Prediction of Amazon’s Stock Price Based on ARIMA, XGBoost, and LSTM Models | Proceedings of Business and Economic Studies

<!DOCTYPE html> <html lang="en-US" xml:lang="en-US"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Prediction of Amazon’s Stock Price Based on ARIMA, XGBoost, and LSTM Models | Proceedings of Business and Economic Studies </title> <meta name="generator" content="Open Journal Systems 3.1.2.0"> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="Proceedings of Business and Economic Studies"/> <meta name="citation_journal_abbrev" content="1"/> <meta name="citation_issn" content="2209-265X"/> <meta name="citation_author" content="Zhe Zhu"/> <meta name="citation_author_institution" content="College of Business and Public Management, Wenzhou-Kean University, Wenzhou 325060, Zhejiang Province, China"/> <meta name="citation_author" content="Kexin He"/> <meta name="citation_author_institution" content="College of Faculty of Liberal Arts, Wenzhou-Kean University, Wenzhou 325060, Zhejiang Province, China"/> <meta name="citation_title" content="Prediction of Amazon’s Stock Price Based on ARIMA, XGBoost, and LSTM Models"/> <meta name="citation_date" content="2022/10/21"/> <meta name="citation_volume" content="5"/> <meta name="citation_issue" content="5"/> <meta name="citation_firstpage" content="127"/> <meta name="citation_lastpage" content="136"/> <meta name="citation_doi" content="10.26689/pbes.v5i5.4432"/> <meta name="citation_abstract_html_url" content="https://ojs.bbwpublisher.com/index.php/PBES/article/view/4432"/> <meta name="citation_language" content="en"/> <meta name="citation_keywords" xml:lang="en" content="Amazon"/> <meta name="citation_keywords" xml:lang="en" content="ARIMA"/> <meta name="citation_keywords" xml:lang="en" content="XGBoost"/> <meta name="citation_keywords" xml:lang="en" content="LSTM"/> <meta name="citation_pdf_url" content="https://ojs.bbwpublisher.com/index.php/PBES/article/download/4432/3864"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Zhe Zhu"/> <meta name="DC.Creator.PersonalName" content="Kexin He"/> <meta name="DC.Date.created" scheme="ISO8601" content="2022-10-21"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2022-10-13"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2022-10-31"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2022-10-21"/> <meta name="DC.Description" xml:lang="en" content="Finding the best model to predict the trend of stock prices is an issue that has always garnered attention, and it is also closely related to investors’ investment dynamics. Even the commonly used autoregressive integrated moving average (ARIMA), extreme gradient boosting (XGBoost), and long short-term memory (LSTM) have their own advantages and disadvantages. We use mean squared error (MSE) to judge the most suitable model for predicting Amazon’s stock price from many aspects and find that LSTM is the model with the best fitting effect and the closest to the real curve. However, the LSTM model still needs to improve in terms of performance so as to reduce the bias. We anticipate the discovery of more models that are apt for predicting stocks in the future."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="4432"/> <meta name="DC.Identifier.pageNumber" content="127-136"/> <meta name="DC.Identifier.DOI" content="10.26689/pbes.v5i5.4432"/> <meta name="DC.Identifier.URI" content="https://ojs.bbwpublisher.com/index.php/PBES/article/view/4432"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2022 Author(s)"/> <meta name="DC.Rights" content=""/> <meta name="DC.Source" content="Proceedings of Business and Economic Studies"/> <meta name="DC.Source.ISSN" content="2209-265X"/> <meta name="DC.Source.Issue" content="5"/> <meta name="DC.Source.Volume" content="5"/> <meta name="DC.Source.URI" content="https://ojs.bbwpublisher.com/index.php/PBES"/> <meta name="DC.Subject" xml:lang="en" content="Amazon"/> <meta name="DC.Subject" xml:lang="en" 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Business and Economic Studies </a> </li> <li class="breadcrumb-item active" aria-current="page"> Articles </li> </ol> </nav> </div> <div class="container page-article"> <div class="article-details"> <div class="page-header row"> <div class="col-lg article-meta-mobile"> <div class="article-details-issue-section large-screen badge