Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications

<!DOCTYPE html> <html lang="en" xmlns:og="http://ogp.me/ns#" xmlns:fb="https://www.facebook.com/2008/fbml"> <head> <meta charset="utf-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> <meta content="mdpi" name="sso-service" /> <meta content="width=device-width, initial-scale=1.0" name="viewport" /> <title>Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications</title><link rel="stylesheet" href="https://pub.mdpi-res.com/assets/css/font-awesome.min.css?eb190a3a77e5e1ee?1732261071"> <link rel="stylesheet" href="https://pub.mdpi-res.com/assets/css/jquery.multiselect.css?f56c135cbf4d1483?1732261071"> <link rel="stylesheet" href="https://pub.mdpi-res.com/assets/css/chosen.min.css?d7ca5ca9441ef9e1?1732261071"> <link rel="stylesheet" href="https://pub.mdpi-res.com/assets/css/main2.css?69b39374e6b554b7?1732261071"> <link rel="mask-icon" href="https://pub.mdpi-res.com/img/mask-icon-128.svg?c1c7eca266cd7013?1732261071" color="#4f5671"> <link rel="apple-touch-icon" sizes="180x180" href="https://pub.mdpi-res.com/icon/apple-touch-icon-180x180.png?1732261071"> <link rel="apple-touch-icon" sizes="152x152" href="https://pub.mdpi-res.com/icon/apple-touch-icon-152x152.png?1732261071"> <link rel="apple-touch-icon" sizes="144x144" href="https://pub.mdpi-res.com/icon/apple-touch-icon-144x144.png?1732261071"> <link rel="apple-touch-icon" sizes="120x120" href="https://pub.mdpi-res.com/icon/apple-touch-icon-120x120.png?1732261071"> <link rel="apple-touch-icon" sizes="114x114" href="https://pub.mdpi-res.com/icon/apple-touch-icon-114x114.png?1732261071"> <link rel="apple-touch-icon" sizes="76x76" href="https://pub.mdpi-res.com/icon/apple-touch-icon-76x76.png?1732261071"> <link rel="apple-touch-icon" sizes="72x72" href="https://pub.mdpi-res.com/icon/apple-touch-icon-72x72.png?1732261071"> <link rel="apple-touch-icon" sizes="57x57" href="https://pub.mdpi-res.com/icon/apple-touch-icon-57x57.png?1732261071"> <link rel="apple-touch-icon" href="https://pub.mdpi-res.com/icon/apple-touch-icon-57x57.png?1732261071"> <link rel="apple-touch-icon-precomposed" href="https://pub.mdpi-res.com/icon/apple-touch-icon-57x57.png?1732261071"> <link rel="manifest" href="/manifest.json"> <meta name="theme-color" content="#ffffff"> <meta name="application-name" content=" "/> <link rel="apple-touch-startup-image" href="https://pub.mdpi-res.com/img/journals/ijfs-logo-sq.png?90a76e5ffbc4c6e6"> <link rel="apple-touch-icon" href="https://pub.mdpi-res.com/img/journals/ijfs-logo-sq.png?90a76e5ffbc4c6e6"> <meta name="msapplication-TileImage" content="https://pub.mdpi-res.com/img/journals/ijfs-logo-sq.png?90a76e5ffbc4c6e6"> <link rel="stylesheet" href="https://pub.mdpi-res.com/assets/css/jquery-ui-1.10.4.custom.min.css?80647d88647bf347?1732261071"> <link rel="stylesheet" href="https://pub.mdpi-res.com/assets/css/magnific-popup.min.css?04d343e036f8eecd?1732261071"> <link rel="stylesheet" href="https://pub.mdpi-res.com/assets/css/xml2html/article-html.css?230b005b39af4260?1732261071"> <style> h2, #abstract .related_suggestion_title { } .batch_articles a { color: #000; } a, .batch_articles .authors a, a:focus, a:hover, a:active, .batch_articles a:focus, .batch_articles a:hover, li.side-menu-li a { } span.label a { color: #fff; } #main-content a.title-link:hover, #main-content a.title-link:focus, #main-content div.generic-item a.title-link:hover, #main-content div.generic-item a.title-link:focus { } #main-content #middle-column .generic-item.article-item a.title-link:hover, #main-content #middle-column .generic-item.article-item a.title-link:focus { } .art-authors a.toEncode { color: #333; font-weight: 700; } #main-content #middle-column ul li::before { } .accordion-navigation.active a.accordion__title, .accordion-navigation.active a.accordion__title::after { } .accordion-navigation li:hover::before, .accordion-navigation li:hover a, .accordion-navigation li:focus a { } .relative-size-container .relative-size-image .relative-size { } .middle-column__help__fixed a:hover i, } input[type="checkbox"]:checked:after { } input[type="checkbox"]:not(:disabled):hover:before { } #main-content .bolded-text { } #main-content .hypothesis-count-container { } #main-content .hypothesis-count-container:before { } .full-size-menu ul li.menu-item .dropdown-wrapper { } .full-size-menu ul li.menu-item > a.open::after { } #title-story .title-story-orbit .orbit-caption { #background: url('/img/design/000000_background.png') !important; background: url('/img/design/ffffff_background.png') !important; color: rgb(51, 51, 51) !important; } #main-content .content__container__orbit { background-color: #000 !important; } #main-content .content__container__journal { color: #fff; } .html-article-menu .row span { } .html-article-menu .row span.active { } .accordion-navigation__journal .side-menu-li.active::before, .accordion-navigation__journal .side-menu-li.active a { color: rgba(85,68,0,0.75) !important; font-weight: 700; } .accordion-navigation__journal .side-menu-li:hover::before , .accordion-navigation__journal .side-menu-li:hover a { color: rgba(85,68,0,0.75) !important; } .side-menu-ul li.active a, .side-menu-ul li.active, .side-menu-ul li.active::before { color: rgba(85,68,0,0.75) !important; } .side-menu-ul li.active a { } .result-selected, .active-result.highlighted, .active-result:hover, .result-selected, .active-result.highlighted, .active-result:focus { } .search-container.search-container__default-scheme { } nav.tab-bar .open-small-search.active:after { } .search-container.search-container__default-scheme .custom-accordion-for-small-screen-link::after { color: #fff; } @media only screen and (max-width: 50em) { #main-content .content__container.journal-info { color: #fff; } #main-content .content__container.journal-info a { color: #fff; } } .button.button--color { } .button.button--color:hover, .button.button--color:focus { } .button.button--color-journal { position: relative; background-color: rgba(85,68,0,0.75); border-color: #fff; color: #fff !important; } .button.button--color-journal:hover::before { content: ''; position: absolute; top: 0; left: 0; height: 100%; width: 100%; background-color: #ffffff; opacity: 0.2; } .button.button--color-journal:visited, .button.button--color-journal:hover, .button.button--color-journal:focus { background-color: rgba(85,68,0,0.75); border-color: #fff; color: #fff !important; } .button.button--color path { } .button.button--color:hover path { fill: #fff; } #main-content #search-refinements .ui-slider-horizontal .ui-slider-range { } .breadcrumb__element:last-of-type a { } #main-header { } #full-size-menu .top-bar, #full-size-menu li.menu-item span.user-email { } .top-bar-section li:not(.has-form) a:not(.button) { } #full-size-menu li.menu-item .dropdown-wrapper li a:hover { } #full-size-menu li.menu-item a:hover, #full-size-menu li.menu.item a:focus, nav.tab-bar a:hover { } #full-size-menu li.menu.item a:active, #full-size-menu li.menu.item a.active { } #full-size-menu li.menu-item a.open-mega-menu.active, #full-size-menu li.menu-item div.mega-menu, a.open-mega-menu.active { } #full-size-menu li.menu-item div.mega-menu li, #full-size-menu li.menu-item div.mega-menu a { border-color: #9a9a9a; } div.type-section h2 { font-size: 20px; line-height: 26px; font-weight: 300; } div.type-section h3 { margin-left: 15px; margin-bottom: 0px; font-weight: 300; } .journal-tabs .tab-title.active a { } </style> <link rel="stylesheet" href="https://pub.mdpi-res.com/assets/css/slick.css?f38b2db10e01b157?1732261071"> <meta name="title" content="Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications"> <meta name="description" content="The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models." > <link rel="image_src" href="https://pub.mdpi-res.com/img/journals/ijfs-logo.png?90a76e5ffbc4c6e6" > <meta name="dc.title" content="Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications"> <meta name="dc.creator" content="Gaurang Sonkavde"> <meta name="dc.creator" content="Deepak Sudhakar Dharrao"> <meta name="dc.creator" content="Anupkumar M. Bongale"> <meta name="dc.creator" content="Sarika T. Deokate"> <meta name="dc.creator" content="Deepak Doreswamy"> <meta name="dc.creator" content="Subraya Krishna Bhat"> <meta name="dc.type" content="Review"> <meta name="dc.source" content="International Journal of Financial Studies 2023, Vol. 11, Page 94"> <meta name="dc.date" content="2023-07-26"> <meta name ="dc.identifier" content="10.3390/ijfs11030094"> <meta name="dc.publisher" content="Multidisciplinary Digital Publishing Institute"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf" > <meta name="dc.language" content="en" > <meta name="dc.description" content="The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models." > <meta name="dc.subject" content="stock market" > <meta name="dc.subject" content="finance" > <meta name="dc.subject" content="linear regression" > <meta name="dc.subject" content="random forest" > <meta name="dc.subject" content="XG-Boost" > <meta name="dc.subject" content="FB Prophet" > <meta name="dc.subject" content="LSTM" > <meta name="dc.subject" content="ensemble learning" > <meta name="dc.subject" content="blending ensemble" > <meta name ="prism.issn" content="2227-7072"> <meta name ="prism.publicationName" content="International Journal of Financial Studies"> <meta name ="prism.publicationDate" content="2023-07-26"> <meta name ="prism.volume" content="11"> <meta name ="prism.number" content="3"> <meta name ="prism.section" content="Review" > <meta name ="prism.startingPage" content="94" > <meta name="citation_issn" content="2227-7072"> <meta name="citation_journal_title" content="International Journal of Financial Studies"> <meta name="citation_publisher" content="Multidisciplinary Digital Publishing Institute"> <meta name="citation_title" content="Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications"> <meta name="citation_publication_date" content="2023/9"> <meta name="citation_online_date" content="2023/07/26"> <meta name="citation_volume" content="11"> <meta name="citation_issue" content="3"> <meta name="citation_firstpage" content="94"> <meta name="citation_author" content="Sonkavde, Gaurang"> <meta name="citation_author" content="Dharrao, Deepak Sudhakar"> <meta name="citation_author" content="Bongale, Anupkumar M."> <meta name="citation_author" content="Deokate, Sarika T."> <meta name="citation_author" content="Doreswamy, Deepak"> <meta name="citation_author" content="Bhat, Subraya Krishna"> <meta name="citation_doi" content="10.3390/ijfs11030094"> <meta name="citation_id" content="mdpi-ijfs11030094"> <meta name="citation_abstract_html_url" content="https://www.mdpi.com/2227-7072/11/3/94"> <meta name="citation_pdf_url" content="https://www.mdpi.com/2227-7072/11/3/94/pdf?version=1690521261"> <link rel="alternate" type="application/pdf" title="PDF Full-Text" href="https://www.mdpi.com/2227-7072/11/3/94/pdf?version=1690521261"> <meta name="fulltext_pdf" content="https://www.mdpi.com/2227-7072/11/3/94/pdf?version=1690521261"> <meta name="citation_fulltext_html_url" content="https://www.mdpi.com/2227-7072/11/3/94/htm"> <link rel="alternate" type="text/html" title="HTML Full-Text" href="https://www.mdpi.com/2227-7072/11/3/94/htm"> <meta name="fulltext_html" content="https://www.mdpi.com/2227-7072/11/3/94/htm"> <link rel="alternate" type="text/xml" title="XML Full-Text" href="https://www.mdpi.com/2227-7072/11/3/94/xml"> <meta name="fulltext_xml" content="https://www.mdpi.com/2227-7072/11/3/94/xml"> <meta name="citation_xml_url" content="https://www.mdpi.com/2227-7072/11/3/94/xml"> <meta name="twitter:card" content="summary" /> <meta name="twitter:site" content="@MDPIOpenAccess" /> <meta name="twitter:image" content="https://pub.mdpi-res.com/img/journals/ijfs-logo-social.png?90a76e5ffbc4c6e6" /> <meta property="fb:app_id" content="131189377574"/> <meta property="og:site_name" content="MDPI"/> <meta property="og:type" content="article"/> <meta property="og:url" content="https://www.mdpi.com/2227-7072/11/3/94" /> <meta property="og:title" content="Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications" /> <meta property="og:description" content="The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models." /> <meta property="og:image" content="https://pub.mdpi-res.com/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g001-550.jpg?1690521344" /> <link rel="alternate" type="application/rss+xml" title="MDPI Publishing - Latest articles" href="https://www.mdpi.com/rss"> <meta name="google-site-verification" content="PxTlsg7z2S00aHroktQd57fxygEjMiNHydKn3txhvwY"> <meta name="facebook-domain-verification" content="mcoq8dtq6sb2hf7z29j8w515jjoof7" /> <script id="Cookiebot" data-cfasync="false" src="https://consent.cookiebot.com/uc.js" data-cbid="51491ddd-fe7a-4425-ab39-69c78c55829f" type="text/javascript" async></script>  <script type="text/plain" data-cookieconsent="statistics"> (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-WPK7SW5'); </script> <script type="text/plain" data-cookieconsent="statistics"> _linkedin_partner_id = "2846186"; window._linkedin_data_partner_ids = window._linkedin_data_partner_ids || []; window._linkedin_data_partner_ids.push(_linkedin_partner_id); </script><script type="text/javascript"> (function(){var s = document.getElementsByTagName("script")[0]; var b = document.createElement("script"); b.type = "text/javascript";b.async = true; b.src = "https://snap.licdn.com/li.lms-analytics/insight.min.js"; s.parentNode.insertBefore(b, s);})(); </script> <script type="text/plain" data-cookieconsent="statistics" data-cfasync="false" src="//script.crazyegg.com/pages/scripts/0116/4951.js" async="async" ></script> </head> <body> <div class="direction direction_right" id="small_right" style="border-right-width: 0px; padding:0;"> <i class="fa fa-caret-right fa-2x"></i> </div> <div class="big_direction direction_right" id="big_right" style="border-right-width: 0px;"> <div style="text-align: right;"> Next Article in Journal<br> <div><a href="/2227-7072/11/3/95">Do Share Repurchases Crowd Out Internal Investment in South Africa?</a></div> </div> </div> <div class="direction" id="small_left" style="border-left-width: 0px"> <i class="fa fa-caret-left fa-2x"></i> </div> <div class="big_direction" id="big_left" style="border-left-width: 0px;"> <div> Previous Article in Journal<br> <div><a href="/2227-7072/11/3/93">The Sustainability of Investing in Cryptocurrencies: A Bibliometric Analysis of Research Trends</a></div> </div> </div> <div style="clear: both;"></div> <div id="menuModal" class="reveal-modal reveal-modal-new reveal-modal-menu" aria-hidden="true" data-reveal role="dialog"> <div class="menu-container"> <div class="UI_NavMenu"> <div class="content__container " > <div class="custom-accordion-for-small-screen-link " > <h2>Journals</h2> </div> <div class="target-item custom-accordion-for-small-screen-content show-for-medium-up"> <div class="menu-container__links"> <div style="width: 100%; float: left;"> <a href="/about/journals">Active Journals</a> <a href="/about/journalfinder">Find a Journal</a> <a href="/about/journals/proposal">Journal Proposal</a> <a href="/about/proceedings">Proceedings Series</a> </div> </div> </div> </div> <a href="/topics"> <h2>Topics</h2> </a> <div class="content__container " > <div class="custom-accordion-for-small-screen-link " > <h2>Information</h2> </div> <div class="target-item custom-accordion-for-small-screen-content show-for-medium-up"> <div class="menu-container__links"> <div style="width: 100%; max-width: 200px; float: left;"> <a href="/authors">For Authors</a> <a href="/reviewers">For Reviewers</a> <a href="/editors">For Editors</a> <a href="/librarians">For Librarians</a> <a href="/publishing_services">For Publishers</a> <a href="/societies">For Societies</a> <a href="/conference_organizers">For Conference Organizers</a> </div> <div style="width: 100%; max-width: 250px; float: left;"> <a href="/openaccess">Open Access Policy</a> <a href="/ioap">Institutional Open Access Program</a> <a href="/special_issues_guidelines">Special Issues Guidelines</a> <a href="/editorial_process">Editorial Process</a> <a href="/ethics">Research and Publication Ethics</a> <a href="/apc">Article Processing Charges</a> <a href="/awards">Awards</a> <a href="/testimonials">Testimonials</a> </div> </div> </div> </div> <a href="/authors/english"> <h2>Editing Services</h2> </a> <div class="content__container " > <div class="custom-accordion-for-small-screen-link " > <h2>Initiatives</h2> </div> <div class="target-item custom-accordion-for-small-screen-content show-for-medium-up"> <div class="menu-container__links"> <div style="width: 100%; float: left;"> <a href="https://sciforum.net" target="_blank" rel="noopener noreferrer">Sciforum</a> <a href="https://www.mdpi.com/books" target="_blank" rel="noopener noreferrer">MDPI Books</a> <a href="https://www.preprints.org" target="_blank" rel="noopener noreferrer">Preprints.org</a> <a href="https://www.scilit.net" target="_blank" rel="noopener noreferrer">Scilit</a> <a href="https://sciprofiles.com" target="_blank" rel="noopener noreferrer">SciProfiles</a> <a href="https://encyclopedia.pub" target="_blank" rel="noopener noreferrer">Encyclopedia</a> <a href="https://jams.pub" target="_blank" rel="noopener noreferrer">JAMS</a> <a href="/about/proceedings">Proceedings Series</a> </div> </div> </div> </div> <div class="content__container " > <div class="custom-accordion-for-small-screen-link " > <h2>About</h2> </div> <div class="target-item custom-accordion-for-small-screen-content show-for-medium-up"> <div class="menu-container__links"> <div style="width: 100%; float: left;"> <a href="/about">Overview</a> <a href="/about/contact">Contact</a> <a href="https://careers.mdpi.com" target="_blank" rel="noopener noreferrer">Careers</a> <a href="/about/announcements">News</a> <a href="/about/press">Press</a> <a href="http://blog.mdpi.com/" target="_blank" rel="noopener noreferrer">Blog</a> </div> </div> </div> </div> </div> <div class="menu-container__buttons"> <a class="button UA_SignInUpButton" href="/user/login">Sign In / Sign Up</a> </div> </div> </div> <div id="captchaModal" class="reveal-modal reveal-modal-new reveal-modal-new--small" data-reveal aria-label="Captcha" aria-hidden="true" role="dialog"></div> <div id="actionDisabledModal" class="reveal-modal" data-reveal aria-labelledby="actionDisableModalTitle" aria-hidden="true" role="dialog" style="width: 300px;"> <h2 id="actionDisableModalTitle">Notice</h2> <form action="/email/captcha" method="post" id="emailCaptchaForm"> <div class="row"> <div id="js-action-disabled-modal-text" class="small-12 columns"> </div> <div id="js-action-disabled-modal-submit" class="small-12 columns" style="margin-top: 10px; display: none;"> You can make submissions to other journals <a href="https://susy.mdpi.com/user/manuscripts/upload">here</a>. </div> </div> </form> <a class="close-reveal-modal" aria-label="Close"> <i class="material-icons">clear</i> </a> </div> <div id="rssNotificationModal" class="reveal-modal reveal-modal-new" data-reveal aria-labelledby="rssNotificationModalTitle" aria-hidden="true" role="dialog"> <div class="row"> <div class="small-12 columns"> <h2 id="rssNotificationModalTitle">Notice</h2> <p> You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader. </p> </div> </div> <div class="row"> <div class="small-12 columns"> <a class="button button--color js-rss-notification-confirm">Continue</a> <a class="button button--grey" onclick="$(this).closest('.reveal-modal').find('.close-reveal-modal').click(); return false;">Cancel</a> </div> </div> <a class="close-reveal-modal" aria-label="Close"> <i class="material-icons">clear</i> </a> </div> <div id="drop-article-label-openaccess" class="f-dropdown medium" data-dropdown-content aria-hidden="true" tabindex="-1"> <p> All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to <a href="https://www.mdpi.com/openaccess">https://www.mdpi.com/openaccess</a>. </p> </div> <div id="drop-article-label-feature" class="f-dropdown medium" data-dropdown-content aria-hidden="true" tabindex="-1"> <p> Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications. </p> <p> Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers. </p> </div> <div id="drop-article-label-choice" class="f-dropdown medium" data-dropdown-content aria-hidden="true" tabindex="-1"> <p> Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal. <div style="margin-top: -10px;"> <div id="drop-article-label-choice-journal-link" style="display: none; margin-top: -10px; padding-top: 10px;"> </div> </div> </p> </div> <div id="drop-article-label-resubmission" class="f-dropdown medium" data-dropdown-content aria-hidden="true" tabindex="-1"> <p> Original Submission Date Received: <span id="drop-article-label-resubmission-date"></span>. </p> </div> <div id="container"> <noscript> <div id="no-javascript"> You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled. </div> </noscript> <div class="fixed"> <nav class="tab-bar show-for-medium-down"> <div class="row full-width collapse"> <div class="medium-3 small-4 columns"> <a href="/"> <img class="full-size-menu__mdpi-logo" src="https://pub.mdpi-res.com/img/design/mdpi-pub-logo-black-small1.svg?da3a8dcae975a41c?1732261071" style="width: 64px;" title="MDPI Open Access Journals"> </a> </div> <div class="medium-3 small-4 columns right-aligned"> <div class="show-for-medium-down"> <a href="#" style="display: none;"> <i class="material-icons" onclick="$('#menuModal').foundation('reveal', 'close'); return false;">clear</i> </a> <a class="js-toggle-desktop-layout-link" title="Toggle desktop layout" style="display: none;" href="/toggle_desktop_layout_cookie"> <i class="material-icons">zoom_out_map</i> </a> <a href="#" class="js-open-small-search open-small-search"> <i class="material-icons show-for-small only">search</i> </a> <a title="MDPI main page" class="js-open-menu" data-reveal-id="menuModal" href="#"> <i class="material-icons">menu</i> </a> </div> </div> </div> </nav> </div> <section class="main-section"> <header> <div class="full-size-menu show-for-large-up"> <div class="row full-width"> <div class="large-1 columns"> <a href="/"> <img class="full-size-menu__mdpi-logo" src="https://pub.mdpi-res.com/img/design/mdpi-pub-logo-black-small1.svg?da3a8dcae975a41c?1732261071" title="MDPI Open Access Journals"> </a> </div> <div class="large-8 columns text-right UI_NavMenu"> <ul> <li class="menu-item"> <a href="/about/journals" data-dropdown="journals-dropdown" aria-controls="journals-dropdown" aria-expanded="false" data-options="is_hover: true; hover_timeout: 200">Journals</a> <ul id="journals-dropdown" class="f-dropdown dropdown-wrapper dropdown-wrapper__small" data-dropdown-content aria-hidden="true" tabindex="-1"> <li> <div class="row"> <div class="small-12 columns"> <ul> <li> <a href="/about/journals"> Active Journals </a> </li> <li> <a href="/about/journalfinder"> Find a Journal </a> </li> <li> <a href="/about/journals/proposal"> Journal Proposal </a> </li> <li> <a href="/about/proceedings"> Proceedings Series </a> </li> </ul> </div> </div> </li> </ul> </li> <li class="menu-item"> <a href="/topics">Topics</a> </li> <li class="menu-item"> <a href="/authors" data-dropdown="information-dropdown" aria-controls="information-dropdown" aria-expanded="false" data-options="is_hover:true; hover_timeout:200">Information</a> <ul id="information-dropdown" class="f-dropdown dropdown-wrapper" data-dropdown-content aria-hidden="true" tabindex="-1"> <li> <div class="row"> <div class="small-5 columns right-border"> <ul> <li> <a href="/authors">For Authors</a> </li> <li> <a href="/reviewers">For Reviewers</a> </li> <li> <a href="/editors">For Editors</a> </li> <li> <a href="/librarians">For Librarians</a> </li> <li> <a href="/publishing_services">For Publishers</a> </li> <li> <a href="/societies">For Societies</a> </li> <li> <a href="/conference_organizers">For Conference Organizers</a> </li> </ul> </div> <div class="small-7 columns"> <ul> <li> <a href="/openaccess">Open Access Policy</a> </li> <li> <a href="/ioap">Institutional Open Access Program</a> </li> <li> <a href="/special_issues_guidelines">Special Issues Guidelines</a> </li> <li> <a href="/editorial_process">Editorial Process</a> </li> <li> <a href="/ethics">Research and Publication Ethics</a> </li> <li> <a href="/apc">Article Processing Charges</a> </li> <li> <a href="/awards">Awards</a> </li> <li> <a href="/testimonials">Testimonials</a> </li> </ul> </div> </div> </li> </ul> </li> <li class="menu-item"> <a href="/authors/english">Editing Services</a> </li> <li class="menu-item"> <a href="/about/initiatives" data-dropdown="initiatives-dropdown" aria-controls="initiatives-dropdown" aria-expanded="false" data-options="is_hover: true; hover_timeout: 200">Initiatives</a> <ul id="initiatives-dropdown" class="f-dropdown dropdown-wrapper dropdown-wrapper__small" data-dropdown-content aria-hidden="true" tabindex="-1"> <li> <div class="row"> <div class="small-12 columns"> <ul> <li> <a href="https://sciforum.net" target="_blank" rel="noopener noreferrer"> Sciforum </a> </li> <li> <a href="https://www.mdpi.com/books" target="_blank" rel="noopener noreferrer"> MDPI Books </a> </li> <li> <a href="https://www.preprints.org" target="_blank" rel="noopener noreferrer"> Preprints.org </a> </li> <li> <a href="https://www.scilit.net" target="_blank" rel="noopener noreferrer"> Scilit </a> </li> <li> <a href="https://sciprofiles.com" target="_blank" rel="noopener noreferrer"> SciProfiles </a> </li> <li> <a href="https://encyclopedia.pub" target="_blank" rel="noopener noreferrer"> Encyclopedia </a> </li> <li> <a href="https://jams.pub" target="_blank" rel="noopener noreferrer"> JAMS </a> </li> <li> <a href="/about/proceedings"> Proceedings Series </a> </li> </ul> </div> </div> </li> </ul> </li> <li class="menu-item"> <a href="/about" data-dropdown="about-dropdown" aria-controls="about-dropdown" aria-expanded="false" data-options="is_hover: true; hover_timeout: 200">About</a> <ul id="about-dropdown" class="f-dropdown dropdown-wrapper dropdown-wrapper__small" data-dropdown-content aria-hidden="true" tabindex="-1"> <li> <div class="row"> <div class="small-12 columns"> <ul> <li> <a href="/about"> Overview </a> </li> <li> <a href="/about/contact"> Contact </a> </li> <li> <a href="https://careers.mdpi.com" target="_blank" rel="noopener noreferrer"> Careers </a> </li> <li> <a href="/about/announcements"> News </a> </li> <li> <a href="/about/press"> Press </a> </li> <li> <a href="http://blog.mdpi.com/" target="_blank" rel="noopener noreferrer"> Blog </a> </li> </ul> </div> </div> </li> </ul> </li> </ul> </div> <div class="large-3 columns text-right full-size-menu__buttons"> <div> <a class="button button--default-inversed UA_SignInUpButton" href="/user/login">Sign In / Sign Up</a> <a class="button button--default js-journal-active-only-link js-journal-active-only-submit-link UC_NavSubmitButton" href=" https://susy.mdpi.com/user/manuscripts/upload?journal=ijfs " data-disabledmessage="new submissions are not possible.">Submit</a> </div> </div> </div> </div> <div class="header-divider"> </div> <div class="search-container hide-for-small-down row search-container__homepage-scheme"> <form id="basic_search" style="background-color: inherit !important;" class="large-12 medium-12 columns " action="/search" method="get"> <div class="row search-container__main-elements"> <div class="large-2 medium-2 small-12 columns text-right1 small-only-text-left"> <div class="show-for-medium-up"> <div class="search-input-label"> </div> </div> <span class="search-container__title">Search<span class="hide-for-medium"> for Articles</span><span class="hide-for-small">:</span></span> </div> <div class="custom-accordion-for-small-screen-content"> <div class="large-2 medium-2 small-6 columns "> <div class=""> <div class="search-input-label">Title / Keyword</div> </div> <input type="text" placeholder="Title / Keyword" id="q" tabindex="1" name="q" value="" /> </div> <div class="large-2 medium-2 small-6 columns "> <div class=""> <div class="search-input-label">Author / Affiliation / Email</div> </div> <input type="text" id="authors" placeholder="Author / Affiliation / Email" tabindex="2" name="authors" value="" /> </div> <div class="large-2 medium-2 small-6 columns "> <div class=""> <div class="search-input-label">Journal</div> </div> <select id="journal" tabindex="3" name="journal" class="chosen-select"> <option value="">All Journals</option> <option value="acoustics" > Acoustics </option> <option value="amh" > Acta Microbiologica Hellenica (AMH) </option> <option value="actuators" > Actuators </option> <option value="admsci" > Administrative Sciences </option> <option value="adolescents" > Adolescents </option> <option value="arm" > Advances in Respiratory Medicine (ARM) </option> <option value="aerobiology" > Aerobiology </option> <option value="aerospace" > Aerospace </option> <option value="agriculture" > Agriculture </option> <option value="agriengineering" > AgriEngineering </option> <option value="agrochemicals" > Agrochemicals </option> <option value="agronomy" > Agronomy </option> <option value="ai" > AI </option> <option value="air" > Air </option> <option value="algorithms" > Algorithms </option> <option value="allergies" > Allergies </option> <option value="alloys" > Alloys </option> <option value="analytica" > Analytica </option> <option value="analytics" > Analytics </option> <option value="anatomia" > Anatomia </option> <option value="anesthres" > Anesthesia Research </option> <option value="animals" > Animals </option> <option value="antibiotics" > Antibiotics </option> <option value="antibodies" > Antibodies </option> <option value="antioxidants" > Antioxidants </option> <option value="applbiosci" > Applied Biosciences </option> <option value="applmech" > Applied Mechanics </option> <option value="applmicrobiol" > Applied Microbiology </option> <option value="applnano" > Applied Nano </option> <option value="applsci" > Applied Sciences </option> <option value="asi" > Applied System Innovation (ASI) </option> <option value="appliedchem" > AppliedChem </option> <option value="appliedmath" > AppliedMath </option> <option value="aquacj" > Aquaculture Journal </option> <option value="architecture" > Architecture </option> <option value="arthropoda" > Arthropoda </option> <option value="arts" > Arts </option> <option value="astronomy" > Astronomy </option> <option value="atmosphere" > Atmosphere </option> <option value="atoms" > Atoms </option> <option value="audiolres" > Audiology Research </option> <option value="automation" > Automation </option> <option value="axioms" > Axioms </option> <option value="bacteria" > Bacteria </option> <option value="batteries" > Batteries </option> <option value="behavsci" > Behavioral Sciences </option> <option value="beverages" > Beverages </option> <option value="BDCC" > Big Data and Cognitive Computing (BDCC) </option> <option value="biochem" > BioChem </option> <option value="bioengineering" > Bioengineering </option> <option value="biologics" > Biologics </option> <option value="biology" > Biology </option> <option value="blsf" > Biology and Life Sciences Forum </option> <option value="biomass" > Biomass </option> <option value="biomechanics" > Biomechanics </option> <option value="biomed" > BioMed </option> <option value="biomedicines" > Biomedicines </option> <option value="biomedinformatics" > BioMedInformatics </option> <option value="biomimetics" > Biomimetics </option> <option value="biomolecules" > Biomolecules </option> <option value="biophysica" > Biophysica </option> <option value="biosensors" > Biosensors </option> <option value="biotech" > BioTech </option> <option value="birds" > Birds </option> <option value="blockchains" > Blockchains </option> <option value="brainsci" > Brain Sciences </option> <option value="buildings" > Buildings </option> <option value="businesses" > Businesses </option> <option value="carbon" > C </option> <option value="cancers" > Cancers </option> <option value="cardiogenetics" > Cardiogenetics </option> <option value="catalysts" > Catalysts </option> <option value="cells" > Cells </option> <option value="ceramics" > Ceramics </option> <option value="challenges" > Challenges </option> <option value="ChemEngineering" > ChemEngineering </option> <option value="chemistry" > Chemistry </option> <option value="chemproc" > Chemistry Proceedings </option> <option value="chemosensors" > Chemosensors </option> <option value="children" > Children </option> <option value="chips" > Chips </option> <option value="civileng" > CivilEng </option> <option value="cleantechnol" > Clean Technologies (Clean Technol.) </option> <option value="climate" > Climate </option> <option value="ctn" > Clinical and Translational Neuroscience (CTN) </option> <option value="clinbioenerg" > Clinical Bioenergetics </option> <option value="clinpract" > Clinics and Practice </option> <option value="clockssleep" > Clocks & Sleep </option> <option value="coasts" > Coasts </option> <option value="coatings" > Coatings </option> <option value="colloids" > Colloids and Interfaces </option> <option value="colorants" > Colorants </option> <option value="commodities" > Commodities </option> <option value="complications" > Complications </option> <option value="compounds" > Compounds </option> <option value="computation" > Computation </option> <option value="csmf" > Computer Sciences & Mathematics Forum </option> <option value="computers" > Computers </option> <option value="condensedmatter" > Condensed Matter </option> <option value="conservation" > Conservation </option> <option value="constrmater" > Construction Materials </option> <option value="cmd" > Corrosion and Materials Degradation (CMD) </option> <option value="cosmetics" > Cosmetics </option> <option value="covid" > COVID </option> <option value="crops" > Crops </option> <option value="cryo" > Cryo </option> <option value="cryptography" > Cryptography </option> <option value="crystals" > Crystals </option> <option value="cimb" > Current Issues in Molecular Biology (CIMB) </option> <option value="curroncol" > Current Oncology </option> <option value="dairy" > Dairy </option> <option value="data" > Data </option> <option value="dentistry" > Dentistry Journal </option> <option value="dermato" > Dermato </option> <option value="dermatopathology" > Dermatopathology </option> <option value="designs" > Designs </option> <option value="diabetology" > Diabetology </option> <option value="diagnostics" > Diagnostics </option> <option value="dietetics" > Dietetics </option> <option value="digital" > Digital </option> <option value="disabilities" > Disabilities </option> <option value="diseases" > Diseases </option> <option value="diversity" > Diversity </option> <option value="dna" > DNA </option> <option value="drones" > Drones </option> <option value="ddc" > Drugs and Drug Candidates (DDC) </option> <option value="dynamics" > Dynamics </option> <option value="earth" > Earth </option> <option value="ecologies" > Ecologies </option> <option value="econometrics" > Econometrics </option> <option value="economies" > Economies </option> <option value="education" > Education Sciences </option> <option value="electricity" > Electricity </option> <option value="electrochem" > Electrochem </option> <option value="electronicmat" > Electronic Materials </option> <option value="electronics" > Electronics </option> <option value="ecm" > Emergency Care and Medicine </option> <option value="encyclopedia" > Encyclopedia </option> <option value="endocrines" > Endocrines </option> <option value="energies" > Energies </option> <option value="esa" > Energy Storage and Applications (ESA) </option> <option value="eng" > Eng </option> <option value="engproc" > Engineering Proceedings </option> <option value="entropy" > Entropy </option> <option value="environsciproc" > Environmental Sciences Proceedings </option> <option value="environments" > Environments </option> <option value="epidemiologia" > Epidemiologia </option> <option value="epigenomes" > Epigenomes </option> <option value="ebj" > European Burn Journal (EBJ) </option> <option value="ejihpe" > European Journal of Investigation in Health, Psychology and Education (EJIHPE) </option> <option value="fermentation" > Fermentation </option> <option value="fibers" > Fibers </option> <option value="fintech" > FinTech </option> <option value="fire" > Fire </option> <option value="fishes" > Fishes </option> <option value="fluids" > Fluids </option> <option value="foods" > Foods </option> <option value="forecasting" > Forecasting </option> <option value="forensicsci" > Forensic Sciences </option> <option value="forests" > Forests </option> <option value="fossstud" > Fossil Studies </option> <option value="foundations" > Foundations </option> <option value="fractalfract" > Fractal and Fractional (Fractal Fract) </option> <option value="fuels" > Fuels </option> <option value="future" > Future </option> <option value="futureinternet" > Future Internet </option> <option value="futurepharmacol" > Future Pharmacology </option> <option value="futuretransp" > Future Transportation </option> <option value="galaxies" > Galaxies </option> <option value="games" > Games </option> <option value="gases" > Gases </option> <option value="gastroent" > Gastroenterology Insights </option> <option value="gastrointestdisord" > Gastrointestinal Disorders </option> <option value="gastronomy" > Gastronomy </option> <option value="gels" > Gels </option> <option value="genealogy" > Genealogy </option> <option value="genes" > Genes </option> <option value="geographies" > Geographies </option> <option value="geohazards" > GeoHazards </option> <option value="geomatics" > Geomatics </option> <option value="geometry" > Geometry </option> <option value="geosciences" > Geosciences </option> <option value="geotechnics" > Geotechnics </option> <option value="geriatrics" > Geriatrics </option> <option value="glacies" > Glacies </option> <option value="gucdd" > Gout, Urate, and Crystal Deposition Disease (GUCDD) </option> <option value="grasses" > Grasses </option> <option value="hardware" > Hardware </option> <option value="healthcare" > Healthcare </option> <option value="hearts" > Hearts </option> <option value="hemato" > Hemato </option> <option value="hematolrep" > Hematology Reports </option> <option value="heritage" > Heritage </option> <option value="histories" > Histories </option> <option value="horticulturae" > Horticulturae </option> <option value="hospitals" > Hospitals </option> <option value="humanities" > Humanities </option> <option value="humans" > Humans </option> <option value="hydrobiology" > Hydrobiology </option> <option value="hydrogen" > Hydrogen </option> <option value="hydrology" > Hydrology </option> <option value="hygiene" > Hygiene </option> <option value="immuno" > Immuno </option> <option value="idr" > Infectious Disease Reports </option> <option value="informatics" > Informatics </option> <option value="information" > Information </option> <option value="infrastructures" > Infrastructures </option> <option value="inorganics" > Inorganics </option> <option value="insects" > Insects </option> <option value="instruments" > Instruments </option> <option value="iic" > Intelligent Infrastructure and Construction </option> <option value="ijerph" > International Journal of Environmental Research and Public Health (IJERPH) </option> <option value="ijfs" selected='selected'> International Journal of Financial Studies (IJFS) </option> <option value="ijms" > International Journal of Molecular Sciences (IJMS) </option> <option value="IJNS" > International Journal of Neonatal Screening (IJNS) </option> <option value="ijpb" > International Journal of Plant Biology (IJPB) </option> <option value="ijt" > International Journal of Topology </option> <option value="ijtm" > International Journal of Translational Medicine (IJTM) </option> <option value="ijtpp" > International Journal of Turbomachinery, Propulsion and Power (IJTPP) </option> <option value="ime" > International Medical Education (IME) </option> <option value="inventions" > Inventions </option> <option value="IoT" > IoT </option> <option value="ijgi" > ISPRS International Journal of Geo-Information (IJGI) </option> <option value="J" > J </option> <option value="jal" > Journal of Ageing and Longevity (JAL) </option> <option value="jcdd" > Journal of Cardiovascular Development and Disease (JCDD) </option> <option value="jcto" > Journal of Clinical & Translational Ophthalmology (JCTO) </option> <option value="jcm" > Journal of Clinical Medicine (JCM) </option> <option value="jcs" > Journal of Composites Science (J. Compos. Sci.) </option> <option value="jcp" > Journal of Cybersecurity and Privacy (JCP) </option> <option value="jdad" > Journal of Dementia and Alzheimer's Disease (JDAD) </option> <option value="jdb" > Journal of Developmental Biology (JDB) </option> <option value="jeta" > Journal of Experimental and Theoretical Analyses (JETA) </option> <option value="jfb" > Journal of Functional Biomaterials (JFB) </option> <option value="jfmk" > Journal of Functional Morphology and Kinesiology (JFMK) </option> <option value="jof" > Journal of Fungi (JoF) </option> <option value="jimaging" > Journal of Imaging (J. Imaging) </option> <option value="jintelligence" > Journal of Intelligence (J. Intell.) </option> <option value="jlpea" > Journal of Low Power Electronics and Applications (JLPEA) </option> <option value="jmmp" > Journal of Manufacturing and Materials Processing (JMMP) </option> <option value="jmse" > Journal of Marine Science and Engineering (JMSE) </option> <option value="jmahp" > Journal of Market Access & Health Policy (JMAHP) </option> <option value="jmp" > Journal of Molecular Pathology (JMP) </option> <option value="jnt" > Journal of Nanotheranostics (JNT) </option> <option value="jne" > Journal of Nuclear Engineering (JNE) </option> <option value="ohbm" > Journal of Otorhinolaryngology, Hearing and Balance Medicine (JOHBM) </option> <option