[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120540-en":3,"doc-seo-120540-105":29,"detail-sidebar-cat-0-en-105":81},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":11},120540,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning Applied to Stock Price Forecasting - Summary and Outlook","This dissertation investigates how machine learning models can predict the relative returns of S&P 500 stocks. It reviews feature selection combined with ML methods, identifying random forest, support vector machine, and long short-term memory as frequent high-performing approaches for time-series data. It also evaluates relative-return forecasting during the 2017–2022 COVID-19 volatility window, comparing ML classifiers with random-choice baselines aligned with EMH and RWH. Results show improved accuracy, precision, and recall, and highlight the effectiveness of technical indicators and selected feature sets.","University of Groningen  \nMachine Learning Applied to Stock Price Forecasting  \nHtun, Htet Htet  \nDOI:  \n10.33612/diss.1135448900  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2024  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nHtun, H. H. (2024) . Machine Learning Applied to Stock Price Forecasting. [Thesis fully internal (DIV), University of Groningen] . University of Groningen. [https://doi.org/10.33612/diss.1135448900](https://doi.org/10.33612/diss.1135448900)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 24-12-2025  \nChapter 6  Summary and Outlook  \n6.1 Summary  \nThis dissertation explores the application of machine learning (ML) models for predicting the relative returns of S&P 500 stocks. In the second chapter, we conduct a comprehensive analysis of studies that combined feature selection and ML techniques in the stock market from 2011 to 2022 . Our analysis identifies random forest (RF), support vector machine (SVM) and long short-term memory (LSTM) models as the most frequently used due to their consistent superior performance with time series data. Furthermore, we address significant gaps in the literature, such as the validation of ML models using the sliding window approach to ensure the model is continuously trained on the latest information and tested on future data.  \nThe third chapter is dedicated to predicting the relative returns of S&P 500 stocks during the volatile period from 2017-2022, marked by the COVID-19 pandemic. We applied RF, SVM and LSTM classifiers, trained on historical relative returns, to select stocks that will exceed a 2% relative returns within the next ten trading days. Among these models, LSTM achieved the highest performance, with accuracy values over 0.8 for some stocks. We compared the performance of our ML classifiers with a random choice classifier, which aligns with the conventional hypotheses: the efficient market hypothesis (EMH) and the random walk hypothesis (RWH) . Across the entire S&P 500, our ML classifiers showed higher mean accuracy, precision and recall compared to a random choice strategy, with statistically significant p-values of 8.46e-17 for accuracy, 0.008 for precision and 1.29e-31 for recall. With this evaluation, we demonstrate the advantages of using ML classifiers for stock selection.  \nIn the fourth chapter, we evaluate the performance of using different feature sets. This includes a 260-dimensional feature set, representing all relative returns from the previous year, a more recent 21-dimensional feature set with a sh","cbCaiebZdJY1LnXh","https://ap.wps.com/l/cbCaiebZdJY1LnXh","pdf",326690,1,3,"English","en",105,"# Chapter 6 Summary and Outlook\n## 6.1 Summary\n## 6.2 Outlook","[{\"question\":\"What do the feature-set and technical-indicator experiments conclude?\",\"answer\":\"Different feature sets are evaluated, with a 13-dimensional feature vector delivering strong precision and recall across S\\u0026P 500 stocks. Additional experiments show that combining technical indicators improves predictive performance compared with using previous relative returns alone.\"}]","Machine Learning Applied to Stock Price Forecasting - Summary and Outlook | PDF",1785730567,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":76,"head_meta":78,"extra_data":80,"updated_unix":28},"machine-learning-applied-to-stock-price-forecasting-summary-and-outlook","",{"@graph":35,"@context":75},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":21},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/machine-learning-applied-to-stock-price-forecasting-summary-and-outlook/120540/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69],{"name":70,"@type":71,"acceptedAnswer":72},"What do the feature-set and technical-indicator experiments conclude?","Question",{"text":73,"@type":74},"Different feature sets are evaluated, with a 13-dimensional feature vector delivering strong precision and recall across S&P 500 stocks. 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