badge-info">Articles</div> <h1 class="article-details-fulltitle" > Prediction of Amazon’s Stock Price Based on ARIMA, XGBoost, and LSTM Models </h1> <div class="authors-string"> <span>Zhe Zhu</span>, <span>Kexin He</span> </div> </div> </div> <div class="row justify-content-md-center" id="mainArticleContent"> <div class="col-lg-3 order-lg-2" id="articleDetailsWrapper"> <div class="article-details-sidebar" id="articleDetails"> <div class="article-details-block article-details-cover"> <a href="https://ojs.bbwpublisher.com/index.php/PBES/issue/view/351"> <img class="img-fluid" src="https://ojs.bbwpublisher.com/public/journals/8/cover_issue_351_en_US.jpg"> </a> </div> <div class="article-details-block article-details-galleys article-details-galleys-sidebar"> <div class="article-details-galley"> <a class="btn btn-primary" style="margin-top:5px;width:120px;" href="https://ojs.bbwpublisher.com/index.php/PBES/article/view/4432/3864"> Download PDF </a> </div> </div> <form action="https://ojs.bbwpublisher.com/createxml.php" method="post" enctype="multipart/form-data"> <input type="hidden" name="journal" value="Proceedings of Business and Economic Studies" /> <input type="hidden" name="title" value="Prediction of Amazon’s Stock Price Based on ARIMA, XGBoost, and LSTM Models" /> <input type="hidden" name="doi" value="10.26689/pbes.v5i5.4432" /> <input type="hidden" name="aaa" value="Kexin He" /> <input type="hidden" name="ccc" value="Vol 5 No 5 (2022): Proceedings of Business and Economic Studies" /> <div class="weburl" style="display:none" name="weburl" > <input type="hidden" name="weburl" value="https://ojs.bbwpublisher.com/index.php/PBES/article/view?path="> </div> <input type="hidden" name="published" value="2022-10-21" > <input type="hidden" name="auto" value="Zhe Zhu, Kexin He" /> <input type="hidden" name="Keywords" value="Array " /> <div class="keyword" style="display:none" name="keyword"> <input type="hidden" style="display:none" name="keywords" value="Amazon,ARIMA,XGBoost,LSTM" > </div> <div style="display:none" > $currentUrl="http://$_SERVER[HTTP_HOST]$_SERVER[REQUEST_URI]" <input type="hidden" style="display:none" name="urldom" value="https://ojs.bbwpublisher.com/index.php/PBES/article/view/4432" > </div> <input type="hidden" name="subject" value="Articles" /> <input type="hidden" name="Abstract" value="<p>Finding the best model to predict the trend of stock prices is an issue that has always garnered attention, and it is also closely related to investors’ investment dynamics. Even the commonly used autoregressive integrated moving average (ARIMA), extreme gradient boosting (XGBoost), and long short-term memory (LSTM) have their own advantages and disadvantages. We use mean squared error (MSE) to judge the most suitable model for predicting Amazon’s stock price from many aspects and find that LSTM is the model with the best fitting effect and the closest to the real curve. However, the LSTM model still needs to improve in terms of performance so as to reduce the bias. We anticipate the discovery of more models that are apt for predicting stocks in the future.</p>" /> <input type="hidden" name="References" value="[1] Asness CS, Frazzini A, Pedersen LH, 2012, Leverage Aversion and Risk Parity. Financial Analysts Journal, 68(1): 47–59. <br /> [2] Qin J, Tao Z, Huang S, et al., 2021, Stock Price Forecast Based on ARIMA Model and BP Neural Network Model. 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), IEEE, 426–430.<br /> [3] Ho SL, Xie M, 1998, The Use of ARIMA Models for Reliability Forecasting and Analysis. Computers & Industrial Engineering, 35(1–2): 213–216.<br /> [4] Benvenuto D, Giovanetti M, Vassallo L, et al., 2020, Application of the ARIMA Model on the COVID-2019 Epidemic Dataset. Data in Brief, 29: 105340.<br /> [5] Zhang GP, 2003, Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing, 50: 159–175.<br /> [6] Contreras J, Espinola R, Nogales FJ, et al., 2003, ARIMA Models to Predict Next-Day Electricity Prices. IEEE Transactions on Power Systems, 18(3): 1014–1020.<br /> [7] Ariyo AA, Adewumi AO, Ayo CK, 2014, Stock Price Prediction Using the ARIMA Model. 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, IEEE, 106–112.<br /> [8] Smagulova K, James AP, 2019, A Survey on LSTM Memristive Neural Network Architectures and Applications. The European Physical Journal Special Topics, 228(10): 2313–2324.