value="jop" > Journal of Parks </option> <option value="jpm" > Journal of Personalized Medicine (JPM) </option> <option value="jpbi" > Journal of Pharmaceutical and BioTech Industry (JPBI) </option> <option value="jor" > Journal of Respiration (JoR) </option> <option value="jrfm" > Journal of Risk and Financial Management (JRFM) </option> <option value="jsan" > Journal of Sensor and Actuator Networks (JSAN) </option> <option value="joma" > Journal of the Oman Medical Association (JOMA) </option> <option value="jtaer" > Journal of Theoretical and Applied Electronic Commerce Research (JTAER) </option> <option value="jvd" > Journal of Vascular Diseases (JVD) </option> <option value="jox" > Journal of Xenobiotics (JoX) </option> <option value="jzbg" > Journal of Zoological and Botanical Gardens (JZBG) </option> <option value="journalmedia" > Journalism and Media </option> <option value="kidneydial" > Kidney and Dialysis </option> <option value="kinasesphosphatases" > Kinases and Phosphatases </option> <option value="knowledge" > Knowledge </option> <option value="labmed" > LabMed </option> <option value="laboratories" > Laboratories </option> <option value="land" > Land </option> <option value="languages" > Languages </option> <option value="laws" > Laws </option> <option value="life" > Life </option> <option value="limnolrev" > Limnological Review </option> <option value="lipidology" > Lipidology </option> <option value="liquids" > Liquids </option> <option value="literature" > Literature </option> <option value="livers" > Livers </option> <option value="logics" > Logics </option> <option value="logistics" > Logistics </option> <option value="lubricants" > Lubricants </option> <option value="lymphatics" > Lymphatics </option> <option value="make" > Machine Learning and Knowledge Extraction (MAKE) </option> <option value="machines" > Machines </option> <option value="macromol" > Macromol </option> <option value="magnetism" > Magnetism </option> <option value="magnetochemistry" > Magnetochemistry </option> <option value="marinedrugs" > Marine Drugs </option> <option value="materials" > Materials </option> <option value="materproc" > Materials Proceedings </option> <option value="mca" > Mathematical and Computational Applications (MCA) </option> <option value="mathematics" > Mathematics </option> <option value="medsci" > Medical Sciences </option> <option value="msf" > Medical Sciences Forum </option> <option value="medicina" > Medicina </option> <option value="medicines" > Medicines </option> <option value="membranes" > Membranes </option> <option value="merits" > Merits </option> <option value="metabolites" > Metabolites </option> <option value="metals" > Metals </option> <option value="meteorology" > Meteorology </option> <option value="methane" > Methane </option> <option value="mps" > Methods and Protocols (MPs) </option> <option value="metrics" > Metrics </option> <option value="metrology" > Metrology </option> <option value="micro" > Micro </option> <option value="microbiolres" > Microbiology Research </option> <option value="micromachines" > Micromachines </option> <option value="microorganisms" > Microorganisms </option> <option value="microplastics" > Microplastics </option> <option value="minerals" > Minerals </option> <option value="mining" > Mining </option> <option value="modelling" > Modelling </option> <option value="mmphys" > Modern Mathematical Physics </option> <option value="molbank" > Molbank </option> <option value="molecules" > Molecules </option> <option value="mti" > Multimodal Technologies and Interaction (MTI) </option> <option value="muscles" > Muscles </option> <option value="nanoenergyadv" > Nanoenergy Advances </option> <option value="nanomanufacturing" > Nanomanufacturing </option> <option value="nanomaterials" > Nanomaterials </option> <option value="ndt" > NDT </option> <option value="network" > Network </option> <option value="neuroglia" > Neuroglia </option> <option value="neurolint" > Neurology International </option> <option value="neurosci" > NeuroSci </option> <option value="nitrogen" > Nitrogen </option> <option value="ncrna" > Non-Coding RNA (ncRNA) </option> <option value="nursrep" > Nursing Reports </option> <option value="nutraceuticals" > Nutraceuticals </option> <option value="nutrients" > Nutrients </option> <option value="obesities" > Obesities </option> <option value="oceans" > Oceans </option> <option value="onco" > Onco </option> <option value="optics" > Optics </option> <option value="oral" > Oral </option> <option value="organics" > Organics </option> <option value="organoids" > Organoids </option> <option value="osteology" > Osteology </option> <option value="oxygen" > Oxygen </option> <option value="parasitologia" > Parasitologia </option> <option value="particles" > Particles </option> <option value="pathogens" > Pathogens </option> <option value="pathophysiology" > Pathophysiology </option> <option value="pediatrrep" > Pediatric Reports </option> <option value="pets" > Pets </option> <option value="pharmaceuticals" > Pharmaceuticals </option> <option value="pharmaceutics" > Pharmaceutics </option> <option value="pharmacoepidemiology" > Pharmacoepidemiology </option> <option value="pharmacy" > Pharmacy </option> <option value="philosophies" > Philosophies </option> <option value="photochem" > Photochem </option> <option value="photonics" > Photonics </option> <option value="phycology" > Phycology </option> <option value="physchem" > Physchem </option> <option value="psf" > Physical Sciences Forum </option> <option value="physics" > Physics </option> <option value="physiologia" > Physiologia </option> <option value="plants" > Plants </option> <option value="plasma" > Plasma </option> <option value="platforms" > Platforms </option> <option value="pollutants" > Pollutants </option> <option value="polymers" > Polymers </option> <option value="polysaccharides" > Polysaccharides </option> <option value="populations" > Populations </option> <option value="poultry" > Poultry </option> <option value="powders" > Powders </option> <option value="proceedings" > Proceedings </option> <option value="processes" > Processes </option> <option value="prosthesis" > Prosthesis </option> <option value="proteomes" > Proteomes </option> <option value="psychiatryint" > Psychiatry International </option> <option value="psychoactives" > Psychoactives </option> <option value="psycholint" > Psychology International </option> <option value="publications" > Publications </option> <option value="qubs" > Quantum Beam Science (QuBS) </option> <option value="quantumrep" > Quantum Reports </option> <option value="quaternary" > Quaternary </option> <option value="radiation" > Radiation </option> <option value="reactions" > Reactions </option> <option value="realestate" > Real Estate </option> <option value="receptors" > Receptors </option> <option value="recycling" > Recycling </option> <option value="rsee" > Regional Science and Environmental Economics (RSEE) </option> <option value="religions" > Religions </option> <option value="remotesensing" > Remote Sensing </option> <option value="reports" > Reports </option> <option value="reprodmed" > Reproductive Medicine (Reprod. Med.) </option> <option value="resources" > Resources </option> <option value="rheumato" > Rheumato </option> <option value="risks" > Risks </option> <option value="robotics" > Robotics </option> <option value="ruminants" > Ruminants </option> <option value="safety" > Safety </option> <option value="sci" > Sci </option> <option value="scipharm" > Scientia Pharmaceutica (Sci. Pharm.) </option> <option value="sclerosis" > Sclerosis </option> <option value="seeds" > Seeds </option> <option value="sensors" > Sensors </option> <option value="separations" > Separations </option> <option value="sexes" > Sexes </option> <option value="signals" > Signals </option> <option value="sinusitis" > Sinusitis </option> <option value="smartcities" > Smart Cities </option> <option value="socsci" > Social Sciences </option> <option value="siuj" > Société Internationale d’Urologie Journal (SIUJ) </option> <option value="societies" > Societies </option> <option value="software" > Software </option> <option value="soilsystems" > Soil Systems </option> <option value="solar" > Solar </option> <option value="solids" > Solids </option> <option value="spectroscj" > Spectroscopy Journal </option> <option value="sports" > Sports </option> <option value="standards" > Standards </option> <option value="stats" > Stats </option> <option value="stresses" > Stresses </option> <option value="surfaces" > Surfaces </option> <option value="surgeries" > Surgeries </option> <option value="std" > Surgical Techniques Development </option> <option value="sustainability" > Sustainability </option> <option value="suschem" > Sustainable Chemistry </option> <option value="symmetry" > Symmetry </option> <option value="synbio" > SynBio </option> <option value="systems" > Systems </option> <option value="targets" > Targets </option> <option value="taxonomy" > Taxonomy </option> <option value="technologies" > Technologies </option> <option value="telecom" > Telecom </option> <option value="textiles" > Textiles </option> <option value="thalassrep" > Thalassemia Reports </option> <option value="therapeutics" > Therapeutics </option> <option value="thermo" > Thermo </option> <option value="timespace" > Time and Space </option> <option value="tomography" > Tomography </option> <option value="tourismhosp" > Tourism and Hospitality </option> <option value="toxics" > Toxics </option> <option value="toxins" > Toxins </option> <option value="transplantology" > Transplantology </option> <option value="traumacare" > Trauma Care </option> <option value="higheredu" > Trends in Higher Education </option> <option value="tropicalmed" > Tropical Medicine and Infectious Disease (TropicalMed) </option> <option value="universe" > Universe </option> <option value="urbansci" > Urban Science </option> <option value="uro" > Uro </option> <option value="vaccines" > Vaccines </option> <option value="vehicles" > Vehicles </option> <option value="venereology" > Venereology </option> <option value="vetsci" > Veterinary Sciences </option> <option value="vibration" > Vibration </option> <option value="virtualworlds" > Virtual Worlds </option> <option value="viruses" > Viruses </option> <option value="vision" > Vision </option> <option value="waste" > Waste </option> <option value="water" > Water </option> <option value="wild" > Wild </option> <option value="wind" > Wind </option> <option value="women" > Women </option> <option value="world" > World </option> <option value="wevj" > World Electric Vehicle Journal (WEVJ) </option> <option value="youth" > Youth </option> <option value="zoonoticdis" > Zoonotic Diseases </option> </select> </div> <div class="large-2 medium-2 small-6 columns "> <div class=""> <div class="search-input-label">Article Type</div> </div> <select id="article_type" tabindex="4" name="article_type" class="chosen-select"> <option value="">All Article Types</option> <option value="research-article">Article</option> <option value="review-article">Review</option> <option value="rapid-communication">Communication</option> <option value="editorial">Editorial</option> <option value="abstract">Abstract</option> <option value="book-review">Book Review</option> <option value="brief-communication">Brief Communication</option> <option value="brief-report">Brief Report</option> <option value="case-report">Case Report</option> <option value="clinicopathological-challenge">Clinicopathological Challenge</option> <option value="article-commentary">Comment</option> <option value="commentary">Commentary</option> <option value="concept-paper">Concept Paper</option> <option value="conference-report">Conference Report</option> <option value="correction">Correction</option> <option value="creative">Creative</option> <option value="data-descriptor">Data Descriptor</option> <option value="discussion">Discussion</option> <option value="Entry">Entry</option> <option value="essay">Essay</option> <option value="expression-of-concern">Expression of Concern</option> <option value="extended-abstract">Extended Abstract</option> <option value="field-guide">Field Guide</option> <option value="guidelines">Guidelines</option> <option value="hypothesis">Hypothesis</option> <option value="interesting-image">Interesting Images</option> <option value="letter">Letter</option> <option value="books-received">New Book Received</option> <option value="obituary">Obituary</option> <option value="opinion">Opinion</option> <option value="perspective">Perspective</option> <option value="proceedings">Proceeding Paper</option> <option value="project-report">Project Report</option> <option value="protocol">Protocol</option> <option value="registered-report">Registered Report</option> <option value="reply">Reply</option> <option value="retraction">Retraction</option> <option value="note">Short Note</option> <option value="study-protocol">Study Protocol</option> <option value="systematic_review">Systematic Review</option> <option value="technical-note">Technical Note</option> <option value="tutorial">Tutorial</option> <option value="viewpoint">Viewpoint</option> </select> </div> <div class="large-1 medium-1 small-6 end columns small-push-6 medium-reset-order large-reset-order js-search-collapsed-button-container"> <div class="search-input-label"> </div> <input type="submit" id="search" value="Search" class="button button--dark button--full-width searchButton1 US_SearchButton" tabindex="12"> </div> <div class="large-1 medium-1 small-6 end columns large-text-left small-only-text-center small-pull-6 medium-reset-order large-reset-order js-search-collapsed-link-container"> <div class="search-input-label"> </div> <a class="main-search-clear search-container__link" href="#" onclick="openAdvanced(''); return false;">Advanced<span class="show-for-small-only"> Search</span></a> </div> </div> </div> <div class="search-container__advanced" style="margin-top: 0; padding-top: 0px; background-color: inherit; color: inherit;"> <div class="row"> <div class="large-2 medium-2 columns show-for-medium-up"> </div> <div class="large-2 medium-2 small-6 columns "> <div class=""> <div class="search-input-label">Section</div> </div> <select id="section" tabindex="5" name="section" class="chosen-select"> <option value=""></option> </select> </div> <div class="large-2 medium-2 small-6 columns "> <div class=""> <div class="search-input-label">Special Issue</div> </div> <select id="special_issue" tabindex="6" name="special_issue" class="chosen-select"> <option value=""></option> </select> </div> <div class="large-1 medium-1 small-6 end columns "> <div class="search-input-label">Volume</div> <input type="text" id="volume" tabindex="7" name="volume" placeholder="..." value="11" /> </div> <div class="large-1 medium-1 small-6 end columns "> <div class="search-input-label">Issue</div> <input type="text" id="issue" tabindex="8" name="issue" placeholder="..." value="3" /> </div> <div class="large-1 medium-1 small-6 end columns "> <div class="search-input-label">Number</div> <input type="text" id="number" tabindex="9" name="number" placeholder="..." value="" /> </div> <div class="large-1 medium-1 small-6 end columns "> <div class="search-input-label">Page</div> <input type="text" id="page" tabindex="10" name="page" placeholder="..." value="" /> </div> <div class="large-1 medium-1 small-6 columns small-push-6 medium-reset order large-reset-order medium-reset-order js-search-expanded-button-container"></div> <div class="large-1 medium-1 small-6 columns large-text-left small-only-text-center small-pull-6 medium-reset-order large-reset-order js-search-expanded-link-container"></div> </div> </div> </form> <form id="advanced-search" class="large-12 medium-12 columns"> <div class="search-container__advanced"> <div id="advanced-search-template" class="row advanced-search-row"> <div class="large-2 medium-2 small-12 columns show-for-medium-up"> </div> <div class="large-2 medium-2 small-3 columns connector-div"> <div class="search-input-label"><span class="show-for-medium-up">Logical Operator</span><span class="show-for-small">Operator</span></div> <select class="connector"> <option value="and">AND</option> <option value="or">OR</option> </select> </div> <div class="large-3 medium-3 small-6 columns search-text-div"> <div class="search-input-label">Search Text</div> <input type="text" class="search-text" placeholder="Search text"> </div> <div class="large-2 medium-2 small-6 large-offset-0 medium-offset-0 small-offset-3 columns search-field-div"> <div class="search-input-label">Search Type</div> <select class="search-field"> <option value="all">All fields</option> <option value="title">Title</option> <option value="abstract">Abstract</option> <option value="keywords">Keywords</option> <option value="authors">Authors</option> <option value="affiliations">Affiliations</option> <option value="doi">Doi</option> <option value="full_text">Full Text</option> <option value="references">References</option> </select> </div> <div class="large-1 medium-1 small-3 columns"> <div class="search-input-label"> </div> <div class="search-action-div"> <div class="search-plus"> <i class="material-icons">add_circle_outline</i> </div> </div> <div class="search-action-div"> <div class="search-minus"> <i class="material-icons">remove_circle_outline</i> </div> </div> </div> <div class="large-1 medium-1 small-6 large-offset-0 medium-offset-0 small-offset-3 end columns"> <div class="search-input-label"> </div> <input class="advanced-search-button button button--dark search-submit" type="submit" value="Search"> </div> <div class="large-1 medium-1 small-6 end columns show-for-medium-up"></div> </div> </div> </form> </div> <div class="header-divider"> </div> <div class="breadcrumb row full-row"> <div class="breadcrumb__element"> <a href="/about/journals">Journals</a> </div> <div class="breadcrumb__element"> <a href="/journal/ijfs">IJFS</a> </div> <div class="breadcrumb__element"> <a href="/2227-7072/11">Volume 11</a> </div> <div class="breadcrumb__element"> <a href="/2227-7072/11/3">Issue 3</a> </div> <div class="breadcrumb__element"> <a href="#">10.3390/ijfs11030094</a> </div> </div> </header> <div id="main-content" class=""> <div class="row full-width row-fixed-left-column"> <div id="left-column" class="content__column large-3 medium-3 small-12 columns"> <div class="content__container"> <a href="/journal/ijfs"> <img src="https://pub.mdpi-res.com/img/journals/ijfs-logo.png?90a76e5ffbc4c6e6" alt="ijfs-logo" title="International Journal of Financial Studies" style="max-height: 60px; margin: 0 0 0 0;"> </a> <div class="generic-item no-border"> <a class="button button--color button--full-width js-journal-active-only-link js-journal-active-only-submit-link UC_ArticleSubmitButton" href="https://susy.mdpi.com/user/manuscripts/upload?form%5Bjournal_id%5D%3D148" data-disabledmessage="creating new submissions is not possible."> Submit to this Journal </a> <a class="button button--color button--full-width js-journal-active-only-link UC_ArticleReviewButton" href="https://susy.mdpi.com/volunteer/journals/review" data-disabledmessage="volunteering as journal reviewer is not possible."> Review for this Journal </a> <a class="button button--color-inversed button--color-journal button--full-width js-journal-active-only-link UC_ArticleEditIssueButton" href="/journalproposal/sendproposalspecialissue/ijfs" data-path="/2227-7072/11/3/94" data-disabledmessage="proposing new special issue is not possible."> Propose a Special Issue </a> </div> <div class="generic-item link-article-menu show-for-small"> <a href="#" class="link-article-menu show-for-small"> <span class="closed">►</span> <span class="open" style="display: none;">▼</span> Article Menu </a> </div> <div class="hide-small-down-initially UI_ArticleMenu"> <div class="generic-item"> <h2>Article Menu</h2> </div> <ul class="accordion accordion__menu" data-accordion data-options="multi_expand:true;toggleable: true"> <li class="accordion-navigation"> <a href="#academic_editors" class="accordion__title">Academic Editor</a> <div id="academic_editors" class="content active"> <div class="academic-editor-container " title="Faculty of Business, Philadelphia University, Amman, Jordan"> <div class="sciprofiles-link" style="display: inline-block"> <div class="sciprofiles-link__link"> <img class="sciprofiles-link__image" src="https://pub.mdpi-res.com/bundles/mdpisciprofileslink/img/unknown-user.png?1732261071" style="width: auto; height: 16px; border-radius: 50%;"> <span class="sciprofiles-link__name" style="line-height: 36px;">Muneer M. Alshater</span> </div> </div> </div> </div> </li> <li class="accordion-direct-link"> <a href="/2227-7072/11/3/94/scifeed_display" data-reveal-id="scifeed-modal" data-reveal-ajax="true">Subscribe SciFeed</a> </li> <li class="accordion-direct-link js-article-similarity-container" style="display: none"> <a href="#" class="js-similarity-related-articles">Recommended Articles</a> </li> <li class="accordion-navigation"> <a href="#related" class="accordion__title">Related Info Link</a> <div id="related" class="content UI_ArticleMenu_RelatedLinks"> <ul> <li class="li-link"> <a href="https://scholar.google.com/scholar?q=Forecasting%20Stock%20Market%20Prices%20Using%20Machine%20Learning%20and%20Deep%20Learning%20Models%3A%20A%20Systematic%20Review%2C%20Performance%20Analysis%20and%20Discussion%20of%20Implications" target="_blank" rel="noopener noreferrer">Google Scholar</a> </li> </ul> </div> </li> <li class="accordion-navigation"> <a href="#authors" class="accordion__title">More by Authors Links</a> <div id="authors" class="content UI_ArticleMenu_AuthorsLinks"> <ul class="side-menu-ul"> <li> <a class="expand" onclick='$(this).closest("li").next("div").toggle(); return false;'>on DOAJ</a> </li> <div id="AuthorDOAJExpand" style="display:none;"> <ul class="submenu"> <li class="li-link"> <a href='http://doaj.org/search/articles?source=%7B%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22%5C%22Gaurang%20Sonkavde%5C%22%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22bibjson.author.name%22%7D%7D%7D' target="_blank" rel="noopener noreferrer">Sonkavde, G.</a> <li> </li> <li class="li-link"> <a href='http://doaj.org/search/articles?source=%7B%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22%5C%22Deepak%20Sudhakar%20Dharrao%5C%22%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22bibjson.author.name%22%7D%7D%7D' target="_blank" rel="noopener noreferrer">Dharrao, D. Sudhakar</a> <li> </li> <li class="li-link"> <a href='http://doaj.org/search/articles?source=%7B%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22%5C%22Anupkumar%20M.%20Bongale%5C%22%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22bibjson.author.name%22%7D%7D%7D' target="_blank" rel="noopener noreferrer">Bongale, A. M.</a> <li> </li> <li class="li-link"> <a href='http://doaj.org/search/articles?source=%7B%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22%5C%22Sarika%20T.%20Deokate%5C%22%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22bibjson.author.name%22%7D%7D%7D' target="_blank" rel="noopener noreferrer">Deokate, S. T.</a> <li> </li> <li class="li-link"> <a href='http://doaj.org/search/articles?source=%7B%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22%5C%22Deepak%20Doreswamy%5C%22%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22bibjson.author.name%22%7D%7D%7D' target="_blank" rel="noopener noreferrer">Doreswamy, D.</a> <li> </li> <li class="li-link"> <a href='http://doaj.org/search/articles?source=%7B%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22%5C%22Subraya%20Krishna%20Bhat%5C%22%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22bibjson.author.name%22%7D%7D%7D' target="_blank" rel="noopener noreferrer">Bhat, S. Krishna</a> <li> </li> </ul> </div> <li> <a class="expand" onclick='$(this).closest("li").next("div").toggle(); return false;'>on Google Scholar</a> </li> <div id="AuthorGoogleExpand" style="display:none;"> <ul class="submenu"> <li class="li-link"> <a href="https://scholar.google.com/scholar?q=Gaurang%20Sonkavde" target="_blank" rel="noopener noreferrer">Sonkavde, G.</a> <li> </li> <li class="li-link"> <a href="https://scholar.google.com/scholar?q=Deepak%20Sudhakar%20Dharrao" target="_blank" rel="noopener noreferrer">Dharrao, D. Sudhakar</a> <li> </li> <li class="li-link"> <a href="https://scholar.google.com/scholar?q=Anupkumar%20M.%20Bongale" target="_blank" rel="noopener noreferrer">Bongale, A. M.</a> <li> </li> <li class="li-link"> <a href="https://scholar.google.com/scholar?q=Sarika%20T.%20Deokate" target="_blank" rel="noopener noreferrer">Deokate, S. T.</a> <li> </li> <li class="li-link"> <a href="https://scholar.google.com/scholar?q=Deepak%20Doreswamy" target="_blank" rel="noopener noreferrer">Doreswamy, D.</a> <li> </li> <li class="li-link"> <a href="https://scholar.google.com/scholar?q=Subraya%20Krishna%20Bhat" target="_blank" rel="noopener noreferrer">Bhat, S. Krishna</a> <li> </li> </ul> </div> <li> <a class="expand" onclick='$(this).closest("li").next("div").toggle(); return false;'>on PubMed</a> </li> <div id="AuthorPubMedExpand" style="display:none;"> <ul class="submenu"> <li class="li-link"> <a href="http://www.pubmed.gov/?cmd=Search&term=Gaurang%20Sonkavde" target="_blank" rel="noopener noreferrer">Sonkavde, G.</a> <li> </li> <li class="li-link"> <a href="http://www.pubmed.gov/?cmd=Search&term=Deepak%20Sudhakar%20Dharrao" target="_blank" rel="noopener noreferrer">Dharrao, D. Sudhakar</a> <li> </li> <li class="li-link"> <a href="http://www.pubmed.gov/?cmd=Search&term=Anupkumar%20M.%20Bongale" target="_blank" rel="noopener noreferrer">Bongale, A. M.</a> <li> </li> <li class="li-link"> <a href="http://www.pubmed.gov/?cmd=Search&term=Sarika%20T.%20Deokate" target="_blank" rel="noopener noreferrer">Deokate, S. T.</a> <li> </li> <li class="li-link"> <a href="http://www.pubmed.gov/?cmd=Search&term=Deepak%20Doreswamy" target="_blank" rel="noopener noreferrer">Doreswamy, D.</a> <li> </li> <li class="li-link"> <a href="http://www.pubmed.gov/?cmd=Search&term=Subraya%20Krishna%20Bhat" target="_blank" rel="noopener noreferrer">Bhat, S. Krishna</a> <li> </li> </ul> </div> </ul> </div> </li> </ul> <span style="display:none" id="scifeed_hidden_flag"></span> <span style="display:none" id="scifeed_subscribe_url">/ajax/scifeed/subscribe</span> </div> </div> <div class="content__container responsive-moving-container large medium active hidden" data-id="article-counters"> <div id="counts-wrapper" class="row generic-item no-border" data-equalizer> <div id="js-counts-wrapper__views" class="small-12 hide columns count-div-container"> <a href="#metrics" > <div class="count-div" data-equalizer-watch> <span class="name">Article Views</span> <span class="count view-number"></span> </div> </a> </div> <div id="js-counts-wrapper__citations" class="small-12 columns hide count-div-container"> <a href="#metrics" > <div class="count-div" data-equalizer-watch> <span class="name">Citations</span> <span class="count citations-number Var_ArticleMaxCitations">-</span> </div> </a> </div> </div> </div> <div class="content__container"> <div class="hide-small-down-initially"> <ul class="accordion accordion__menu" data-accordion data-options="multi_expand:true;toggleable: true"> <li class="accordion-navigation"> <a href="#table_of_contents" class="accordion__title">Table of Contents</a> <div id="table_of_contents" class="content active"> <div class="menu-caption" id="html-quick-links-title"></div> </div> </li> </ul> </div> </div>  <div id="pbgrd-sky"></div> <script src="https://cdn.pbgrd.com/core-mdpi.js"></script> <style>.content__container { min-width: 300px; }</style>  </div> <div id="middle-column" class="content__column large-9 medium-9 small-12 columns end middle-bordered"> <div class="middle-column__help"> <div class="middle-column__help__fixed show-for-medium-up"> <span id="js-altmetrics-donut" href="#" target="_blank" rel="noopener noreferrer" style="display: none;"> <span data-badge-type='donut' class='altmetric-embed' data-doi='10.3390/ijfs11030094'></span> <span>Altmetric</span> </span> <a href="#" class="UA_ShareButton" data-reveal-id="main-share-modal" title="Share"> <i class="material-icons">share</i> <span>Share</span> </a> <a href="#" data-reveal-id="main-help-modal" title="Help"> <i class="material-icons">announcement</i> <span>Help</span> </a> <a href="javascript:void(0);" data-reveal-id="cite-modal" data-counterslink = "https://www.mdpi.com/2227-7072/11/3/94/cite" > <i class="material-icons">format_quote</i> <span>Cite</span> </a> <a href="https://sciprofiles.com/discussion-groups/public/10.3390/ijfs11030094?utm_source=mpdi.com&utm_medium=publication&utm_campaign=discuss_in_sciprofiles" target="_blank" rel="noopener noreferrer" title="Discuss in Sciprofiles"> <i class="material-icons">question_answer</i> <span>Discuss in SciProfiles</span> </a> <a href="#" class="" data-hypothesis-trigger-endorses-tab title="Endorse"> <i data-hypothesis-endorse-trigger class="material-icons" >thumb_up</i> <div data-hypothesis-endorsement-count data-hypothesis-trigger-endorses-tab class="hypothesis-count-container"> ... </div> <span>Endorse</span> </a> <a href="#" data-hypothesis-trigger class="js-hypothesis-open UI_ArticleAnnotationsButton" title="Comment"> <i class="material-icons">textsms</i> <div data-hypothesis-annotation-count class="hypothesis-count-container"> ... </div> <span>Comment</span> </a> </div> <div id="main-help-modal" class="reveal-modal reveal-modal-new" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog"> <div class="row"> <div class="small-12 columns"> <h2 style="margin: 0;">Need Help?</h2> </div> <div class="small-6 columns"> <h3>Support</h3> <p> Find support for a specific problem in the support section of our website. </p> <a target="_blank" href="/about/contactform" class="button button--color button--full-width"> Get Support </a> </div> <div class="small-6 columns"> <h3>Feedback</h3> <p> Please let us know what you think of our products and services. </p> <a target="_blank" href="/feedback/send" class="button button--color button--full-width"> Give Feedback </a> </div> <div class="small-6 columns end"> <h3>Information</h3> <p> Visit our dedicated information section to learn more about MDPI. </p> <a target="_blank" href="/authors" class="button button--color button--full-width"> Get Information </a> </div> </div> <a class="close-reveal-modal" aria-label="Close"> <i class="material-icons">clear</i> </a> </div> </div> <div class="middle-column__main "> <div class="page-highlight"> <style type="text/css"> img.review-status { width: 30px; } </style> <div id="jmolModal" class="reveal-modal" data-reveal aria-labelledby="Captcha" aria-hidden="true" role="dialog"> <h2>JSmol Viewer</h2> <div class="row"> <div class="small-12 columns text-center"> <iframe style="width: 520px; height: 520px;" frameborder="0" id="jsmol-content"></iframe> <div class="content"></div> </div> </div> <a class="close-reveal-modal" aria-label="Close"> <i class="material-icons">clear</i> </a> </div> <div itemscope itemtype="http://schema.org/ScholarlyArticle" id="abstract" class="abstract_div"> <div class="js-check-update-container"></div> <div class="html-content__container content__container content__container__combined-for-large__first" style="overflow: auto; position: inherit;"> <div class='html-profile-nav'> <div class='top-bar'> <div class='nav-sidebar-btn show-for-large-up' data-status='opened' > <i class='material-icons'>first_page</i> </div> <a id="js-button-download" class="button button--color-inversed" style="display: none;" href="/2227-7072/11/3/94/pdf?version=1690521261" data-name="Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications" data-journal="ijfs"> <i class="material-icons custom-download"></i> Download PDF </a> <div class='nav-btn'> <i class='material-icons'>settings</i> </div> <a href="/2227-7072/11/3/94/reprints" id="js-button-reprints" class="button button--color-inversed"> Order Article Reprints </a> </div> <div class='html-article-menu'> <div class='html-first-step row'> <div class='html-font-family large-6 medium-6 small-12 columns'> <div class='row'> <div class='html-font-label large-4 medium-4 small-12 columns'> Font Type: </div> <div class='large-8 medium-8 small-12 columns'> <span class="html-article-menu-option"><i style='font-family:Arial, Arial, Helvetica, sans-serif;' data-fontfamily='Arial, Arial, Helvetica, sans-serif'>Arial</i></span> <span class="html-article-menu-option"><i style='font-family:Georgia1, Georgia, serif;' data-fontfamily='Georgia1, Georgia, serif'>Georgia</i></span> <span class="html-article-menu-option"><i style='font-family:Verdana, Verdana, Geneva, sans-serif;' data-fontfamily='Verdana, Verdana, Geneva, sans-serif' >Verdana</i></span> </div> </div> </div> <div class='html-font-resize large-6 medium-6 small-12 columns'> <div class='row'> <div class='html-font-label large-4 medium-4 small-12 columns'>Font Size:</div> <div class='large-8 medium-8 small-12 columns'> <span class="html-article-menu-option a1" data-percent="100">Aa</span> <span class="html-article-menu-option a2" data-percent="120">Aa</span> <span class="html-article-menu-option a3" data-percent="160">Aa</span> </div> </div> </div> </div> <div class='row'> <div class='html-line-space large-6 medium-6 small-12 columns'> <div class='row'> <div class='html-font-label large-4 medium-4 small-12 columns' >Line Spacing:</div> <div class='large-8 medium-8 small-12 columns'> <span class="html-article-menu-option a1" data-line-height="1.5em"> <i class="fa"></i> </span> <span class="html-article-menu-option a2" data-line-height="1.8em"> <i class="fa"></i> </span> <span class="html-article-menu-option a3" data-line-height="2.1em"> <i class="fa"></i> </span> </div> </div> </div> <div class='html-column-width large-6 medium-6 small-12 columns'> <div class='row'> <div class='html-font-label large-4 medium-4 small-12 columns' >Column Width:</div> <div class='large-8 medium-8 small-12 columns'> <span class="html-article-menu-option a1" data-column-width="20%"> <i class="fa"></i> </span> <span class="html-article-menu-option a2" data-column-width="10%"> <i class="fa"></i> </span> <span class="html-article-menu-option a3" data-column-width="0%"> <i class="fa"></i> </span> </div> </div> </div> </div> <div class='row'> <div class='html-font-bg large-6 medium-6 small-12 columns end'> <div class='row'> <div class='html-font-label large-4 medium-4 small-12 columns'>Background:</div> <div class='large-8 medium-8 small-12 columns'> <div class="html-article-menu-option html-nav-bg html-nav-bright" data-bg="bright"> <i class="fa fa-file-text"></i> </div> <div class="html-article-menu-option html-nav-bg html-nav-dark" data-bg="dark"> <i class="fa fa-file-text-o"></i> </div> <div class="html-article-menu-option html-nav-bg html-nav-creme" data-bg="creme"> <i class="fa fa-file-text"></i> </div> </div> </div> </div> </div> </div> </div> <article ><div class='html-article-content'> <span itemprop="publisher" content="Multidisciplinary Digital Publishing Institute"></span><span itemprop="url" content="https://www.mdpi.com/2227-7072/11/3/94"></span> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Review</span></div> <h1 class="title hypothesis_container" itemprop="name"> Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications </h1> <div class="art-authors hypothesis_container"> by <span class="inlineblock "><div class='profile-card-drop' data-dropdown='profile-card-drop11530304' data-options='is_hover:true, hover_timeout:5000'> Gaurang Sonkavde</div><div id="profile-card-drop11530304" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"><div class="profile-card__title"><div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name">Gaurang Sonkavde</span></div></div></div><div class="profile-card__buttons" style="margin-bottom: 10px;"><a href="https://sciprofiles.com/profile/3114980?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a><a href="https://scilit.net/scholars?q=Gaurang%20Sonkavde" class="button button--color-inversed" target="_blank"> Scilit </a><a href="https://www.preprints.org/search?search1=Gaurang%20Sonkavde&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a><a href="https://scholar.google.com/scholar?q=Gaurang%20Sonkavde" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a></div></div><sup> 1</sup><span style="display: inline; margin-left: 5px;"></span><a class="toEncode emailCaptcha visibility-hidden" data-author-id="11530304" href="/cdn-cgi/l/email-protection#56793538327b35313f793a79333b373f3a7b262439223335223f393875666666606061676467636660666f66666762666e666f663566606767666066656664646166666637666066336634626f6662666e6637"><sup><i class="fa fa-envelope-o"></i></sup></a>, </span><span class="inlineblock "><div class='profile-card-drop' data-dropdown='profile-card-drop11530305' data-options='is_hover:true, hover_timeout:5000'> Deepak Sudhakar Dharrao</div><div id="profile-card-drop11530305" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"><div class="profile-card__title"><div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name">Deepak Sudhakar Dharrao</span></div></div></div><div class="profile-card__buttons" style="margin-bottom: 10px;"><a href="https://sciprofiles.com/profile/2434189?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a><a href="https://scilit.net/scholars?q=Deepak%20Sudhakar%20Dharrao" class="button button--color-inversed" target="_blank"> Scilit </a><a href="https://www.preprints.org/search?search1=Deepak%20Sudhakar%20Dharrao&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a><a href="https://scholar.google.com/scholar?q=Deepak%20Sudhakar%20Dharrao" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a></div></div><sup> 2,*</sup><span style="display: inline; margin-left: 5px;"></span><a class="toEncode emailCaptcha visibility-hidden" data-author-id="11530305" href="/cdn-cgi/l/email-protection#5a7539343e77393d337536753f373b3336772a28352e3f392e333534796a6a6a6b6c6e6a6b6b6e6a6f6a3c6e3b6a6a6a396a6f6b6c6b6c6a6f6a38686e6b6d6a3e6b6a6b6e6b6b6a3b6a6b6e3b6a6b6a6a6b6b6e3b6a3e6a3b"><sup><i class="fa fa-envelope-o"></i></sup></a><a href="https://orcid.org/0000-0002-2540-6942" target="_blank" rel="noopener noreferrer"><img src="https://pub.mdpi-res.com/img/design/orcid.png?0465bc3812adeb52?1732261071" title="ORCID" style="position: relative; width: 13px; margin-left: 3px; max-width: 13px !important; height: auto; top: -5px;"></a>, </span><span class="inlineblock "><div class='profile-card-drop' data-dropdown='profile-card-drop11530306' data-options='is_hover:true, hover_timeout:5000'> Anupkumar M. Bongale</div><div id="profile-card-drop11530306" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"><div class="profile-card__title"><div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name">Anupkumar M. Bongale</span></div></div></div><div class="profile-card__buttons" style="margin-bottom: 10px;"><a href="https://sciprofiles.com/profile/1404211?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a><a href="https://scilit.net/scholars?q=Anupkumar%20M.%20Bongale" class="button button--color-inversed" target="_blank"> Scilit </a><a href="https://www.preprints.org/search?search1=Anupkumar%20M.%20Bongale&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a><a href="https://scholar.google.com/scholar?q=Anupkumar%20M.%20Bongale" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a></div></div><sup> 1,*</sup><span style="display: inline; margin-left: 5px;"></span><a class="toEncode emailCaptcha visibility-hidden" data-author-id="11530306" href="/cdn-cgi/l/email-protection#07286469632a64606e286b28626a666e6b2a777568736264736e68692437373761313636333636376636333764373736343361373437623761373137373763373335363635373f3632363636333761373333613733373236333361373f3761"><sup><i class="fa fa-envelope-o"></i></sup></a><a href="https://orcid.org/0000-0002-5897-0283" target="_blank" rel="noopener noreferrer"><img src="https://pub.mdpi-res.com/img/design/orcid.png?0465bc3812adeb52?1732261071" title="ORCID" style="position: relative; width: 13px; margin-left: 3px; max-width: 13px !important; height: auto; top: -5px;"></a>, </span><span class="inlineblock "><div class='profile-card-drop' data-dropdown='profile-card-drop11530307' data-options='is_hover:true, hover_timeout:5000'> Sarika T. Deokate</div><div id="profile-card-drop11530307" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"><div class="profile-card__title"><div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name">Sarika T. Deokate</span></div></div></div><div class="profile-card__buttons" style="margin-bottom: 10px;"><a href="https://sciprofiles.com/profile/3025138?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a><a href="https://scilit.net/scholars?q=Sarika%20T.%20Deokate" class="button button--color-inversed" target="_blank"> Scilit </a><a href="https://www.preprints.org/search?search1=Sarika%20T.%20Deokate&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a><a href="https://scholar.google.com/scholar?q=Sarika%20T.%20Deokate" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a></div></div><sup> 3</sup><span style="display: inline; margin-left: 5px;"></span><a class="toEncode emailCaptcha visibility-hidden" data-author-id="11530307" href="/cdn-cgi/l/email-protection#331c505d571e50545a1c5f1c565e525a5f1e43415c475650475a5c5d1003030201040003020252020b02010657020402050250020b02010304020500000300020302030250020503000305025702050657025003020207"><sup><i class="fa fa-envelope-o"></i></sup></a><a href="https://orcid.org/0000-0002-5102-4104" target="_blank" rel="noopener noreferrer"><img src="https://pub.mdpi-res.com/img/design/orcid.png?0465bc3812adeb52?1732261071" title="ORCID" style="position: relative; width: 13px; margin-left: 3px; max-width: 13px !important; height: auto; top: -5px;"></a>, </span><span class="inlineblock "><div class='profile-card-drop' data-dropdown='profile-card-drop11530308' data-options='is_hover:true, hover_timeout:5000'> Deepak Doreswamy</div><div id="profile-card-drop11530308" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"><div class="profile-card__title"><div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name">Deepak Doreswamy</span></div></div></div><div class="profile-card__buttons" style="margin-bottom: 10px;"><a href="https://sciprofiles.com/profile/1844985?