Optimizing Stock Price Prediction for South Asian Markets Using LSTM, GRU, CNN with Greedy Algorithm

Optimizing Stock Price Prediction for South Asian Markets Using LSTM, GRU, CNN with Greedy Algorithm | 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 Optimizing Stock Price Prediction for South Asian Markets Using LSTM, GRU, CNN with Greedy Algorithm Bushra Saeed, Wei Yin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5847917/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 Accurately predicting socio-economic trends, including stock market behavior, has become increasingly vital for investors, policymakers, and researchers in today's economic growth. This task is particularly challenging in South Asian nations due to the region's economic instability and the unpredictable nature of financial information. This paper aims to predict stock values in five prominent South Asian stock exchanges, namely Karachi (KSE), Nifty50 (NSE), Colombo (CSE), Dhaka (DSE), and Afghanistan, using machine learning methods and daily data from 2018 to 2023. To improve forecasting accuracy, this research used a greedy approach to optimize the window size of a Simple Moving Average (SMA) and normalized the data to train three deep learning models: Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The models were evaluated using performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R2 score. Our results demonstrate that GRU outperforms LSTM and CNN in all markets, with reduced MSE and elevated R² values. However, CNN exhibits the most volatility in unstable markets, such as Afghanistan and Sri Lanka. LSTM provides more dynamic forecasting patterns but is prone to overestimating abrupt fluctuations in stock values. In summary, our research provides a comprehensive evaluation of machine learning models for stock price prediction and identifies GRU as the most reliable model. Deep Learning Model Time Series Forecasting Greedy Algorithm South Asian Stock Market Stock Price Prediction Full Text Additional Declarations No competing interests reported. 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. 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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-5847917","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":406591218,"identity":"303c06dd-856c-499b-8236-b615da0aa91d","order_by":0,"name":"Bushra Saeed","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Bushra","middleName":"","lastName":"Saeed","suffix":""},{"id":406591220,"identity":"c22c7569-8ad7-4020-ba15-83f06a9e466d","order_by":1,"name":"Wei Yin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYDACZhBhAGYdgIgcIF4LWwKRWhCAx4A4LQbHeY9JfChgsOeXyPn84WMbgxzfjQTGzwX4tBzmS5OcYcCQOHNG7jbJmW0MxpI3EpilZ+DVwmN2G+ikBIMbuduYedsYEjfcSGBj5iGk5Y8Bg739jZzHn/+2MdQTpwUYYowbJHIYpBnbQNYR0CJ5mMf8Zw/QLzPOPDOT7DknYTjzzMNmaXxa+M6fMTb48QcYYu3Jjz/8KLOR5zuefPAzPi0KB8DUfxhfAogZG/BoYGCQxy89CkbBKBgFowAIAHiaSIf7saH8AAAAAElFTkSuQmCC","orcid":"","institution":"Southeast University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Yin","suffix":""}],"badges":[],"createdAt":"2025-01-17 09:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5847917/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5847917/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75848155,"identity":"0ccd43b9-5092-4305-9dc0-a12225a4452a","added_by":"auto","created_at":"2025-02-09 23:46:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":862091,"visible":true,"origin":"","legend":"","description":"","filename":"OptimizingStockPricePredictionforSouthAsianMarkets.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5847917/v1_covered_4e894185-9d34-4161-b97a-425a3c3ad626.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eOptimizing Stock Price Prediction for South Asian Markets Using LSTM, GRU, CNN with Greedy Algorithm\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"Deep Learning Model, Time Series Forecasting, Greedy Algorithm, South Asian Stock Market, Stock Price Prediction","lastPublishedDoi":"10.21203/rs.3.rs-5847917/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5847917/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurately predicting socio-economic trends, including stock market behavior, has become increasingly vital for investors, policymakers, and researchers in today's economic growth. This task is particularly challenging in South Asian nations due to the region's economic instability and the unpredictable nature of financial information. This paper aims to predict stock values in five prominent South Asian stock exchanges, namely Karachi (KSE), Nifty50 (NSE), Colombo (CSE), Dhaka (DSE), and Afghanistan, using machine learning methods and daily data from 2018 to 2023. To improve forecasting accuracy, this research used a greedy approach to optimize the window size of a Simple Moving Average (SMA) and normalized the data to train three deep learning models: Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The models were evaluated using performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R2 score. Our results demonstrate that GRU outperforms LSTM and CNN in all markets, with reduced MSE and elevated R\u0026sup2; values. However, CNN exhibits the most volatility in unstable markets, such as Afghanistan and Sri Lanka. LSTM provides more dynamic forecasting patterns but is prone to overestimating abrupt fluctuations in stock values. 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