The Research on End-to-end Stock Recommendation Algorithm Based on Time-frequency Consistency-Advances in Computer and Communication-Hill Publishing Group

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<li><a href="/ArticleDetails/4023" target="_blank">Research on a Low-code Data Analysis and Modeling Platform for Public Safety</a></li> <li><a href="/ArticleDetails/3972" target="_blank">The Research on End-to-end Stock Recommendation Algorithm Based on Time-frequency Consistency</a></li> <li><a href="/ArticleDetails/3971" target="_blank">Research on High-speed Digital Circuit Signal Transmission and Noise Suppression</a></li> <li><a href="/ArticleDetails/3954" target="_blank">Industry 4.0: Manufacturing Informatics-driven Innovation</a></li> <li><a href="/ArticleDetails/3953" target="_blank">ChatGPT Class Generative Artificial Intelligence Criminal Subject Problem Thinking</a></li> <li><a href="/ArticleDetails/3952" target="_blank">Research on Computer Security Protection Mechanism in Marine Communication Network</a></li> </ul> </div> </div>  <div class="wzxqbox"> <div class="type_doi"><i>Article</i> <span>http://dx.doi.org/10.26855/acc.2024.10.008</span></div> <h1 class="art_title">The Research on End-to-end Stock Recommendation Algorithm Based on Time-frequency Consistency</h1> <div class="art-authors"> <p style="padding-bottom: 15px; margin-bottom: 0px; line-height: 22px; outline: none;"><font color="#000000" face="Poppins"><span style="font-size: 14px;">Yunxiang Gan</span><sup><span style="font-size: 14px;">1</span></sup><span style="font-size: 14px;">, Xiaoyang Chen</span><sup><span style="font-size: 14px;">2,*</span></sup></font></p><p style="padding-bottom: 15px; margin-bottom: 0px; line-height: 22px; outline: none;"><font color="#000000" face="Poppins"><sup><span style="font-size: 14px;">1</span></sup><span style="font-size: 14px;">Moloco, Inc., Redwood City, CA 94063, USA.</span></font></p><p style="padding-bottom: 15px; margin-bottom: 0px; line-height: 22px; outline: none;"><font color="#000000" face="Poppins"><sup><span style="font-size: 14px;">2</span></sup><span style="font-size: 14px;">The Ohio State University, Columbus, OH 43210, USA.</span></font></p><p style="padding-bottom: 15px; margin-bottom: 0px; line-height: 22px; outline: none;"><strong style="color: rgb(0, 0, 0); font-family: Arial, sans-serif, "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei" !important; font-size: 16px;"><span style="padding-left: 4px; outline: none; font-family: Poppins; font-size: 14px;">*Corresponding author: </span></strong><font color="#000000" face="Poppins"><span style="font-size: 14px;">Xiaoyang Chen</span></font></p> </div> <div class="art-affiliations"> </div> <div class="art-pubhistory"> Published: November 14,2024 </div> <ul class="wzzhbox1"> <li id="left" class="preAfter" style="display:block;"> <a href="/ArticleDetails/3952"> <span>Previous article in journal</span> </a> </li> <li id="right" class="next" style="display:block;"> <a href="/ArticleDetails/3971"> <span>Next article in journal</span> </a> </li> </ul> <div class="abt"> <h2>Abstract</h2> <p><p>In the financial market, the volatility and complexity of stock prices make accurately predicting stock trends a highly challenging task. Traditional stock prediction methods often rely on either time-domain or frequency-domain information alone, which fails to fully capture the multi-scale dynamic characteristics of stock prices, leading to insufficient prediction accuracy. To address the shortcomings of existing stock recommendation algorithms, this paper proposes an end-to-end stock recommendation algorithm based on time-frequency consistency. Firstly, we introduce a time-frequency consistency analysis method, which can simultaneously extract both time-domain and frequency-domain features of stock prices, thus providing a more comprehensive characterization of stock trend changes. Secondly, by integrating prompt learning strategies, the model is guided by pre-designed prompts to identify the lowest-risk buying points within specific time frames, optimizing the stock recommendation decision-making process. Finally, the end-to-end model training ensures seamless integration and automation throughout the entire prediction process, achieving a complete workflow from data input to stock recommendation. Experimental results demonstrate that this method outperforms traditional approaches in terms of prediction accuracy and risk control, offering more reliable decision support for investors.