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a><a href="https://scilit.net/scholars?q=Deepak%20Doreswamy" class="button button--color-inversed" target="_blank"> Scilit </a><a href="https://www.preprints.org/search?search1=Deepak%20Doreswamy&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a><a href="https://scholar.google.com/scholar?q=Deepak%20Doreswamy" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a></div></div><sup> 4</sup><span style="display: inline; margin-left: 5px;"></span><a class="toEncode emailCaptcha visibility-hidden" data-author-id="11530308" href="/cdn-cgi/l/email-protection#5b7438353f76383c327437743e363a3237762b29342f3e382f323435786b6b6b6a6d6f6b6a6a6f6b6e6b3d6f3a6b6b696f6b626b6e6b3a6b3f6a6f6b6e6b636f3a6b6a6b6b6a6a"><sup><i class="fa fa-envelope-o"></i></sup></a><a href="https://orcid.org/0000-0001-7194-463X" target="_blank" rel="noopener noreferrer"><img src="https://pub.mdpi-res.com/img/design/orcid.png?0465bc3812adeb52?1732261071" title="ORCID" style="position: relative; width: 13px; margin-left: 3px; max-width: 13px !important; height: auto; top: -5px;"></a> and </span><span class="inlineblock "><div class='profile-card-drop' data-dropdown='profile-card-drop11530309' data-options='is_hover:true, hover_timeout:5000'> Subraya Krishna Bhat</div><div id="profile-card-drop11530309" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"><div class="profile-card__title"><div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name">Subraya Krishna Bhat</span></div></div></div><div class="profile-card__buttons" style="margin-bottom: 10px;"><a href="https://sciprofiles.com/profile/2028908?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a><a href="https://scilit.net/scholars?q=Subraya%20Krishna%20Bhat" class="button button--color-inversed" target="_blank"> Scilit </a><a href="https://www.preprints.org/search?search1=Subraya%20Krishna%20Bhat&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a><a href="https://scholar.google.com/scholar?q=Subraya%20Krishna%20Bhat" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a></div></div><sup> 5</sup><span style="display: inline; margin-left: 5px;"></span><a class="toEncode emailCaptcha visibility-hidden" data-author-id="11530309" href="/cdn-cgi/l/email-protection#27084449430a44404e084b08424a464e4b0a575548534244534e4849041717161f101412431616164516151710141416421615164316461714161516411243161116101711"><sup><i class="fa fa-envelope-o"></i></sup></a><a href="https://orcid.org/0000-0003-1798-3480" target="_blank" rel="noopener noreferrer"><img src="https://pub.mdpi-res.com/img/design/orcid.png?0465bc3812adeb52?1732261071" title="ORCID" style="position: relative; width: 13px; margin-left: 3px; max-width: 13px !important; height: auto; top: -5px;"></a></span> </div> <div class="nrm"></div> <span style="display:block; height:6px;"></span> <div></div> <div style="margin: 5px 0 15px 0;" class="hypothesis_container"> <div class="art-affiliations"> <div class="affiliation "> <div class="affiliation-item"><sup>1</sup></div> <div class="affiliation-name ">Department of Artificial Intelligence and Machine Learning, Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune 412115, Maharashtra, India</div> </div> <div class="affiliation "> <div class="affiliation-item"><sup>2</sup></div> <div class="affiliation-name ">Department of Computer Science & Engineering, Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune 412115, Maharashtra, India</div> </div> <div class="affiliation "> <div class="affiliation-item"><sup>3</sup></div> <div class="affiliation-name ">Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune 411044, Maharashtra, India</div> </div> <div class="affiliation "> <div class="affiliation-item"><sup>4</sup></div> <div class="affiliation-name ">Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India</div> </div> <div class="affiliation "> <div class="affiliation-item"><sup>5</sup></div> <div class="affiliation-name ">Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India</div> </div> <div class="affiliation"> <div class="affiliation-item"><sup>*</sup></div> <div class="affiliation-name ">Authors to whom correspondence should be addressed. </div> </div> </div> </div> <div class="bib-identity" style="margin-bottom: 10px;"> <em>Int. J. Financial Stud.</em> <b>2023</b>, <em>11</em>(3), 94; <a href="https://doi.org/10.3390/ijfs11030094">https://doi.org/10.3390/ijfs11030094</a> </div> <div class="pubhistory" style="font-weight: bold; padding-bottom: 10px;"> <span style="display: inline-block">Submission received: 22 April 2023</span> / <span style="display: inline-block">Revised: 13 July 2023</span> / <span style="display: inline-block">Accepted: 19 July 2023</span> / <span style="display: inline-block">Published: 26 July 2023</span> </div> <div class="highlight-box1"> <div class="download"> <a class="button button--color-inversed button--drop-down" data-dropdown="drop-download-1203368" aria-controls="drop-supplementary-1203368" aria-expanded="false"> Download <i class="material-icons">keyboard_arrow_down</i> </a> <div id="drop-download-1203368" class="f-dropdown label__btn__dropdown label__btn__dropdown--button" data-dropdown-content aria-hidden="true" tabindex="-1"> <a class="UD_ArticlePDF" href="/2227-7072/11/3/94/pdf?version=1690521261" data-name="Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications" data-journal="ijfs">Download PDF</a> <br/> <a id="js-pdf-with-cover-access-captcha" href="#" data-target="/2227-7072/11/3/94/pdf-with-cover" class="accessCaptcha">Download PDF with Cover</a> <br/> <a id="js-xml-access-captcha" href="#" data-target="/2227-7072/11/3/94/xml" class="accessCaptcha">Download XML</a> <br/> <a href="/2227-7072/11/3/94/epub" id="epub_link">Download Epub</a> <br/> </div> <div class="js-browse-figures" style="display: inline-block;"> <a href="#" class="button button--color-inversed margin-bottom-10 openpopupgallery UI_BrowseArticleFigures" data-target='article-popup' data-counterslink = "https://www.mdpi.com/2227-7072/11/3/94/browse" >Browse Figures</a> </div> <div id="article-popup" class="popupgallery" style="display: inline; line-height: 200%"> <a href="https://pub.mdpi-res.com/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g001.png?1690521342" title=" <strong>Figure 1</strong><br/> <p>Stock forecasting algorithm.</p> "> </a> <a href="https://pub.mdpi-res.com/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g002.png?1690521348" title=" <strong>Figure 2</strong><br/> <p>LSTM structure.</p> "> </a> <a href="https://pub.mdpi-res.com/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g003.png?1690521349" title=" <strong>Figure 3</strong><br/> <p>GRU structure.</p> "> </a> <a href="https://pub.mdpi-res.com/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g004.png?1690521350" title=" <strong>Figure 4</strong><br/> <p>Random forest.</p> "> </a> <a href="https://pub.mdpi-res.com/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g005.png?1690521353" title=" <strong>Figure 5</strong><br/> <p>XG-Boost algorithm.</p> "> </a> <a href="https://pub.mdpi-res.com/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g006.png?1690521354" title=" <strong>Figure 6</strong><br/> <p>Workflow of basic ML model.</p> "> </a> <a href="https://pub.mdpi-res.com/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g007a.png?1690521345" title=" <strong>Figure 7</strong><br/> <p>TANIWALCHM stock price forecasting: (<b>a</b>) SVR, (<b>b</b>) MLPR, (<b>c</b>) KNN, (<b>d</b>) random forest, (<b>e</b>) XG-Boost, (<b>f</b>) LSTM, (<b>g</b>) Ensemble Random Forest + XG-Boost + LSTM.</p> "> </a> <a href="https://pub.mdpi-res.com/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g007b.png?1690521359" title=" <strong>Figure 7 Cont.</strong><br/> <p>TANIWALCHM stock price forecasting: (<b>a</b>) SVR, (<b>b</b>) MLPR, (<b>c</b>) KNN, (<b>d</b>) random forest, (<b>e</b>) XG-Boost, (<b>f</b>) LSTM, (<b>g</b>) Ensemble Random Forest + XG-Boost + LSTM.</p> "> </a> <a href="https://pub.mdpi-res.com/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g008a.png?1690521351" title=" <strong>Figure 8</strong><br/> <p>AGROPHOS stock price forecasting: (<b>a</b>) SVR, (<b>b</b>) MLPR, (<b>c</b>) KNN, (<b>d</b>) random forest, (<b>e</b>) XG-Boost, (<b>f</b>) LSTM, (<b>g</b>) Ensemble Random Forest + XG-Boost + LSTM.</p> "> </a> <a href="https://pub.mdpi-res.com/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g008b.png?1690521357" title=" <strong>Figure 8 Cont.</strong><br/> <p>AGROPHOS stock price forecasting: (<b>a</b>) SVR, (<b>b</b>) MLPR, (<b>c</b>) KNN, (<b>d</b>) random forest, (<b>e</b>) XG-Boost, (<b>f</b>) LSTM, (<b>g</b>) Ensemble Random Forest + XG-Boost + LSTM.</p> "> </a> </div> <a class="button button--color-inversed" href="/2227-7072/11/3/94/notes">Versions Notes</a> </div> </div> <div class="responsive-moving-container small hidden" data-id="article-counters" style="margin-top: 15px;"></div> <div class="html-dynamic"> <section> <div class="art-abstract art-abstract-new in-tab hypothesis_container"> <p> <div><section class="html-abstract" id="html-abstract"> <h2 id="html-abstract-title">Abstract</h2><b>:</b> <div class="html-p">The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models.</div> </section> <div id="html-keywords"> <div class="html-gwd-group"><div id="html-keywords-title">Keywords: </div><a href="/search?q=stock+market">stock market</a>; <a href="/search?q=finance">finance</a>; <a href="/search?q=linear+regression">linear regression</a>; <a href="/search?q=random+forest">random forest</a>; <a href="/search?q=XG-Boost">XG-Boost</a>; <a href="/search?q=FB+Prophet">FB Prophet</a>; <a href="/search?q=LSTM">LSTM</a>; <a href="/search?q=ensemble+learning">ensemble learning</a>; <a href="/search?q=blending+ensemble">blending ensemble</a></div> <div> </div> </div> </div> </p> </div> </section> </div> <div class="hypothesis_container"> <ul class="menu html-nav" data-prev-node="#html-quick-links-title"> </ul> <div class="html-body"> <section id='sec1-ijfs-11-00094' type='intro'><h2 data-nested='1'> 1. Introduction</h2><div class='html-p'>The performance of a country’s financial market is a crucial determinant of its overall economic condition, enabling economists and financial experts to gauge the country’s current economic health. Among the various financial markets, the stock market stands out as a key driving force. A country’s economic situation directly or indirectly impacts sectors such as finance, agriculture, metal, and investment banking, among others. The growth of these sectors hinges on their volatility, which follows the fundamental principle of supply and demand. The demand for a particular sector directly influences the stock market, with increased supply prompting traders and financial institutions to invest in that sector or stock, driving up prices. Additionally, regular dividend payments contribute to the generation of profits and returns on invested capital. It is imperative for investors to identify the opportune moment to sell shares and achieve their desired returns. Financial markets encompass various types of market, including stock markets, derivatives markets, bond markets, and commodity markets (<a href="#B42-ijfs-11-00094" class="html-bibr">Obthong et al. 2020</a>). The stock market serves as a platform for investors to invest in and own a portion or fraction of a company. As companies grow, they often require additional funding to support their future endeavors. Following the approval of current shareholders, who face diluted ownership due to the issuance of new shares, companies can sell these shares to investors to raise capital. Successful outcomes result in increased stock market value for the shares.</div><div class='html-p'>Shares listed on the stock market can be bought for both short-term and long-term investment strategies. Long-term investment involves holding shares over an extended period, while short-term investments involve buying and selling shares within shorter timeframes, with investors aiming for profits within days or weeks. Traders employ a wide range of trading strategies, including swing trading, day trading, position trading, and scalping (<a href="#B34-ijfs-11-00094" class="html-bibr">Mann and Kutz 2016</a>).</div><div class='html-p'>Due to the unpredictable nature of the stock market, it is highly difficult for individuals to obtain returns on their investments. Primary, fundamental, and technical analysis are popular approaches to understanding market trends (<a href="#B33-ijfs-11-00094" class="html-bibr">Manish and Thenmozhi 2014</a>), but they possess inherent limitations due to the involvement of lagging indicators and prediction inaccuracy. This has motivated researchers to develop improved techniques for real-time market scenarios based on machine learning and deep learning models. In the current era, machine learning and deep learning algorithms offer substantial advantages over traditional techniques such as technical and fundamental analysis. Leveraging the power of machine learning and artificial intelligence, these algorithms facilitate the forecasting of stock prices and indices. Machine learning serves as an additional approach alongside technical and fundamental analysis, with the combination of these tools forming a powerful trading platform. Machine learning models can provide solutions to problems such as stock price forecasting and classification, portfolio management, algorithmic trading, stock market sentiment analysis, risk assessment, etc. Of these problems, this review article is focused on exploring different approaches described for stock price forecasting and classification.</div><div class='html-p'>The typical steps of a machine learning model pipeline for predicting stock price involve several phases: collecting historical data via an API, pre-processing the data, creating a forecasting model, and evaluating the model. Pre-processing entails removing zero values, eliminating duplicates, and scaling features. Subsequently, important features are shortlisted, and valid data are selected for stock price prediction or forecasting (<a href="#B49-ijfs-11-00094" class="html-bibr">Raghavendra et al. 2021</a>). In this article, numerous popular machine learning and deep learning algorithms, such as linear regression, random forest, logistic regression, k-nearest neighbor, support vector machine, naïve Bayes, ARIMA (autoregressive integrated moving average), FB Prophet (Facebook Prophet), LSTM (long short-term memory), GRU (gated recurrent network), as well as ensemble algorithms such as random forest and XG-Boost (extreme gradient boosting), are described (<a href="#B74-ijfs-11-00094" class="html-bibr">Zhong and Enke 2019</a>; <a href="#B61-ijfs-11-00094" class="html-bibr">Sidra and Sen 2020</a>; <a href="#B43-ijfs-11-00094" class="html-bibr">Parray et al. 2020</a>). For evaluating a classification model’s accuracy, recall, precision, and F-score, are commonly preferred metrics, and for regression or price forecasting models, root mean square error (RMSE) and mean absolute percentage error (MAPE) are often employed (<a href="#B19-ijfs-11-00094" class="html-bibr">Jose et al. 2019</a>).</div><div class='html-p'>There are a significant number of review articles on stock price prediction and forecasting (<a href="#B46-ijfs-11-00094" class="html-bibr">Polamuri et al. 2019</a>; <a href="#B26-ijfs-11-00094" class="html-bibr">Kumar et al. 2021</a>; <a href="#B63-ijfs-11-00094" class="html-bibr">Soni et al. 2022</a>). But due to the boom in artificial intelligence and machine learning, the frequency of publications has increased considerably. Hence, this review article presents recent state-of-the-art machine learning and deep learning techniques for stock price prediction. The salient contributions and uniqueness of this review article are listed below:</div><div class='html-p'><ul class='html-bullet'><li><div class='html-p'>One of the unique contributions of this review article is that it is not just limited to summarizing the research articles. Extra effort is put into implementing the well-known machine learning and deep learning models to understand their nature and performance. Along with our review, a comparative analysis of various algorithms is presented in this article. The machine learning and deep learning ensemble algorithms are tested on TAINIWALCHM and AGROPHOS stock data, which fall under the umbrella of the chemical industry market sector.</div></li><li><div class='html-p'>In this review article, detailed future research directions are included. Future research avenues for researchers are identified based on the conducted study stock trend analysis and classification, pattern identification, and candlestick chart pattern analysis using computer vision.</div></li></ul></div><div class='html-p'>The rest of the article is organized as follows. <a href="#sec2-ijfs-11-00094" class="html-sec">Section 2</a> describes the complete theoretical background of machine learning and deep learning models. A generic structure of the machine learning modeling pipeline is presented in <a href="#sec3-ijfs-11-00094" class="html-sec">Section 3</a>. The importance of the ensemble model “Random Forest + XG-Boost + LSTM” for forecasting TAINIWALCHM and AGROPHOS stock prices and a comparative analysis with popular machine learning and deep learning models are mentioned in <a href="#sec4-ijfs-11-00094" class="html-sec">Section 4</a>. <a href="#sec5-ijfs-11-00094" class="html-sec">Section 5</a> discusses the implications and limitations of the current review. Future research directions are given in <a href="#sec6-ijfs-11-00094" class="html-sec">Section 6</a>. Finally, the paper is concluded in <a href="#sec7-ijfs-11-00094" class="html-sec">Section 7</a>.</div></section><section id='sec2-ijfs-11-00094' type=''><h2 data-nested='1'> 2. Comprehensive Summary of Theoretical Basis</h2><div class='html-p'>Forecasting stock prices and predicting market trends are challenging tasks. Over the years, researchers have proposed several solutions to these challenges (<a href="#B42-ijfs-11-00094" class="html-bibr">Obthong et al. 2020</a>; <a href="#B17-ijfs-11-00094" class="html-bibr">Hu et al. 2021</a>; <a href="#B46-ijfs-11-00094" class="html-bibr">Polamuri et al. 2019</a>), and these methods are briefly explained below. Machine learning, deep learning, time series forecasting, and ensemble algorithms are some of the most popular approaches to solving the mentioned problems. Ensemble algorithms can improve accuracy and reduce RMSE. Hadoop architectures can also handle large volumes of stock data (<a href="#B19-ijfs-11-00094" class="html-bibr">Jose et al. 2019</a>) and deep learning algorithms can predict financial markets (<a href="#B17-ijfs-11-00094" class="html-bibr">Hu et al. 2021</a>). Stock forecasting using LSTM, a unique recurrent neural network (RNN), overcomes long-term dependency (<a href="#B48-ijfs-11-00094" class="html-bibr">Qiu et al. 2020</a>; <a href="#B6-ijfs-11-00094" class="html-bibr">Banik et al. 2022</a>). But the vanishing gradient and exploding gradient problems often need to be addressed in RNN-based architectures (<a href="#B30-ijfs-11-00094" class="html-bibr">Li and Pan 2021</a>; <a href="#B76-ijfs-11-00094" class="html-bibr">Zhu 2020</a>). Khan et al., in (<a href="#B25-ijfs-11-00094" class="html-bibr">Khan et al. 2020</a>), used Pyspark, MLlib, linear regression, and random forest to achieve 80–98% accuracy.</div><div class='html-p'>Multiple algorithms have been used to forecast stock prices, including neural networks, which train data on layers of connected neurons, and support vector machines, which predict stock price movement using hyperplanes. Random forest, trained on multiple decision trees and Naïve Bayes, predicts stock movement based on negative or positive probability using Reliance and Infosys’ 10-year historical data (<a href="#B44-ijfs-11-00094" class="html-bibr">Patel et al. 2015</a>). Random forest is compared to other algorithms on 5767 European companies. These algorithms include neural networks, which are multiple layers of neurons connected with each other; logistic regression, which outputs a binary value to predict whether the stock will move up or down based on probability; support vector machines; and K-Nearest neighbor, which finds the k nearest data points using Euclidean similarity metrics. Random forest is the best algorithm, followed by SVM (<a href="#B5-ijfs-11-00094" class="html-bibr">Ballings et al. 2015</a>).</div><div class='html-p'>Stock market forecasting is a regression use case because stock prices are continuous (<a href="#B55-ijfs-11-00094" class="html-bibr">Seethalakshmi 2018</a>). (<a href="#B11-ijfs-11-00094" class="html-bibr">Di Persio and Honchar 2017</a>), used RNN for forecasting Google stock prices. RNN, LSTM, and GRU are the three most efficient neural networks for sequential data. RNN is used for historical data. LSTM and GRU can avoid the vanishing gradient problem based on forget, reset, and update gates. GRU is found to be faster as it operates on reset and update gates (<a href="#B11-ijfs-11-00094" class="html-bibr">Di Persio and Honchar 2017</a>).</div><div class='html-p'>Another study involves the use of ARIMA, LSTM, and random forest for price forecasting, and XG-Boost, an ensemble learning algorithm like random forest. Based on the evaluation parameters, XG-Boost outperformed ARIMA and LSTM (<a href="#B77-ijfs-11-00094" class="html-bibr">Zhu and He 2022</a>). Isaac et al. also used ensemble machine learning to improve stock market forecasting results. Cooperative and competitive classifier algorithms use stacking and blending. Bagging and boosting techniques are used to reduce variance and bias. Most ensemble classifiers and regressors are developed by combining decision trees, support vector machines, and neural networks (<a href="#B41-ijfs-11-00094" class="html-bibr">Nti et al. 2020</a>). Similarly, (<a href="#B71-ijfs-11-00094" class="html-bibr">Xu et al. 2020</a>) also used bagging ensemble learning techniques to predict Chinese stocks. This approach combines a two-stage prediction model called “Ensemble Learning SVR and Random Forest (E-SVR&RF)” with KNN to cluster it with ten technical indicators. Another proposed method uses Ensemble LSTM with CNN on stock indexes for training adversarial networks, which can help forecast high-frequency stock and has advantages like adversarial training and reducing direction prediction loss and forecast error loss (<a href="#B75-ijfs-11-00094" class="html-bibr">Zhou et al. 2018</a>). To enhance the effectiveness of ensemble model of XG-Boost and LSTM, XG-Boost is used to select features applied to high-dimensional time series data and LSTM is used for stock price forecasting (<a href="#B70-ijfs-11-00094" class="html-bibr">Vuong et al. 2022</a>). </div><div class='html-p'>Not only have machine learning ensemble methods helped to improve forecasting performance, but in some research work, it has also been observed that neural network blending ensemble models also perform well. (<a href="#B73-ijfs-11-00094" class="html-bibr">Yang 2019</a>) implemented a model consisting of two layers of RNN. The first one was an LSTM-based blending ensemble algorithm, and the second one was GRU-based. The model showed the lowest RMSE value of 186.32, a precision of 60%, and an F1-score of 66.47 (<a href="#B30-ijfs-11-00094" class="html-bibr">Li and Pan 2021</a>).</div><div class='html-p'>Sometimes, price movement is primarily affected by sentiments. These sentiments can be positive, leading to a bullish movement, or negative, leading to a bearish movement. Hence, stock sentiment analysis is important to understand stock price forecasting and trend classification. Further, in this section, we explore some forecasting techniques based on sentiment analysis. Social media data, company news, and trend analysis can classify investors’ stock sentiments as positive, negative, or neutral (<a href="#B72-ijfs-11-00094" class="html-bibr">Yadav and Vishwakarma 2019</a>).</div><div class='html-p'>Since stock prices respond to news and global events, price variation alone cannot be used to train ML models. ANN- and LSTM-based deep learning techniques can also be trained using price values and text data. Word2vec and NGram are used to convert text data to numerical data and train the model together with price-sentiment data. Diversified data can predominantly increase accuracy and lower inaccurate results. An algorithm like a random forest approach is also a good choice for Twitter sentiment analysis (<a href="#B27-ijfs-11-00094" class="html-bibr">Kumar and Ningombam 2018</a>; <a href="#B50-ijfs-11-00094" class="html-bibr">Reddy et al. 2020</a>).</div><div class='html-p'>Financial news and user-generated text, such as comments on social media platforms, can trigger new trends in the stock market. For example, “Monday has the lowest average return” shows a statement representing a weak or negative sentiment. </div><div class='html-p'>While creating the dataset for training the model, a rolling window approach on historical data works well with news-based text data. Ren et al. showed an increase in accuracy of up to 18.6% to 89.93% points when multimodal data were considered (<a href="#B51-ijfs-11-00094" class="html-bibr">Ren et al. 2019</a>).</div><div class='html-p'>When text data are collected, they can include correct and fake data. In one study, the authors used feature selection to eliminate fake news and spam tweets collected from social media data. This improved the data quality for training, and a classification algorithm, the random forest algorithm, was used to train the model. Sentiments can be positive, neutral, or negative, which helps people decide whether to buy or sell stock (<a href="#B4-ijfs-11-00094" class="html-bibr">Baheti et al. 2021</a>). Negative sentiments affect market conditions. Lim et al. considered a use case in which a comparison of two stocks, Tesla and Nio, based on sentiments, was carried out. It was found that negative events, such as Tesla’s 2021 protest, affected its competitor, Nio, as well. This research was based on historical data using time series forecasting with 10, 15, and 20 days of data (<a href="#B32-ijfs-11-00094" class="html-bibr">Lim and Tan 2021</a>).</div><div class='html-p'>In (<a href="#B59-ijfs-11-00094" class="html-bibr">Sharaf et al. 2022</a>), news headlines pertaining to TSLA, AMZ, and GOOG stock were considered to obtain good-quality data to reduce spam tweets through feature selection methods. Sentiment analysis was also used for polarity detection and historical data mining, for which DL algorithms were used. Similar feature engineering- and deep learning-based approaches are presented in (<a href="#B60-ijfs-11-00094" class="html-bibr">Shen and Shafiq 2020</a>; <a href="#B39-ijfs-11-00094" class="html-bibr">Nabipour et al. 2020</a>; <a href="#B37-ijfs-11-00094" class="html-bibr">Mohapatra et al. 2022</a>). </div><div class='html-p'>(<a href="#B24-ijfs-11-00094" class="html-bibr">Khairi et al. 2019</a>) showed that technical analysis with sentiment analysis can also provide prominent investment opportunities. Technical indicators like stochastic oscillators, moving average convergence divergence (MACD), Bollinger bands, and relative strength indicators (RSI) are good for short-term analysis but not long-term. In cases of negative news, generally, market trends will be bearish, and the stock price will fall. To address this issue, initial sentiment analysis can be performed to find the negative news using PoS (part of speech) tags in the news statement. Programming libraries like SentiWordNet can be used for PoS tagging. This approach has resulted in high profitability and low loss-making situations (<a href="#B24-ijfs-11-00094" class="html-bibr">Khairi et al. 2019</a>). In another approach proposed by Li et al. (<a href="#B29-ijfs-11-00094" class="html-bibr">Li and Bastos 2020</a>), a technical analysis of historical data and a fundamental analysis using a deep learning approach were conducted to generate better returns. Here, LSTM is chosen for prediction as it can store memory and does not have a vanishing gradient issue (<a href="#B29-ijfs-11-00094" class="html-bibr">Li and Bastos 2020</a>). Similar work is presented in (<a href="#B1-ijfs-11-00094" class="html-bibr">Agrawal et al. 2022</a>; <a href="#B54-ijfs-11-00094" class="html-bibr">Sathish Kumar et al. 2020</a>; <a href="#B67-ijfs-11-00094" class="html-bibr">Umer et al. 2019</a>).</div><div class='html-p'>Based on the studies presented so far, the best stock market forecasting solution is derived from fundamental analysis and technical analysis with sentiment analysis and deep learning models. Ensemble techniques provide especially promising forecasting outcomes. The algorithms that are used for stock market prediction by considering research papers are given in <a href="#ijfs-11-00094-f001" class="html-fig">Figure 1</a>.</div><section id='sec2dot1-ijfs-11-00094' type=''><h4 class='html-italic' data-nested='2'> 2.1. Basic Machine Learning Algorithm</h4><section id='sec2dot1dot1-ijfs-11-00094' type=''><h4 class='' data-nested='3'> 2.1.1. Linear Regression</h4><div class='html-p'>Linear regression is used for stock or financial market prediction to forecast the future price of stock regression and uses a model based on one or more attributes, such as closed price, open price, volume, etc., to forecast the stock price. Regression modeling aims to simulate the linear relationship between the dependent and independent variables. The linear regression model produces a best-fit line that describes the connection between the independent factors and the dependent variable.</div><div class='html-p'>In this technique, a straight line represented by Equation (1) is drawn, ensuring that the line crosses the highest possible number of the dataset’s data points. When charting the dataset’s values on a graph, a straight line is mathematically fitted between the points so that the square of the distance or difference between each point and the line is as small as possible. For each given <span class='html-italic'>x</span>, the hypothesis line is used to forecast the value of <span class='html-italic'>y</span>. This forecasting method is known as linear regression. For the evaluation of the results and to check how well the model fits the line, parameters such as RMSE, MAE, MSE, and R-squared are used (<a href="#B15-ijfs-11-00094" class="html-bibr">Gururaj et al. 2019</a>; <a href="#B12-ijfs-11-00094" class="html-bibr">Dospinescu and Dospinescu 2019</a>). <div class='html-disp-formula-info' id='FD1-ijfs-11-00094'> <div class='f'> <math display='block'><semantics> <mrow> <mi>O</mi> <mo>=</mo> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>+</mo> <mi>K</mi> </mrow> </semantics></math> </div> <div class='l'> <label >(1)</label> </div> </div> where <math display='inline'><semantics> <mi>O</mi> </semantics></math> is the output, <math display='inline'><semantics> <mrow> <msub> <mi>S</mi> <mi>x</mi> </msub> </mrow> </semantics></math> represents the slope, and <math display='inline'><semantics> <mi>K</mi> </semantics></math> is a constant.</div></section><section id='sec2dot1dot2-ijfs-11-00094' type=''><h4 class='' data-nested='3'> 2.1.2. K-Nearest Neighbor (KNN)</h4><div class='html-p'>KNN is a classification and regression technique and has been termed as a lazy learner because it does not need a huge time period for learning. One of KNN’s advantages is that it is one of the easiest ML algorithms. The only action that needs to be taken for KNN is to calculate the value of K and the Euclidean distance. This algorithm’s slow learning aspect makes it quicker than other algorithms. It may not generalize well for big data since it skips the learning step (<a href="#B66-ijfs-11-00094" class="html-bibr">Tanuwijaya and Hansun 2019</a>). The euclidean distance calculation is given in Equation (2), <div class='html-disp-formula-info' id='FD2-ijfs-11-00094'> <div class='f'> <math display='block'><semantics> <mrow> <mi>D</mi> <mrow> <mo>(</mo> <mrow> <msub> <mi>h</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>p</mi> <mrow> <mi>r</mi> </mrow> </msub> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <msqrt> <mrow> <msubsup> <mstyle mathsize="140%" displaystyle="true"> <mo>∑</mo> </mstyle> <mrow> <mover> <mi>l</mi> <mo>¨</mo> </mover> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </msubsup> <msup> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>P</mi> <mi>r</mi> </msub> <mo>−</mo> <msub> <mi>h</mi> <mi>i</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mrow> </semantics></math> </div> <div class='l'> <label >(2)</label> </div> </div> where <span class='html-italic'>P<sub>r</sub></span> represents the predicted value and <span class='html-italic'>h<sub>i</sub></span> represents the data value.</div></section><section id='sec2dot1dot3-ijfs-11-00094' type=''><h4 class='' data-nested='3'> 2.1.3. Support Vector Machine (SVM)</h4><div class='html-p'>Stock market prediction using an SVM can be the most useful technique for predicting stock price, as it can be used as a classification and regression algorithm. The comparison of SVM and its variants, such as “Peeling + SVM” and “CC + SVM”, shows that its prediction can be improved by advanced SVM methods (<a href="#B14-ijfs-11-00094" class="html-bibr">Grigoryan 2017</a>). A support vector machine involves supervised learning used to categorize aspects using a separator. The separator is then discovered when the data are initially mapped to a high-dimensional feature space. It finds the categorization of data points occurring in an n-dimensional space and finds the optimal hyperplane. The data points are grouped according to their location in relation to the hyperplanes. The performance of the SVM algorithm can be elevated by tuning parameters such as regularization, gamma, and kernel parameters (<a href="#B9-ijfs-11-00094" class="html-bibr">Bustos et al. 2017</a>). SVM can also be used for sentiment analysis to assess investors’ sentiments, which would indirectly affect market conditions. It is well suited to both high-dimensional datasets and small-scale datasets.</div></section><section id='sec2dot1dot4-ijfs-11-00094' type=''><h4 class='' data-nested='3'> 2.1.4. Naïve Bayes Algorithm</h4><div class='html-p'>Naïve Bayes is a supervised machine learning technique that can be used to forecast prices of various stocks in research on banking stock.</div><div class='html-p'>Naïve Bayes is a classification algorithm in which a combination of probability summing up the frequencies and value combinations is taken from a dataset. Depending on the values of the class variables, the Bayes theorem makes an assumption regarding whether the attributes of naïve Bayes are independent or interdependent. The basic concept of naïve Bayes is that attribute values are independent in the presence of an output value (<a href="#B56-ijfs-11-00094" class="html-bibr">Setiani et al. 2020</a>). The set up GNB models were graded based on their performance using Kendall’s test of concordance for several assessment parameters. The outcomes showed that the GNB LDA predictive model (<a href="#B22-ijfs-11-00094" class="html-bibr">Kardani et al. 2020</a>) performed better than all other GNB models. The posterior probability was calculated as shown in Equation (3) (<a href="#B3-ijfs-11-00094" class="html-bibr">Ampomah et al. 2021</a>). <div class='html-disp-formula-info' id='FD3-ijfs-11-00094'> <div class='f'> <math display='block'><semantics> <mrow> <mi>P</mi> <mrow> <mo>(</mo> <mrow> <mi>H</mi> <mo>|</mo> <mi>U</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>P</mi> <mrow> <mo>(</mo> <mrow> <mi>U</mi> <mo>|</mo> <mi>H</mi> </mrow> <mo>)</mo> </mrow> <mo>⋅</mo> <mi>P</mi> <mrow> <mo>(</mo> <mi>H</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>U</mi> <mo>)</mo> </mrow> </mrow> </mfrac> </mrow> </semantics></math> </div> <div class='l'> <label >(3)</label> </div> </div> where<math display='inline'><semantics> <mrow> <mo> </mo> <mi>U</mi> <mo> </mo> </mrow> </semantics></math>is unknown class data, <math display='inline'><semantics> <mi>H</mi> </semantics></math> is the hypothesis for data of certain class, <math display='inline'><semantics> <mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>H</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> is the hypothesis probability, <math display='inline'><semantics> <mrow> <mi>P</mi> <mo stretchy="false">(</mo> <mi>U</mi> <mo>|</mo> <mi>H</mi> <mo stretchy="false">)</mo> <mo> </mo> </mrow> </semantics></math>is the probability of <math display='inline'><semantics> <mi>U</mi> </semantics></math> being dependent on the condition in hypothesis <math display='inline'><semantics> <mi>H</mi> </semantics></math>, and<math display='inline'><semantics> <mrow> <mo> </mo> <mi>P</mi> <mrow> <mo>(</mo> <mi>U</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> is the probability of <math display='inline'><semantics> <mrow> <mi>U</mi> </mrow> </semantics></math>.</div><div class='html-p'>The formula for naïve Bayes used in stock prediction is given by Equation (4):<div class='html-disp-formula-info' id='FD4-ijfs-11-00094'> <div class='f'> <math display='block'><semantics> <mrow> <mi>P</mi> <mo stretchy="false">(</mo> <msub> <mi>A</mi> <mi>i</mi> </msub> <mo>=</mo> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo stretchy="false">|</mo> <mi>B</mi> <mo>=</mo> <msub> <mi>b</mi> <mi>i</mi> </msub> <mo stretchy="false">)</mo> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <msqrt> <mrow> <mn>2</mn> <mi>π</mi> <mo>€</mo> </mrow> </msqrt> </mrow> </mfrac> <msup> <mi>e</mi> <mrow> <mo>−</mo> <mfrac> <mrow> <msup> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>a</mi> <mi>i</mi> </msub> <mo>−</mo> <msub> <mi mathvariant="sans-serif">µ</mi> <mrow> <msub> <mi>i</mi> <mi>j</mi> </msub> </mrow> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <mn>2</mn> </msup> </mrow> <mrow> <mn>2</mn> <msup> <mo>€</mo> <mn>4</mn> </msup> <mover> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mo>˙</mo> </mover> </mrow> </mfrac> </mrow> </msup> </mrow> </semantics></math> </div> <div class='l'> <label >(4)</label> </div> </div> where <math display='inline'><semantics> <mi>π</mi> </semantics></math> represents pi (3.14), <span class='html-italic'>e</span> is the exponential (2.7183), <math display='inline'><semantics> <mi mathvariant="sans-serif">µ</mi> </semantics></math> represents the mean, and € is the standard deviation.</div></section><section id='sec2dot1dot5-ijfs-11-00094' type=''><h4 class='' data-nested='3'> 2.1.5. Logistic Regression</h4><div class='html-p'>Logistic regression is a supervised method of machine learning. By utilizing variables for logistic curves, logistic regression groups several independent factors into two or more mutually exclusive groups and forecasts the likelihood of equities that perform well (<a href="#B2-ijfs-11-00094" class="html-bibr">Ali et al. 2018</a>). To classify stock performance using logistic regression, the maximum likelihood is calculated as per Equation (5):<div class='html-disp-formula-info' id='FD5-ijfs-11-00094'> <div class='f'> <math display='block'><semantics> <mrow> <msub> <mi>Z</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>β</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>β</mi> <mn>2</mn> </msub> <mi>E</mi> <mi>P</mi> <msub> <mi>S</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>β</mi> <mn>2</mn> </msub> <mi>P</mi> <msub> <mi>B</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>β</mi> <mn>2</mn> </msub> <mi>R</mi> <mi>O</mi> <msub> <mi>E</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>β</mi> <mn>2</mn> </msub> <mi>C</mi> <msub> <mi>R</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>β</mi> <mn>2</mn> </msub> <mi>D</mi> <msub> <mi>E</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>β</mi> <mn>2</mn> </msub> <mi>s</mi> <mi>a</mi> <mi>l</mi> <mi>e</mi> <msub> <mi>s</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>V</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> </div> <div class='l'> <label >(5)</label> </div> </div> where <math display='inline'><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mi>l</mi> <mi>o</mi> <mi>g</mi> <mi>l</mi> <mi>o</mi> <mi>g</mi> <mo> </mo> <mrow> <mo>(</mo> <mrow> <mfrac> <mrow> <mi>P</mi> <mi>r</mi> </mrow> <mrow> <mn>1</mn> <mo>−</mo> <mi>P</mi> <mi>r</mi> </mrow> </mfrac> </mrow> <mo>)</mo> </mrow> <mo> </mo> </mrow> </semantics></math>and <math display='inline'><semantics> <mrow> <mi>P</mi> <mi>r</mi> </mrow> </semantics></math> = probability of outcome is positive.</div><div class='html-p'>There are variants, such as binary logistic regression, that can improve finance ratios and investors’ ability to anticipate stock price (<a href="#B62-ijfs-11-00094" class="html-bibr">Smita 2021</a>). </div></section></section><section id='sec2dot2-ijfs-11-00094' type=''><h4 class='html-italic' data-nested='2'> 2.2. Forecasting of Stock Market Using Time Series Forecasting</h4><div class='html-p'>Stock price data are time series data. Some of the classical methods, such as autoregressive moving average (ARIMA) and FB Prophet, are discussed in this subsection. These methods were very well adopted before the success of deep learning models.