Forecasting Exchange Rate Depending On The Data Volatility: A Comparison Of Deep Learning Techniques

Forecasting Exchange Rate Depending On The Data Volatility: A Comparison Of Deep Learning Techniques | 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 Forecasting Exchange Rate Depending On The Data Volatility: A Comparison Of Deep Learning Techniques Filiz Erataş Sönmez, Şule Öztürk Birim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4218174/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 foreign exchange rate is critical for decision makers since international trade is a vital task, and an accurate prediction enables effective planning of the future. To model the exchange rate behavior over time, a deep learning methodology is used in this study. Deep learning techniques can uncover indeterminate complex structures in a dataset with multiple processing layers. Traditional artificial neural networks (ANNs) do not consider the time dependence between data points in time series data. To overcome this problem, deep learning tools, such as recurrent neural networks (RNNs), consider long-term time dependency in the data. In this study, among the types of RNNs, long short-term memory (LSTM), bidirectional LSTM, and gated recurrent units (GRUs) are used to predict time series data of USD/TRY and EUR/TRY. This prediction is conducted for three different periods in the last 11 years in Turkey. One period includes near-steady data, and two periods have volatile exchange rate data. The prediction performance of the models is evaluated based on the mean absolute error (MAE), root square error (RMSE), and mean absolute percentage error (MAPE) metrics. After the comparison of different models, the bi-LSTM and GRU models are found to yield the most accurate predictions in volatile periods, depending on the nature of the volatility. This study proposes new models for exchange rate estimation and compares the performance of each model based on the volatility of the data. Finance Other Economics Artificial Intelligence and Machine Learning Exchange Rate Prediction Deep Learning Volatility Long Short-Term Memory (LSTM) Bi-Directional LSTM Gated Recurrent Unit (GRU) 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-4218174","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287542761,"identity":"44391265-bcec-4fcf-a017-0d89fbd90413","order_by":0,"name":"Filiz Erataş Sönmez","email":"","orcid":"","institution":"Manisa Celal Bayar University","correspondingAuthor":false,"prefix":"","firstName":"Filiz","middleName":"Erataş","lastName":"Sönmez","suffix":""},{"id":287542762,"identity":"281359f8-a491-4bcf-b6a3-b5aaff05843c","order_by":1,"name":"Şule Öztürk Birim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYFAC5gYog43xASOYnUBIC0SZBFALswFcywEitbBJEKVFfkZi4wOGiro6g9vH0ip+7jjMwM+eY8D8cQ9uLQY3EpsNGM6wSRicSzt2s/fMYQbJnjcGDAee4dEikdgmwdjGI2Fwhr3tNmPbYaAhOUAteFwGdFj7D8Y2CbCWYpAWe0JaGG4ktjEwthkAtbAdYwbbIkFAi8GZh80SCWcSJGeeYUuW7D2TziNx5lnBgTP4HNaefPDDh4o6fr4zbIYffu6wluNvT974oAKfw0AgAYnNAyIIaRgFo2AUjIJRQAAAADk2Uqvmu/NEAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-7544-8588","institution":"Manisa Celal Bayar University","correspondingAuthor":true,"prefix":"","firstName":"Şule","middleName":"Öztürk","lastName":"Birim","suffix":""}],"badges":[],"createdAt":"2024-04-04 13:20:36","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4218174/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4218174/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54140815,"identity":"af2d1def-edeb-4ead-adb6-9393fd6424a8","added_by":"auto","created_at":"2024-04-05 07:50:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1069617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript040424edited.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4218174/v1_covered_cea46cf8-e961-4559-824c-eaea4e616dc8.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eForecasting Exchange Rate Depending On The Data Volatility: A Comparison Of Deep Learning Techniques\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Exchange Rate Prediction, Deep Learning, Volatility, Long Short-Term Memory (LSTM), Bi-Directional LSTM, Gated Recurrent Unit (GRU)","lastPublishedDoi":"10.21203/rs.3.rs-4218174/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4218174/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe prediction of the foreign exchange rate is critical for decision makers since international trade is a vital task, and an accurate prediction enables effective planning of the future. 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