</p></p> </div> <div class="re" > <h2>References</h2> <p>[1]<span style="white-space:pre"> </span>Zhao C, et al. Progress and prospects of data-driven stock price forecasting research. International Journal of Cognitive Computing in Engineering. 2023 June;4:100-108.</p><p>[2]<span style="white-space:pre"> </span>C. L. Yingda Tang. Examining the Factors of Corporate Frauds in Chinese A-share Listed Enterprises. OAJRC Social Science. 2023;4(3):63-77.</p><p>[3]<span style="white-space:pre"> </span>Sheth, D, & Shah, M. 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OPSEARCH. 2023;60(1):59-86.</p> </div> <div class="ftex" id="fu"> <h2>How to cite this paper</h2> <p style="padding-bottom: 15px; margin-bottom: 0px; line-height: 22px; font-size: 16px; outline: none; font-family: Arial, sans-serif, "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei"; color: rgb(0, 0, 0); font-weight: bold;"><span style="outline: none; font-family: Poppins; font-size: 14px;">The Research on End-to-end Stock Recommendation Algorithm Based on Time-frequency Consistency</span></p><p style="padding-bottom: 15px; margin-bottom: 0px; line-height: 22px; font-family: Arial, Helvetica, sans-serif, 微软雅黑; font-size: 16px; outline: none; color: rgb(0, 0, 0);"><b style="font-family: Arial, sans-serif, "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei";"><span style="outline: none; font-family: Poppins; font-size: 14px;">How to cite this paper: </span></b><span style="font-family: Poppins; outline: none; font-size: 14px;">Yunxiang Gan, Xiaoyang Chen. (2024) The Research on End-to-end Stock Recommendation Algorithm Based on Time-frequency Consistency. </span><i style="font-family: Arial, sans-serif, "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei";"><span style="outline: none; font-family: Poppins; font-size: 14px;">Advances in Computer and Communication</span></i><span style="outline: none; font-size: 14px;"><span style="outline: none; font-family: Arial, sans-serif, "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei" !important; font-size: 14px;">, </span><span style="outline: none; font-size: 14px;"><font face="Poppins"><b>5</b></font></span></span><span style="font-family: Poppins; outline: none; font-size: 14px;"><span style="outline: none; font-family: Arial, sans-serif, "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei" !important;"><span style="font-size: 14px;">(</span><b><span style="font-size: 14px;">4</span></b></span></span><span style="font-family: Poppins; outline: none; font-size: 14px;">), 243-259.</span></p><p style="padding-bottom: 15px; margin-bottom: 0px; line-height: 22px; font-size: 16px; outline: none; font-family: Arial, sans-serif, "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei"; color: rgb(0, 0, 0);"><span style="outline: none; font-family: Poppins; font-size: 14px;">DOI: https://dx.doi.org/10.26855/acc.2024.10.008</span></p> </div> </div>  </div>  </div>  <div class="footer"> <div class="foot_nav"><a href="/Index" title="Home">Home</a>　|　<a href="/Journals" title="Journals">Journals</a>　|　<a href="/Books" title="Books">Books</a>　|　<a href="/ForAuthors" title="For Authors and Reviewers">For Authors and Reviewers</a>　|　<a href="/OnlineSubmission" title="Online Submission">Online Submission</a>　|　<a href="ContactUs" title="Contact Us">Contact Us</a>　|　<a href="/AboutUs" title="About Us">About Us</a></div> <p>Copyright © 2023 Hill Publishing Group Inc. 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