</div><section id='sec2dot2dot1-ijfs-11-00094' type=''><h4 class='' data-nested='3'> 2.2.1. ARIMA</h4><div class='html-p'>ARIMA is an algorithm that uses time series forecasting to predict the future value of stocks. In a study presented by Tamerlan et al., in (<a href="#B36-ijfs-11-00094" class="html-bibr">Mashadihasanli 2022</a>), it is demonstrated that the ARIMA model best fits the stock market index. The ARIMA model comprises three steps—identify, estimate, and diagnose. These steps can be used for forecasting any finance market such as equity, derivative, etc. (<a href="#B36-ijfs-11-00094" class="html-bibr">Mashadihasanli 2022</a>). ARIMA can be combined with another algorithm, symmetric generalized autoregressive conditional heteroskedasticity (SGARCH), to improve forecasting performance. This combination has been modeled and tested on the S&P500 Index (<a href="#B69-ijfs-11-00094" class="html-bibr">Vo and Ślepaczuk 2022</a>). ARIMA has even been extended for stock sentiment analysis (<a href="#B23-ijfs-11-00094" class="html-bibr">Kedar 2021</a>).</div><div class='html-p'>The formula of ARIMA, which combines AR (autoregression) and MA (moving average), is shown in Equation (6), <div class='html-disp-formula-info' id='FD6-ijfs-11-00094'> <div class='f'> <math display='block'><semantics> <mrow> <msup> <mi>y</mi> <mo>′</mo> </msup> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mi>k</mi> <mo>+</mo> <msub> <mi>β</mi> <mi>p</mi> </msub> <mo>*</mo> <mi>ω</mi> <mi>D</mi> <mo> </mo> <msub> <msup> <mi>y</mi> <mo>′</mo> </msup> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> <mo> </mo> <mo>+</mo> <mo>⋯</mo> <mo>+</mo> <mo> </mo> <msub> <mi>β</mi> <mi>p</mi> </msub> <mo>*</mo> <mi>ω</mi> <mi>D</mi> <msub> <msup> <mi>y</mi> <mo>′</mo> </msup> <mrow> <mi>t</mi> <mo>−</mo> <mi>p</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>θ</mi> <mn>1</mn> </msub> <mo>∗</mo> <msub> <mi>ε</mi> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <mo>⋯</mo> <mo>+</mo> <mi>θ</mi> <mi>q</mi> <mo>*</mo> <msub> <mi>ε</mi> <mrow> <mrow> <mo>(</mo> <mrow> <mi>t</mi> <mi>i</mi> <mo>−</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </msub> <mo>+</mo> <msub> <mi>ε</mi> <mrow> <mi>t</mi> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> </div> <div class='l'> <label >(6)</label> </div> </div> where <math display='inline'><semantics> <mi>p</mi> </semantics></math> represents the autoregressive model’s given degree, <math display='inline'><semantics> <mi>D</mi> </semantics></math> is the degree of different orders, and <math display='inline'><semantics> <mi>q</mi> </semantics></math> is the moving average’s given degree. </div></section><section id='sec2dot2dot2-ijfs-11-00094' type=''><h4 class='' data-nested='3'> 2.2.2. FB Prophet Model</h4><div class='html-p'>FB Prophet is a time series forecasting library developed by Facebook. FB Prophet better suits data that have a null value and it relatively shows more accurate results in such situations. The formula of FB Prophet is presented in Equation (7):<div class='html-disp-formula-info' id='FD13-ijfs-11-00094'> <div class='f'> <math display='block'><semantics> <mrow> <mi>Y</mi> <mi>t</mi> <mo>=</mo> <mi>l</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>s</mi> <mi>p</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>v</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>ε</mi> <mi>t</mi> <mo>,</mo> <mo> </mo> <mi>y</mi> <mo> </mo> <mi>t</mi> <mo>=</mo> <mi>g</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>s</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>h</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>ε</mi> <mi>n</mi> </mrow> </semantics></math> </div> <div class='l'> <label >(7)</label> </div> </div> where <math display='inline'><semantics> <mrow> <mi>l</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> represents the linear trend, <math display='inline'><semantics> <mrow> <mi>s</mi> <mi>p</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> represents seasonal patterns, <math display='inline'><semantics> <mrow> <mi>v</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> represents holiday effects, and <math display='inline'><semantics> <mrow> <mi>ε</mi> <mi>n</mi> </mrow> </semantics></math> is the white noise error. The algorithm designed based on FB Prophet is implemented using the PyStan library (<a href="#B65-ijfs-11-00094" class="html-bibr">Suresh et al. 2022</a>). Researchers have extensively used FB Prophet in stock price forecasting (<a href="#B21-ijfs-11-00094" class="html-bibr">Kaninde et al. 2022</a>; <a href="#B58-ijfs-11-00094" class="html-bibr">Shahi et al. 2020</a>).</div></section></section><section id='sec2dot3-ijfs-11-00094' type=''><h4 class='html-italic' data-nested='2'> 2.3. Deep Learning Methods</h4><div class='html-p'>Deep learning models have wide-ranging popularity in many areas of science and engineering. They are especially widely adopted in stock price forecasting and trend prediction due to their ability to capture complex patterns, handle large volumes of data, and undertake feature learning and representation, and their adaptability to changing market conditions. In this subsection, some of the popular deep learning models are discussed in relation to the finance domain.</div><section id='sec2dot3dot1-ijfs-11-00094' type=''><h4 class='' data-nested='3'> 2.3.1. Long Short-Term Memory (LSTM)</h4><div class='html-p'>LSTM is an advanced model of Recurrent Neural Networks (RNNs), a deep learning algorithm. An LSTM model can handle lengthy sequences of data units as it can remember the data sequence, which can be used for future inputs. <a href="#ijfs-11-00094-f002" class="html-fig">Figure 2</a> shows a general LSTM cell structure. It comprises three gates—the input gate, the forget gate, and the output gate. All of the gates employ the sigmoid activation function. All of the gates used in the LSTM are mathematically represented as per Equations (8)–(10). </div><div class='html-p'>Input gate (New information in cell state): <div class='html-disp-formula-info' id='FD14-ijfs-11-00094'> <div class='f'> <math display='block'><semantics> <mrow> <mi>i</mi> <mi>g</mi> <mi>a</mi> <mo>=</mo> <mi>σ</mi> <mo> </mo> <mrow> <mo>(</mo> <mrow> <msub> <mi>W</mi> <mrow> <mi>i</mi> <mi>p</mi> </mrow> </msub> <mo> </mo> <mrow> <mo>[</mo> <mrow> <msub> <mi>h</mi> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <mo> </mo> <mi>X</mi> <mi>c</mi> </mrow> <mo>]</mo> </mrow> <mo>+</mo> <mi>b</mi> <mi>i</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> </div> <div class='l'> <label >(8)</label> </div> </div></div><div class='html-p'>Forget gate (useless information is eliminated): <div class='html-disp-formula-info' id='FD15-ijfs-11-00094'> <div class='f'> <math display='block'><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>g</mi> <mi>a</mi> </mrow> </msub> <mo>=</mo> <mi>σ</mi> <mo> </mo> <mrow> <mo>(</mo> <mrow> <msub> <mi>W</mi> <mrow> <mi>f</mi> <mi>g</mi> </mrow> </msub> <mo> </mo> <mrow> <mo>[</mo> <mrow> <msub> <mi>h</mi> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <mo> </mo> <mi>X</mi> <mi>c</mi> </mrow> <mo>]</mo> </mrow> <mo>+</mo> <mi>b</mi> <mi>f</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> </div> <div class='l'> <label >(9)</label> </div> </div></div><div class='html-p'>Output gate (activation to last block of final output):<div class='html-disp-formula-info' id='FD16-ijfs-11-00094'> <div class='f'> <math display='block'><semantics> <mrow> <msub> <mi>O</mi> <mrow> <mi>p</mi> <mi>g</mi> </mrow> </msub> <mo>=</mo> <mi>σ</mi> <mo> </mo> <mrow> <mo>(</mo> <mrow> <msub> <mi>W</mi> <mrow> <mi>o</mi> <mi>p</mi> </mrow> </msub> <mo> </mo> <mrow> <mo>[</mo> <mrow> <msub> <mi>h</mi> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <mo> </mo> <mi>X</mi> <mi>c</mi> </mrow> <mo>]</mo> </mrow> <mo>+</mo> <mi>b</mi> <mi>o</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> </div> <div class='l'> <label >(10)</label> </div> </div> where <math display='inline'><semantics> <mi>σ</mi> </semantics></math> is sigmoid,<math display='inline'><semantics> <mrow> <mo> </mo> <mi>W</mi> <mi>x</mi> <mo> </mo> </mrow> </semantics></math>is the neuron gate (<math display='inline'><semantics> <mi>x</mi> </semantics></math>) weight, <math display='inline'><semantics> <mrow> <msub> <mi>h</mi> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> is the result of the preceding LSTM block, <math display='inline'><semantics> <mrow> <mi>X</mi> <mi>t</mi> </mrow> </semantics></math> is the input, and <math display='inline'><semantics> <mrow> <mi>b</mi> <mi>x</mi> </mrow> </semantics></math> is bias.</div><div class='html-p'>As shown in <a href="#ijfs-11-00094-f002" class="html-fig">Figure 2</a>, the top part of the memory line in every cell can be used to connect with the transport line with the help of the model, which can be used to handle the data from the prior memory to the current memory. Each LSTM node should have a set of cells that is used for storage of the data stream (<a href="#B47-ijfs-11-00094" class="html-bibr">Pramod and Pm 2021</a>). In order to provide a recursive network with plenty of time to train and allow for the creation of a long-distance causal link, LSTM maintains errors at a more constant level (<a href="#B38-ijfs-11-00094" class="html-bibr">Mukherjee et al. 2021</a>). In several cases, neural networks and deep neural networks have shown superior forecasting performance compared to other machine learning models. However, when it comes to predicting financial distress, the logistic regression model has exhibited better results in comparison to neural networks (<a href="#B78-ijfs-11-00094" class="html-bibr">Zizi et al. 2021</a>).</div></section><section id='sec2dot3dot2-ijfs-11-00094' type=''><h4 class='' data-nested='3'> 2.3.2. Gated Recurrent Neural Network (GRU)</h4><div class='html-p'>A GRU is yet another RNN-based model with comparable differences to LSTM. It is computationally efficient and faster to train than LSTM, while capturing long-term dependencies in sequential data. GRU utilizes gating mechanisms to control the flow of information between the current and previous time steps. However, it utilizes only two gates, a reset gate and an update gate, whereas LSTM has three gates. <a href="#ijfs-11-00094-f003" class="html-fig">Figure 3</a> shows the general structure of a GRU.</div><div class='html-p'>The two gates that GRU uses are:</div><div class='html-p'><dl class='html-order'><dt id=''>(1)</dt><dd><div class='html-p'>Update gate <div class='html-disp-formula-info' id='FD17-ijfs-11-00094'> <div class='f'> <math display='block'><semantics> <mrow> <mi>Z</mi> <mrow> <mo>[</mo> <mi>t</mi> <mo>]</mo> </mrow> <mo>=</mo> <mi>σ</mi> <mo> </mo> <mo stretchy="false">(</mo> <msup> <mi>W</mi> <mrow> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </msup> <msub> <mi>x</mi> <mi>t</mi> </msub> <mo>+</mo> <msup> <mi>U</mi> <mrow> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </msup> <msub> <mi>h</mi> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> <mo stretchy="false">)</mo> </mrow> </semantics></math> </div> <div class='l'> <label >(11)</label> </div> </div></div></dd><dt id=''>(2)</dt><dd><div class='html-p'>Reset gate <div class='html-disp-formula-info' id='FD18-ijfs-11-00094'> <div class='f'> <math display='block'><semantics> <mrow> <mi>r</mi> <mrow> <mo>[</mo> <mi>t</mi> <mo>]</mo> </mrow> <mo>=</mo> <mi>σ</mi> <mo> </mo> <mo stretchy="false">(</mo> <msup> <mi>W</mi> <mrow> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> </mrow> </msup> <msub> <mi>x</mi> <mi>t</mi> </msub> <mo>+</mo> <msup> <mi>U</mi> <mrow> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> </mrow> </msup> <msub> <mi>h</mi> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> <mo stretchy="false">)</mo> </mrow> </semantics></math> </div> <div class='l'> <label >(12)</label> </div> </div> where <math display='inline'><semantics> <mrow> <mi>Z</mi> <mrow> <mo>[</mo> <mi>t</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> is update gate,<math display='inline'><semantics> <mrow> <mo> </mo> <mi>r</mi> <mrow> <mo>[</mo> <mi>t</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> is reset gate, <span class='html-italic'>σ</span> represents sigmoid function, <math display='inline'><semantics> <mrow> <msup> <mi>W</mi> <mrow> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </msup> </mrow> </semantics></math> is neuron gate, <math display='inline'><semantics> <mrow> <msup> <mi>U</mi> <mrow> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </msup> </mrow> </semantics></math> is previous weight, <span class='html-italic'>h<sub>t</sub></span><sub>−1</sub> is the result of the preceding GRU block, and <math display='inline'><semantics> <mrow> <msub> <mi>x</mi> <mi>t</mi> </msub> <mo> </mo> </mrow> </semantics></math>is the current input.</div></dd></dl></div><div class='html-p'>For stock price data, GRU receives the input as a sequence of historical stock prices and generates the output as a sequence of predicted stock prices. The input sequence is fed through the GRU network, which updates its internal state at each time step, and the network’s final state is used to generate the output (<a href="#B11-ijfs-11-00094" class="html-bibr">Di Persio and Honchar 2017</a>) Both LSTM and GRU have their advantages due to their capability to eliminate the vanishing gradient problem and blend deep ensemble algorithms (<a href="#B30-ijfs-11-00094" class="html-bibr">Li and Pan 2021</a>).</div></section></section><section id='sec2dot4-ijfs-11-00094' type=''><h4 class='html-italic' data-nested='2'> 2.4. Ensemble Learning Methods</h4><section id='sec2dot4dot1-ijfs-11-00094' type=''><h4 class='' data-nested='3'> 2.4.1. Random Forest Algorithm</h4><div class='html-p'>Random forest is a supervised learning method and employs a technique called ensemble learning. It works well for both classification and regression use cases. It is derived from the concept of a decision tree as it creates several decision trees to provide results (<a href="#B20-ijfs-11-00094" class="html-bibr">Kaczmarek and Perez 2021</a>). The process of random forest is shown in <a href="#ijfs-11-00094-f004" class="html-fig">Figure 4</a>. The generic steps of a random forest algorithm applied to stock market prediction (<a href="#B72-ijfs-11-00094" class="html-bibr">Yadav and Vishwakarma 2019</a>) are:</div><div class='html-p'>Step 1: <math display='inline'><semantics> <mi>N</mi> </semantics></math> random records are picked.</div><div class='html-p'>Step 2: A decision tree is built based on <math display='inline'><semantics> <mi>N</mi> </semantics></math> inputs.</div><div class='html-p'>Step 3: The number of trees to be considered is picked.</div><div class='html-p'>Step 4: Based on the steps performed before, the output is predicted for each tree.</div><div class='html-p'>Random forest can perform well on large datasets, but the possibility of the formation of large number of trees can slow down the algorithm’s performance (<a href="#B53-ijfs-11-00094" class="html-bibr">Salles et al. 2018</a>). The random forest approach can also be used for various other use cases, such as predicting the direction of stocks (<a href="#B52-ijfs-11-00094" class="html-bibr">Sadorsky 2021</a>). This algorithm is used in the Zagreb Stock Exchange (<a href="#B35-ijfs-11-00094" class="html-bibr">Manojlović and Štajduhar 2015</a>) and is compared with SVM, KNN, and logistic regression based on the evaluation parameters accuracy, precession, recall, and F-Score (<a href="#B45-ijfs-11-00094" class="html-bibr">Pathak and Pathak 2020</a>).</div></section><section id='sec2dot4dot2-ijfs-11-00094' type=''><h4 class='' data-nested='3'> 2.4.2. XG-Boost Regression Algorithm</h4><div class='html-p'>XG-Boost is an ensembled machine learning algorithm that is like random forest with subtle differences. It is a combination of weak learners such as decision trees. It is a good prediction model for stock forecasting as it works on a sequential model that considers the gradient for each iteration so that the weights are updated for each iteration of the decision tree (<a href="#B77-ijfs-11-00094" class="html-bibr">Zhu and He 2022</a>). The process of XG-Boost is depicted in <a href="#ijfs-11-00094-f005" class="html-fig">Figure 5</a>.</div></section><section id='sec2dot4dot3-ijfs-11-00094' type=''><h4 class='' data-nested='3'> 2.4.3. E-SVR-RF (Ensemble Support Vector Machine–Random Forest)</h4><div class='html-p'>Ensemble support vector regression with random forest is an ensemble technique that follows the bagging method. An ensemble learning algorithm that consists of a support vector regressor and random forest is used to handle the complex relationship for each cluster and individual forecast. These are finally combined using the bagging method to show the final prediction using a weighted average model (<a href="#B71-ijfs-11-00094" class="html-bibr">Xu et al. 2020</a>). The suggested ensembling can leverage the advantages of both the support vector machine and random forest, where the support vector machine can capture complex relationships by finding the hyperplane that maximizes the margin between the forecasted and actual price, and random forest decreases the overfitting problem by combining the decision trees.</div><div class='html-p'>XG-Boost + LSTM, blending ensemble (LSTM + GRU) (<a href="#B30-ijfs-11-00094" class="html-bibr">Li and Pan 2021</a>), and ensemble techniques for sentiment analysis (<a href="#B13-ijfs-11-00094" class="html-bibr">Gite et al. 2023</a>) are proposed by the research community for financial instrument price forecasting. To summarize further, based on our review, it can be understood that deep learning- and machine learning-based ensemble techniques have gained popularity due to their performance.</div></section></section></section><section id='sec3-ijfs-11-00094' type=''><h2 data-nested='1'> 3. General Machine Learning Pipeline</h2><div class='html-p'>The general approach to training a machine learning model is given in <a href="#ijfs-11-00094-f006" class="html-fig">Figure 6</a>: </div><div class='html-p'>Training the ML model involves six steps, which are described in the following section: </div><div class='html-p'>Step 1: Load the data from a csv file or call the historical data with the help of an API. (examples: Yahoo Finance, Quandl, IEXFinance, etc.).</div><div class='html-p'>Step 2: Preprocess the historical data to remove any redundancies, null values, etc. and feature selection should be conducted. </div><div class='html-p'>Step 3: Before training the ML model, features such as open, close, adj close, volume, etc., can be selected along with secondary data. </div><div class='html-p'>Step 4: Divide the preprocessed data into training and testing data, preferably, where preferably 75% of the data should be used to train the model, while the remaining 25% should be used to evaluate the model’s performance.</div><div class='html-p'>Step 5: After splitting the data, use the training data to train the model; then, performance evaluation can be carried out based on the model’s output using the testing data.</div><div class='html-p'>Step 6: Once the model is constructed, the model’s respective evaluation parameters for regression or classification can be used to evaluate the model. The evaluation parameters are precision, recall, F1 Score, and accuracy. The mean absolute error, mean square error, root mean squared error, R-squared, chi square, and mean absolute percent error (mean absolute percentage error) can be used.</div><div class='html-p'>Step 7: Fine-tune the hyperparameters to improve the evaluation parameters of the model. Following hyperparameter tuning, the model should be evaluated to check for improved prediction parameters, after which the prediction can be plotted.</div></section><section id='sec4-ijfs-11-00094' type=''><h2 data-nested='1'> 4. Significance of Ensemble Modeling</h2><div class='html-p'>This section of the article is about the comparative analysis of the most significant methods explored in <a href="#sec2-ijfs-11-00094" class="html-sec">Section 2</a> and <a href="#sec3-ijfs-11-00094" class="html-sec">Section 3</a>. Based on the study, it is observed that most common algorithms, such as SVR, MLPR, KNN, random forest, XG-Boost, and LSTM, are used by various researchers in their research work. In this review article, we attempted to use these algorithms for forecasting the stock price of two stocks, namely, Tainwala Chemicals and Plastics (Mumbai, India) Lt. (TAINIWALCHM) and Agro Phos (Indore, India) Ltd. (AGROPHOS), and proposed an ensemble algorithm based on “Random Forest + XG-Boost + LSTM”. The idea behind including a comparison of the algorithms is to understand the generic performance of the popular machine learning and deep learning algorithms identified during the literature review.</div><div class='html-p'>After studying various algorithms, we developed an ensemble model of random forest, XG-Boost, and LSTM. Random forest is simpler than the gradient boosting algorithm but has the ability to handle high-dimensional datasets and capture non-linear patterns common in stock market forecasting. Gradient boosting is a top choice algorithm for classification and regression predictive modeling projects because it often achieves the best performance, but it takes lot of time to converge to the solution. The ensemble of XG-Boost provides an efficient implementation of gradient boosting that can be configured to train random forest ensembles and address the speed problems of gradient boosting. In order to achieve the best performance in stock price forecasting, the LSTM is combined in this model due to its capability of storing past information. To make the most of the two models, it is good practice to combine these two and apply a higher weight to the model, which yields a lower loss function (mean absolute error). The parameter setting used for implementing the proposed ensemble model is mentioned <a href="#ijfs-11-00094-t001" class="html-table">Table 1</a>.</div><div class='html-p'>The necessary comparison and evaluations are depicted in <a href="#ijfs-11-00094-f007" class="html-fig">Figure 7</a> and <a href="#ijfs-11-00094-f008" class="html-fig">Figure 8</a>. We implemented all of these algorithms using Python programming language. <a href="#ijfs-11-00094-f007" class="html-fig">Figure 7</a> shows the stock forecasting results of Tainwala Chemicals and Plastics (India) (TAINIWALCHM). The dataset for implementation was obtained from Yahoo Finance API, and we considered the dataset for TAINIWALCHM from the year 2014 to the year 2023. The RMSE and R2 scores were used for evaluating the performance of the various models.</div><div class='html-p'><a href="#ijfs-11-00094-f008" class="html-fig">Figure 8</a> shows the stock forecasting results of Agro Phos (India) Ltd. (AGROPHOS). The dataset for implementation was obtained from Yahoo Finance API, and we have considered the dataset for TAINIWALCHM from the year 2018 to the year 2023. The RMSE and R2 scores were used for evaluating the performance of the various models.</div><div class='html-p'>The analysis results in <a href="#ijfs-11-00094-t002" class="html-table">Table 2</a> indicate that the ensemble algorithm demonstrated the best performance compared to other distinct algorithms’.</div><div class='html-p'>From this study and experimental analysis, we observed that the modification of hyperparameters is a crucial stage in the process of stock forecasting as it can maximize the performance of machine learning models. <a href="#ijfs-11-00094-t003" class="html-table">Table 3</a> lists machine learning models that are frequently used in stock price forecasting. Nevertheless, the efficacy of these models is highly dependent on the choice of hyperparameters, which are parameters that are not learned from the data, but rather, defined before the learning process.</div><div class='html-p'>The next part of this section illustrates the comparative analysis of work conducted by multiple authors on various classification, regression, and time series-based algorithms to evaluate their performance in stock market forecasting, as shown in <a href="#ijfs-11-00094-t003" class="html-table">Table 3</a>. The algorithms were assessed using various evaluation parameters, including accuracy, recall, precision, F1 Score, MSE, MAE, RMSE, and R-squared error. The aim was to identify effective algorithms that could benefit a wider audience, including general applied researchers and intelligent laypersons, enabling them to make informed investment decisions. These findings will also provide valuable insights for researchers, investors, and general audiences, enabling them to make well-informed decisions and navigate the complexities of the financial market.</div></section><section id='sec5-ijfs-11-00094' type=''><h2 data-nested='1'> 5. Implications and Limitations of the Study</h2><div class='html-p'>This study focused on the application of machine learning and deep learning models in stock forecasting. It can be noted that machine learning or deep learning models alone are not sufficient. Ensemble techniques are capable of providing superior performance. But merely developing a model is not enough, and emphasis should also be placed on hyperparameter tuning. The performance of the model can be improved using hyperparameters such as learning rates, regularization in cases of deep learning, the number of hidden layers, max_depth, n_estimators, and learning rate. Choosing the proper settings for these hyperparameters can considerably enhance the precision of stock forecasts. For instance, a model with a greater learning rate may converge more quickly but may also be susceptible to overfitting, whereas a model with a lower learning rate may converge more slowly but generalize better to new data. Tuning hyperparameters entails choosing the optimal combination of hyperparameters for a given dataset and model architecture. This procedure often entails training many models with distinct hyperparameter values and evaluating their performance on a validation dataset. Typically, the optimal combination of hyperparameters is determined by the model’s capacity to reduce the deviation between predicted and actual stock prices. The study of machine learning models and deep learning models and the comparative results presented in this article will guide researchers in choosing ideal and preferred machine learning and deep learning algorithms for their respective research work.</div></section><section id='sec6-ijfs-11-00094' type=''><h2 data-nested='1'> 6. Future Research Directions</h2><div class='html-p'>The presented review article is focused on the review of related and published articles on stock price prediction, forecasting, and classification. The analysis of financial instruments such as stocks and equities is a considerable challenge. It is said that the stock market evolves over a period of time (<a href="#B31-ijfs-11-00094" class="html-bibr">Lim and Brooks 2011</a>), and hence, the approaches developed for handling specific problems will see low performance sooner or later even though their performance is found to be appreciated initially. As the stock market evolves under the influence of various factors such as geopolitical issues, equity trading, and investment, the underlying challenges also change, and so do the methodologies for addressing the new challenges (<a href="#B64-ijfs-11-00094" class="html-bibr">Sprenger and Welpe 2011</a>; <a href="#B57-ijfs-11-00094" class="html-bibr">Shah et al. 2019</a>). Sufficient research has been presented on stock price prediction and stock classification, which is the primary focus of this review article. Based on the study presented in this article, we have identified some of the key areas where researchers should focus their attention and explore better solutions. In this section, an attempt is made to open new research avenues for researchers in the field of stock market research.</div><section id='sec6dot1-ijfs-11-00094' type=''><h4 class='html-italic' data-nested='2'> 6.1. Trend Analysis and Classification</h4><div class='html-p'>Most researchers are inclined towards solving the problem of price prediction or forecasting stock value series. It is important to know the movement of stock prices, as well. We believe that researchers can explore the possible usage of state-of-the-art deep learning and machine learning algorithms in stock trend prediction and classification (<a href="#B18-ijfs-11-00094" class="html-bibr">Jiang 2021</a>; <a href="#B40-ijfs-11-00094" class="html-bibr">Nikou et al. 2019</a>).</div></section><section id='sec6dot2-ijfs-11-00094' type=''><h4 class='html-italic' data-nested='2'> 6.2. Pattern Identification Using Computer Vision</h4><div class='html-p'>Most researchers see stock price as time series data, which is true. But time series numerical data can be transformed into images, and patterns in the images can be identified to understand new trends occurring in the price changes (<a href="#B8-ijfs-11-00094" class="html-bibr">Barra et al. 2020</a>). Due to the advancement of deep learning in the form of pre-trained models and transfer learning, researchers can explore opportunities to apply these models to understanding the images generated based on time series data.</div></section><section id='sec6dot3-ijfs-11-00094' type=''><h4 class='html-italic' data-nested='2'> 6.3. Chart Pattern Analysis Using Computer Vision</h4><div class='html-p'>Chart pattern analysis is one of the most-applied approaches among traders and investors. Candlesticks often form fascinating patterns (<a href="#B10-ijfs-11-00094" class="html-bibr">Cagliero et al. 2023</a>; <a href="#B16-ijfs-11-00094" class="html-bibr">Hu et al. 2019</a>), which helps traders in capturing price action well before it occurs. But the biggest drawback of chart pattern analysis is the perception of the viewer. Generally, these chart patterns are identified based on the perceptions of traders. Every trader will have a different perception of the market, and the emotions of traders tend to project different patterns for the same price action. For example, it is difficult to differentiate between double-top and triple-top candlestick patterns (<a href="#B28-ijfs-11-00094" class="html-bibr">Lambert 2009</a>). Researchers can address the problem of candlestick pattern identification using suitable deep-learning techniques so that perception bias can be avoided amongst traders.</div></section></section><section id='sec7-ijfs-11-00094' type='conclusions'><h2 data-nested='1'> 7. Conclusions</h2><div class='html-p'>In this review, several conventional, machine learning, and deep learning techniques that are employed in stock market forecasting are investigated. This review describes various machine learning techniques, deep learning techniques, and time series forecasting techniques. This article presents recent applications of machine learning and deep learning models, and an ensemble model is also tested on the TAINIWALCHM and AGROPHOS stock datasets. Despite the existence of several popular methods for stock price forecasting, even today, there is no universal solution to accurately predict the stock price or trend of the market. There is still a possibility that AI-based models can also fail if they are not trained efficiently with fresh data. To conclude this article, we assert that researchers should keep exploring new avenues to solve price action problems using ensemble techniques. Stock forecasting models should be enhanced with suitable hyperparameter tuning so that they can serve as precise stock price prediction models. Traders and investment advisors can use machine learning and deep learning models as additional confirmation indicators to support their decisions, and decisions should not rely only on AI-based price forecasting methods. Along with stock forecasting techniques, researchers in the future can expand their studies to portfolio management, trading strategy design, and investment decision making.</div></section> </div> <div class="html-back"> <section class='html-notes'><h2 >Author Contributions</h2><div class='html-p'>Conceptualization G.S., D.S.D. and A.M.B.; Visualization, G.S.; Writing—original draft, G.S., D.S.D. and A.M.B.; Supervision, D.S.D. and A.M.B.; Writing—review & editing, S.T.D., D.D., S.K.B. All authors have read and agreed to the published version of the manuscript.</div></section><section class='html-notes'><h2>Funding</h2><div class='html-p'>This research received no external funding.</div></section><section class='html-notes'><h2 >Informed Consent Statement</h2><div class='html-p'>Not applicable.</div></section><section class='html-notes'><h2 >Data Availability Statement</h2><div class='html-p'>Not applicable.</div></section><section class='html-notes'><h2 >Conflicts of Interest</h2><div class='html-p'>The authors declare no conflict of interest.</div></section><section id='html-references_list'><h2>References</h2><ol class='html-xx'><li id='B1-ijfs-11-00094' class='html-x' data-content=''>Agrawal, Manish, Piyush Kumar Shukla, Rajit Nair, Anand Nayyar, and Mehedi Masud. 2022. Stock Prediction Based on Technical Indicators Using Deep Learning Model. <span class='html-italic'>Computers, Materials & Continua</span> 70: 287–304. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Prediction+Based+on+Technical+Indicators+Using+Deep+Learning+Model&author=Agrawal,+Manish&author=Piyush+Kumar+Shukla&author=Rajit+Nair&author=Anand+Nayyar&author=and+Mehedi+Masud&publication_year=2022&journal=Computers,+Materials+&+Continua&volume=70&pages=287%E2%80%93304" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B2-ijfs-11-00094' class='html-x' data-content=''>Ali, Syed Shahan, Muhammad Mubeen, and Adnan Hussain. 2018. Prediction of stock performance by using logistic regression model: Evidence from Pakistan Stock Exchange (PSX). <span class='html-italic'>Asian Journal of Empirical Research</span> 15: 212. [<a href="https://scholar.google.com/scholar_lookup?title=Prediction+of+stock+performance+by+using+logistic+regression+model:+Evidence+from+Pakistan+Stock+Exchange+(PSX)&author=Ali,+Syed+Shahan&author=Muhammad+Mubeen&author=and+Adnan+Hussain&publication_year=2018&journal=Asian+Journal+of+Empirical+Research&volume=15&pages=212" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B3-ijfs-11-00094' class='html-x' data-content=''>Ampomah, Ernest Kwame, Gabriel Nyame, Zhiguang Qin, Prince Clement Addo, Enoch Opanin Gyamfi, and Micheal Gyan. 2021. Stock Market Prediction with Gaussian Naïve Bayes Machine Learning Algorithm. <span class='html-italic'>Informatica</span> 45: 2. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Market+Prediction+with+Gaussian+Na%C3%AFve+Bayes+Machine+Learning+Algorithm&author=Ampomah,+Ernest+Kwame&author=Gabriel+Nyame&author=Zhiguang+Qin&author=Prince+Clement+Addo&author=Enoch+Opanin+Gyamfi&author=and+Micheal+Gyan&publication_year=2021&journal=Informatica&volume=45&pages=2&doi=10.31449/inf.v45i2.3407" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.31449/inf.v45i2.3407" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B4-ijfs-11-00094' class='html-x' data-content=''>Baheti, Radhika, Gauri Shirkande, Sneha Bodake, Janhavi Deokar, and Archana K. 2021. Stock Market Analysis from Social Media and News using Machine Learning Techniques. <span class='html-italic'>International Journal on Data Science and Machine Learning with Applications</span> 1: 59–67. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Market+Analysis+from+Social+Media+and+News+using+Machine+Learning+Techniques&author=Baheti,+Radhika&author=Gauri+Shirkande&author=Sneha+Bodake&author=Janhavi+Deokar&author=and+Archana+K&publication_year=2021&journal=International+Journal+on+Data+Science+and+Machine+Learning+with+Applications&volume=1&pages=59%E2%80%9367" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B5-ijfs-11-00094' class='html-x' data-content=''>Ballings, Michel, Dirk Van den Poel, Nathalie Hespeels, and Ruben Gryp. 2015. Evaluating multiple classifiers for stock price direction prediction. <span class='html-italic'>Expert Systems with Applications</span> 42: 7046–56. [<a href="https://scholar.google.com/scholar_lookup?title=Evaluating+multiple+classifiers+for+stock+price+direction+prediction&author=Ballings,+Michel&author=Dirk+Van+den+Poel&author=Nathalie+Hespeels&author=and+Ruben+Gryp&publication_year=2015&journal=Expert+Systems+with+Applications&volume=42&pages=7046%E2%80%9356&doi=10.1016/j.eswa.2015.05.013" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1016/j.eswa.2015.05.013" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B6-ijfs-11-00094' class='html-x' data-content=''>Banik, Shouvik, Nonita Sharma, Monika Mangla, Sachi Nandan Mohanty, and Selvarajan Shitharth. 2022. LSTM based decision support system for swing trading in stock market. <span class='html-italic'>Knowledge-Based Systems</span> 239: 107994. [<a href="https://scholar.google.com/scholar_lookup?title=LSTM+based+decision+support+system+for+swing+trading+in+stock+market&author=Banik,+Shouvik&author=Nonita+Sharma&author=Monika+Mangla&author=Sachi+Nandan+Mohanty&author=and+Selvarajan+Shitharth&publication_year=2022&journal=Knowledge-Based+Systems&volume=239&pages=107994&doi=10.1016/j.knosys.2021.107994" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1016/j.knosys.2021.107994" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B7-ijfs-11-00094' class='html-x' data-content=''>Bansal, Malti, Apoorva Goyal, and Apoorva Choudhary. 2022. Stock Market Prediction with High Accuracy using Machine Learning Techniques. <span class='html-italic'>Procedia Computer Science</span> 215: 247–65. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Market+Prediction+with+High+Accuracy+using+Machine+Learning+Techniques&author=Bansal,+Malti&author=Apoorva+Goyal&author=and+Apoorva+Choudhary&publication_year=2022&journal=Procedia+Computer+Science&volume=215&pages=247%E2%80%9365&doi=10.1016/j.procs.2022.12.028" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1016/j.procs.2022.12.028" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B8-ijfs-11-00094' class='html-x' data-content=''>Barra, Silvio, Salvatore Mario Carta, Andrea Corriga, Alessandro Sebastian Podda, and Diego Reforgiato Recupero. 2020. Deep learning and time series-to-image encoding for financial forecasting. <span class='html-italic'>IEEE/CAA Journal of Automatica Sinica</span> 7: 683–92. [<a href="https://scholar.google.com/scholar_lookup?title=Deep+learning+and+time+series-to-image+encoding+for+financial+forecasting&author=Barra,+Silvio&author=Salvatore+Mario+Carta&author=Andrea+Corriga&author=Alessandro+Sebastian+Podda&author=and+Diego+Reforgiato+Recupero&publication_year=2020&journal=IEEE/CAA+Journal+of+Automatica+Sinica&volume=7&pages=683%E2%80%9392&doi=10.1109/JAS.2020.1003132" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1109/JAS.2020.1003132" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B9-ijfs-11-00094' class='html-x' data-content=''>Bustos, Oscar, Alexandra Pomares, and Enrique Gonzalez. 2017. A comparison between SVM and multilayer perceptron in predicting an emerging financial market: Colombian stock market. Paper presented at 2017 Congreso Internacional de Innovacion y Tendencias en Ingenieria (CONIITI), Bogotá, Colombia, October 4–6; pp. 1–6. [<a href="https://scholar.google.com/scholar_lookup?title=A+comparison+between+SVM+and+multilayer+perceptron+in+predicting+an+emerging+financial+market:+Colombian+stock+market&conference=Paper+presented+at+2017+Congreso+Internacional+de+Innovacion+y+Tendencias+en+Ingenieria+(CONIITI)&author=Bustos,+Oscar&author=Alexandra+Pomares&author=and+Enrique+Gonzalez&publication_year=2017&pages=1%E2%80%936" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B10-ijfs-11-00094' class='html-x' data-content=''>Cagliero, Luca, Jacopo Fior, and Paolo Garza. 2023. Shortlisting machine learning-based stock trading recommendations using candlestick pattern recognition. <span class='html-italic'>Expert Systems with Applications</span> 216: 119493. [<a href="https://scholar.google.com/scholar_lookup?title=Shortlisting+machine+learning-based+stock+trading+recommendations+using+candlestick+pattern+recognition&author=Cagliero,+Luca&author=Jacopo+Fior&author=and+Paolo+Garza&publication_year=2023&journal=Expert+Systems+with+Applications&volume=216&pages=119493&doi=10.1016/j.eswa.2022.119493" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1016/j.eswa.2022.119493" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B11-ijfs-11-00094' class='html-x' data-content=''>Di Persio, Luca, and Oleksandr Honchar. 2017. Recurrent neural networks approach to the financial forecast of Google assets. <span class='html-italic'>International Journal of Mathematics and Computers in Simulation</span> 11: 7–13. [<a href="https://scholar.google.com/scholar_lookup?title=Recurrent+neural+networks+approach+to+the+financial+forecast+of+Google+assets&author=Di+Persio,+Luca&author=and+Oleksandr+Honchar&publication_year=2017&journal=International+Journal+of+Mathematics+and+Computers+in+Simulation&volume=11&pages=7%E2%80%9313" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B12-ijfs-11-00094' class='html-x' data-content=''>Dospinescu, Nicoleta, and Octavian Dospinescu. 2019. A Profitability Regression Model In Financial Communication Of Romanian Stock Exchange’s Companies. <span class='html-italic'>Ecoforum Journal</span> 8: 4. [<a href="https://scholar.google.com/scholar_lookup?title=A+Profitability+Regression+Model+In+Financial+Communication+Of+Romanian+Stock+Exchange%E2%80%99s+Companies&author=Dospinescu,+Nicoleta&author=and+Octavian+Dospinescu&publication_year=2019&journal=Ecoforum+Journal&volume=8&pages=4" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B13-ijfs-11-00094' class='html-x' data-content=''>Gite, Shilpa, Shruti Patil, Deepak Dharrao, Madhuri Yadav, Sneha Basak, Arundarasi Rajendran, and Ketan Kotecha. 