<br /> [9] Gers FA, Schraudolph NN, Schmidhuber J, 2002, Learning Precise Timing with LSTM Recurrent Networks. Journal of Machine Learning Research, 3: 115–143.<br /> [10] Graves A, Fernandez S, Schmidhuber J, 2005, Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition. International Conference on Artificial Neural Networks, Springer, 799–804.<br /> [11] Chen K, Zhou Y, Dai F, 2015, A LSTM-Based Method for Stock Returns Prediction: A Case Study of China Stock Market. 2015 IEEE International Conference on Big Data (Big Data), IEEE, 2823–2824. <br /> [12] Fu R, Zhang Z, Li L, 2016, Using LSTM and GRU Neural Network Methods for Traffic Flow Prediction. 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), IEEE, 324–328.<br /> [13] Liao J, Zhang R, 2018, Dynamic Weighting Multi Factor Stock Selection Strategy Based on XGBoost Machine Learning Algorithm. 2018 IEEE International Conference of Safety Produce Informatization (IICSPI), IEEE, 868–872. <br /> [14] Basak S, Kar S, Saha S, et al., 2019, Predicting the Direction of Stock Market Prices Using Tree-Based Classifiers. The North American Journal of Economics and Finance, 47: 552–567.<br /> [15] Kumar DS, Thiruvarangan BC, Vishnu A, et al., 2022, Analysis and Prediction of Stock Price Using Hybridization of SARIMA and XGBoost. 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT), IEEE, 1–4. <br /> [16] Gumelar AB, Setyorini H, Adi DP, et al., 2020, Boosting the Accuracy of Stock Market Prediction using XGBoost and Long Short-Tergumem Memory. 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), IEEE, 609–613. <br /> [17] Cao J, Li Z, Li J, 2019, Financial Time Series Forecasting Model Based on CEEMDAN and LSTM. Physica A: Statistical Mechanics and Its Applications, 519: 127–139.<br /> [18] Jiang H, He Z, Ye G, et al., 2020, Network Intrusion Detection Based on PSO-XGBoost Model. IEEE Access, 8: 58392–58401.<br /> [19] Ma X, Sha J, Wang D, et al., 2018, Study on a Prediction of P2P Network Loan Default Based on the Machine Learning LightGBM and XGBoost Algorithms According to Different High Dimensional Data Cleaning. Electron Commerce Res Appl, 31: 24–39." /> <input type="hidden" name="Online" value="2209-265X" /> <input type="hidden" name="Print" value="2209-2641" /> <input class="btn btn-primary" type="submit" value="xml" style="border:none;width:120px;height:34px;background-color:#337ab7;border-color:#2e6da4;color:#fff;margin-top:5px;" /> </form> <div class="article-details-block article-details-keywords"> <h2 class="article-details-heading"> Keywords </h2> <div class="article-details-keywords-value"> <span>Amazon</span><br> <span>ARIMA</span><br> <span>XGBoost</span><br> <span>LSTM</span> </div> </div> <div class="article-details-block article-details-doi"> <h2 class="article-details-heading"> DOI </h2> <p><a href="https://doi.org/10.26689/pbes.v5i5.4432">10.26689/pbes.v5i5.4432</a></p> </div> <h5 class="published">Submitted : 2022-09-21</h5> <h5 class="published">Accepted : 2022-10-06</h5> <h5 class="published">Published : 2022-10-21</h5> </div> </div> <div class="col-lg-9 order-lg-1" id="articleMainWrapper"> <div class="article-details-main" id="articleMain"> <div class="article-details-block article-details-abstract"> <h2 class="article-details-heading">Abstract</h2> <p>Finding the best model to predict the trend of stock prices is an issue that has always garnered attention, and it is also closely related to investors’ investment dynamics. Even the commonly used autoregressive integrated moving average (ARIMA), extreme gradient boosting (XGBoost), and long short-term memory (LSTM) have their own advantages and disadvantages. We use mean squared error (MSE) to judge the most suitable model for predicting Amazon’s stock price from many aspects and find that LSTM is the model with the best fitting effect and the closest to the real curve. However, the LSTM model still needs to improve in terms of performance so as to reduce the bias. We anticipate the discovery of more models that are apt for predicting stocks in the future.</p> </div> <div class="article-details-block article-details-references"> <h2 class="article-details-heading"> References </h2> <div class="article-details-references-value"> <p>Asness CS, Frazzini A, Pedersen LH, 2012, Leverage Aversion and Risk Parity. Financial Analysts Journal, 68(1): 47–59.