Comparing different Machine Learning Algorithms in a stock Market Scenario to check which one has the highest efficiency

Comparing different Machine Learning Algorithms in a stock Market Scenario to check which one has the highest efficiency | 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 Comparing different Machine Learning Algorithms in a stock Market Scenario to check which one has the highest efficiency Jayesh Dave, Sanket Porwal, Utsav Jain, Garima Chandore, Anusha Jain This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4328509/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Predicting stock market movements using machine learning algorithms is a challenging yet crucial task in financial markets. This study evaluates the efficacy of different machine learning algorithms in predicting stock market trends, utilizing historical stock price data alongside technical indicators as input variables, including Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Random Forest. The study extends the prediction horizon to ten and 30 days into the future, aiming to assess the performance of these algorithms over various timeframes. Results indicate that despite the sophistication of the machine learning models, a simple strategy of always predicting a stock price increase outperforms them, aligning with the random walk theory. This finding contributes to the ongoing discussion on the efficacy of predictive algorithms in financial markets. The implications of these results for stock market prediction and the challenges in accurately forecasting stock price movements are discussed. Ultimately, this study offers valuable perspective on the relative effectiveness of machine learning algorithms within the context of the stock market, illuminating the inherent intricacies involved in forecasting fluctuations in stock market. Stock market prediction Support Vector Machines (SVM) Long Short-Term Memory (LSTM) Random Forests (RF) Technical indicators Prediction horizon Random walk theory Efficiency Financial markets Forecasting Stock price movements Comparative analysis Predictive algorithms Research findings Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 19 Jun, 2024 Submission checks completed at journal 18 Jun, 2024 First submitted to journal 26 Apr, 2024 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. 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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-4328509","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":316483499,"identity":"d0fdd93d-2b9c-42e5-8804-f3f0fb31b256","order_by":0,"name":"Jayesh Dave","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3PsQqCQBjA8QvhXA5bP0HqFQwhBINepQh0Od8gghBqymdpchakazFcHQPBoSkQpEm68wH0xqD7g9433I+7Q0il+sVS/gFCBOtRP1rSxDII24iRyBHeagbUFsM4Me754unuM4JJ3rzKvUuQnt2uQ8TMqWMD40S/JB5l/GLE98shYqe+D4DFKY/EoZgTIMthUtTBBzpOgNYO7WRIuWPIPPVEq8KTBDHLKgMzDvjF2FILY+DDyFuMYntsoPXW83NUNbQ9zKZ6xgaJSAMUiRVD/x/bLpq80aGnb5ndKpVK9X99AYrVRwoy5LnTAAAAAElFTkSuQmCC","orcid":"","institution":"Medi-Caps University","correspondingAuthor":true,"prefix":"","firstName":"Jayesh","middleName":"","lastName":"Dave","suffix":""},{"id":316483500,"identity":"acdb6482-aca0-4149-9ae6-037d6b3434b9","order_by":1,"name":"Sanket Porwal","email":"","orcid":"","institution":"Medi-Caps University","correspondingAuthor":false,"prefix":"","firstName":"Sanket","middleName":"","lastName":"Porwal","suffix":""},{"id":316483501,"identity":"bad7357d-d4ac-4d69-86e5-58f55364958f","order_by":2,"name":"Utsav Jain","email":"","orcid":"","institution":"Medi-Caps University","correspondingAuthor":false,"prefix":"","firstName":"Utsav","middleName":"","lastName":"Jain","suffix":""},{"id":316483502,"identity":"963b2d7e-fb1b-4304-8347-04b511dae003","order_by":3,"name":"Garima Chandore","email":"","orcid":"","institution":"Medi-Caps University","correspondingAuthor":false,"prefix":"","firstName":"Garima","middleName":"","lastName":"Chandore","suffix":""},{"id":316483503,"identity":"dfe3425d-0e7a-4539-b66c-dc1dfc2845e2","order_by":4,"name":"Anusha Jain","email":"","orcid":"","institution":"Medi-Caps University","correspondingAuthor":false,"prefix":"","firstName":"Anusha","middleName":"","lastName":"Jain","suffix":""}],"badges":[],"createdAt":"2024-04-26 09:11:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4328509/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4328509/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59656948,"identity":"02573161-43ce-4fba-9459-7571d00b625d","added_by":"auto","created_at":"2024-07-04 10:56:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":330144,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchpaperonStockMarketAnalysis.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4328509/v1_covered_71489716-e798-484b-a9e8-f1ff7e3afa4a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparing different Machine Learning Algorithms in a stock Market Scenario to check which one has the highest efficiency","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stock market prediction, Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Random Forests (RF), Technical indicators, Prediction horizon, Random walk theory, Efficiency, Financial markets, Forecasting, Stock price movements, Comparative analysis, Predictive algorithms, Research findings","lastPublishedDoi":"10.21203/rs.3.rs-4328509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4328509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePredicting stock market movements using machine learning algorithms is a challenging yet crucial task in financial markets. 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