2023. Textual Feature Extraction Using Ant Colony Optimization for Hate Speech Classification. <span class='html-italic'>Big Data and Cognitive Computing</span> 7: 45. [<a href="https://scholar.google.com/scholar_lookup?title=Textual+Feature+Extraction+Using+Ant+Colony+Optimization+for+Hate+Speech+Classification&author=Gite,+Shilpa&author=Shruti+Patil&author=Deepak+Dharrao&author=Madhuri+Yadav&author=Sneha+Basak&author=Arundarasi+Rajendran&author=and+Ketan+Kotecha&publication_year=2023&journal=Big+Data+and+Cognitive+Computing&volume=7&pages=45&doi=10.3390/bdcc7010045" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.3390/bdcc7010045" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B14-ijfs-11-00094' class='html-x' data-content=''>Grigoryan, Hakob. 2017. Stock Market Trend Prediction Using Support Vector Machines and Variable Selection Methods. <span class='html-italic'>Advances in Intelligent Systems Research (AISR)</span> 2017: 210–13. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Market+Trend+Prediction+Using+Support+Vector+Machines+and+Variable+Selection+Methods&author=Grigoryan,+Hakob&publication_year=2017&journal=Advances+in+Intelligent+Systems+Research+(AISR)&volume=2017&pages=210%E2%80%9313&doi=10.2991/ammsa-17.2017.45" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.2991/ammsa-17.2017.45" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>] [<a href="https://download.atlantis-press.com/article/25879160.pdf" target='_blank' rel="noopener noreferrer">Green Version</a>]</li><li id='B15-ijfs-11-00094' class='html-x' data-content=''>Gururaj, Vaishnavi, V. R. Shriya, and K. Ashwini. 2019. Stock Market Prediction using Linear Regression and Support Vector Machines. <span class='html-italic'>International Journal of Applied Engineering Research</span> 14: 1931–34. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Market+Prediction+using+Linear+Regression+and+Support+Vector+Machines&author=Gururaj,+Vaishnavi&author=V.+R.+Shriya&author=and+K.+Ashwini&publication_year=2019&journal=International+Journal+of+Applied+Engineering+Research&volume=14&pages=1931%E2%80%9334" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B16-ijfs-11-00094' class='html-x' data-content=''>Hu, Weilong, Yain-Whar Si, Simon Fong, and Raymond Yiu Keung Lau. 2019. A formal approach to candlestick pattern classification in financial time series. <span class='html-italic'>Applied Soft Computing</span> 84: 105700. [<a href="https://scholar.google.com/scholar_lookup?title=A+formal+approach+to+candlestick+pattern+classification+in+financial+time+series&author=Hu,+Weilong&author=Yain-Whar+Si&author=Simon+Fong&author=and+Raymond+Yiu+Keung+Lau&publication_year=2019&journal=Applied+Soft+Computing&volume=84&pages=105700&doi=10.1016/j.asoc.2019.105700" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1016/j.asoc.2019.105700" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B17-ijfs-11-00094' class='html-x' data-content=''>Hu, Zexin, Yiqi Zhao, and Matloob Khushi. 2021. A survey of forex and stock price prediction using deep learning. <span class='html-italic'>Applied System Innovation</span> 4: 9. [<a href="https://scholar.google.com/scholar_lookup?title=A+survey+of+forex+and+stock+price+prediction+using+deep+learning&author=Hu,+Zexin&author=Yiqi+Zhao&author=and+Matloob+Khushi&publication_year=2021&journal=Applied+System+Innovation&volume=4&pages=9&doi=10.3390/asi4010009" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.3390/asi4010009" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B18-ijfs-11-00094' class='html-x' data-content=''>Jiang, Weiwei. 2021. Applications of deep learning in stock market prediction: Recent progress. <span class='html-italic'>Expert Systems with Applications</span> 184: 115537. [<a href="https://scholar.google.com/scholar_lookup?title=Applications+of+deep+learning+in+stock+market+prediction:+Recent+progress&author=Jiang,+Weiwei&publication_year=2021&journal=Expert+Systems+with+Applications&volume=184&pages=115537&doi=10.1016/j.eswa.2021.115537" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1016/j.eswa.2021.115537" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B19-ijfs-11-00094' class='html-x' data-content=''>Jose, Jithina, Suja Cherukullapurath Mana, and B. Keerthi Samhitha. 2019. An Efficient System to Predict and Analyze Stock Data using Hadoop Techniques. <span class='html-italic'>International Journal of Recent Technology and Engineering (IJRTE)</span> 8: 1039–43. [<a href="https://scholar.google.com/scholar_lookup?title=An+Efficient+System+to+Predict+and+Analyze+Stock+Data+using+Hadoop+Techniques&author=Jose,+Jithina&author=Suja+Cherukullapurath+Mana&author=and+B.+Keerthi+Samhitha&publication_year=2019&journal=International+Journal+of+Recent+Technology+and+Engineering+(IJRTE)&volume=8&pages=1039%E2%80%9343&doi=10.35940/ijrte.B1824.078219" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.35940/ijrte.B1824.078219" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B20-ijfs-11-00094' class='html-x' data-content=''>Kaczmarek, Tomasz, and Katarzyna Perez. 2021. Building portfolios based on machine learning predictions. <span class='html-italic'>Economic Research-Ekonomska Istraživanja</span> 35: 19–37. [<a href="https://scholar.google.com/scholar_lookup?title=Building+portfolios+based+on+machine+learning+predictions&author=Kaczmarek,+Tomasz&author=and+Katarzyna+Perez&publication_year=2021&journal=Economic+Research-Ekonomska+Istra%C5%BEivanja&volume=35&pages=19%E2%80%9337&doi=10.1080/1331677X.2021.1875865" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1080/1331677X.2021.1875865" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B21-ijfs-11-00094' class='html-x' data-content=''>Kaninde, Sumedh, Manish Mahajan, Aditya Janghale, and Bharti Joshi. 2022. Stock Price Prediction Using Facebook Prophet. <span class='html-italic'>International Journal of Research in Engineering and Science</span> 44: 03060. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Price+Prediction+Using+Facebook+Prophet&author=Kaninde,+Sumedh&author=Manish+Mahajan&author=Aditya+Janghale&author=and+Bharti+Joshi&publication_year=2022&journal=International+Journal+of+Research+in+Engineering+and+Science&volume=44&pages=03060&doi=10.1051/itmconf/20224403060" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1051/itmconf/20224403060" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B22-ijfs-11-00094' class='html-x' data-content=''>Kardani, Navid, Annan Zhou, Majidreza Nazem, and Shui-Long Shen. 2020. Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data. <span class='html-italic'>Journal of Rock Mechanics and Geotechnical Engineering</span> 13: 188–201. [<a href="https://scholar.google.com/scholar_lookup?title=Improved+prediction+of+slope+stability+using+a+hybrid+stacking+ensemble+method+based+on+finite+element+analysis+and+field+data&author=Kardani,+Navid&author=Annan+Zhou&author=Majidreza+Nazem&author=and+Shui-Long+Shen&publication_year=2020&journal=Journal+of+Rock+Mechanics+and+Geotechnical+Engineering&volume=13&pages=188%E2%80%93201&doi=10.1016/j.jrmge.2020.05.011" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1016/j.jrmge.2020.05.011" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B23-ijfs-11-00094' class='html-x' data-content=''>Kedar, S. V. 2021. Stock Market Increase and Decrease using Twitter Sentiment Analysis and ARIMA Model. <span class='html-italic'>Turkish Journal of Computer and Mathematics Education (TURCOMAT)</span> 12: 146–61. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Market+Increase+and+Decrease+using+Twitter+Sentiment+Analysis+and+ARIMA+Model&author=Kedar,+S.+V.&publication_year=2021&journal=Turkish+Journal+of+Computer+and+Mathematics+Education+(TURCOMAT)&volume=12&pages=146%E2%80%9361&doi=10.17762/turcomat.v12i1S.1596" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.17762/turcomat.v12i1S.1596" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B24-ijfs-11-00094' class='html-x' data-content=''>Khairi, Teaba W. A., Rana M. Zaki, and Wisam A. Mahmood. 2019. Stock Price Prediction using Technical, Fundamental and News based Approach. Paper presented at 2019 2nd Scientific Conference of Computer Sciences (SCCS), Baghdad, Iraq, March 27–28. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Price+Prediction+using+Technical,+Fundamental+and+News+based+Approach&conference=Paper+presented+at+2019+2nd+Scientific+Conference+of+Computer+Sciences+(SCCS)&author=Khairi,+Teaba+W.+A.&author=Rana+M.+Zaki&author=and+Wisam+A.+Mahmood&publication_year=2019" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B25-ijfs-11-00094' class='html-x' data-content=''>Khan, Wasiat, Mustansar Ali Ghazanfar, Muhammad Awais Azam, Amin Karami, Khaled H. Alyoubi, and Ahmed S. Alfakeeh. 2020. Stock market prediction using machine learning classifiers and social media, news. <span class='html-italic'>Journal of Ambient Intelligence and Humanized Computing</span> 13: 3433–56. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+market+prediction+using+machine+learning+classifiers+and+social+media,+news&author=Khan,+Wasiat&author=Mustansar+Ali+Ghazanfar&author=Muhammad+Awais+Azam&author=Amin+Karami&author=Khaled+H.+Alyoubi&author=and+Ahmed+S.+Alfakeeh&publication_year=2020&journal=Journal+of+Ambient+Intelligence+and+Humanized+Computing&volume=13&pages=3433%E2%80%9356&doi=10.1007/s12652-020-01839-w" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1007/s12652-020-01839-w" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B26-ijfs-11-00094' class='html-x' data-content=''>Kumar, Deepak, Pradeepta Kumar Sarangi, and Rajit Verma. 2021. A systematic review of stock market prediction using machine learning and statistical techniques. <span class='html-italic'>Materials Today: Proceedings</span> 49: 3187–91. [<a href="https://scholar.google.com/scholar_lookup?title=A+systematic+review+of+stock+market+prediction+using+machine+learning+and+statistical+techniques&author=Kumar,+Deepak&author=Pradeepta+Kumar+Sarangi&author=and+Rajit+Verma&publication_year=2021&journal=Materials+Today:+Proceedings&volume=49&pages=3187%E2%80%9391&doi=10.1016/j.matpr.2020.11.399" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1016/j.matpr.2020.11.399" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B27-ijfs-11-00094' class='html-x' data-content=''>Kumar, Saurav, and Dhruba Ningombam. 2018. Short-Term Forecasting of Stock Prices Using Long Short Term Memory. Paper presented at 2018 International Conference on Information Technology (ICIT), Hong Kong, China, December 29–31. [<a href="https://scholar.google.com/scholar_lookup?title=Short-Term+Forecasting+of+Stock+Prices+Using+Long+Short+Term+Memory&conference=Paper+presented+at+2018+International+Conference+on+Information+Technology+(ICIT)&author=Kumar,+Saurav&author=and+Dhruba+Ningombam&publication_year=2018" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B28-ijfs-11-00094' class='html-x' data-content=''>Lambert, Clive. 2009. <span class='html-italic'>Candlestick Charts: An Introduction to Using Candlestick Charts</span>. Petersfield: Harriman House Limited. [<a href="https://scholar.google.com/scholar_lookup?title=Candlestick+Charts:+An+Introduction+to+Using+Candlestick+Charts&author=Lambert,+Clive&publication_year=2009" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B29-ijfs-11-00094' class='html-x' data-content=''>Li, Audeliano Wolian, and Guilherme Sousa Bastos. 2020. Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review. <span class='html-italic'>IEEE Access</span> 8: 185232–242. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Market+Forecasting+Using+Deep+Learning+and+Technical+Analysis:+A+Systematic+Review&author=Li,+Audeliano+Wolian&author=and+Guilherme+Sousa+Bastos&publication_year=2020&journal=IEEE+Access&volume=8&pages=185232%E2%80%93242&doi=10.1109/ACCESS.2020.3030226" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1109/ACCESS.2020.3030226" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B30-ijfs-11-00094' class='html-x' data-content=''>Li, Yang, and Yi Pan. 2021. A novel ensemble deep learning model for stock prediction based on stock prices and news. <span class='html-italic'>International Journal of Data Science and Analytics</span> 13: 139–49. [<a href="https://scholar.google.com/scholar_lookup?title=A+novel+ensemble+deep+learning+model+for+stock+prediction+based+on+stock+prices+and+news&author=Li,+Yang&author=and+Yi+Pan&publication_year=2021&journal=International+Journal+of+Data+Science+and+Analytics&volume=13&pages=139%E2%80%9349&doi=10.1007/s41060-021-00279-9&pmid=34549080" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1007/s41060-021-00279-9" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>] [<a href="http://www.ncbi.nlm.nih.gov/pubmed/34549080" class='cross-ref' data-typ='pmid' target='_blank' rel='noopener noreferrer'>PubMed</a>]</li><li id='B31-ijfs-11-00094' class='html-x' data-content=''>Lim, Kian-Ping, and Robert Brooks. 2011. The evolution of stock market efficiency over time: A survey of the empirical literature. <span class='html-italic'>Journal of Economic Surveys</span> 25: 69–108. [<a href="https://scholar.google.com/scholar_lookup?title=The+evolution+of+stock+market+efficiency+over+time:+A+survey+of+the+empirical+literature&author=Lim,+Kian-Ping&author=and+Robert+Brooks&publication_year=2011&journal=Journal+of+Economic+Surveys&volume=25&pages=69%E2%80%93108&doi=10.1111/j.1467-6419.2009.00611.x" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1111/j.1467-6419.2009.00611.x" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B32-ijfs-11-00094' class='html-x' data-content=''>Lim, Yi Xuan, and Consilz Tan. 2021. Do negative events really have deteriorating effects on stock performance? A comparative study on Tesla (US) and Nio (China). <span class='html-italic'>Journal of Asian Business and Economic Studies</span> 29: 105–19. [<a href="https://scholar.google.com/scholar_lookup?title=Do+negative+events+really+have+deteriorating+effects+on+stock+performance?+A+comparative+study+on+Tesla+(US)+and+Nio+(China)&author=Lim,+Yi+Xuan&author=and+Consilz+Tan&publication_year=2021&journal=Journal+of+Asian+Business+and+Economic+Studies&volume=29&pages=105%E2%80%9319&doi=10.1108/JABES-07-2021-0106" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1108/JABES-07-2021-0106" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B33-ijfs-11-00094' class='html-x' data-content=''>Manish, Kumar, and M. Thenmozhi. 2014. Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models. <span class='html-italic'>International Journal of Banking Accounting and Finance</span> 5: 284–308. [<a href="https://scholar.google.com/scholar_lookup?title=Forecasting+stock+index+returns+using+ARIMA-SVM,+ARIMA-ANN,+and+ARIMA-random+forest+hybrid+models&author=Manish,+Kumar&author=and+M.+Thenmozhi&publication_year=2014&journal=International+Journal+of+Banking+Accounting+and+Finance&volume=5&pages=284%E2%80%93308" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B34-ijfs-11-00094' class='html-x' data-content=''>Mann, Jordan, and J. Nathan Kutz. 2016. Dynamic mode decomposition for financial trading strategies. <span class='html-italic'>Quantitative Finance</span> 16: 1643–55. [<a href="https://scholar.google.com/scholar_lookup?title=Dynamic+mode+decomposition+for+financial+trading+strategies&author=Mann,+Jordan&author=and+J.+Nathan+Kutz&publication_year=2016&journal=Quantitative+Finance&volume=16&pages=1643%E2%80%9355&doi=10.1080/14697688.2016.1170194" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1080/14697688.2016.1170194" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>] [<a href="http://arxiv.org/pdf/1508.04487" target='_blank' rel="noopener noreferrer">Green Version</a>]</li><li id='B35-ijfs-11-00094' class='html-x' data-content=''>Manojlović, Teo, and Ivan Štajduhar. 2015. Predicting stock market trends using random forests: A sample of the Zagreb stock exchange. Paper presented at International Convention on Information and Communication Technology Electronics and Microelectronics, Opatija, Croatia, May 25–29. [<a href="https://scholar.google.com/scholar_lookup?title=Predicting+stock+market+trends+using+random+forests:+A+sample+of+the+Zagreb+stock+exchange&conference=Paper+presented+at+International+Convention+on+Information+and+Communication+Technology+Electronics+and+Microelectronics&author=Manojlovi%C4%87,+Teo&author=and+Ivan+%C5%A0tajduhar&publication_year=2015" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B36-ijfs-11-00094' class='html-x' data-content=''>Mashadihasanli, Tamerlan. 2022. Stock Market Price Forecasting Using the Arima Model: An Application to Istanbul, Turkiye. <span class='html-italic'>Journal of Economic Policy Researches</span> 9: 439–54. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Market+Price+Forecasting+Using+the+Arima+Model:+An+Application+to+Istanbul,+Turkiye&author=Mashadihasanli,+Tamerlan&publication_year=2022&journal=Journal+of+Economic+Policy+Researches&volume=9&pages=439%E2%80%9354&doi=10.26650/JEPR1056771" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.26650/JEPR1056771" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B37-ijfs-11-00094' class='html-x' data-content=''>Mohapatra, Sabyasachi, Rohan Mukherjee, Arindam Roy, Anirban Sengupta, and Amit Puniyani. 2022. Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators? <span class='html-italic'>Journal of Risk and Financial Management</span> 8: 350. [<a href="https://scholar.google.com/scholar_lookup?title=Can+Ensemble+Machine+Learning+Methods+Predict+Stock+Returns+for+Indian+Banks+Using+Technical+Indicators?&author=Mohapatra,+Sabyasachi&author=Rohan+Mukherjee&author=Arindam+Roy&author=Anirban+Sengupta&author=and+Amit+Puniyani&publication_year=2022&journal=Journal+of+Risk+and+Financial+Management&volume=8&pages=350&doi=10.3390/jrfm15080350" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.3390/jrfm15080350" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B38-ijfs-11-00094' class='html-x' data-content=''>Mukherjee, Somenath, Bikash Sadhukhan, Nairita Sarkar, Debajyoti Roy, and Soumil De. 2021. Stock market prediction using deep learning algorithms. <span class='html-italic'>CAAI Transactions on Intelligence Technology</span> 8: 82–94. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+market+prediction+using+deep+learning+algorithms&author=Mukherjee,+Somenath&author=Bikash+Sadhukhan&author=Nairita+Sarkar&author=Debajyoti+Roy&author=and+Soumil+De&publication_year=2021&journal=CAAI+Transactions+on+Intelligence+Technology&volume=8&pages=82%E2%80%9394&doi=10.1049/cit2.12059" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1049/cit2.12059" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B39-ijfs-11-00094' class='html-x' data-content=''>Nabipour, Mojtaba, Pooyan Nayyeri, Hamed Jabani, S. Shahab, and Amir Mosavi. 2020. Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis. <span class='html-italic'>IEEE Access</span> 8: 150199–212. [<a href="https://scholar.google.com/scholar_lookup?title=Predicting+Stock+Market+Trends+Using+Machine+Learning+and+Deep+Learning+Algorithms+Via+Continuous+and+Binary+Data;+a+Comparative+Analysis&author=Nabipour,+Mojtaba&author=Pooyan+Nayyeri&author=Hamed+Jabani&author=S.+Shahab&author=and+Amir+Mosavi&publication_year=2020&journal=IEEE+Access&volume=8&pages=150199%E2%80%93212&doi=10.1109/ACCESS.2020.3015966" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1109/ACCESS.2020.3015966" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B40-ijfs-11-00094' class='html-x' data-content=''>Nikou, Mahla, Gholamreza Mansourfar, and Jamshid Bagherzadeh. 2019. Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. <span class='html-italic'>Intelligent Systems in Accounting, Finance and Management</span> 26: 164–74. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+price+prediction+using+DEEP+learning+algorithm+and+its+comparison+with+machine+learning+algorithms&author=Nikou,+Mahla&author=Gholamreza+Mansourfar&author=and+Jamshid+Bagherzadeh&publication_year=2019&journal=Intelligent+Systems+in+Accounting,+Finance+and+Management&volume=26&pages=164%E2%80%9374&doi=10.1002/isaf.1459" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1002/isaf.1459" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B41-ijfs-11-00094' class='html-x' data-content=''>Nti, Isaac Kofi, Adebayo Felix Adekoya, and Benjamin Asubam Weyori. 2020. A comprehensive evaluation of ensemble learning for stock-market prediction. <span class='html-italic'>Journal of Big Data</span> 7: 1–40. [<a href="https://scholar.google.com/scholar_lookup?title=A+comprehensive+evaluation+of+ensemble+learning+for+stock-market+prediction&author=Nti,+Isaac+Kofi&author=Adebayo+Felix+Adekoya&author=and+Benjamin+Asubam+Weyori&publication_year=2020&journal=Journal+of+Big+Data&volume=7&pages=1%E2%80%9340&doi=10.1186/s40537-020-00299-5" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1186/s40537-020-00299-5" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>] [<a href="https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-020-00299-5" target='_blank' rel="noopener noreferrer">Green Version</a>]</li><li id='B42-ijfs-11-00094' class='html-x' data-content=''>Obthong, Mehtabhorn, Nongnuch Tantisantiwong, Watthanasak Jeamwatthanachai, and Gary Wills. 2020. A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques. Paper presented at 2nd International Conference on Finance, Economics, Management and IT Business, Prague, Czech Republic, May 5–6. [<a href="https://scholar.google.com/scholar_lookup?title=A+Survey+on+Machine+Learning+for+Stock+Price+Prediction:+Algorithms+and+Techniques&conference=Paper+presented+at+2nd+International+Conference+on+Finance,+Economics,+Management+and+IT+Business&author=Obthong,+Mehtabhorn&author=Nongnuch+Tantisantiwong&author=Watthanasak+Jeamwatthanachai&author=and+Gary+Wills&publication_year=2020" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B43-ijfs-11-00094' class='html-x' data-content=''>Parray, Irfan Ramzan, Surinder Singh Khurana, Munish Kumar, and Ali A. Altalbe. 2020. Time series data analysis of stock price movement using machine learning techniques. <span class='html-italic'>Soft Computing</span> 24: 16509–17. [<a href="https://scholar.google.com/scholar_lookup?title=Time+series+data+analysis+of+stock+price+movement+using+machine+learning+techniques&author=Parray,+Irfan+Ramzan&author=Surinder+Singh+Khurana&author=Munish+Kumar&author=and+Ali+A.+Altalbe&publication_year=2020&journal=Soft+Computing&volume=24&pages=16509%E2%80%9317&doi=10.1007/s00500-020-04957-x" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1007/s00500-020-04957-x" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B44-ijfs-11-00094' class='html-x' data-content=''>Patel, Jigar, Sahil Shah, Priyank Thakkar, and Ketan Kotecha. 2015. Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. <span class='html-italic'>Expert Systems with Applications</span> 42: 259–68. [<a href="https://scholar.google.com/scholar_lookup?title=Predicting+stock+and+stock+price+index+movement+using+Trend+Deterministic+Data+Preparation+and+machine+learning+techniques&author=Patel,+Jigar&author=Sahil+Shah&author=Priyank+Thakkar&author=and+Ketan+Kotecha&publication_year=2015&journal=Expert+Systems+with+Applications&volume=42&pages=259%E2%80%9368&doi=10.1016/j.eswa.2014.07.040" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1016/j.eswa.2014.07.040" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B45-ijfs-11-00094' class='html-x' data-content=''>Pathak, Ashwini, and Sakshi Pathak. 2020. Study of Machine learning Algorithms for Stock Market Prediction. <span class='html-italic'>International Journal of Engineering Research & Technology (IJERT)</span> 9: 6. [<a href="https://scholar.google.com/scholar_lookup?title=Study+of+Machine+learning+Algorithms+for+Stock+Market+Prediction&author=Pathak,+Ashwini&author=and+Sakshi+Pathak&publication_year=2020&journal=International+Journal+of+Engineering+Research+&+Technology+(IJERT)&volume=9&pages=6" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B46-ijfs-11-00094' class='html-x' data-content=''>Polamuri, Subba Rao, Kudipudi Srinivas, and A. Krishna Mohan. 2019. Stock Market Prices Prediction using Random Forest and Extra Tree Regression. <span class='html-italic'>International Journal of Recent Technology and Engineering</span> 8: 1224–28. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Market+Prices+Prediction+using+Random+Forest+and+Extra+Tree+Regression&author=Polamuri,+Subba+Rao&author=Kudipudi+Srinivas&author=and+A.+Krishna+Mohan&publication_year=2019&journal=International+Journal+of+Recent+Technology+and+Engineering&volume=8&pages=1224%E2%80%9328&doi=10.35940/ijrte.C4314.098319" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.35940/ijrte.C4314.098319" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B47-ijfs-11-00094' class='html-x' data-content=''>Pramod, B. S., and Mallikarjuna Shastry Pm. 2021. Stock Price Prediction Using LSTM. <span class='html-italic'>Test Engineering and Management</span> 83: 5246–51. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Price+Prediction+Using+LSTM&author=Pramod,+B.+S.&author=and+Mallikarjuna+Shastry+Pm&publication_year=2021&journal=Test+Engineering+and+Management&volume=83&pages=5246%E2%80%9351" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B48-ijfs-11-00094' class='html-x' data-content=''>Qiu, Jiayu, Bin Wang, and Changjun Zhou. 2020. Forecasting stock prices with long-short term memory neural network based on attention mechanism. <span class='html-italic'>PLoS ONE</span> 15: e0227222. [<a href="https://scholar.google.com/scholar_lookup?title=Forecasting+stock+prices+with+long-short+term+memory+neural+network+based+on+attention+mechanism&author=Qiu,+Jiayu&author=Bin+Wang&author=and+Changjun+Zhou&publication_year=2020&journal=PLoS+ONE&volume=15&pages=e0227222&doi=10.1371/journal.pone.0227222&pmid=31899770" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1371/journal.pone.0227222" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>] [<a href="http://www.ncbi.nlm.nih.gov/pubmed/31899770" class='cross-ref' data-typ='pmid' target='_blank' rel='noopener noreferrer'>PubMed</a>] [<a href="https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0227222&type=printable" target='_blank' rel="noopener noreferrer">Green Version</a>]</li><li id='B49-ijfs-11-00094' class='html-x' data-content=''>Raghavendra, Kumar, Pardeep Kumar, and Yugal Kumar. 2021. Analysis of financial time series forecasting using deep learning model. Paper presented at 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Uttar Pradesh, India, January 28–29; pp. 877–81. [<a href="https://scholar.google.com/scholar_lookup?title=Analysis+of+financial+time+series+forecasting+using+deep+learning+model&conference=Paper+presented+at+2021+11th+International+Conference+on+Cloud+Computing,+Data+Science+&+Engineering+(Confluence)&author=Raghavendra,+Kumar&author=Pardeep+Kumar&author=and+Yugal+Kumar&publication_year=2021&pages=877%E2%80%9381" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B50-ijfs-11-00094' class='html-x' data-content=''>Reddy, Niveditha N., E. Naresh, and Vijaya Kumar B. P. 2020. Predicting Stock Price Using Sentimental Analysis Through Twitter Data. Paper presented at 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, July 2–4. [<a href="https://scholar.google.com/scholar_lookup?title=Predicting+Stock+Price+Using+Sentimental+Analysis+Through+Twitter+Data&conference=Paper+presented+at+2020+IEEE+International+Conference+on+Electronics,+Computing+and+Communication+Technologies+(CONECCT)&author=Reddy,+Niveditha+N.&author=E.+Naresh&author=and+Vijaya+Kumar+B.+P.&publication_year=2020" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B51-ijfs-11-00094' class='html-x' data-content=''>Ren, Rui, Desheng Dash Wu, and Tianxiang Liu. 2019. Forecasting Stock Market Movement Direction Using Sentiment Analysis and Support Vector Machine. <span class='html-italic'>IEEE Systems Journal</span> 13: 760–70. [<a href="https://scholar.google.com/scholar_lookup?title=Forecasting+Stock+Market+Movement+Direction+Using+Sentiment+Analysis+and+Support+Vector+Machine&author=Ren,+Rui&author=Desheng+Dash+Wu&author=and+Tianxiang+Liu&publication_year=2019&journal=IEEE+Systems+Journal&volume=13&pages=760%E2%80%9370&doi=10.1109/JSYST.2018.2794462" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1109/JSYST.2018.2794462" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B52-ijfs-11-00094' class='html-x' data-content=''>Sadorsky, Perry. 2021. A Random Forests Approach to Predicting Clean Energy Stock Prices. <span class='html-italic'>Journal of Risk and Financial Management</span> 14: 48. [<a href="https://scholar.google.com/scholar_lookup?title=A+Random+Forests+Approach+to+Predicting+Clean+Energy+Stock+Prices&author=Sadorsky,+Perry&publication_year=2021&journal=Journal+of+Risk+and+Financial+Management&volume=14&pages=48&doi=10.3390/jrfm14020048" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.3390/jrfm14020048" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B53-ijfs-11-00094' class='html-x' data-content=''>Salles, Thiago, Marcos Gonçalves, Victor Rodrigues, and Leonardo Rocha. 2018. Improving random forests by neighborhood projection for effective text classification. <span class='html-italic'>Information Systems</span> 77: 1–21. [<a href="https://scholar.google.com/scholar_lookup?title=Improving+random+forests+by+neighborhood+projection+for+effective+text+classification&author=Salles,+Thiago&author=Marcos+Gon%C3%A7alves&author=Victor+Rodrigues&author=and+Leonardo+Rocha&publication_year=2018&journal=Information+Systems&volume=77&pages=1%E2%80%9321&doi=10.1016/j.is.2018.05.006" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1016/j.is.2018.05.006" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B54-ijfs-11-00094' class='html-x' data-content=''>Sathish Kumar, R., R. Girivarman, S. Parameshwaran, and V. Sriram. 2020. Stock Price Prediction Using Deep Learning and Sentimental Analysis. <span class='html-italic'>JETIR</span> 7: 346–54. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Price+Prediction+Using+Deep+Learning+and+Sentimental+Analysis&author=Sathish+Kumar,+R.&author=R.+Girivarman&author=S.+Parameshwaran&author=and+V.+Sriram&publication_year=2020&journal=JETIR&volume=7&pages=346%E2%80%9354" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B55-ijfs-11-00094' class='html-x' data-content=''>Seethalakshmi, Ramaswamy. 2018. Analayis of stock market predictor variables using linear regression. <span class='html-italic'>International Journal of Pure and Applied Mathematics</span> 119: 369–78. [<a href="https://scholar.google.com/scholar_lookup?title=Analayis+of+stock+market+predictor+variables+using+linear+regression&author=Seethalakshmi,+Ramaswamy&publication_year=2018&journal=International+Journal+of+Pure+and+Applied+Mathematics&volume=119&pages=369%E2%80%9378" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B56-ijfs-11-00094' class='html-x' data-content=''>Setiani, Ida, Meilany Nonsi Tentua, and Sunggito Oyama. 2020. Prediction of Banking Stock Prices Using Naïve Bayes Method. <span class='html-italic'>Journal of Physics Conference Series</span> 1823: 012059. [<a href="https://scholar.google.com/scholar_lookup?title=Prediction+of+Banking+Stock+Prices+Using+Na%C3%AFve+Bayes+Method&author=Setiani,+Ida&author=Meilany+Nonsi+Tentua&author=and+Sunggito+Oyama&publication_year=2020&journal=Journal+of+Physics+Conference+Series&volume=1823&pages=012059&doi=10.1088/1742-6596/1823/1/012059" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1088/1742-6596/1823/1/012059" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B57-ijfs-11-00094' class='html-x' data-content=''>Shah, Dev, Haruna Isah, and Farhana Zulkernine. 2019. Stock Market Analysis: A Review and Taxonomy of Prediction Techniques. <span class='html-italic'>International Journal of Financial Studies</span> 7: 26. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Market+Analysis:+A+Review+and+Taxonomy+of+Prediction+Techniques&author=Shah,+Dev&author=Haruna+Isah&author=and+Farhana+Zulkernine&publication_year=2019&journal=International+Journal+of+Financial+Studies&volume=7&pages=26&doi=10.3390/ijfs7020026" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.3390/ijfs7020026" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>] [<a href="https://www.mdpi.com/2227-7072/7/2/26/pdf" target='_blank' rel="noopener noreferrer">Green Version</a>]</li><li id='B58-ijfs-11-00094' class='html-x' data-content=''>Shahi, Tej Bahadur, Ashish Shrestha, Arjun Neupane, and William Guo. 2020. Stock Price Forecasting with Deep Learning: A Comparative Study. <span class='html-italic'>Mathematics and Computer Science</span> 8: 1441. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Price+Forecasting+with+Deep+Learning:+A+Comparative+Study&author=Shahi,+Tej+Bahadur&author=Ashish+Shrestha&author=Arjun+Neupane&author=and+William+Guo&publication_year=2020&journal=Mathematics+and+Computer+Science&volume=8&pages=1441&doi=10.3390/math8091441" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.3390/math8091441" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B59-ijfs-11-00094' class='html-x' data-content=''>Sharaf, Marwa, Ezz El-Din Hemdan, Ayman El-Sayed, and Nirmeen A. El-Bahnasawy. 2022. An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis. <span class='html-italic'>Multimedia Tools and Applications</span> 28: 1–33. [<a href="https://scholar.google.com/scholar_lookup?title=An+efficient+hybrid+stock+trend+prediction+system+during+COVID-19+pandemic+based+on+stacked-LSTM+and+news+sentiment+analysis&author=Sharaf,+Marwa&author=Ezz+El-Din+Hemdan&author=Ayman+El-Sayed&author=and+Nirmeen+A.+El-Bahnasawy&publication_year=2022&journal=Multimedia+Tools+and+Applications&volume=28&pages=1%E2%80%9333&doi=10.1007/s11042-022-14216-w" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1007/s11042-022-14216-w" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B60-ijfs-11-00094' class='html-x' data-content=''>Shen, Jingyi, and M. Omair Shafiq. 2020. Short-term stock market price trend prediction using a comprehensive deep learning system. <span class='html-italic'>Journal of Big Data</span> 7: 1–33. [<a href="https://scholar.google.com/scholar_lookup?title=Short-term+stock+market+price+trend+prediction+using+a+comprehensive+deep+learning+system&author=Shen,+Jingyi&author=and+M.+Omair+Shafiq&publication_year=2020&journal=Journal+of+Big+Data&volume=7&pages=1%E2%80%9333&doi=10.1186/s40537-020-00333-6" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1186/s40537-020-00333-6" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B61-ijfs-11-00094' class='html-x' data-content=''>Sidra, Mehtab, and Jaydip Sen. 2020. A time series analysis-based stock price prediction using machine learning and deep learning models. <span class='html-italic'>arXiv</span> arXiv:2004.11697. [<a href="https://scholar.google.com/scholar_lookup?title=A+time+series+analysis-based+stock+price+prediction+using+machine+learning+and+deep+learning+models&author=Sidra,+Mehtab&author=and+Jaydip+Sen&publication_year=2020&journal=arXiv" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B62-ijfs-11-00094' class='html-x' data-content=''>Smita, Mrinalini. 2021. Logistic Regression Model For Predicting Performance of S&P BSE30 Company Using IBM SPSS. <span class='html-italic'>International Journal of Mathematics Trends and Technology</span> 67: 118–34. [<a href="https://scholar.google.com/scholar_lookup?title=Logistic+Regression+Model+For+Predicting+Performance+of+S%2526P+BSE30+Company+Using+IBM+SPSS&author=Smita,+Mrinalini&publication_year=2021&journal=International+Journal+of+Mathematics+Trends+and+Technology&volume=67&pages=118%E2%80%9334&doi=10.14445/22315373/ijmtt-v67i7p515" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.14445/22315373/ijmtt-v67i7p515" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B63-ijfs-11-00094' class='html-x' data-content=''>Soni, Payal, Yogya Tewari, and Deepa Krishnan. 2022. Machine Learning approaches in stock price prediction: A systematic review. <span class='html-italic'>Journal of Physics Conference Series</span> 2161: 012065. [<a href="https://scholar.google.com/scholar_lookup?title=Machine+Learning+approaches+in+stock+price+prediction:+A+systematic+review&author=Soni,+Payal&author=Yogya+Tewari&author=and+Deepa+Krishnan&publication_year=2022&journal=Journal+of+Physics+Conference+Series&volume=2161&pages=012065&doi=10.1088/1742-6596/2161/1/012065" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1088/1742-6596/2161/1/012065" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B64-ijfs-11-00094' class='html-x' data-content=''>Sprenger, Timm O., and Isabell M. Welpe. 2011. News or noise? The stock market reaction to different types of company-specific news events. <span class='html-italic'>SSRN Electronic Journal</span>. [<a href="https://scholar.google.com/scholar_lookup?title=News+or+noise?+The+stock+market+reaction+to+different+types+of+company-specific+news+events&author=Sprenger,+Timm+O.&author=and+Isabell+M.+Welpe&publication_year=2011&journal=SSRN+Electronic+Journal&doi=10.2139/ssrn.1734632" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.2139/ssrn.1734632" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B65-ijfs-11-00094' class='html-x' data-content=''>Suresh, N., B. Priya, and G. Lakshmi. 2022. Historical Analysis and Forecasting of Stock Market Using Fbprophet. <span class='html-italic'>South Asian Journal of Engineering and Technology</span> 12: 152–57. [<a href="https://scholar.google.com/scholar_lookup?title=Historical+Analysis+and+Forecasting+of+Stock+Market+Using+Fbprophet&author=Suresh,+N.&author=B.+Priya&author=and+G.+Lakshmi&publication_year=2022&journal=South+Asian+Journal+of+Engineering+and+Technology&volume=12&pages=152%E2%80%9357&doi=10.26524/sajet.2022.12.43" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.26524/sajet.2022.12.43" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B66-ijfs-11-00094' class='html-x' data-content=''>Tanuwijaya, Julius, and Seng Hansun. 2019. LQ45 Stock Index Prediction using k-Nearest Neighbors Regression. <span class='html-italic'>International Journal of Recent Technology and Engineering</span> 8: 2388–91. [<a href="https://scholar.google.com/scholar_lookup?title=LQ45+Stock+Index+Prediction+using+k-Nearest+Neighbors+Regression&author=Tanuwijaya,+Julius&author=and+Seng+Hansun&publication_year=2019&journal=International+Journal+of+Recent+Technology+and+Engineering&volume=8&pages=2388%E2%80%9391&doi=10.35940/ijrte.C4663.098319" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.35940/ijrte.C4663.098319" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B67-ijfs-11-00094' class='html-x' data-content=''>Umer, Muhammad, Muhammad Awais, and Muhammad Muzammul. 2019. Stock Market Prediction Using Machine Learning (ML) Algorithms. <span class='html-italic'>ADCAIJ Advances in Distributed Computing and Artificial Intelligence Journal</span> 8: 97–116. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Market+Prediction+Using+Machine+Learning+(ML)+Algorithms&author=Umer,+Muhammad&author=Muhammad+Awais&author=and+Muhammad+Muzammul&publication_year=2019&journal=ADCAIJ+Advances+in+Distributed+Computing+and+Artificial+Intelligence+Journal&volume=8&pages=97%E2%80%93116&doi=10.14201/ADCAIJ20198497116" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.14201/ADCAIJ20198497116" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B68-ijfs-11-00094' class='html-x' data-content=''>Venkat, Projects. 2022. Stock Market Trend Prediction Using K-Nearest Neighbor (KNN) Algorithm. <span class='html-italic'>Journal of Engineering Sciences</span> 3: 32–44. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Market+Trend+Prediction+Using+K-Nearest+Neighbor+(KNN)+Algorithm&author=Venkat,+Projects&publication_year=2022&journal=Journal+of+Engineering+Sciences&volume=3&pages=32%E2%80%9344" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B69-ijfs-11-00094' class='html-x' data-content=''>Vo, Nguyen, and Robert Ślepaczuk. 2022. Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index. <span class='html-italic'>Entropy</span> 24: 158. [<a href="https://scholar.google.com/scholar_lookup?title=Applying+Hybrid+ARIMA-SGARCH+in+Algorithmic+Investment+Strategies+on+S%2526P500+Index&author=Vo,+Nguyen&author=and+Robert+%C5%9Alepaczuk&publication_year=2022&journal=Entropy&volume=24&pages=158&doi=10.3390/e24020158&pmid=35205454" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.3390/e24020158" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>] [<a href="http://www.ncbi.nlm.nih.gov/pubmed/35205454" class='cross-ref' data-typ='pmid' target='_blank' rel='noopener noreferrer'>PubMed</a>]</li><li id='B70-ijfs-11-00094' class='html-x' data-content=''>Vuong, Pham Hoang, Trinh Tan Dat, Tieu Khoi Mai, and Pham Hoang Uyen. 2022. Stock-Price Forecasting Based on XGBoost and LSTM. <span class='html-italic'>Computer Systems Science & Engineering</span> 40: 237–46. [<a href="https://scholar.google.com/scholar_lookup?title=Stock-Price+Forecasting+Based+on+XGBoost+and+LSTM&author=Vuong,+Pham+Hoang&author=Trinh+Tan+Dat&author=Tieu+Khoi+Mai&author=and+Pham+Hoang+Uyen&publication_year=2022&journal=Computer+Systems+Science+&+Engineering&volume=40&pages=237%E2%80%9346" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B71-ijfs-11-00094' class='html-x' data-content=''>Xu, Ying, Cuijuan Yang, Shaoliang Peng, and Yusuke Nojima. 2020. A hybrid two-stage financial stock forecasting algorithm based on clustering and ensemble learning. <span class='html-italic'>Applied Intelligence</span> 50: 3852–67. [<a href="https://scholar.google.com/scholar_lookup?title=A+hybrid+two-stage+financial+stock+forecasting+algorithm+based+on+clustering+and+ensemble+learning&author=Xu,+Ying&author=Cuijuan+Yang&author=Shaoliang+Peng&author=and+Yusuke+Nojima&publication_year=2020&journal=Applied+Intelligence&volume=50&pages=3852%E2%80%9367&doi=10.1007/s10489-020-01766-5" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1007/s10489-020-01766-5" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B72-ijfs-11-00094' class='html-x' data-content=''>Yadav, Ashima, and Dinesh Kumar Vishwakarma. 