</p> <p>Qin J, Tao Z, Huang S, et al., 2021, Stock Price Forecast Based on ARIMA Model and BP Neural Network Model. 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), IEEE, 426–430.</p> <p>Ho SL, Xie M, 1998, The Use of ARIMA Models for Reliability Forecasting and Analysis. Computers & Industrial Engineering, 35(1–2): 213–216.</p> <p>Benvenuto D, Giovanetti M, Vassallo L, et al., 2020, Application of the ARIMA Model on the COVID-2019 Epidemic Dataset. Data in Brief, 29: 105340.</p> <p>Zhang GP, 2003, Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing, 50: 159–175.</p> <p>Contreras J, Espinola R, Nogales FJ, et al., 2003, ARIMA Models to Predict Next-Day Electricity Prices. IEEE Transactions on Power Systems, 18(3): 1014–1020.</p> <p>Ariyo AA, Adewumi AO, Ayo CK, 2014, Stock Price Prediction Using the ARIMA Model. 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, IEEE, 106–112.</p> <p>Smagulova K, James AP, 2019, A Survey on LSTM Memristive Neural Network Architectures and Applications. The European Physical Journal Special Topics, 228(10): 2313–2324.</p> <p>Gers FA, Schraudolph NN, Schmidhuber J, 2002, Learning Precise Timing with LSTM Recurrent Networks. Journal of Machine Learning Research, 3: 115–143.</p> <p>Graves A, Fernandez S, Schmidhuber J, 2005, Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition. International Conference on Artificial Neural Networks, Springer, 799–804.</p> <p>Chen K, Zhou Y, Dai F, 2015, A LSTM-Based Method for Stock Returns Prediction: A Case Study of China Stock Market. 2015 IEEE International Conference on Big Data (Big Data), IEEE, 2823–2824.</p> <p>Fu R, Zhang Z, Li L, 2016, Using LSTM and GRU Neural Network Methods for Traffic Flow Prediction. 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), IEEE, 324–328.</p> <p>Liao J, Zhang R, 2018, Dynamic Weighting Multi Factor Stock Selection Strategy Based on XGBoost Machine Learning Algorithm. 2018 IEEE International Conference of Safety Produce Informatization (IICSPI), IEEE, 868–872.</p> <p>Basak S, Kar S, Saha S, et al., 2019, Predicting the Direction of Stock Market Prices Using Tree-Based Classifiers. The North American Journal of Economics and Finance, 47: 552–567.</p> <p>Kumar DS, Thiruvarangan BC, Vishnu A, et al., 2022, Analysis and Prediction of Stock Price Using Hybridization of SARIMA and XGBoost. 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT), IEEE, 1–4.</p> <p>Gumelar AB, Setyorini H, Adi DP, et al., 2020, Boosting the Accuracy of Stock Market Prediction using XGBoost and Long Short-Tergumem Memory. 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), IEEE, 609–613.</p> <p>Cao J, Li Z, Li J, 2019, Financial Time Series Forecasting Model Based on CEEMDAN and LSTM. Physica A: Statistical Mechanics and Its Applications, 519: 127–139.</p> <p>Jiang H, He Z, Ye G, et al., 2020, Network Intrusion Detection Based on PSO-XGBoost Model. IEEE Access, 8: 58392–58401.</p> <p>Ma X, Sha J, Wang D, et al., 2018, Study on a Prediction of P2P Network Loan Default Based on the Machine Learning LightGBM and XGBoost Algorithms According to Different High Dimensional Data Cleaning. Electron Commerce Res Appl, 31: 24–39.</p> </div> </div> </div> </div> <div class="col-lg-12 order-lg-3 article-footer-hook"> </div> </div> </div> </div> <footer class="site-footer container"> <div class="row"> <div class="col-md-6"> <a href="/"> <img class="footer-brand-image" alt="BBW Logo" src="https://ojs.bbwpublisher.com/plugins/themes/default/templates/images/logo.png"> </a> <div class="journal-desc"> </div> </div> <div class="col-md-6"> <h3>Proceedings of Business and Economic Studies</h3> <p> ISSN: 2209-265X </p> <h4>Publishing Office:</h4> <p>Level 10, 50 Clarence Street Sydney, NSW 2000 Australia.</p> <h4>Editorial Office:</h4>  <p>34-2B, Jalan Puteri 1/2, Bandar Puteri, 47100 Puchong, Selangor, Malaysia.</p> <h4>Website:</h4> <p><a href="https://www.bbwpublisher.com">www.bbwpublisher.com</a></p> <h4>Email:</h4> <p><a href="mailto:se.gao@bbwpublisher.com">se.gao@bbwpublisher.com</a></p> </div> </div> <hr> <div class="text-center"> Copyright © 2024 Bio-Byword Scientific Publishing Pty. 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Prediction of Amazon’s Stock Price Based on ARIMA, XGBoost, and LSTM Models | Proceedings of Business and Economic Studies