A Comparative Study of Machine Learning and Neural Network Models in Short-term Market Prediction

A Comparative Study of Machine Learning and Neural Network Models in Short-term Market Prediction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Comparative Study of Machine Learning and Neural Network Models in Short-term Market Prediction Fayez Abu-Ajamieh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4613466/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The prediction of the stock market and the prices of other commodities like crude oil, 1 constitutes a challenging task. Recently, the rapid progress in the field of Machine Leaning (ML), 2 led to an increased interest in applying ML techniques to market price predictions. In this study, 3 we conduct a comprehensive comparative analysis of the performance of 15 different ML models in 4 predicting the close prices of crude oil futures. These models include an Auto-Regressive Integrated 5 Moving Average (ARIMA) model, the Meta Prophet Library, a simple Recurrent Neural Network 6 (RNN), a Long-Short Term Memory (LSTM), a Gated Recurrent Unit (GRU), a Bi-directional LSTM 7 (BLSTM), a Bi-directional GRU (BGRU) and a number of hybrid models, including an LSTM-BGRU 8 and a BLSTM-BGRU. In addition, we evaluate the effectiveness of using Denoising Autoencoders 9 (DAE) in enhancing the performance of the networks by evaluating hybrid models with DAE layers, 10 including DAE-LSTM, DAE-BLSTM, DAE-GRU, DAE-BGRU, DAE-LSTM-GRU and DAE-BLSTM- 11 BGRU. In our analysis, we focus on short-term predictions dedicated for day trading. We compare 12 the performance of these models using the Root Mean Square Error (RMSE), Mean Absolute Error 13 (MAE), Mean Absolute Error Percentage (MAPE) and the R2. We find that the BGRU model yields the 14 best performance, with the GRU model not far behind. We also find that hybrid models containing 15 BGRUs or GRUs tend to perform better than other hybrid models. We find mixed evidence vis-a-vis 16 the effectiveness of DAEs in improving the performance of networks, where in some models the 17 performance improves, whereas for others the performance deteriorates. We find that the performance 18 of all models deteriorate when predicting sharp and sudden changes in prices. The key takeaway of 19 this study is that BGRUs, and to a lesser extent GRUs, seem to be the best route to follow in applying 20 AI/Ml to the task of market prediction. Market prediction time series analysis ARIMA RNN LSTM GRU BLSTM BGRU DAE. Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4613466","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317009570,"identity":"582d5925-388b-4d3f-bf0c-4d0b801afe66","order_by":0,"name":"Fayez Abu-Ajamieh","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Fayez","middleName":"","lastName":"Abu-Ajamieh","suffix":""}],"badges":[],"createdAt":"2024-06-20 18:32:17","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-4613466/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4613466/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58831886,"identity":"a9a6e11a-dae3-4520-904e-8e9de5c0eeea","added_by":"auto","created_at":"2024-06-21 17:44:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2333188,"visible":true,"origin":"","legend":"","description":"","filename":"AComparativeStudy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4613466/v1_covered_62c6c6dd-f4f8-4927-872b-32ee68bc1b16.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Comparative Study of Machine Learning and Neural Network\u003c/p\u003e\n\u003cp\u003eModels in Short-term Market Prediction\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"indian insitute for sicnece","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Market prediction, time series analysis, ARIMA, RNN, LSTM, GRU, BLSTM, BGRU, DAE.","lastPublishedDoi":"10.21203/rs.3.rs-4613466/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4613466/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe prediction of the stock market and the prices of other commodities like crude oil, 1 constitutes a challenging task. Recently, the rapid progress in the field of Machine Leaning (ML), 2 led to an increased interest in applying ML techniques to market price predictions. In this study, 3 we conduct a comprehensive comparative analysis of the performance of 15 different ML models in 4 predicting the close prices of crude oil futures. These models include an Auto-Regressive Integrated 5 Moving Average (ARIMA) model, the Meta Prophet Library, a simple Recurrent Neural Network 6 (RNN), a Long-Short Term Memory (LSTM), a Gated Recurrent Unit (GRU), a Bi-directional LSTM 7 (BLSTM), a Bi-directional GRU (BGRU) and a number of hybrid models, including an LSTM-BGRU 8 and a BLSTM-BGRU. In addition, we evaluate the effectiveness of using Denoising Autoencoders 9 (DAE) in enhancing the performance of the networks by evaluating hybrid models with DAE layers, 10 including DAE-LSTM, DAE-BLSTM, DAE-GRU, DAE-BGRU, DAE-LSTM-GRU and DAE-BLSTM- 11 BGRU. 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