2019. Sentiment analysis using deep learning architectures: A review. <span class='html-italic'>Artificial Intelligence Review</span> 53: 4335–85. [<a href="https://scholar.google.com/scholar_lookup?title=Sentiment+analysis+using+deep+learning+architectures:+A+review&author=Yadav,+Ashima&author=and+Dinesh+Kumar+Vishwakarma&publication_year=2019&journal=Artificial+Intelligence+Review&volume=53&pages=4335%E2%80%9385&doi=10.1007/s10462-019-09794-5" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1007/s10462-019-09794-5" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B73-ijfs-11-00094' class='html-x' data-content=''>Yang, Liu. 2019. Novel volatility forecasting using deep learning-long short term memory recurrent neural networks. <span class='html-italic'>Expert Systems with Applications</span> 132: 99–109. [<a href="https://scholar.google.com/scholar_lookup?title=Novel+volatility+forecasting+using+deep+learning-long+short+term+memory+recurrent+neural+networks&author=Yang,+Liu&publication_year=2019&journal=Expert+Systems+with+Applications&volume=132&pages=99%E2%80%93109" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>]</li><li id='B74-ijfs-11-00094' class='html-x' data-content=''>Zhong, Xiao, and David Enke. 2019. Predicting the daily return direction of the stock market using hybrid machine learning algorithms. <span class='html-italic'>Financial Innovation</span> 5: 1–20. [<a href="https://scholar.google.com/scholar_lookup?title=Predicting+the+daily+return+direction+of+the+stock+market+using+hybrid+machine+learning+algorithms&author=Zhong,+Xiao&author=and+David+Enke&publication_year=2019&journal=Financial+Innovation&volume=5&pages=1%E2%80%9320&doi=10.1186/s40854-019-0138-0" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1186/s40854-019-0138-0" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B75-ijfs-11-00094' class='html-x' data-content=''>Zhou, Xingyu, Zhisong Pan, Guyu Hu, Siqi Tang, and Cheng Zhao. 2018. Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets. <span class='html-italic'>Mathematical Problems in Engineering</span> 2018: 4907423. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+Market+Prediction+on+High-Frequency+Data+Using+Generative+Adversarial+Nets&author=Zhou,+Xingyu&author=Zhisong+Pan&author=Guyu+Hu&author=Siqi+Tang&author=and+Cheng+Zhao&publication_year=2018&journal=Mathematical+Problems+in+Engineering&volume=2018&pages=4907423&doi=10.1155/2018/4907423" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1155/2018/4907423" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>] [<a href="http://downloads.hindawi.com/journals/mpe/2018/4907423.pdf" target='_blank' rel="noopener noreferrer">Green Version</a>]</li><li id='B76-ijfs-11-00094' class='html-x' data-content=''>Zhu, Yongqiong. 2020. Stock price prediction using the RNN model. <span class='html-italic'>Journal of Physics Conference Series</span> 1650: 032103. [<a href="https://scholar.google.com/scholar_lookup?title=Stock+price+prediction+using+the+RNN+model&author=Zhu,+Yongqiong&publication_year=2020&journal=Journal+of+Physics+Conference+Series&volume=1650&pages=032103&doi=10.1088/1742-6596/1650/3/032103" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.1088/1742-6596/1650/3/032103" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B77-ijfs-11-00094' class='html-x' data-content=''>Zhu, Zhe, and Kexin He. 2022. Prediction of Amazon’s Stock Price Based on ARIMA, XGBoost, and LSTM Models. <span class='html-italic'>Proceedings of Business and Economic Studies</span> 5: 127–36. [<a href="https://scholar.google.com/scholar_lookup?title=Prediction+of+Amazon%E2%80%99s+Stock+Price+Based+on+ARIMA,+XGBoost,+and+LSTM+Models&author=Zhu,+Zhe&author=and+Kexin+He&publication_year=2022&journal=Proceedings+of+Business+and+Economic+Studies&volume=5&pages=127%E2%80%9336&doi=10.26689/pbes.v5i5.4432" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.26689/pbes.v5i5.4432" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li><li id='B78-ijfs-11-00094' class='html-x' data-content=''>Zizi, Youssef, Amine Jamali-Alaoui, Badreddine El Goumi, Mohamed Oudgou, and Abdeslam El Moudden. 2021. An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression. <span class='html-italic'>Risks</span> 9: 200. [<a href="https://scholar.google.com/scholar_lookup?title=An+Optimal+Model+of+Financial+Distress+Prediction:+A+Comparative+Study+between+Neural+Networks+and+Logistic+Regression&author=Zizi,+Youssef&author=Amine+Jamali-Alaoui&author=Badreddine+El+Goumi&author=Mohamed+Oudgou&author=and+Abdeslam+El+Moudden&publication_year=2021&journal=Risks&volume=9&pages=200&doi=10.3390/risks9110200" class='google-scholar' target='_blank' rel='noopener noreferrer'>Google Scholar</a>] [<a href="https://doi.org/10.3390/risks9110200" class='cross-ref' target='_blank' rel='noopener noreferrer'>CrossRef</a>]</li></ol></section><section id='FiguresandTables' type='display-objects'><div class="html-fig-wrap" id="ijfs-11-00094-f001"> <div class='html-fig_img'> <div class="html-figpopup html-figpopup-link" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f001"> <img alt="Ijfs 11 00094 g001 550" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g001.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g001.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g001-550.jpg" /> <a class="html-expand html-figpopup" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f001"></a> </div> </div> <div class="html-fig_description"> <b>Figure 1.</b> Stock forecasting algorithm.  </div> </div> <div class="html-fig_show mfp-hide" id ="fig_body_display_ijfs-11-00094-f001" > <div class="html-caption" > <b>Figure 1.</b> Stock forecasting algorithm.</div> <div class="html-img"><img alt="Ijfs 11 00094 g001" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g001.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g001.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g001.png" /></div> </div><div class="html-fig-wrap" id="ijfs-11-00094-f002"> <div class='html-fig_img'> <div class="html-figpopup html-figpopup-link" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f002"> <img alt="Ijfs 11 00094 g002 550" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g002.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g002.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g002-550.jpg" /> <a class="html-expand html-figpopup" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f002"></a> </div> </div> <div class="html-fig_description"> <b>Figure 2.</b> LSTM structure.  </div> </div> <div class="html-fig_show mfp-hide" id ="fig_body_display_ijfs-11-00094-f002" > <div class="html-caption" > <b>Figure 2.</b> LSTM structure.</div> <div class="html-img"><img alt="Ijfs 11 00094 g002" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g002.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g002.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g002.png" /></div> </div><div class="html-fig-wrap" id="ijfs-11-00094-f003"> <div class='html-fig_img'> <div class="html-figpopup html-figpopup-link" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f003"> <img alt="Ijfs 11 00094 g003 550" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g003.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g003.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g003-550.jpg" /> <a class="html-expand html-figpopup" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f003"></a> </div> </div> <div class="html-fig_description"> <b>Figure 3.</b> GRU structure.  </div> </div> <div class="html-fig_show mfp-hide" id ="fig_body_display_ijfs-11-00094-f003" > <div class="html-caption" > <b>Figure 3.</b> GRU structure.</div> <div class="html-img"><img alt="Ijfs 11 00094 g003" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g003.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g003.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g003.png" /></div> </div><div class="html-fig-wrap" id="ijfs-11-00094-f004"> <div class='html-fig_img'> <div class="html-figpopup html-figpopup-link" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f004"> <img alt="Ijfs 11 00094 g004 550" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g004.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g004.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g004-550.jpg" /> <a class="html-expand html-figpopup" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f004"></a> </div> </div> <div class="html-fig_description"> <b>Figure 4.</b> Random forest.  </div> </div> <div class="html-fig_show mfp-hide" id ="fig_body_display_ijfs-11-00094-f004" > <div class="html-caption" > <b>Figure 4.</b> Random forest.</div> <div class="html-img"><img alt="Ijfs 11 00094 g004" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g004.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g004.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g004.png" /></div> </div><div class="html-fig-wrap" id="ijfs-11-00094-f005"> <div class='html-fig_img'> <div class="html-figpopup html-figpopup-link" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f005"> <img alt="Ijfs 11 00094 g005 550" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g005.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g005.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g005-550.jpg" /> <a class="html-expand html-figpopup" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f005"></a> </div> </div> <div class="html-fig_description"> <b>Figure 5.</b> XG-Boost algorithm.  </div> </div> <div class="html-fig_show mfp-hide" id ="fig_body_display_ijfs-11-00094-f005" > <div class="html-caption" > <b>Figure 5.</b> XG-Boost algorithm.</div> <div class="html-img"><img alt="Ijfs 11 00094 g005" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g005.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g005.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g005.png" /></div> </div><div class="html-fig-wrap" id="ijfs-11-00094-f006"> <div class='html-fig_img'> <div class="html-figpopup html-figpopup-link" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f006"> <img alt="Ijfs 11 00094 g006 550" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g006.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g006.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g006-550.jpg" /> <a class="html-expand html-figpopup" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f006"></a> </div> </div> <div class="html-fig_description"> <b>Figure 6.</b> Workflow of basic ML model.  </div> </div> <div class="html-fig_show mfp-hide" id ="fig_body_display_ijfs-11-00094-f006" > <div class="html-caption" > <b>Figure 6.</b> Workflow of basic ML model.</div> <div class="html-img"><img alt="Ijfs 11 00094 g006" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g006.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g006.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g006.png" /></div> </div><div class="html-fig-wrap" id="ijfs-11-00094-f007"> <div class='html-fig_img'> <div class="html-figpopup html-figpopup-link" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f007"> <img alt="Ijfs 11 00094 g007a 550" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g007a.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g007a.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g007a-550.jpg" /><img alt="Ijfs 11 00094 g007b 550" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g007b.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g007b.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g007b-550.jpg" /> <a class="html-expand html-figpopup" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f007"></a> </div> </div> <div class="html-fig_description"> <b>Figure 7.</b> TANIWALCHM stock price forecasting: (<b>a</b>) SVR, (<b>b</b>) MLPR, (<b>c</b>) KNN, (<b>d</b>) random forest, (<b>e</b>) XG-Boost, (<b>f</b>) LSTM, (<b>g</b>) Ensemble Random Forest + XG-Boost + LSTM.  </div> </div> <div class="html-fig_show mfp-hide" id ="fig_body_display_ijfs-11-00094-f007" > <div class="html-caption" > <b>Figure 7.</b> TANIWALCHM stock price forecasting: (<b>a</b>) SVR, (<b>b</b>) MLPR, (<b>c</b>) KNN, (<b>d</b>) random forest, (<b>e</b>) XG-Boost, (<b>f</b>) LSTM, (<b>g</b>) Ensemble Random Forest + XG-Boost + LSTM.</div> <div class="html-img"><img alt="Ijfs 11 00094 g007a" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g007a.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g007a.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g007a.png" /><img alt="Ijfs 11 00094 g007b" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g007b.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g007b.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g007b.png" /></div> </div><div class="html-fig-wrap" id="ijfs-11-00094-f008"> <div class='html-fig_img'> <div class="html-figpopup html-figpopup-link" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f008"> <img alt="Ijfs 11 00094 g008a 550" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g008a.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g008a.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g008a-550.jpg" /><img alt="Ijfs 11 00094 g008b 550" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g008b.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g008b.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g008b-550.jpg" /> <a class="html-expand html-figpopup" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#fig_body_display_ijfs-11-00094-f008"></a> </div> </div> <div class="html-fig_description"> <b>Figure 8.</b> AGROPHOS stock price forecasting: (<b>a</b>) SVR, (<b>b</b>) MLPR, (<b>c</b>) KNN, (<b>d</b>) random forest, (<b>e</b>) XG-Boost, (<b>f</b>) LSTM, (<b>g</b>) Ensemble Random Forest + XG-Boost + LSTM.  </div> </div> <div class="html-fig_show mfp-hide" id ="fig_body_display_ijfs-11-00094-f008" > <div class="html-caption" > <b>Figure 8.</b> AGROPHOS stock price forecasting: (<b>a</b>) SVR, (<b>b</b>) MLPR, (<b>c</b>) KNN, (<b>d</b>) random forest, (<b>e</b>) XG-Boost, (<b>f</b>) LSTM, (<b>g</b>) Ensemble Random Forest + XG-Boost + LSTM.</div> <div class="html-img"><img alt="Ijfs 11 00094 g008a" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g008a.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g008a.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g008a.png" /><img alt="Ijfs 11 00094 g008b" data-large="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g008b.png" data-original="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g008b.png" data-lsrc="/ijfs/ijfs-11-00094/article_deploy/html/images/ijfs-11-00094-g008b.png" /></div> </div><div class="html-table-wrap" id="ijfs-11-00094-t001"> <div class="html-table_wrap_td" > <div class="html-tablepopup html-tablepopup-link" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href='#table_body_display_ijfs-11-00094-t001'> <img alt="Table" data-lsrc="https://www.mdpi.com/img/table.png" /> <a class="html-expand html-tablepopup" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#table_body_display_ijfs-11-00094-t001"></a> </div> </div> <div class="html-table_wrap_discription"> <b>Table 1.</b> Ensemble model parameter configuration. </div> </div> <div class="html-table_show mfp-hide " id ="table_body_display_ijfs-11-00094-t001" > <div class="html-caption" ><b>Table 1.</b> Ensemble model parameter configuration.</div> <table > <thead ><tr ><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Parameter</th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Description</th></tr></thead><tbody ><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Model type</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Stacked ensemble model</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Libraries</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Keras, TensorFlow, sklearn</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Algorithms</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Random Forest + XG-Boost + LSTM</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Training/testing size</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >80% for training and 20% for testing</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Loss function</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >MSE</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Optimizer</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Adam</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Maximum epochs</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >50</td></tr><tr ><td colspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Random Forest Configuration</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Number of estimators</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >[50, 100, 200]</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Maximum depth</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >[3, 5, 7]</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Maximum features</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >[‘sqrt’, ‘log2’]</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Hyperparameter tuning method</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Grid search</td></tr><tr ><td colspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >XG-Boost Configuration</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Maximum depth</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >[3, 4, 5]</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Learning rate</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >[0.1, 0.01, 0.001]</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Number of estimators</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >[50, 100, 150, 500, 1000]</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Hyperparameter tuning method</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Grid search</td></tr><tr ><td colspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >LSTM Configuration</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >LSTM Layers</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >2</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Dropout rate</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >0.2</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Dense layer</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >25 units</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Batch size</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >32</td></tr><tr ><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Hyperparameter tuning method</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Grid search</td></tr></tbody> </table> </div><div class="html-table-wrap" id="ijfs-11-00094-t002"> <div class="html-table_wrap_td" > <div class="html-tablepopup html-tablepopup-link" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href='#table_body_display_ijfs-11-00094-t002'> <img alt="Table" data-lsrc="https://www.mdpi.com/img/table.png" /> <a class="html-expand html-tablepopup" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#table_body_display_ijfs-11-00094-t002"></a> </div> </div> <div class="html-table_wrap_discription"> <b>Table 2.</b> RMSE and R2 scores of algorithms. </div> </div> <div class="html-table_show mfp-hide " id ="table_body_display_ijfs-11-00094-t002" > <div class="html-caption" ><b>Table 2.</b> RMSE and R2 scores of algorithms.</div> <table > <thead ><tr ><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' > </th><th colspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >TANIWALCHM</th><th colspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >AGROPHOS</th></tr><tr ><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Algorithm</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >RMSE</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >R2</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >RMSE</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >R2</th></tr></thead><tbody ><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >SVR</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >4.525</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.9279</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1.5074</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.9432</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >MLPR</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >2.5893</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.9611</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >2.4764</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.9472</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >KNN</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >4.4249</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.9311</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >4.7877</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.7262</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >LSTM</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >5.6241</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.8867</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >5.2494</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.8809</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Random forest</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >87.8839</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.9818</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >98.5633</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.9428</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >XG-Boost</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >2.0686</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.9842</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1.7618</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.9379</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Random Forest + XG-Boost + LSTM</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >2.0247</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.9921</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1.2658</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.9897</td></tr></tbody> </table> </div><div class="html-table-wrap" id="ijfs-11-00094-t003"> <div class="html-table_wrap_td" > <div class="html-tablepopup html-tablepopup-link" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href='#table_body_display_ijfs-11-00094-t003'> <img alt="Table" data-lsrc="https://www.mdpi.com/img/table.png" /> <a class="html-expand html-tablepopup" data-counterslinkmanual = "https://www.mdpi.com/2227-7072/11/3/94/display" href="#table_body_display_ijfs-11-00094-t003"></a> </div> </div> <div class="html-table_wrap_discription"> <b>Table 3.</b> Summary of existing stock prediction and forecasting algorithms with performance analysis. </div> </div> <div class="html-table_show mfp-hide " id ="table_body_display_ijfs-11-00094-t003" > <div class="html-caption" ><b>Table 3.</b> Summary of existing stock prediction and forecasting algorithms with performance analysis.</div> <table > <thead ><tr ><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Sr No</th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Algorithm Name</th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Gap Analysis</th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Performance Evaluation</th></tr></thead><tbody ><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Linear regression</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Linear regression assumes a linear relationship between dependent and independent variables and is not suitable for most real-time applications, and is used to perform observations on readily accessible sample data (<a href="#B15-ijfs-11-00094" class="html-bibr">Gururaj et al. 2019</a>).</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >This is a regression type of algorithm that shows graphs in a linear way, where in RMSE is 3.22, MAE is 2.53, MSE is 10.37, and R-squared is 0.73 (<a href="#B15-ijfs-11-00094" class="html-bibr">Gururaj et al. 2019</a>).</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >2</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Support vector machine</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >SVM’s excellent memory efficiency and effectiveness make it an ideal estimating technique in high-dimensional space. The shortcoming of SVM is that it might experience overfitting, but it performs exceptionally well on tiny datasets (<a href="#B45-ijfs-11-00094" class="html-bibr">Pathak and Pathak 2020</a>; <a href="#B14-ijfs-11-00094" class="html-bibr">Grigoryan 2017</a>).</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >This is a classification type of algorithm, and when used for stock prediction, the results are as follows: accuracy is 68.2, recall is 65.2, precision is 64.2, and F1-Score is 64.9% (0.65).<br>When used as a support vector regression (SVR) algorithm, the evaluation parameters are as follows: SMAPE = 5.59, R-squared = 1.69, and RMSE = 43.36.</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >3</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >K-nearest neighbor</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >KNN is an algorithm that skips the learning step, so it may not generalize effectively. With a huge dataset, it takes longer since it must sort all the distances from the unknown item (<a href="#B66-ijfs-11-00094" class="html-bibr">Tanuwijaya and Hansun 2019</a>; <a href="#B45-ijfs-11-00094" class="html-bibr">Pathak and Pathak 2020</a>).</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >This is a classification type of ML algorithm in which the results of stock prediction are as follows: accuracy is 65.2, recall is 63.6, precision is 64.8, and F1 Score is 64.1% (0.64).<br>For KNN regressor, the evaluation parameters are as follows: SMAPE = 14.32, R-squared = −2.42, and RMSE = 56.44 (<a href="#B68-ijfs-11-00094" class="html-bibr">Venkat 2022</a>).</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >4</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Gaussian naïve Bayes</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Models with an integrated GNB algorithm will yield feature extraction and feature scaling outcomes that are superior to those already achieved using models that incorporate either the GNB algorithm or feature scaling (<a href="#B56-ijfs-11-00094" class="html-bibr">Setiani et al. 2020</a>; <a href="#B3-ijfs-11-00094" class="html-bibr">Ampomah et al. 2021</a>).</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Gaussian naïve Bayes used by authors in their research. Kendall’s Test of Concordance is used in this feature, which is scaled and extracted. The results are as follows: accuracy is 84, F1 Score is 62.44% (0.62), specificity is 0.70, and AUC values is 0.90 (<a href="#B7-ijfs-11-00094" class="html-bibr">Bansal et al. 2022</a>).</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >5</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Logistic regression</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Both binary and multiclass classification use this algorithm. The findings obtained via logistic regression are the most accurate; however, identifying the best-fitting feature is necessary (<a href="#B45-ijfs-11-00094" class="html-bibr">Pathak and Pathak 2020</a>; <a href="#B2-ijfs-11-00094" class="html-bibr">Ali et al. 2018</a>).</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >In this algorithm, with the help of research papers, various financial factors are considered through which factors are grouped for prediction. The results of this algorithm are as follows: accuracy is 78.6, recall is 76.6, precision is 77.8, and F1 Score is 77.1% (0.77) (<a href="#B45-ijfs-11-00094" class="html-bibr">Pathak and Pathak 2020</a>; <a href="#B2-ijfs-11-00094" class="html-bibr">Ali et al. 2018</a>).</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >6</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >ARIMA</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >ARIMA can be considered because it is a unique model with significant coefficients and passes all the diagnostic tests (<a href="#B36-ijfs-11-00094" class="html-bibr">Mashadihasanli 2022</a>).</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >ARIMA is a time series forecasting technique for predicting market or stock prices. It is a combination or integration of autoregressive moving averages; the results are as follows: RMSE, 88.05; MAE, 65.88; and MAPE, 5.73, and if performed with sentiment analysis, the RMSE score is 6.41 (<a href="#B23-ijfs-11-00094" class="html-bibr">Kedar 2021</a>).</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >7</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >FB Prophet</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Prophet can use regression models to determine seasonality on a daily, monthly, and annual basis, as well as effects related to holidays (<a href="#B65-ijfs-11-00094" class="html-bibr">Suresh et al. 2022</a>; <a href="#B21-ijfs-11-00094" class="html-bibr">Kaninde et al. 2022</a>).</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >This algorithm was created by Facebook for time series forecasting. One of its advantages is that it does not consider holidays or null values. The result of its RMSE is achieved 93 (<a href="#B65-ijfs-11-00094" class="html-bibr">Suresh et al. 2022</a>; <a href="#B21-ijfs-11-00094" class="html-bibr">Kaninde et al. 2022</a>).</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >8</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >GRU</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >GRU is a neural network approach that is an improvement upon RNN but has fewer parameters than LSTM, so it trains faster. Also, the chances of overfitting are lower compared to RNN, and it can handle long-term dependency (<a href="#B58-ijfs-11-00094" class="html-bibr">Shahi et al. 2020</a>).</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >GRU is a deep learning algorithm that has fewer parameters and handles short-term data properly. The evaluation parameters of GRU are, without sentiment analysis, MAE = 42.8, RMSE = 47.31, and R-squared = 0.879, and with sentiment analysis based on news evaluation parameters, MAE = 24.472, RMSE = 29.153, and R-squared = 0.967 (<a href="#B58-ijfs-11-00094" class="html-bibr">Shahi et al. 2020</a>).</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >9</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >LSTM</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >In this algorithm, weights are adjusted for each long short-term memory data point via stochastic gradient descent. <br>LSTM can handle more very long-term dependency compared to any other neural network algorithm (<a href="#B58-ijfs-11-00094" class="html-bibr">Shahi et al. 2020</a>; <a href="#B47-ijfs-11-00094" class="html-bibr">Pramod and Pm 2021</a>; <a href="#B38-ijfs-11-00094" class="html-bibr">Mukherjee et al. 2021</a>).</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >LSTM is a more developed type of RNN and is a deep learning technique. This is one of the most used ML algorithms for stock forecasting, and when used along with sentiment analysis, it shows better results than without sentiment analysis. The result without sentiment analysis are MAE = 48.47, RMSE = 55.993, and R-squared = 0.867, and with sentiment analysis based on news evaluation parameters, are MAE = 17.689, RMSE = 23.070, and R-squared = 0.867 (<a href="#B58-ijfs-11-00094" class="html-bibr">Shahi et al. 2020</a>).</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >10</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Random forest</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >The effectiveness of random forest on large datasets is one of its advantages. It can be applied to classification and regression issues. The model becomes more random as a result, improving it. This model’s use of a huge number of trees slows it down, which is a drawback (<a href="#B45-ijfs-11-00094" class="html-bibr">Pathak and Pathak 2020</a>; <a href="#B46-ijfs-11-00094" class="html-bibr">Polamuri et al. 2019</a>).</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >This is an ensemble type of algorithm that is used for stock forecasting. The results for random forest are as follows: accuracy is 80.7, recall is 78.3, precision is 75.2, and F1 Score is 76.7% (0.77) (<a href="#B45-ijfs-11-00094" class="html-bibr">Pathak and Pathak 2020</a>; <a href="#B46-ijfs-11-00094" class="html-bibr">Polamuri et al. 2019</a>).</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >11</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >XG-Boost</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >XG-Boost is an ensembled learning technique that uses decision trees but in a sequential manner and uses gradient boosting in an iterative manner to obtain better results. <br>XG-Boost is sensitive to hyperparameters and will not work as well on large datasets as random forest (<a href="#B77-ijfs-11-00094" class="html-bibr">Zhu and He 2022</a>).</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >XG-Boost has the following evaluation parameter: MSE = 360.0 (<a href="#B77-ijfs-11-00094" class="html-bibr">Zhu and He 2022</a>).</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >12</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >E-SVR-RF</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Ensemble support vector regression and random forest shows improved accuracy as it leverages the advantages of both algorithms, and its robustness is increased, but due to the increased complexity of both algorithms, overfitting is an issue (<a href="#B71-ijfs-11-00094" class="html-bibr">Xu et al. 2020</a>).</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >The E-SVR-RF ensembled algorithm shows the following evaluation parameters: MAPE = 1.335, MAE = 0.1537, RMSE = 0.0188, and MAE = 0.0485 (<a href="#B71-ijfs-11-00094" class="html-bibr">Xu et al. 2020</a>). </td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >13</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >XG-Boost + LSTM</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Ensembling XG-Boost and LSTM can leverage the advantages of both algorithms. XG-Boost can handle linear and non-linear relationships and LSTM can handle long-term dependence. Due to this algorithm’s complexity, hyperparameter tuning can be an issue (<a href="#B70-ijfs-11-00094" class="html-bibr">Vuong et al. 2022</a>).</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >The ensemble algorithm of XG-Boost + LSTM shows the following evaluation parameter: MSE = 3.465 (<a href="#B70-ijfs-11-00094" class="html-bibr">Vuong et al. 2022</a>).</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >14</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Blending ensemble (LSTM + GRU)</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >Blending ensemble (LSTM+GRU) is a combination of two to of the most-used improvements to RNN and solves the vanishing gradient problem. Both of them can handle long-term dependence well, and combining them would improve forecasting; also, overfitting can be reduced. It may require high computational power and time to train both LSTM and GRU (<a href="#B30-ijfs-11-00094" class="html-bibr">Li and Pan 2021</a>).</td><td valign='middle' style='border-bottom:solid thin' align='left' class='html-align-left' >The blending ensemble algorithm, which consists of a modified version of RNN, i.e., LSTM and GRU, has the following evaluation parameters:<br>MSE = 186.32, MPA = 99.65, precision = 60%, Recall = 75%, F1-Score = 66.67% (<a href="#B30-ijfs-11-00094" class="html-bibr">Li and Pan 2021</a>).</td></tr></tbody> </table> </div></section><section class='html-fn_group'><table><tr id=''><td></td><td><div class='html-p'><b>Disclaimer/Publisher’s Note:</b> The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.</div></td></tr></table></section> <section id="html-copyright"><br>© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<a href='https://creativecommons.org/licenses/by/4.0/' target='_blank' rel="noopener noreferrer" >https://creativecommons.org/licenses/by/4.0/</a>).</section> </div> </div> <div class="additional-content"> <h2><a name="cite"></a>Share and Cite</h2> <div class="social-media-links" style="text-align: left;"> <a href="/cdn-cgi/l/email-protection#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" title="Email"> <i class="fa fa-envelope-square" style="font-size: 30px;"></i> </a> <a href="https://twitter.com/intent/tweet?text=Forecasting+Stock+Market+Prices+Using+Machine+Learning+and+Deep+Learning+Models%3A+A+Systematic+Review%2C+Performance+Analysis+and+Discussion+of+Implications&hashtags=mdpiijfs&url=https%3A%2F%2Fwww.mdpi.com%2F2406736&via=IJFS_MDPI" onclick="windowOpen(this.href,600,800); return false" target="_blank" rel="noopener noreferrer"> <i class="fa fa-twitter-x-square" style="font-size: 30px;"></i> </a> <a href=" http://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Fwww.mdpi.com%2F2406736&title=Forecasting%20Stock%20Market%20Prices%20Using%20Machine%20Learning%20and%20Deep%20Learning%20Models%3A%20A%20Systematic%20Review%2C%20Performance%20Analysis%20and%20Discussion%20of%20Implications%26source%3Dhttps%3A%2F%2Fwww.mdpi.com%26summary%3DThe%20financial%20sector%20has%20greatly%20impacted%20the%20monetary%20well-being%20of%20consumers%2C%20traders%2C%20and%20financial%20institutions.%20In%20the%20current%20era%2C%20artificial%20intelligence%20is%20redefining%20the%20limits%20of%20the%20financial%20markets%20based%20on%20state-of-the-art%20machine%20%5B...%5D" onclick="windowOpen(this.href,600,800); return false" title="LinkedIn" target="_blank" rel="noopener noreferrer"> <i class="fa fa-linkedin-square" style="font-size: 30px;"></i> </a> <a href="https://www.facebook.com/sharer.php?u=https://www.mdpi.com/2406736" title="facebook" target="_blank" rel="noopener noreferrer"> <i class="fa fa-facebook-square" style="font-size: 30px;"></i> </a> <a href="javascript:void(0);" title="Wechat" data-reveal-id="weixin-share-modal"> <i class="fa fa-weixin-square" style="font-size: 26px;"></i> </a> <a href="http://www.reddit.com/submit?url=https://www.mdpi.com/2406736" title="Reddit" target="_blank" rel="noopener noreferrer"> <i class="fa fa-reddit-square" style="font-size: 30px;"></i> </a> <a href="http://www.mendeley.com/import/?url=https://www.mdpi.com/2406736" title="Mendeley" target="_blank" rel="noopener noreferrer"> <i class="fa fa-mendeley-square" style="font-size: 30px;"></i> </a> </div> <div class="in-tab" style="padding-top: 0px!important; margin-top: 15px;"> <div><b>MDPI and ACS Style</b></div> <p> Sonkavde, G.; Dharrao, D.S.; Bongale, A.M.; Deokate, S.T.; Doreswamy, D.; Bhat, S.K. Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications. <em>Int. J. Financial Stud.</em> <b>2023</b>, <em>11</em>, 94. https://doi.org/10.3390/ijfs11030094 </p> <div style="display: block"> <b>AMA Style</b><br> <p> Sonkavde G, Dharrao DS, Bongale AM, Deokate ST, Doreswamy D, Bhat SK. Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications. <em>International Journal of Financial Studies</em>. 2023; 11(3):94. https://doi.org/10.3390/ijfs11030094 </p> <b>Chicago/Turabian Style</b><br> <p> Sonkavde, Gaurang, Deepak Sudhakar Dharrao, Anupkumar M. Bongale, Sarika T. Deokate, Deepak Doreswamy, and Subraya Krishna Bhat. 2023. "Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications" <em>International Journal of Financial Studies</em> 11, no. 3: 94. https://doi.org/10.3390/ijfs11030094 </p> <b>APA Style</b><br> <p> Sonkavde, G., Dharrao, D. S., Bongale, A. M., Deokate, S. T., Doreswamy, D., & Bhat, S. K. (2023). Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications. <em>International Journal of Financial Studies</em>, <em>11</em>(3), 94. https://doi.org/10.3390/ijfs11030094 </p> </div> </div> <div class="info-box no-margin"> Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details <a target="_blank" href="https://www.mdpi.com/about/announcements/784">here</a>. </div> <h2><a name="metrics"></a>Article Metrics</h2> <div class="row"> <div class="small-12 columns"> <div id="loaded_cite_count" style="display:none">No</div> <div id="framed_div_cited_count" class="in-tab" style="display: none; overflow: auto;"></div> <div id="loaded" style="display:none">No</div> <div id="framed_div" class="in-tab" style="display: none; margin-top: 10px;"></div> </div> <div class="small-12 columns"> <div id="article_stats_div" style="display: none; margin-bottom: 1em;"> <h3>Article Access Statistics</h3> <div id="article_stats_swf" ></div> For more information on the journal statistics, click <a href="/journal/ijfs/stats">here</a>. <div class="info-box"> Multiple requests from the same IP address are counted as one view. </div> </div> </div> </div> </div> </div> </article> </div> </div></div> <div class="webpymol-controls webpymol-controls-template" style="margin-top: 10px; display: none;"> <a class="bzoom">Zoom</a> <span style="display: inline-block; margin-left: 5px; margin-right: 5px;">|</span> <a class="borient"> Orient </a> <span style="display: inline-block; margin-left: 5px; margin-right: 5px;">|</span> <a class="blines"> As Lines </a> <span style="display: inline-block; margin-left: 5px; margin-right: 5px;">|</span> <a class="bsticks"> As Sticks </a> <span style="display: inline-block; margin-left: 5px; margin-right: 5px;">|</span> <a class="bcartoon"> As Cartoon </a> <span style="display: inline-block; margin-left: 5px; margin-right: 5px;">|</span> <a class="bsurface"> As Surface </a> <span style="display: inline-block; margin-left: 5px; margin-right: 5px;">|</span> <a class="bprevscene">Previous Scene</a> <span style="display: inline-block; margin-left: 5px; margin-right: 5px;">|</span> <a class="bnextscene">Next Scene</a> </div> <div id="scifeed-modal" class="reveal-modal reveal-modal-new" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog"> </div> <div id="recommended-articles-modal" class="reveal-modal reveal-modal-new" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog"> </div> <div id="author-biographies-modal" class="reveal-modal reveal-modal-new" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog"> </div> <div id="cite-modal" class="reveal-modal reveal-modal-new" data-reveal aria-labelledby="Captcha" aria-hidden="true" role="dialog"> <div class="row"> <div class="small-12 columns"> <h2 style="margin: 0;">Cite</h2> </div> <div class="small-12 columns">  <form style="margin:0; padding:0; display:inline;" name="export-bibtex" method="POST" action="/export"> <input type="hidden" name="articles_ids[]" value="1203368"> <input type="hidden" name="export_format_top" value="bibtex"> <input type="hidden" name="export_submit_top" value=""> </form>  <form style="margin:0; padding:0; display:inline;" name="export-endnote" method="POST" action="/export"> <input type="hidden" name="articles_ids[]" value="1203368"> <input type="hidden" name="export_format_top" value="endnote_no_abstract"> <input type="hidden" name="export_submit_top" value=""> </form>  <form style="margin:0; padding:0; display:inline;" name="export-ris" method="POST" action="/export"> <input type="hidden" name="articles_ids[]" value="1203368"> <input type="hidden" name="export_format_top" value="ris"> <input type="hidden" name="export_submit_top" value=""> </form> <div> Export citation file: <a href="javascript:window.document.forms['export-bibtex'].submit()">BibTeX</a> | <a href="javascript:window.document.forms['export-endnote'].submit()">EndNote</a> | <a href="javascript:window.document.forms['export-ris'].submit()">RIS</a> </div> </div> <div class="small-12 columns"> <div class="in-tab"> <div><b>MDPI and ACS Style</b></div> <p> Sonkavde, G.; Dharrao, D.S.; Bongale, A.M.; Deokate, S.T.; Doreswamy, D.; Bhat, S.K. Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications. <em>Int. J. Financial Stud.</em> <b>2023</b>, <em>11</em>, 94. https://doi.org/10.3390/ijfs11030094 </p> <div style="display: block"> <b>AMA Style</b><br> <p> Sonkavde G, Dharrao DS, Bongale AM, Deokate ST, Doreswamy D, Bhat SK. Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications. <em>International Journal of Financial Studies</em>. 2023; 11(3):94. https://doi.org/10.3390/ijfs11030094 </p> <b>Chicago/Turabian Style</b><br> <p> Sonkavde, Gaurang, Deepak Sudhakar Dharrao, Anupkumar M. Bongale, Sarika T. Deokate, Deepak Doreswamy, and Subraya Krishna Bhat. 2023. "Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications" <em>International Journal of Financial Studies</em> 11, no. 3: 94. https://doi.org/10.3390/ijfs11030094 </p> <b>APA Style</b><br> <p> Sonkavde, G., Dharrao, D. S., Bongale, A. M., Deokate, S. T., Doreswamy, D., & Bhat, S. K. (2023). Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications. <em>International Journal of Financial Studies</em>, <em>11</em>(3), 94. https://doi.org/10.3390/ijfs11030094 </p> </div> </div> <div class="info-box no-margin"> Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details <a target="_blank" href="https://www.mdpi.com/about/announcements/784">here</a>. </div> </div> </div> <a class="close-reveal-modal" aria-label="Close"> <i class="material-icons">clear</i> </a> </div> </div> </div> </div> </div> </section> <div id="footer"> <div class="journal-info"> <span> <em><a class="Var_JournalInfo" href="/journal/ijfs">Int. J. Financial Stud.</a></em>, EISSN 2227-7072, Published by MDPI </span> <div class="large-right"> <span> <a href="/rss/journal/ijfs" class="rss-link">RSS</a> </span> <span> <a href="/journal/ijfs/toc-alert">Content Alert</a> </span> </div> </div> <div class="row full-width footer-links" data-equalizer="footer" data-equalizer-mq="small"> <div class="large-2 large-push-4 medium-3 small-6 columns" data-equalizer-watch="footer"> <h3> Further Information </h3> <a href="/apc"> Article Processing Charges </a> <a href="/about/payment"> Pay an Invoice </a> <a href="/openaccess"> Open Access Policy </a> <a href="/about/contact"> Contact MDPI </a> <a href="https://careers.mdpi.com" target="_blank" rel="noopener noreferrer"> Jobs at MDPI </a> </div> <div class="large-2 large-push-4 medium-3 small-6 columns" data-equalizer-watch="footer"> <h3> Guidelines </h3> <a href="/authors"> For Authors </a> <a href="/reviewers"> For Reviewers </a> <a href="/editors"> For Editors </a> <a href="/librarians"> For Librarians </a> <a href="/publishing_services"> For Publishers </a> <a href="/societies"> For Societies </a> <a href="/conference_organizers"> For Conference Organizers </a> </div> <div class="large-2 large-push-4 medium-3 small-6 columns"> <h3> MDPI Initiatives </h3> <a href="https://sciforum.net" target="_blank" rel="noopener noreferrer"> Sciforum </a> <a href="https://www.mdpi.com/books" target="_blank" rel="noopener noreferrer"> MDPI Books </a> <a href="https://www.preprints.org" target="_blank" rel="noopener noreferrer"> Preprints.org </a> <a href="https://www.scilit.net" target="_blank" rel="noopener noreferrer"> Scilit </a> <a href="https://sciprofiles.com?utm_source=mpdi.com&utm_medium=bottom_menu&utm_campaign=initiative" target="_blank" rel="noopener noreferrer"> SciProfiles </a> <a href="https://encyclopedia.pub" target="_blank" rel="noopener noreferrer"> Encyclopedia </a> <a href="https://jams.pub" target="_blank" rel="noopener noreferrer"> JAMS </a> <a href="/about/proceedings"> Proceedings Series </a> </div> <div class="large-2 large-push-4 medium-3 small-6 right-border-large-without columns UA_FooterFollowMDPI"> <h3> Follow MDPI </h3> <a href="https://www.linkedin.com/company/mdpi" target="_blank" rel="noopener noreferrer"> LinkedIn </a> <a href="https://www.facebook.com/MDPIOpenAccessPublishing" target="_blank" rel="noopener noreferrer"> Facebook </a> <a href="https://twitter.com/MDPIOpenAccess" target="_blank" rel="noopener noreferrer"> Twitter </a> </div> <div id="footer-subscribe" class="large-4 large-pull-8 medium-12 small-12 left-border-large columns"> <div class="footer-subscribe__container"> <img class="show-for-large-up" src="https://pub.mdpi-res.com/img/design/mdpi-pub-logo-white-small.png?71d18e5f805839ab?1732261071" alt="MDPI" title="MDPI Open Access Journals" style="height: 50px; margin-bottom: 10px;"> <form id="newsletter" method="POST" action="/subscribe"> <p> Subscribe to receive issue release notifications and newsletters from MDPI journals </p> <select multiple id="newsletter-journal" class="foundation-select" name="journals[]"> <option value="acoustics">Acoustics</option> <option value="amh">Acta Microbiologica Hellenica</option> <option value="actuators">Actuators</option> <option value="admsci">Administrative Sciences</option> <option value="adolescents">Adolescents</option> <option value="arm">Advances in Respiratory Medicine</option> <option value="aerobiology">Aerobiology</option> <option value="aerospace">Aerospace</option> <option value="agriculture">Agriculture</option> <option value="agriengineering">AgriEngineering</option> <option value="agrochemicals">Agrochemicals</option> <option value="agronomy">Agronomy</option> <option value="ai">AI</option> <option value="air">Air</option> <option value="algorithms">Algorithms</option> <option value="allergies">Allergies</option> <option value="alloys">Alloys</option> <option value="analytica">Analytica</option> <option value="analytics">Analytics</option> <option value="anatomia">Anatomia</option> <option value="anesthres">Anesthesia Research</option> <option value="animals">Animals</option> <option value="antibiotics">Antibiotics</option> <option value="antibodies">Antibodies</option> <option value="antioxidants">Antioxidants</option> <option value="applbiosci">Applied Biosciences</option> <option value="applmech">Applied Mechanics</option> <option value="applmicrobiol">Applied Microbiology</option> <option value="applnano">Applied Nano</option> <option value="applsci">Applied Sciences</option> <option value="asi">Applied System Innovation</option> <option value="appliedchem">AppliedChem</option> <option value="appliedmath">AppliedMath</option> <option value="aquacj">Aquaculture Journal</option> <option value="architecture">Architecture</option> <option value="arthropoda">Arthropoda</option> <option value="arts">Arts</option> <option value="astronomy">Astronomy</option> <option value="atmosphere">Atmosphere</option> <option value="atoms">Atoms</option> <option value="audiolres">Audiology Research</option> <option value="automation">Automation</option> <option value="axioms">Axioms</option> <option value="bacteria">Bacteria</option> <option value="batteries">Batteries</option> <option value="behavsci">Behavioral Sciences</option> <option value="beverages">Beverages</option> <option value="BDCC">Big Data and Cognitive Computing</option> <option value="biochem">BioChem</option> <option value="bioengineering">Bioengineering</option> <option value="biologics">Biologics</option> <option value="biology">Biology</option> <option value="blsf">Biology and Life Sciences Forum</option> <option value="biomass">Biomass</option> <option value="biomechanics">Biomechanics</option> <option value="biomed">BioMed</option> <option value="biomedicines">Biomedicines</option> <option value="biomedinformatics">BioMedInformatics</option> <option value="biomimetics">Biomimetics</option> <option value="biomolecules">Biomolecules</option> <option value="biophysica">Biophysica</option> <option value="biosensors">Biosensors</option> <option value="biotech">BioTech</option> <option value="birds">Birds</option> <option value="blockchains">Blockchains</option> <option value="brainsci">Brain Sciences</option> <option value="buildings">Buildings</option> <option value="businesses">Businesses</option> <option value="carbon">C</option> <option value="cancers">Cancers</option> <option value="cardiogenetics">Cardiogenetics</option> <option value="catalysts">Catalysts</option> <option value="cells">Cells</option> <option value="ceramics">Ceramics</option> <option value="challenges">Challenges</option> <option value="ChemEngineering">ChemEngineering</option> <option value="chemistry">Chemistry</option> <option value="chemproc">Chemistry Proceedings</option> <option value="chemosensors">Chemosensors</option> <option value="children">Children</option> <option value="chips">Chips</option> <option value="civileng">CivilEng</option> <option value="cleantechnol">Clean Technologies</option> <option value="climate">Climate</option> <option value="ctn">Clinical and Translational Neuroscience</option> <option value="clinbioenerg">Clinical Bioenergetics</option> <option value="clinpract">Clinics and Practice</option> <option value="clockssleep">Clocks & Sleep</option> <option value="coasts">Coasts</option> <option value="coatings">Coatings</option> <option value="colloids">Colloids and Interfaces</option> <option value="colorants">Colorants</option> <option value="commodities">Commodities</option> <option value="complications">Complications</option> <option value="compounds">Compounds</option> <option value="computation">Computation</option> <option value="csmf">Computer Sciences & Mathematics Forum</option> <option value="computers">Computers</option> <option value="condensedmatter">Condensed Matter</option> <option value="conservation">Conservation</option> <option value="constrmater">Construction Materials</option> <option value="cmd">Corrosion and Materials Degradation</option> <option value="cosmetics">Cosmetics</option> <option value="covid">COVID</option> <option value="crops">Crops</option> <option value="cryo">Cryo</option> <option value="cryptography">Cryptography</option> <option value="crystals">Crystals</option> <option value="cimb">Current Issues in Molecular Biology</option> <option value="curroncol">Current Oncology</option> <option value="dairy">Dairy</option> <option value="data">Data</option> <option value="dentistry">Dentistry Journal</option> <option value="dermato">Dermato</option> <option value="dermatopathology">Dermatopathology</option> <option value="designs">Designs</option> <option value="diabetology">Diabetology</option> <option value="diagnostics">Diagnostics</option> <option value="dietetics">Dietetics</option> <option value="digital">Digital</option> <option value="disabilities">Disabilities</option> <option value="diseases">Diseases</option> <option value="diversity">Diversity</option> <option value="dna">DNA</option> <option value="drones">Drones</option> <option value="ddc">Drugs and Drug Candidates</option> <option value="dynamics">Dynamics</option> <option value="earth">Earth</option> <option value="ecologies">Ecologies</option> <option value="econometrics">Econometrics</option> <option value="economies">Economies</option> <option value="education">Education Sciences</option> <option value="electricity">Electricity</option> <option value="electrochem">Electrochem</option> <option value="electronicmat">Electronic Materials</option> <option value="electronics">Electronics</option> <option value="ecm">Emergency Care and Medicine</option> <option value="encyclopedia">Encyclopedia</option> <option value="endocrines">Endocrines</option> <option value="energies">Energies</option> <option value="esa">Energy Storage and Applications</option> <option value="eng">Eng</option> <option value="engproc">Engineering Proceedings</option> <option value="entropy">Entropy</option> <option value="environsciproc">Environmental Sciences Proceedings</option> <option value="environments">Environments</option> <option value="epidemiologia">Epidemiologia</option> <option value="epigenomes">Epigenomes</option> <option value="ebj">European Burn Journal</option> <option value="ejihpe">European Journal of Investigation in Health, Psychology and Education</option> <option value="fermentation">Fermentation</option> <option value="fibers">Fibers</option> <option value="fintech">FinTech</option> <option value="fire">Fire</option> <option value="fishes">Fishes</option> <option value="fluids">Fluids</option> <option value="foods">Foods</option> <option value="forecasting">Forecasting</option> <option value="forensicsci">Forensic Sciences</option> <option value="forests">Forests</option> <option value="fossstud">Fossil Studies</option> <option value="foundations">Foundations</option> <option value="fractalfract">Fractal and Fractional</option> <option value="fuels">Fuels</option> <option value="future">Future</option> <option value="futureinternet">Future Internet</option> <option value="futurepharmacol">Future Pharmacology</option> <option value="futuretransp">Future Transportation</option> <option value="galaxies">Galaxies</option> <option value="games">Games</option> <option value="gases">Gases</option> <option value="gastroent">Gastroenterology Insights</option> <option value="gastrointestdisord">Gastrointestinal Disorders</option> <option value="gastronomy">Gastronomy</option> <option value="gels">Gels</option> <option value="genealogy">Genealogy</option> <option value="genes">Genes</option> <option value="geographies">Geographies</option> <option value="geohazards">GeoHazards</option> <option value="geomatics">Geomatics</option> <option value="geometry">Geometry</option> <option value="geosciences">Geosciences</option> <option value="geotechnics">Geotechnics</option> <option value="geriatrics">Geriatrics</option> <option value="glacies">Glacies</option> <option value="gucdd">Gout, Urate, and Crystal Deposition Disease</option> <option value="grasses">Grasses</option> <option value="hardware">Hardware</option> <option value="healthcare">Healthcare</option> <option value="hearts">Hearts</option> <option value="hemato">Hemato</option> <option value="hematolrep">Hematology Reports</option> <option value="heritage">Heritage</option> <option value="histories">Histories</option> <option value="horticulturae">Horticulturae</option> <option value="hospitals">Hospitals</option> <option value="humanities">Humanities</option> <option value="humans">Humans</option> <option value="hydrobiology">Hydrobiology</option> <option value="hydrogen">Hydrogen</option> <option value="hydrology">Hydrology</option> <option value="hygiene">Hygiene</option> <option value="immuno">Immuno</option> <option value="idr">Infectious Disease Reports</option> <option value="informatics">Informatics</option> <option value="information">Information</option> <option value="infrastructures">Infrastructures</option> <option value="inorganics">Inorganics</option> <option value="insects">Insects</option> <option value="instruments">Instruments</option> <option value="iic">Intelligent Infrastructure and Construction</option> <option value="ijerph">International Journal of Environmental Research and Public Health</option> <option value="ijfs">International Journal of Financial Studies</option> <option value="ijms">International Journal of Molecular Sciences</option> <option value="IJNS">International Journal of Neonatal Screening</option> <option value="ijpb">International Journal of Plant Biology</option> <option value="ijt">International Journal of Topology</option> <option value="ijtm">International Journal of Translational Medicine</option> <option value="ijtpp">International Journal of Turbomachinery, Propulsion and Power</option> <option value="ime">International Medical Education</option> <option value="inventions">Inventions</option> <option value="IoT">IoT</option> <option value="ijgi">ISPRS International Journal of Geo-Information</option> <option value="J">J</option> <option value="jal">Journal of Ageing and Longevity</option> <option value="jcdd">Journal of Cardiovascular Development and Disease</option> <option value="jcto">Journal of Clinical & Translational Ophthalmology</option> <option value="jcm">Journal of Clinical Medicine</option> <option value="jcs">Journal of Composites Science</option> <option value="jcp">Journal of Cybersecurity and Privacy</option> <option value="jdad">Journal of Dementia and Alzheimer's Disease</option> <option value="jdb">Journal of Developmental Biology</option> <option value="jeta">Journal of Experimental and Theoretical Analyses</option> <option value="jfb">Journal of Functional Biomaterials</option> <option value="jfmk">Journal of Functional Morphology and Kinesiology</option> <option value="jof">Journal of Fungi</option> <option value="jimaging">Journal of Imaging</option> <option value="jintelligence">Journal of Intelligence</option> <option value="jlpea">Journal of Low Power Electronics and Applications</option> <option value="jmmp">Journal of Manufacturing and Materials Processing</option> <option value="jmse">Journal of Marine Science and Engineering</option> <option value="jmahp">Journal of Market Access & Health Policy</option> <option value="jmp">Journal of Molecular Pathology</option> <option value="jnt">Journal of Nanotheranostics</option> <option value="jne">Journal of Nuclear Engineering</option> <option value="ohbm">Journal of Otorhinolaryngology, Hearing and Balance Medicine</option> <option value="jop">Journal of Parks</option> <option value="jpm">Journal of Personalized Medicine</option> <option value="jpbi">Journal of Pharmaceutical and BioTech Industry</option> <option value="jor">Journal of Respiration</option> <option value="jrfm">Journal of Risk and Financial Management</option> <option value="jsan">Journal of Sensor and Actuator Networks</option> <option value="joma">Journal of the Oman Medical Association</option> <option value="jtaer">Journal of Theoretical and Applied Electronic Commerce Research</option> <option value="jvd">Journal of Vascular Diseases</option> <option value="jox">Journal of Xenobiotics</option> <option value="jzbg">Journal of Zoological and Botanical Gardens</option> <option value="journalmedia">Journalism and Media</option> <option value="kidneydial">Kidney and Dialysis</option> <option value="kinasesphosphatases">Kinases and Phosphatases</option> <option value="knowledge">Knowledge</option> <option value="labmed">LabMed</option> <option value="laboratories">Laboratories</option> <option value="land">Land</option> <option value="languages">Languages</option> <option value="laws">Laws</option> <option value="life">Life</option> <option value="limnolrev">Limnological Review</option> <option value="lipidology">Lipidology</option> <option value="liquids">Liquids</option> <option value="literature">Literature</option> <option value="livers">Livers</option> <option value="logics">Logics</option> <option value="logistics">Logistics</option> <option value="lubricants">Lubricants</option> <option value="lymphatics">Lymphatics</option> <option value="make">Machine Learning and Knowledge Extraction</option> <option value="machines">Machines</option> <option value="macromol">Macromol</option> <option value="magnetism">Magnetism</option> <option value="magnetochemistry">Magnetochemistry</option> <option value="marinedrugs">Marine Drugs</option> <option value="materials">Materials</option> <option value="materproc">Materials Proceedings</option> <option value="mca">Mathematical and Computational Applications</option> <option value="mathematics">Mathematics</option> <option value="medsci">Medical Sciences</option> <option value="msf">Medical Sciences Forum</option> <option value="medicina">Medicina</option> <option value="medicines">Medicines</option> <option value="membranes">Membranes</option> <option value="merits">Merits</option> <option value="metabolites">Metabolites</option> <option value="metals">Metals</option> <option value="meteorology">Meteorology</option> <option value="methane">Methane</option> <option value="mps">Methods and Protocols</option> <option value="metrics">Metrics</option> <option value="metrology">Metrology</option> <option value="micro">Micro</option> <option value="microbiolres">Microbiology Research</option> <option value="micromachines">Micromachines</option> <option value="microorganisms">Microorganisms</option> <option value="microplastics">Microplastics</option> <option value="minerals">Minerals</option> <option value="mining">Mining</option> <option value="modelling">Modelling</option> <option value="mmphys">Modern Mathematical Physics</option> <option value="molbank">Molbank</option> <option value="molecules">Molecules</option> <option value="mti">Multimodal Technologies and Interaction</option> <option value="muscles">Muscles</option> <option value="nanoenergyadv">Nanoenergy Advances</option> <option value="nanomanufacturing">Nanomanufacturing</option> <option value="nanomaterials">Nanomaterials</option> <option value="ndt">NDT</option> <option value="network">Network</option> <option value="neuroglia">Neuroglia</option> <option value="neurolint">Neurology International</option> <option value="neurosci">NeuroSci</option> <option value="nitrogen">Nitrogen</option> <option value="ncrna">Non-Coding RNA</option> <option value="nursrep">Nursing Reports</option> <option value="nutraceuticals">Nutraceuticals</option> <option value="nutrients">Nutrients</option> <option value="obesities">Obesities</option> <option value="oceans">Oceans</option> <option value="onco">Onco</option> <option value="optics">Optics</option> <option value="oral">Oral</option> <option value="organics">Organics</option> <option value="organoids">Organoids</option> <option value="osteology">Osteology</option> <option value="oxygen">Oxygen</option> <option value="parasitologia">Parasitologia</option> <option value="particles">Particles</option> <option value="pathogens">Pathogens</option> <option value="pathophysiology">Pathophysiology</option> <option value="pediatrrep">Pediatric Reports</option> <option value="pets">Pets</option> <option value="pharmaceuticals">Pharmaceuticals</option> <option value="pharmaceutics">Pharmaceutics</option> <option value="pharmacoepidemiology">Pharmacoepidemiology</option> <option value="pharmacy">Pharmacy</option> <option value="philosophies">Philosophies</option> <option value="photochem">Photochem</option> <option value="photonics">Photonics</option> <option value="phycology">Phycology</option> <option value="physchem">Physchem</option> <option value="psf">Physical Sciences Forum</option> <option value="physics">Physics</option> <option value="physiologia">Physiologia</option> <option value="plants">Plants</option> <option value="plasma">Plasma</option> <option value="platforms">Platforms</option> <option value="pollutants">Pollutants</option> <option value="polymers">Polymers</option> <option value="polysaccharides">Polysaccharides</option> <option value="populations">Populations</option> <option value="poultry">Poultry</option> <option value="powders">Powders</option> <option value="proceedings">Proceedings</option> <option value="processes">Processes</option> <option value="prosthesis">Prosthesis</option> <option value="proteomes">Proteomes</option> <option value="psychiatryint">Psychiatry International</option> <option value="psychoactives">Psychoactives</option> <option value="psycholint">Psychology International</option> <option value="publications">Publications</option> <option value="qubs">Quantum Beam Science</option> <option value="quantumrep">Quantum Reports</option> <option value="quaternary">Quaternary</option> <option value="radiation">Radiation</option> <option value="reactions">Reactions</option> <option value="realestate">Real Estate</option> <option value="receptors">Receptors</option> <option value="recycling">Recycling</option> <option value="rsee">Regional Science and Environmental Economics</option> <option value="religions">Religions</option> <option value="remotesensing">Remote Sensing</option> <option value="reports">Reports</option> <option value="reprodmed">Reproductive Medicine</option> <option value="resources">Resources</option> <option value="rheumato">Rheumato</option> <option value="risks">Risks</option> <option value="robotics">Robotics</option> <option value="ruminants">Ruminants</option> <option value="safety">Safety</option> <option value="sci">Sci</option> <option value="scipharm">Scientia Pharmaceutica</option> <option value="sclerosis">Sclerosis</option> <option value="seeds">Seeds</option> <option value="sensors">Sensors</option> <option value="separations">Separations</option> <option value="sexes">Sexes</option> <option value="signals">Signals</option> <option value="sinusitis">Sinusitis</option> <option value="smartcities">Smart Cities</option> <option value="socsci">Social Sciences</option> <option value="siuj">Société Internationale d’Urologie Journal</option> <option value="societies">Societies</option> <option value="software">Software</option> <option value="soilsystems">Soil Systems</option> <option value="solar">Solar</option> <option value="solids">Solids</option> <option value="spectroscj">Spectroscopy Journal</option> <option value="sports">Sports</option> <option value="standards">Standards</option> <option value="stats">Stats</option> <option value="stresses">Stresses</option> <option value="surfaces">Surfaces</option> <option value="surgeries">Surgeries</option> <option value="std">Surgical Techniques Development</option> <option value="sustainability">Sustainability</option> <option value="suschem">Sustainable Chemistry</option> <option value="symmetry">Symmetry</option> <option value="synbio">SynBio</option> <option value="systems">Systems</option> <option value="targets">Targets</option> <option value="taxonomy">Taxonomy</option> <option value="technologies">Technologies</option> <option value="telecom">Telecom</option> <option value="textiles">Textiles</option> <option value="thalassrep">Thalassemia Reports</option> <option value="therapeutics">Therapeutics</option> <option value="thermo">Thermo</option> <option value="timespace">Time and Space</option> <option value="tomography">Tomography</option> <option value="tourismhosp">Tourism and Hospitality</option> <option value="toxics">Toxics</option> <option value="toxins">Toxins</option> <option value="transplantology">Transplantology</option> <option value="traumacare">Trauma Care</option> <option value="higheredu">Trends in Higher Education</option> <option value="tropicalmed">Tropical Medicine and Infectious Disease</option> <option value="universe">Universe</option> <option value="urbansci">Urban Science</option> <option value="uro">Uro</option> <option value="vaccines">Vaccines</option> <option value="vehicles">Vehicles</option> <option value="venereology">Venereology</option> <option value="vetsci">Veterinary Sciences</option> <option value="vibration">Vibration</option> <option value="virtualworlds">Virtual Worlds</option> <option value="viruses">Viruses</option> <option value="vision">Vision</option> <option value="waste">Waste</option> <option value="water">Water</option> <option value="wild">Wild</option> <option value="wind">Wind</option> <option value="women">Women</option> <option value="world">World</option> <option value="wevj">World Electric Vehicle Journal</option> <option value="youth">Youth</option> <option value="zoonoticdis">Zoonotic Diseases</option> </select> <input name="email" type="email" placeholder="Enter your email address..." required="required" /> <button class="genericCaptcha button button--dark UA_FooterNewsletterSubscribeButton" type="submit">Subscribe</button> </form> </div> </div> </div> <div id="footer-copyright"> <div class="row"> <div class="columns large-6 medium-6 small-12 text-left"> © 1996-2024 MDPI (Basel, Switzerland) unless otherwise stated </div> <div class="columns large-6 medium-6 small-12 small-text-left medium-text-right large-text-right"> <a data-dropdown="drop-view-disclaimer" aria-controls="drop-view-disclaimer" aria-expanded="false" data-options="align:top; is_hover:true; hover_timeout:2000;"> Disclaimer </a> <div id="drop-view-disclaimer" class="f-dropdown label__btn__dropdown label__btn__dropdown--wide text-left" data-dropdown-content aria-hidden="true" tabindex="-1"> Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. </div> <a href="/about/terms-and-conditions"> Terms and Conditions </a> <a href="/about/privacy"> Privacy Policy </a> </div> </div> </div> </div> <div id="cookie-notification" class="js-allow-cookies" style="display: none;"> <div class="columns large-10 medium-10 small-12"> We use cookies on our website to ensure you get the best experience.<br class="show-for-medium-up"/> Read more about our cookies <a href="/about/privacy">here</a>. </div> <div class="columns large-2 medium-2 small-12 small-only-text-left text-right"> <a class="button button--default" href="/accept_cookies">Accept</a> </div> </div> </div> <div id="main-share-modal" class="reveal-modal reveal-modal-new reveal-modal-new--small" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog"> <div class="row"> <div class="small-12 columns"> <h2 style="margin: 0;">Share Link</h2> </div> <div class="small-12 columns"> <div class="social-media-links UA_ShareModalLinks" style="text-align: left;"> <a href="/cdn-cgi/l/email-protection#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" title="Email"> <i class="fa fa-envelope-square" style="font-size: 30px;"></i> </a> <a href="https://twitter.com/intent/tweet?text=Forecasting+Stock+Market+Prices+Using+Machine+Learning+and+Deep+Learning+Models%3A+A+Systematic+Review%2C+Performance+Analysis+and+Discussion+of+Implications&hashtags=mdpiijfs&url=https%3A%2F%2Fwww.mdpi.com%2F2406736&via=IJFS_MDPI" onclick="windowOpen(this.href,600,800); return false" title="Twitter" target="_blank" rel="noopener noreferrer"> <i class="fa fa-twitter-x-square" style="font-size: 30px;"></i> </a> <a href=" http://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Fwww.mdpi.com%2F2406736&title=Forecasting%20Stock%20Market%20Prices%20Using%20Machine%20Learning%20and%20Deep%20Learning%20Models%3A%20A%20Systematic%20Review%2C%20Performance%20Analysis%20and%20Discussion%20of%20Implications%26source%3Dhttps%3A%2F%2Fwww.mdpi.com%26summary%3DThe%20financial%20sector%20has%20greatly%20impacted%20the%20monetary%20well-being%20of%20consumers%2C%20traders%2C%20and%20financial%20institutions.%20In%20the%20current%20era%2C%20artificial%20intelligence%20is%20redefining%20the%20limits%20of%20the%20financial%20markets%20based%20on%20state-of-the-art%20machine%20%5B...%5D" onclick="windowOpen(this.href,600,800); return false" title="LinkedIn" target="_blank" rel="noopener noreferrer"> <i class="fa fa-linkedin-square" style="font-size: 30px;"></i> </a> <a href="https://www.facebook.com/sharer.php?u=https://www.mdpi.com/2406736" title="facebook" target="_blank" rel="noopener noreferrer"> <i class="fa fa-facebook-square" style="font-size: 30px;"></i> </a> <a href="javascript:void(0);" title="Wechat" data-reveal-id="weixin-share-modal"> <i class="fa fa-weixin-square" style="font-size: 26px;"></i> </a> <a href="http://www.reddit.com/submit?url=https://www.mdpi.com/2406736" title="Reddit" target="_blank" rel="noopener noreferrer"> <i class="fa fa-reddit-square" style="font-size: 30px;"></i> </a> <a href="http://www.mendeley.com/import/?url=https://www.mdpi.com/2406736" title="Mendeley" target="_blank" rel="noopener noreferrer"> <i class="fa fa-mendeley-square" style="font-size: 30px;"></i> </a> <a href="http://www.citeulike.org/posturl?url=https://www.mdpi.com/2406736" title="CiteULike" target="_blank" rel="noopener noreferrer"> <i class="fa fa-citeulike-square" style="font-size: 30px;"></i> </a> </div> </div> <div class="small-9 columns"> <input id="js-clipboard-text" type="text" readonly value="https://www.mdpi.com/2406736" /> </div> <div class="small-3 columns text-left"> <a class="button button--color js-clipboard-copy" data-clipboard-target="#js-clipboard-text">Copy</a> </div> </div> <a class="close-reveal-modal" aria-label="Close"> <i class="material-icons">clear</i> </a> </div> <div id="weixin-share-modal" class="reveal-modal reveal-modal-new" data-reveal aria-labelledby="weixin-share-modal-title" aria-hidden="true" role="dialog"> <div class="row"> <div class="small-12 columns"> <h2 id="weixin-share-modal-title" style="margin: 0;">Share</h2> </div> <div class="small-12 columns"> <div class="weixin-qr-code-section"> <?xml version="1.0" standalone="no"?> <!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd"> <svg width="300" height="300" version="1.1" xmlns="http://www.w3.org/2000/svg"> <desc>https://www.mdpi.com/2406736</desc> <g id="elements" fill="black" stroke="none"> <rect x="0" y="0" width="12" height="12" /> <rect x="12" y="0" width="12" height="12" /> <rect x="24" y="0" width="12" height="12" /> <rect x="36" y="0" width="12" height="12" /> <rect x="48" y="0" width="12" height="12" /> <rect x="60" y="0" width="12" height="12" /> <rect x="72" y="0" width="12" height="12" /> <rect x="96" y="0" width="12" height="12" /> <rect x="108" y="0" width="12" height="12" /> <rect x="120" y="0" width="12" height="12" /> <rect x="132" y="0" width="12" height="12" /> <rect x="156" y="0" width="12" height="12" /> <rect x="168" y="0" width="12" height="12" /> <rect x="216" y="0" width="12" height="12" /> <rect x="228" y="0" width="12" height="12" /> <rect x="240" y="0" width="12" height="12" /> <rect x="252" y="0" width="12" height="12" /> <rect x="264" y="0" width="12" height="12" /> <rect x="276" y="0" width="12" height="12" /> <rect x="288" y="0" width="12" height="12" /> <rect x="0" y="12" width="12" height="12" /> <rect x="72" y="12" width="12" height="12" /> <rect x="132" y="12" width="12" height="12" /> <rect x="144" y="12" width="12" height="12" /> <rect x="168" y="12" width="12" height="12" /> <rect x="216" y="12" width="12" height="12" /> <rect x="288" y="12" width="12" height="12" /> <rect x="0" y="24" width="12" height="12" /> <rect x="24" y="24" width="12" height="12" /> <rect x="36" y="24" width="12" height="12" /> <rect x="48" y="24" width="12" height="12" /> <rect x="72" y="24" width="12" height="12" /> <rect x="108" y="24" width="12" height="12" /> <rect x="132" y="24" width="12" height="12" /> <rect x="156" y="24" width="12" height="12" /> <rect x="168" y="24" width="12" height="12" /> <rect x="216" y="24" width="12" height="12" /> <rect x="240" y="24" width="12" height="12" /> <rect x="252" y="24" width="12" height="12" /> <rect x="264" y="24" width="12" height="12" /> <rect x="288" y="24" width="12" height="12" /> <rect x="0" y="36" width="12" height="12" /> <rect x="24" y="36" width="12" height="12" /> <rect x="36" y="36" width="12" height="12" /> <rect x="48" y="36" width="12" height="12" /> <rect x="72" y="36" width="12" height="12" /> <rect x="120" y="36" width="12" height="12" /> <rect x="132" y="36" width="12" height="12" /> <rect x="144" y="36" width="12" height="12" /> <rect x="156" y="36" width="12" height="12" /> <rect x="180" y="36" width="12" height="12" /> <rect x="192" y="36" width="12" height="12" /> <rect x="216" y="36" width="12" height="12" /> <rect x="240" y="36" width="12" height="12" /> <rect x="252" y="36" width="12" height="12" /> <rect x="264" y="36" width="12" height="12" /> <rect x="288" y="36" width="12" height="12" /> <rect x="0" y="48" width="12" height="12" /> <rect x="24" y="48" width="12" height="12" /> <rect x="36" y="48" width="12" height="12" /> <rect x="48" y="48" width="12" height="12" /> <rect x="72" y="48" width="12" height="12" /> <rect x="120" y="48" width="12" height="12" /> <rect x="132" y="48" width="12" height="12" /> <rect x="144" y="48" width="12" height="12" /> <rect x="156" y="48" width="12" height="12" /> <rect x="168" y="48" width="12" height="12" /> <rect x="216" y="48" width="12" height="12" /> <rect x="240" y="48" width="12" height="12" /> <rect x="252" y="48" width="12" height="12" /> <rect x="264" y="48" width="12" height="12" /> <rect x="288" y="48" width="12" height="12" /> <rect x="0" y="60" width="12" height="12" /> <rect x="72" y="60" width="12" height="12" /> <rect x="108" y="60" width="12" height="12" /> <rect x="132" y="60" width="12" height="12" /> <rect x="144" y="60" width="12" height="12" /> <rect x="156" y="60" width="12" height="12" /> <rect x="168" y="60" width="12" height="12" /> <rect x="216" y="60" width="12" height="12" /> <rect x="288" y="60" width="12" height="12" /> <rect x="0" y="72" width="12" height="12" /> <rect x="12" y="72" width="12" height="12" /> <rect x="24" y="72" width="12" height="12" /> <rect x="36" y="72" width="12" height="12" /> <rect x="48" y="72" width="12" height="12" /> <rect x="60" y="72" width="12" height="12" /> <rect x="72" y="72" width="12" height="12" /> <rect x="96" y="72" width="12" height="12" /> <rect x="120" y="72" width="12" height="12" /> <rect x="144" y="72" width="12" height="12" /> <rect x="168" y="72" width="12" height="12" /> <rect x="192" y="72" width="12" height="12" /> <rect x="216" y="72" width="12" height="12" /> <rect x="228" y="72" width="12" height="12" /> <rect x="240" y="72" width="12" height="12" /> <rect x="252" y="72" width="12" height="12" /> <rect x="264" y="72" width="12" height="12" /> <rect x="276" y="72" width="12" height="12" /> <rect x="288" y="72" width="12" height="12" /> <rect x="96" y="84" width="12" height="12" /> <rect x="108" y="84" width="12" height="12" /> <rect x="144" y="84" width="12" height="12" /> <rect x="156" y="84" width="12" height="12" /> <rect x="0" y="96" width="12" height="12" /> <rect x="12" y="96" width="12" height="12" /> <rect x="36" y="96" width="12" height="12" /> <rect x="48" y="96" width="12" height="12" /> <rect x="72" y="96" width="12" height="12" /> <rect x="108" y="96" width="12" height="12" /> <rect x="144" y="96" width="12" height="12" /> <rect x="168" y="96" width="12" height="12" /> <rect x="192" y="96" width="12" height="12" /> <rect x="216" y="96" width="12" height="12" /> <rect x="288" y="96" width="12" height="12" /> <rect x="0" y="108" width="12" height="12" /> <rect x="36" y="108" width="12" height="12" /> <rect x="48" y="108" width="12" height="12" /> <rect x="60" y="108" width="12" height="12" /> <rect x="84" y="108" width="12" height="12" /> <rect x="120" y="108" width="12" height="12" /> <rect x="144" y="108" width="12" height="12" /> <rect x="156" y="108" width="12" height="12" /> <rect x="168" y="108" width="12" height="12" /> <rect x="180" y="108" width="12" height="12" /> <rect x="204" y="108" width="12" height="12" /> <rect x="228" y="108" width="12" height="12" /> <rect x="240" y="108" width="12" height="12" /> <rect x="252" y="108" width="12" height="12" /> <rect x="264" y="108" width="12" height="12" /> <rect x="276" y="108" width="12" height="12" /> <rect x="0" y="120" width="12" height="12" /> <rect x="12" y="120" width="12" height="12" /> <rect x="24" y="120" width="12" height="12" /> <rect x="48" y="120" width="12" height="12" /> <rect x="72" y="120" width="12" height="12" /> <rect x="84" y="120" width="12" height="12" /> <rect x="96" y="120" width="12" height="12" /> <rect x="108" y="120" width="12" height="12" /> <rect x="144" y="120" width="12" height="12" /> <rect x="180" y="120" width="12" height="12" /> <rect x="216" y="120" width="12" height="12" /> <rect x="228" y="120" width="12" height="12" /> <rect x="252" y="120" width="12" height="12" /> <rect x="288" y="120" width="12" height="12" /> <rect x="0" y="132" width="12" height="12" /> <rect x="24" y="132" width="12" height="12" /> <rect x="60" y="132" width="12" height="12" /> <rect x="84" y="132" width="12" height="12" /> <rect x="168" y="132" width="12" height="12" /> <rect x="192" y="132" width="12" height="12" /> <rect x="228" y="132" width="12" height="12" /> <rect x="252" y="132" width="12" height="12" /> <rect x="264" y="132" width="12" height="12" /> <rect x="276" y="132" width="12" height="12" /> <rect x="288" y="132" width="12" height="12" /> <rect x="0" y="144" width="12" height="12" /> <rect x="72" y="144" width="12" height="12" /> <rect x="84" y="144" width="12" height="12" /> <rect x="96" y="144" width="12" height="12" /> <rect x="108" y="144" width="12" height="12" /> <rect x="120" y="144" width="12" height="12" /> <rect x="132" y="144" width="12" height="12" /> <rect x="144" y="144" width="12" height="12" /> <rect x="168" y="144" width="12" height="12" /> <rect x="180" y="144" width="12" height="12" /> <rect x="216" y="144" width="12" height="12" /> <rect x="288" y="144" width="12" height="12" /> <rect x="0" y="156" width="12" height="12" /> <rect x="36" y="156" width="12" height="12" /> <rect x="60" y="156" width="12" height="12" /> <rect x="108" y="156" width="12" height="12" /> <rect x="120" y="156" width="12" height="12" /> <rect x="156" y="156" width="12" height="12" /> <rect x="168" y="156" width="12" height="12" /> <rect x="180" y="156" width="12" height="12" /> <rect x="192" y="156" width="12" height="12" /> <rect x="204" y="156" width="12" height="12" /> <rect x="240" y="156" width="12" height="12" /> <rect x="276" y="156" width="12" height="12" /> <rect x="0" y="168" width="12" height="12" /> <rect x="12" y="168" width="12" height="12" /> <rect x="24" y="168" width="12" height="12" /> <rect x="48" y="168" width="12" height="12" /> <rect x="60" y="168" width="12" height="12" /> <rect x="72" y="168" width="12" height="12" /> <rect x="84" y="168" width="12" height="12" /> <rect x="144" y="168" width="12" height="12" /> <rect x="156" y="168" width="12" height="12" /> <rect x="180" y="168" width="12" height="12" /> <rect x="192" y="168" width="12" height="12" /> <rect x="204" y="168" width="12" height="12" /> <rect x="216" y="168" width="12" height="12" /> <rect x="240" y="168" width="12" height="12" /> <rect x="252" y="168" width="12" height="12" /> <rect x="264" y="168" width="12" height="12" /> <rect x="276" y="168" width="12" height="12" /> <rect x="288" y="168" width="12" height="12" /> <rect x="0" y="180" width="12" height="12" /> <rect x="24" y="180" width="12" height="12" /> <rect x="36" y="180" width="12" height="12" /> <rect x="96" y="180" width="12" height="12" /> <rect x="120" y="180" width="12" height="12" /> <rect x="168" y="180" width="12" height="12" /> <rect x="204" y="180" width="12" height="12" /> <rect x="228" y="180" width="12" height="12" /> <rect x="252" y="180" width="12" height="12" /> <rect x="264" y="180" width="12" height="12" /> <rect x="288" y="180" width="12" height="12" /> <rect x="0" y="192" width="12" height="12" /> <rect x="72" y="192" width="12" height="12" /> <rect x="84" y="192" width="12" height="12" /> <rect x="96" y="192" width="12" height="12" /> <rect x="108" y="192" width="12" height="12" /> <rect x="120" y="192" width="12" height="12" /> <rect x="132" y="192" width="12" height="12" /> <rect x="180" y="192" width="12" height="12" /> <rect x="192" y="192" width="12" height="12" /> <rect x="204" y="192" width="12" height="12" /> <rect x="216" y="192" width="12" height="12" /> <rect x="228" y="192" width="12" height="12" /> <rect x="240" y="192" width="12" height="12" /> <rect x="264" y="192" width="12" height="12" /> <rect x="276" y="192" width="12" height="12" /> <rect x="96" y="204" width="12" height="12" /> <rect x="108" y="204" width="12" height="12" /> <rect x="132" y="204" width="12" height="12" /> <rect x="192" y="204" width="12" height="12" /> <rect x="240" y="204" width="12" height="12" /> <rect x="264" y="204" width="12" height="12" /> <rect x="276" y="204" width="12" height="12" /> <rect x="0" y="216" width="12" height="12" /> <rect x="12" y="216" width="12" height="12" /> <rect x="24" y="216" width="12" height="12" /> <rect x="36" y="216" width="12" height="12" /> <rect x="48" y="216" width="12" height="12" /> <rect x="60" y="216" width="12" height="12" /> <rect x="72" y="216" width="12" height="12" /> <rect x="120" y="216" width="12" height="12" /> <rect x="132" y="216" width="12" height="12" /> <rect x="144" y="216" width="12" height="12" /> <rect x="192" y="216" width="12" height="12" /> <rect x="216" y="216" width="12" height="12" /> <rect x="240" y="216" width="12" height="12" /> <rect x="288" y="216" width="12" height="12" /> <rect x="0" y="228" width="12" height="12" /> <rect x="72" y="228" width="12" height="12" /> <rect x="120" y="228" width="12" height="12" /> <rect x="180" y="228" width="12" height="12" /> <rect x="192" y="228" width="12" height="12" /> <rect x="240" y="228" width="12" height="12" /> <rect x="288" y="228" width="12" height="12" /> <rect x="0" y="240" width="12" height="12" /> <rect x="24" y="240" width="12" height="12" /> <rect x="36" y="240" width="12" height="12" /> <rect x="48" y="240" width="12" height="12" /> <rect x="72" y="240" width="12" height="12" /> <rect x="96" y="240" width="12" height="12" /> <rect x="108" y="240" width="12" height="12" /> <rect x="120" y="240" width="12" height="12" /> <rect x="144" y="240" width="12" height="12" /> <rect x="156" y="240" width="12" height="12" /> <rect x="180" y="240" width="12" height="12" /> <rect x="192" y="240" width="12" height="12" /> <rect x="204" y="240" width="12" height="12" /> <rect x="216" y="240" width="12" height="12" /> <rect x="228" y="240" width="12" height="12" /> <rect x="240" y="240" width="12" height="12" /> <rect x="276" y="240" width="12" height="12" /> <rect x="288" y="240" width="12" height="12" /> <rect x="0" y="252" width="12" height="12" /> <rect x="24" y="252" width="12" height="12" /> <rect x="36" y="252" width="12" height="12" /> <rect x="48" y="252" width="12" height="12" /> <rect x="72" y="252" width="12" height="12" /> <rect x="96" y="252" width="12" height="12" /> <rect x="108" y="252" width="12" height="12" /> <rect x="144" y="252" width="12" height="12" /> <rect x="180" y="252" width="12" height="12" /> <rect x="192" y="252" width="12" height="12" /> <rect x="216" y="252" width="12" height="12" /> <rect x="276" y="252" width="12" height="12" /> <rect x="288" y="252" width="12" height="12" /> <rect x="0" y="264" width="12" height="12" /> <rect x="24" y="264" width="12" height="12" /> <rect x="36" y="264" width="12" height="12" /> <rect x="48" y="264" width="12" height="12" /> <rect x="72" y="264" width="12" height="12" /> <rect x="120" y="264" width="12" height="12" /> <rect x="144" y="264" width="12" height="12" /> <rect x="168" y="264" width="12" height="12" /> <rect x="192" y="264" width="12" height="12" /> <rect x="204" y="264" width="12" height="12" /> <rect x="240" y="264" width="12" height="12" /> <rect x="252" y="264" width="12" height="12" /> <rect x="264" y="264" width="12" height="12" /> <rect x="276" y="264" width="12" height="12" /> <rect x="288" y="264" width="12" height="12" /> <rect x="0" y="276" width="12" height="12" /> <rect x="72" y="276" width="12" height="12" /> <rect x="96" y="276" width="12" height="12" /> <rect x="108" y="276" width="12" height="12" /> <rect x="120" y="276" width="12" height="12" /> <rect x="168" y="276" width="12" height="12" /> <rect x="228" y="276" width="12" height="12" /> <rect x="240" y="276" width="12" height="12" /> <rect x="264" y="276" width="12" height="12" /> <rect x="276" y="276" width="12" height="12" /> <rect x="288" y="276" width="12" height="12" /> <rect x="0" y="288" width="12" height="12" /> <rect x="12" y="288" width="12" height="12" /> <rect x="24" y="288" width="12" height="12" /> <rect x="36" y="288" width="12" height="12" /> <rect x="48" y="288" width="12" height="12" /> <rect x="60" y="288" width="12" height="12" /> <rect x="72" y="288" width="12" height="12" /> <rect x="96" y="288" width="12" height="12" /> <rect x="108" y="288" width="12" height="12" /> <rect x="120" y="288" width="12" height="12" /> <rect x="132" y="288" width="12" height="12" /> <rect x="156" y="288" width="12" height="12" /> <rect x="168" y="288" width="12" height="12" /> <rect x="192" y="288" width="12" height="12" /> <rect x="204" y="288" width="12" height="12" /> <rect x="252" y="288" width="12" height="12" /> <rect x="288" y="288" width="12" height="12" /> </g> </svg> </div> </div> </div> <a class="close-reveal-modal" aria-label="Close"> <i class="material-icons">clear</i> </a> </div> <a href="#" class="back-to-top"><span class="show-for-medium-up">Back to Top</span><span class="show-for-small">Top</span></a> <script data-cfasync="false" src="/cdn-cgi/scripts/5c5dd728/cloudflare-static/email-decode.min.js"></script><script src="https://pub.mdpi-res.com/assets/js/modernizr-2.8.3.min.js?5227e0738f7f421d?1732261071"></script> <script src="https://pub.mdpi-res.com/assets/js/jquery-1.12.4.min.js?4f252523d4af0b47?1732261071"></script> <script src="https://pub.mdpi-res.com/assets/js/foundation-5.5.3.min.js?6b2ec41c18b29054?1732261071"></script> <script src="https://pub.mdpi-res.com/assets/js/foundation-5.5.3.equalizer.min.js?0f6c549b75ec554c?1732261071"></script> <script src="https://pub.mdpi-res.com/assets/js/jquery.multiselect.js?0edd3998731d1091?1732261071"></script> <script src="https://pub.mdpi-res.com/assets/js/jquery.cycle2.min.js?63413052928f97ee?1732261071"></script> <script> // old browser fix - this way the console log rows won't throw (silent) errors in browsers not supporting console log if (!window.console) window.console = {}; if (!window.console.log) window.console.log = function () { }; var currentJournalNameSystem = "ijfs"; $(document).ready(function() { $('select.foundation-select').multiselect({ search: true, minHeight: 130, maxHeight: 130, }); $(document).foundation({ orbit: { timer_speed: 4000, }, reveal: { animation: 'fadeAndPop', animation_speed: 100, } }); $(".chosen-select").each(function(element) { var maxSelected = (undefined !== $(this).data('maxselectedoptions') ? $(this).data('maxselectedoptions') : 100); $(this).on('chosen:ready', function(event, data) { var select = $(data.chosen.form_field); if (select.attr('id') === 'journal-browser-volume') { $(data.chosen.dropdown).addClass('UI_JournalBrowser_Volume_Options'); } if (select.attr('id') === 'journal-browser-issue') { $(data.chosen.dropdown).addClass('UI_JournalBrowser_Issue_Options'); } }).chosen({ display_disabled_options: false, disable_search_threshold: 7, max_selected_options: maxSelected, width: "100%" }); }); $(".toEncode").each(function(e) { var oldHref = $(this).attr("href"); var newHref = oldHref.replace('.botdefense.please.enable.javascript.','@'); $(this).attr("href", newHref); if (!$(this).hasClass("emailCaptcha")) { $(this).html(newHref.replace('mailto:', '')); } $(this).removeClass("visibility-hidden"); }); $(document).on('opened.fndtn.reveal', '[data-reveal]', function() { $(document).foundation('equalizer', 'reflow'); }); // fix the images that have tag height / width defined // otherwise the default foundation styles overwrite the tag definitions $("img").each(function() { if ($(this).attr('width') != undefined || $(this).attr('height') != undefined) { $(this).addClass("img-fixed"); } }); $("#basic_search, #advanced_search").submit(function(e) { var searchArguments = false; $(this).find("input,select").not("#search,.search-button").each(function() { if (undefined === $(this).val() || "" === $(this).val()) { $(this).attr('name', null); } else { $(this).attr('name'); searchArguments = true; } }); if (!searchArguments) { window.location = $(this).attr('action'); return false; } }); $(".hide-show-desktop-option").click(function(e) { e.preventDefault(); var parentDiv = $(this).closest("div"); $.ajax({ url: $(this).attr('href'), success: function(msg) { parentDiv.removeClass().hide(); } }); }); $(".generic-toggleable-header").click(function(e) { $(this).toggleClass("active"); $(this).next(".generic-toggleable-content").toggleClass("active"); }); /* * handle whole row as a link if the row contains only one visible link */ $("table.new tr").hover(function() { if ($(this).find("td:visible a").length == 1) { $(this).addClass("single-link"); } }, function() { $(this).removeClass("single-link"); }); $("table.new:not(.table-of-tables)").on("click", "tr.single-link", function(e) { var target = $(e.target); if (!e.ctrlKey && !target.is("a")) { $(this).find("td:visible a")[0].click(); } }); $(document).on("click", ".custom-accordion-for-small-screen-link", function(e) { if ($(this).closest("#basic_search").length > 0) { if ($(".search-container__advanced").first().is(":visible")) { openAdvanced() } } if (Foundation.utils.is_small_only()) { if ($(this).hasClass("active")) { $(this).removeClass("active"); $(this).next(".custom-accordion-for-small-screen-content").addClass("show-for-medium-up"); } else { $(this).addClass("active"); $(this).next(".custom-accordion-for-small-screen-content").removeClass("show-for-medium-up"); $(document).foundation('orbit', 'reflow'); } } if (undefined !== $(this).data("callback")) { var customCallback = $(this).data("callback"); func = window[customCallback]; func(); } }); $(document).on("click", ".js-open-small-search", function(e) { e.preventDefault(); $(this).toggleClass("active").closest(".tab-bar").toggleClass("active"); $(".search-container").toggleClass("hide-for-small-down"); }); $(document).on("click", ".js-open-menu", function(e) { $(".search-container").addClass("hide-for-small-down"); }); $(window).on('resize', function() { recalculate_main_browser_position(); recalculate_responsive_moving_containers(); }); updateSearchLabelVisibilities(); recalculate_main_browser_position(); recalculate_responsive_moving_containers(); if (window.document.documentMode == 11) { $("<link/>", { rel: "stylesheet", type: "text/css", href: "https://fonts.googleapis.com/icon?family=Material+Icons"}).appendTo("head"); } }); function recalculate_main_browser_position() { if (Foundation.utils.is_small_only()) { if ($("#js-main-top-container").parent("#js-large-main-top-container").length > 0) { $("#js-main-top-container").appendTo($("#js-small-main-top-container")); } } else { if ($("#js-main-top-container").parent("#js-small-main-top-container").length > 0) { $("#js-main-top-container").appendTo($("#js-large-main-top-container")); } } } function recalculate_responsive_moving_containers() { $(".responsive-moving-container.large").each(function() { var previousParent = $(".responsive-moving-container.active[data-id='"+$(this).data("id")+"']"); var movingContent = previousParent.html(); if (Foundation.utils.is_small_only()) { var currentParent = $(".responsive-moving-container.small[data-id='"+$(this).data("id")+"']"); } else if (Foundation.utils.is_medium_only()) { var currentParent = $(".responsive-moving-container.medium[data-id='"+$(this).data("id")+"']"); } else { var currentParent = $(".responsive-moving-container.large[data-id='"+$(this).data("id")+"']"); } if (previousParent.attr("class") !== currentParent.attr("class")) { currentParent.html(movingContent); previousParent.html(); currentParent.addClass("active"); previousParent.removeClass("active"); } }); } // cookies allowed is checked from a) local storage and b) from server separately so that the footer bar doesn't // get included in the custom page caches function checkCookiesAllowed() { var cookiesEnabled = localStorage.getItem("mdpi_cookies_enabled"); if (null === cookiesEnabled) { $.ajax({ url: "/ajax_cookie_value/mdpi_cookies_accepted", success: function(data) { if (data.value) { localStorage.setItem("mdpi_cookies_enabled", true); checkDisplaySurvey(); } else { $(".js-allow-cookies").show(); } } }); } else { checkDisplaySurvey(); } } function checkDisplaySurvey() { } window.addEventListener('CookiebotOnAccept', function (e) { var CookieDate = new Date; if (Cookiebot.consent.preferences) { CookieDate.setFullYear(CookieDate.getFullYear() + 1); document.cookie = "mdpi_layout_type_v2=mobile; path=/; expires=" + CookieDate.toUTCString() + ";"; $(".js-toggle-desktop-layout-link").css("display", "inline-block"); } }, false); window.addEventListener('CookiebotOnDecline', function (e) { if (!Cookiebot.consent.preferences) { $(".js-toggle-desktop-layout-link").hide(); if ("" === "desktop") { window.location = "/toggle_desktop_layout_cookie"; } } }, false); var hash = $(location).attr('hash'); if ("#share" === hash) { if (1 === $("#main-share-modal").length) { $('#main-share-modal').foundation('reveal', 'open'); } } </script> <script src="https://pub.mdpi-res.com/assets/js/lib.js?f8d3d71b3a772f9d?1732261071"></script> <script src="https://pub.mdpi-res.com/assets/js/mdpi.js?c267ce58392b15da?1732261071"></script> <script>var banners_url = 'https://serve.mdpi.com';</script> <script type='text/javascript' src='https://pub.mdpi-res.com/assets/js/ifvisible.min.js?c621d19ecb761212?1732261071'></script> <script src="https://pub.mdpi-res.com/assets/js/xmltohtml/affix.js?ac4ea55275297c15?1732261071"></script> <script src="https://pub.mdpi-res.com/assets/js/clipboard.min.js?3f3688138a1b9fc4?1732261071"></script> <script type="text/javascript"> $(document).ready(function() { var helpFunctions = $(".middle-column__help__fixed"); var leftColumnAffix = $(".left-column__fixed"); var middleColumn = $("#middle-column"); var clone = null; helpFunctions.affix({ offset: { top: function() { return middleColumn.offset().top - 8 - (Foundation.utils.is_medium_only() ? 30 : 0); }, bottom: function() { return $("#footer").innerHeight() + 74 + (Foundation.utils.is_medium_only() ? 0 : 0); } } }); if (leftColumnAffix.length > 0) { clone = leftColumnAffix.clone(); clone.addClass("left-column__fixed__affix"); clone.insertBefore(leftColumnAffix); clone.css('width', leftColumnAffix.outerWidth() + 50); clone.affix({ offset: { top: function() { return leftColumnAffix.offset().top - 30 - (Foundation.utils.is_medium_only() ? 50 : 0); }, bottom: function() { return $("#footer").innerHeight() + 92 + (Foundation.utils.is_medium_only() ? 0 : 0); } } }); } $(window).on("resize", function() { if (clone !== null) { clone.css('width', leftColumnAffix.outerWidth() + 50); } }); new ClipboardJS('.js-clipboard-copy'); }); </script> <script src="https://pub.mdpi-res.com/assets/js/jquery-ui-1.13.2.min.js?1e2047978946a1d2?1732261071"></script> <script src="https://pub.mdpi-res.com/assets/js/slick.min.js?d5a61c749e44e471?1732261071"></script> <script> $(document).ready(function() { $(".link-article-menu").click(function(e) { e.preventDefault(); $(this).find('span').toggle(); $(this).next("div").toggleClass("active"); }); $(".js-similarity-related-articles").click(function(e) { e.preventDefault(); if ('' !== $('#recommended-articles-modal').attr('data-url')) { $('#recommended-articles-modal').foundation('reveal', 'open', $('#recommended-articles-modal').attr('data-url')); } }); $.ajax({ url: "/article/1203368/similarity-related/show-link", success: function(result) { if (result.show) { $('#recommended-articles-modal').attr('data-url', result.link); $('.js-article-similarity-container').show(); } } }); $(document).on('opened.fndtn.reveal', '[data-reveal]', function() { var modal = $(this); if (modal.attr('id') === "author-biographies-modal") { modal.find('.multiple-items').slick({ slidesToShow: 1, nextArrow: '<a class="slick-next" href="#"><i class="material-icons">chevron_right</i></a>', prevArrow: '<a class="slick-prev" href="#"><i class="material-icons">chevron_left</i></a>', slidesToScroll: 1, draggable: false, }); modal.find('.multiple-items').slick('refresh'); } }); }); </script> <script> $(document).ready(function() { $(document).on('keyup', function (e) { if (e.keyCode == 27) { var hElem = $(this).find(".annotator-adder"); if (hElem.length){ hElem.css({'visibility':'hidden'}); } else { document.querySelector("hypothesis-adder").shadowRoot.querySelector(".annotator-adder").style.visibility = "hidden"; } } }); }); </script> <script> window.hypothesisConfig = function () { return { sidebarAppUrl: 'https://commenting.mdpi.com/app.html', showHighlights: 'whenSidebarOpen' , openSidebar: false , assetRoot: 'https://commentingres.mdpi.com/hypothesis', services: [{ apiUrl: 'https://commenting.mdpi.com/api/', authority: 'mdpi', grantToken: '', doi: '10.3390/ijfs11030094' }], }; }; </script> <script async id="hypothesis_frame"></script> <script type="text/javascript"> if (-1 !== window.location.href.indexOf("?src=")) { window.history.replaceState({}, '', `${location.pathname}`); } $(document).ready(function() { var scifeedCounter = 0; var search = window.location.search; var mathjaxReady = false; // late image file loading $("img[data-lsrc]").each(function() { $(this).attr("src", $(this).data("lsrc")); }); // late mathjax initialization var head = document.getElementsByTagName("head")[0]; var script = document.createElement("script"); script.type = "text/x-mathjax-config"; script[(window.opera ? "innerHTML" : "text")] = "MathJax.Hub.processSectionDelay = 0;\n" + "MathJax.Hub.Config({\n" + " \"menuSettings\": {\n" + " CHTMLpreview: false\n" + " },\n" + " \"CHTML-preview\":{\n" + " disabled: true\n" + " },\n" + " \"HTML-CSS\": {\n" + " scale: 90,\n" + " availableFonts: [],\n" + " preferredFont: null,\n" + " preferredFonts: null,\n" + " webFont:\"Gyre-Pagella\",\n" + " imageFont:'TeX',\n" + " undefinedFamily:\"'Arial Unicode MS',serif\",\n" + " linebreaks: { automatic: false }\n" + " },\n" + " \"TeX\": {\n" + " extensions: ['noErrors.js'],\n" + " noErrors: {\n" + " inlineDelimiters: [\"\",\"\"],\n" + " multiLine: true,\n" + " style: {\n" + " 'font-size': '90%',\n" + " 'text-align': 'left',\n" + " 'color': 'black',\n" + " 'padding': '1px 3px',\n" + " 'border': '1px solid'\n" + " }\n" + " }\n" + " }\n" + "});\n" + "MathJax.Hub.Register.StartupHook('End', function() {\n" + " refreshMathjaxWidths();\n" + " mathjaxReady = true;\n" + "});\n" + "MathJax.Hub.Startup.signal.Interest(function (message) {\n" + " if (message == 'End') {\n" + " var hypoLink = document.getElementById('hypothesis_frame');\n" + " if (null !== hypoLink) {\n" + " hypoLink.setAttribute('src', 'https://commenting.mdpi.com/embed.js');\n" + " }\n" + " }\n" + "});"; head.appendChild(script); script = document.createElement("script"); script.type = "text/javascript"; script.src = "https://pub.mdpi-res.com/bundles/mathjax/MathJax.js?config=TeX-AMS-MML_HTMLorMML"; head.appendChild(script); // article version checker if (0 === search.indexOf('?type=check_update&version=')) { $.ajax({ url: "/2227-7072/11/3/94" + "/versioncheck" + search, success: function(result) { $(".js-check-update-container").html(result); } }); } $('#feed_option').click(function() { // tracker if ($('#scifeed_clicked').length<1) { $(this).append('<span style="display:none" id="scifeed_clicked">done</span>'); } $('#feed_data').toggle('slide', { direction: 'up'}, '1000'); // slideToggle(700); OR toggle(700) $("#scifeed_error_msg").html('').hide(); $("#scifeed_notice_msg").html('').hide(); }); $('#feed_option').click(function(event) { setTimeout(function(){ var captchaSection = $("#captchaSection"); captchaSection.removeClass('ui-helper-hidden').find('input').prop('disabled', false); // var img = captchaSection.find('img'); // img.attr('src', img.data('url') + "?" + (new Date()).getTime()); // $(".captcha_reload").trigger("click"); var img = document.getElementById('gregwar_captcha_scifeed'); img.src = '/generate-captcha/gcb_captcha?n=' + (new Date()).getTime(); },800); }); $(document).on('click', '.split_feeds', function() { var name = $( this ).attr('name'); var flag = 1 - ($(this).is(":checked")*1); $('.split_feeds').each(function (index) { if ($( this ).attr('name') !== name) { $(this)[0].checked = flag; } }); }); $(document).on('click', '#scifeed_submit, #scifeed_submit1', function(event) { event.preventDefault(); $(".captcha_reload").trigger("click"); $("#scifeed_error_msg").html(""); $("#scifeed_error_msg").hide(); }); $(document).on('click', '.subscription_toggle', function(event) { if ($(this).val() === 'Create SciFeed' && $('#scifeed_hidden_flag').length>0) { event.preventDefault(); // alert('Here there would be a captcha because user is not logged in'); var captchaSection = $("#captchaSection"); if (captchaSection.hasClass('ui-helper-hidden')) { captchaSection.removeClass('ui-helper-hidden').find('input').prop('disabled', false); var img = captchaSection.find('img'); img.attr('src', img.data('url') + "?" + (new Date()).getTime()); $("#reloadCaptcha").trigger("click"); } } }); $(document).on('click', '.scifeed_msg', function(){ $(this).hide(); }); $(document).on('click', '.article-scilit-search', function(e) { e.preventDefault(); var data = $(".article-scilit-search-data").val(); var dataArray = data.split(';').map(function(keyword) { return "(\"" + keyword.trim() + "\")"; }); var searchQuery = dataArray.join(" OR "); var searchUrl = encodeURI("https://www.scilit.net/articles/search?q="+ searchQuery + "&advanced=1&highlight=1"); var win = window.open(searchUrl, '_blank'); if (win) { win.focus(); } else { window.location(searchUrl); } }); display_stats(); citedCount(); follow_goto(); // Select the node that will be observed for mutations const targetNodes = document.getElementsByClassName('hypothesis-count-container'); // Options for the observer (which mutations to observe) const config = { attributes: false, childList: true, subtree: false }; // Callback function to execute when mutations are observed const callback = function(mutationList, observer) { for(const mutation of mutationList) { if (mutation.type === 'childList') { let node = $(mutation.target); if (parseInt(node.html()) > 0) { node.show(); } } } }; // Create an observer instance linked to the callback function const observer = new MutationObserver(callback); // Start observing the target node for configured mutations for(const targetNode of targetNodes) { observer.observe(targetNode, config); } // Select the node that will be observed for mutations const mathjaxTargetNode = document.getElementById('middle-column'); // Callback function to execute when mutations are observed const mathjaxCallback = function(mutationList, observer) { if (mathjaxReady && typeof(MathJax) !== 'undefined') { refreshMathjaxWidths(); } }; // Create an observer instance linked to the callback function const mathjaxObserver = new ResizeObserver(mathjaxCallback); // Start observing the target node for configured mutations mathjaxObserver.observe(mathjaxTargetNode); }); /* END $(document).ready */ function refreshMathjaxWidths() { let width = ($('.html-body').width()*0.9) + "px"; $('.MathJax_Display').css('max-width', width); $('.MJXc-display').css('max-width', width); } function sendScifeedFrom(form) { if (!$('#scifeed_email').val().trim()) { // empty email alert('Please, provide an email for subscribe to this scifeed'); return false; } else if (!$('#captchaSection').hasClass('ui-helper-hidden') && !$('#captchaSection').find('input').val().trim()) { // empty captcha alert('Please, fill the captcha field.'); return false; } else if( ((($('#scifeed_form').find('input:checkbox:checked').length)-($('#split_feeds:checked').length))<1) || ($('#scifeed_kwd_txt').length < 0 && !$('#scifeed_kwd_txt').val().trim()) || ($('#scifeed_author_txt').length<0 &&!$('#scifeed_author_txt').val().trim()) ) { alert('You did not select anything to subscribe'); return false; } else if(($('#scifeed_form').find('input:checkbox:checked').length)-($('#split_feeds2:checked').length)<1){ alert("You did not select anything to subscribe"); return false; } else { var url = $('#scifeed_subscribe_url').html(); var formData = $(form).serializeArray(); $.post(url, formData).done(function (data) { if (JSON.parse(data)) { $('.scifeed_msg').hide(); var res = JSON.parse(data); var successFeeds = 0; var errorFeeds = 0; if (res) { $('.scifeed_msg').html(''); $.each(res, function (index, val) { if (val) { if (val.error) { errorFeeds++; $("#scifeed_error_msg").append(index+' - '+val.error+'<br>'); } if (val.notice) // for successful feed creation { successFeeds++; // $("#scifeed_notice_msg").append(index+' - '+val.notice+'<br>'); $("#scifeed_notice_msg").append('<li>'+index+'</li>'); } } }); if (successFeeds>0) { text = $('#scifeed_notice_msg').html(); text = 'The following feed'+(successFeeds>1?'s have':' has')+ ' been sucessfully created:<br><ul>'+ text + '</ul>' +($('#scifeed_hidden_flag').length>0 ? 'You are not logged in, so you probably need to validate '+ (successFeeds>1?'them':' it')+'.<br>' :'' ) +'Please check your email'+(successFeeds>1?'s':'')+' for more details.'; //(successFeeds>1?' for each of them':'')+'.<br>'; $("#scifeed_notice_msg").html(text); $("#scifeed_notice_msg").show(); } if (errorFeeds>0) { $("#scifeed_error_msg").show();; } } $("#feed_data").hide(); } }); } } function follow_goto() { var hashStr = location.hash.replace("#",""); if(typeof hashStr !== 'undefined') { if( hashStr == 'supplementary') { document.getElementById('suppl_id').scrollIntoView(); } if( hashStr == 'citedby') { document.getElementById('cited_id').scrollIntoView(); } } } function cited() { $("#framed_div").toggle('fast', function(){ if ($(this).css('display') != 'none') { var loaded = document.getElementById("loaded"); if(loaded.innerHTML == "No") { // Load Xref result var container = document.getElementById("framed_div"); // This replace the content container.innerHTML = "<img src=\"https://pub.mdpi-res.com/img/loading_circle.gif?9a82694213036313?1732261071\" height=\"20\" width=\"20\" alt=\"Processing...\" style=\"vertical-align:middle; margin-right:0.6em;\">"; var url = "/citedby/10.3390%252Fijfs11030094/148"; $.post(url, function(result) { if (result.success) { container.innerHTML = result.view; } loaded.innerHTML = "Yes"; }); } } return true; // for not going at the beginning of the page... }) return true; // for not going at the beginning of the page... } function detect_device() { // Added by Bastien (18/08/2014): based on the http://detectmobilebrowsers.com/ detector var check = false; (function(a){if(/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i.test(a)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0,4)))check = true})(navigator.userAgent||navigator.vendor||window.opera); return check; } function display_stats(){ $("#article_stats_div").toggle(); return false; } /* * Cited By Scopus */ function citedCount(){ $("#framed_div_cited_count").toggle('fast', function(){ if ($(this).css('display') != 'none') { var loaded = document.getElementById("loaded_cite_count"); // to load only once the result! if(loaded.innerHTML == "No") { // Load Xref result var d = document.getElementById("framed_div_cited_count"); // This replace the content d.innerHTML = "<img src=\"https://pub.mdpi-res.com/img/loading_circle.gif?9a82694213036313?1732261071\" height=\"20\" width=\"20\" alt=\"Processing...\" style=\"vertical-align:middle; margin-right:0.6em;\">"; $.ajax({ method : "POST", url : "/cite-count/10.3390%252Fijfs11030094", success : function(data) { if (data.succ) { d.innerHTML = data.view; loaded.innerHTML = "Yes"; follow_goto(); } } }); } } // end else return true; // for not going at the beginning of the page... }) return true; // for not going at the beginning of the page... } </script><script type="text/javascript" src="https://pub.mdpi-res.com/assets/js/third-party/highcharts/highcharts.js?bdd06f45e34c33df?1732261071"></script><script type="text/javascript" src="https://pub.mdpi-res.com/assets/js/third-party/highcharts/modules/exporting.js?944dc938d06de3a8?1732261071"></script><script type="text/javascript" defer="defer"> var advancedStatsData; var selectedStatsType = "abstract"; $(function(){ var countWrapper = $('#counts-wrapper'); $('#author_stats_id #type_links a').on('click', function(e) { e.preventDefault(); selectedStatsType = $(this).data('type'); $('#article_advanced_stats').vectorMap('set', 'values', advancedStatsData[selectedStatsType]); $('#advanced_stats_max').html(advancedStatsData[selectedStatsType].max); $('#type_links a').removeClass('active'); $(this).addClass('active'); }); $.get('/2227-7072/11/3/94/stats', function (result) { if (!result.success) { return; } // process article metrics part in left column var viewNumber = countWrapper.find(".view-number"); viewNumber.html(result.metrics.views); viewNumber.parent().toggleClass("count-div--grey", result.metrics.views == 0); var downloadNumber = countWrapper.find(".download-number"); downloadNumber.html(result.metrics.downloads); downloadNumber.parent().toggleClass("count-div--grey", result.metrics.downloads == 0); var citationsNumber = countWrapper.find(".citations-number"); citationsNumber.html(result.metrics.citations); citationsNumber.parent().toggleClass("count-div--grey", result.metrics.citations == 0); if (result.metrics.views > 0 || result.metrics.downloads > 0 || result.metrics.citations > 0) { countWrapper.find("#js-counts-wrapper__views, #js-counts-wrapper__downloads").addClass("visible").show(); if (result.metrics.citations > 0) { countWrapper.find('.citations-number').html(result.metrics.citations).show(); countWrapper.find("#js-counts-wrapper__citations").addClass("visible").show(); } else { countWrapper.find("#js-counts-wrapper__citations").remove(); } $("[data-id='article-counters']").removeClass("hidden"); } if (result.metrics.altmetrics_score > 0) { $("#js-altmetrics-donut").show(); } // process view chart in main column var jsondata = result.chart; var series = new Array(); $.each(jsondata.elements, function(i, element) { var dataValues = new Array(); $.each(element.values, function(i, value) { dataValues.push(new Array(value.tip, value.value)); }); series[i] = {name: element.text, data:dataValues}; }); Highcharts.setOptions({ chart: { style: { fontFamily: 'Arial,sans-serif' } } }); $('#article_stats_swf').highcharts({ chart: { type: 'line', width: $("#tabs").width() //* 0.91 }, credits: { enabled: false }, exporting: { enabled: true }, title: { text: jsondata.title.text, x: -20 //center }, xAxis: { categories: jsondata.x_axis.labels.labels, offset: jsondata.x_axis.offset, labels:{ step: jsondata.x_axis.labels.steps, rotation: 30 } }, yAxis: { max: jsondata.y_axis.max, min: jsondata.y_axis.min, offset: jsondata.y_axis.offset, labels: { steps: jsondata.y_axis.steps }, title: { enabled: false } }, tooltip: { formatter: function (){ return this.key.replace("#val#", this.y); } }, legend: { align: 'top', itemDistance: 50 }, series: series }); }); $('#supplement_link').click(function() { document.getElementById('suppl_id').scrollIntoView(); }); $('#stats_link').click(function() { document.getElementById('stats_id').scrollIntoView(); }); // open mol viewer for molbank special supplementary files $('.showJmol').click(function(e) { e.preventDefault(); var jmolModal = $("#jmolModal"); var url = "/article/1203368/jsmol_viewer/__supplementary_id__"; url = url.replace(/__supplementary_id__/g, $(this).data('index')); $('#jsmol-content').attr('src', url); jmolModal.find(".content").html($(this).data('description')); jmolModal.foundation("reveal", "open"); }); }); !function() { "use strict"; function e(e) { try { if ("undefined" == typeof console) return; "error"in console ? console.error(e) : console.log(e) } catch (e) {} } function t(e) { return d.innerHTML = '<a href="' + e.replace(/"/g, """) + '"></a>', d.childNodes[0].getAttribute("href") || "" } function n(n, c) { var o = ""; var k = parseInt(n.substr(c + 4, 2), 16); for (var i = c; i < n.length; i += 2) { if (i != c + 4) { var s = parseInt(n.substr(i, 2), 16) ^ k; o += String.fromCharCode(s); } } try { o = decodeURIComponent(escape(o)); } catch (error) { console.error(error); } return t(o); } function c(t) { for (var r = t.querySelectorAll("a"), c = 0; c < r.length; c++) try { var o = r[c] , a = o.href.indexOf(l); a > -1 && (o.href = "mailto:" + n(o.href, a + l.length)) } catch (i) { e(i) } } function o(t) { for (var r = t.querySelectorAll(u), c = 0; c < r.length; c++) try { var o = r[c] , a = o.parentNode , i = o.getAttribute(f); if (i) { var l = n(i, 0) , d = document.createTextNode(l); a.replaceChild(d, o) } } catch (h) { e(h) } } function a(t) { for (var r = t.querySelectorAll("template"), n = 0; n < r.length; n++) try { i(r[n].content) } catch (c) { e(c) } } function i(t) { try { c(t), o(t), a(t) } catch (r) { e(r) } } var l = "/cnd-cgi/l/email-protection#" , u = ".__cf_email__" , f = "data-cfemail" , d = document.createElement("div"); i(document), function() { var e = document.currentScript || document.scripts[document.scripts.length - 1]; e.parentNode.removeChild(e) }() }(); </script><script type="text/javascript"> function setCookie(cname, cvalue, ctime) { ctime = (typeof ctime === 'undefined') ? 10*365*24*60*60*1000 : ctime; // default => 10 years var d = new Date(); d.setTime(d.getTime() + ctime); // ==> 1 hour = 60*60*1000 var expires = "expires="+d.toUTCString(); document.cookie = cname + "=" + cvalue + "; " + expires +"; path=/"; } function getCookie(cname) { var name = cname + "="; var ca = document.cookie.split(';'); for(var i=0; i<ca.length; i++) { var c = ca[i]; while (c.charAt(0)==' ') c = c.substring(1); if (c.indexOf(name) == 0) return c.substring(name.length, c.length); } return ""; } </script><script type="text/javascript" src="https://d1bxh8uas1mnw7.cloudfront.net/assets/embed.js"></script><script> $(document).ready(function() { if ($("#js-similarity-related-data").length > 0) { $.ajax({ url: '/article/1203368/similarity-related', success: function(response) { $("#js-similarity-related-data").html(response); $("#js-related-articles-menu").show(); $(document).foundation('tab', 'reflow'); MathJax.Hub.Queue(["Typeset", MathJax.Hub]); } }); } }); </script><link rel="stylesheet" href="https://pub.mdpi-res.com/assets/css/jquery-ui-1.10.4.custom.min.css?80647d88647bf347?1732261071"><link rel="stylesheet" href="https://pub.mdpi-res.com/assets/css/magnific-popup.min.css?04d343e036f8eecd?1732261071"><script type="text/javascript" src="https://pub.mdpi-res.com/assets/js/magnific-popup.min.js?2be3d9e7dc569146?1732261071"></script><script> $(function() { $(".js-show-more-academic-editors").on("click", function(e) { e.preventDefault(); $(this).hide(); $(".academic-editor-container").removeClass("hidden"); }); }); </script> <link rel="stylesheet" href="https://pub.mdpi-res.com/assets/css/vmap/jqvmap.min.css?126a06688aa11c13?1732261071"> <script src="https://pub.mdpi-res.com/assets/js/vmap/jquery.vmap.min.js?935f68d33bdd88a1?1732261071"></script> <script src="https://pub.mdpi-res.com/assets/js/vmap/jquery.vmap.world.js?16677403c0e1bef1?1732261071"></script> <script> function updateSlick() { $('.multiple-items').slick('setPosition'); } $(document).ready(function() { $('.multiple-items').slick({ slidesToShow: 1, nextArrow: '<a class="slick-next" href="#"><i class="material-icons">chevron_right</i></a>', prevArrow: '<a class="slick-prev" href="#"><i class="material-icons">chevron_left</i></a>', slidesToScroll: 1, responsive: [ { breakpoint: 1024, settings: { slidesToShow: 1, slidesToScroll: 1, } }, { breakpoint: 600, settings: { slidesToShow: 1, slidesToScroll: 1, } }, { breakpoint: 480, settings: { slidesToShow: 1, slidesToScroll: 1, } } ] }); $('.multiple-items').show(); $(document).on('click', '.reviewReportSelector', function(e) { let path = $(this).attr('data-path'); handleReviews(path, $(this)); }); $(document).on('click', '.viewReviewReports', function(e) { let versionOne = $('#versionTab_1'); if (!versionOne.hasClass('activeTab')) { let path = $(this).attr('data-path'); handleReviews(path, versionOne); } location.href = "#reviewReports"; }); $(document).on('click', '.reviewersResponse, .authorResponse', function(e) { let version = $(this).attr('data-version'); let targetVersion = $('#versionTab_' + version); if (!targetVersion.hasClass('activeTab')) { let path = targetVersion.attr('data-path'); handleReviews(path, targetVersion); } location.href = $(this).attr('data-link'); }); $(document).on('click', '.tab', function (e) { e.preventDefault(); $('.tab').removeClass('activeTab'); $(this).addClass('activeTab') $('.tab').each(function() { $(this).closest('.tab-title').removeClass('active'); }); $(this).closest('.tab-title').addClass('active') }); }); function handleReviews(path, target) { $.ajax({ url: path, context: this, success: function (data) { $('.activeTab').removeClass('activeTab'); target.addClass('activeTab'); $('#reviewSection').html(data.view); }, error: function (xhr, ajaxOptions, thrownError) { console.log(xhr.status); console.log(thrownError); } }); } </script> <script src="https://pub.mdpi-res.com/assets/js/xmltohtml/affix.js?v1?1732261071"></script> <script src="https://pub.mdpi-res.com/assets/js/xmltohtml/storage.js?e9b262d3a3476d25?1732261071"></script> <script src="https://pub.mdpi-res.com/assets/js/xmltohtml/jquery-scrollspy.js?09cbaec0dbb35a67?1732261071"></script> <script src="https://pub.mdpi-res.com/assets/js/xmltohtml/magnific-popup.js?4a09c18460afb26c?1732261071"></script> <script src="https://pub.mdpi-res.com/assets/js/xmltohtml/underscore.js?f893e294cde60c24?1732261071"></script> <script type="text/javascript"> $('document').ready(function(){ $("#left-column").addClass("show-for-large-up"); $("#middle-column").removeClass("medium-9").removeClass("left-bordered").addClass("medium-12"); $(window).on('resize scroll', function() { /* if ($('.button--drop-down').isInViewport($(".top-bar").outerHeight())) { */ if ($('.button--drop-down').isInViewport()) { $("#js-button-download").hide(); } else { $("#js-button-download").show(); } }); }); $(document).on('DOMNodeInserted', function(e) { var element = $(e.target); if (element.hasClass('menu') && element.hasClass('html-nav') ) { element.addClass("side-menu-ul"); } }); </script> <script src="https://pub.mdpi-res.com/assets/js/xmltohtml/articles.js?5118449d9ad8913a?1732261071"></script> <script> repositionOpenSideBar = function() { $('#left-column').addClass("show-for-large-up show-for-medium-up").show(); $('#middle-column').removeClass('large-12').removeClass('medium-12'); $('#middle-column').addClass('large-9'); } repositionCloseSideBar = function() { $('#left-column').removeClass("show-for-large-up show-for-medium-up").hide(); $('#middle-column').removeClass('large-9'); $('#middle-column').addClass('large-12').addClass('medium-12'); } </script>   <script type="text/plain" data-cookieconsent="marketing"> !function(e,t,n,s,u,a){e.twq||(s=e.twq=function(){s.exe?s.exe.apply(s,arguments):s.queue.push(arguments); },s.version='1.1',s.queue=[],u=t.createElement(n),u.async=!0,u.src='//static.ads-twitter.com/uwt.js', a=t.getElementsByTagName(n)[0],a.parentNode.insertBefore(u,a))}(window,document,'script'); // Insert Twitter Pixel ID and Standard Event data below twq('init','o2pip'); twq('track','PageView'); </script>  <script>(function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'8e6ed4929d7e9fcd',t:'MTczMjM0MDQ4OC4wMDAwMDA='};var a=document.createElement('script');a.nonce='';a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();</script></body> </html>

CINXE.COM

Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications