Solar Shocks and Predictive Ceilings: A Comparative Benchmarking of Machine and Deep Learning Architectures in the Egyptian Solar Energy Stock Price Market

Solar Shocks and Predictive Ceilings: A Comparative Benchmarking of Machine and Deep Learning Architectures in the Egyptian Solar Energy Stock Price Market | 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 Solar Shocks and Predictive Ceilings: A Comparative Benchmarking of Machine and Deep Learning Architectures in the Egyptian Solar Energy Stock Price Market Aman Shreevastava, Bharat Kumar Meher This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9072475/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 rapid expansion of the Egyptian renewable energy sector has created a critical need for high-precision financial forecasting tools to guide institutional investment and policy stability. This study presents a comparative benchmarking of five computational paradigms, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), Random Forest, and XGBoost to predict the stock price evolution of six major solar energy entities: ACWA Power, JA Solar, Jinko Solar, Longi, TAQA, and Trina. Utilizing a daily historical dataset spanning from 2019 to early 2026, we transformed raw price sequences into stationary log returns to mitigate the impact of market noise and heteroscedasticity. Our empirical findings reveal a distinct performance hierarchy based on the asset's volatility regime. The LSTM model emerged as the most robust architecture for stable, trend-driven entities like JA Solar and Trina, achieving R^2 scores above 0.95 and MAPE values below 2%. Conversely, all models exhibited a predictive ceiling when applied to ACWA Power, where sudden "step-shifts" and regime changes led to negative R^2 values, highlighting the limitations of pure historical price action in capturing fundamental market shocks. By performing an inverse log transformation to reconstruct actual price levels, this research provides a practical framework for identifying which algorithmic structures are best suited for different market conditions. These results offer actionable insights for financial analysts navigating the complexities of emerging solar markets, suggesting that while deep learning effectively tracks cyclical growth, hybrid models incorporating fundamental sentiment are necessary for shock-driven assets. JEL Classification: C45, C53, C58, G17, Q42, Q47 Solar Energy Forecasting Deep Learning LSTM CNN XGBoost Egyptian Stock Market Renewable Energy Economics Financial Time-Series Analysis 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. 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-9072475","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603085392,"identity":"2971481c-dd1a-4524-ac7c-3a1b3a993dbd","order_by":0,"name":"Aman Shreevastava","email":"","orcid":"","institution":"Purnea University","correspondingAuthor":false,"prefix":"","firstName":"Aman","middleName":"","lastName":"Shreevastava","suffix":""},{"id":603085395,"identity":"d4a8d3d4-fa6e-481e-a20f-63fe0c273165","order_by":1,"name":"Bharat Kumar Meher","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBACAwkog7GBseHAhwogi5m5gQgtCSAtzAcfzjgD0sJIpBYGBrZkY942iIV4tZhLNz/78PPHPXnm9jNmkjPn1UbztwO1/KjYhlOL5ZxjxjN7EooNG3tyzCQ+bjueO+Mw0Fs9Z27jdtiNBGMGnoQExsaGHKAt247lNgC1MDO24dOS/pnxT0KCfWP/GzNp3jnHcucT1pJjzAy0JbFxRhrQ+w01uRsIabGckVPMLJOWkNw44zEwkI8dyN0I1HIQn1/MJdI3M76xSbDd2J8IjMqautx55w8ffPCjArcWODBsAFOHweQBwuqBQB5C1RGleBSMglEwCkYWAACIuWLxh0YchwAAAABJRU5ErkJggg==","orcid":"","institution":"Purnea University","correspondingAuthor":true,"prefix":"","firstName":"Bharat","middleName":"Kumar","lastName":"Meher","suffix":""}],"badges":[],"createdAt":"2026-03-09 11:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9072475/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9072475/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104891144,"identity":"0032f86e-1193-48da-a5ab-87770541e9e3","added_by":"auto","created_at":"2026-03-18 10:42:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1697434,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchMethodologyann.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9072475/v1_covered_d32c8545-556c-414e-a497-b20e71001d77.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Solar Shocks and Predictive Ceilings: A Comparative Benchmarking of Machine and Deep Learning Architectures in the Egyptian Solar Energy Stock Price Market","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":"Solar Energy Forecasting, Deep Learning, LSTM, CNN, XGBoost, Egyptian Stock Market, Renewable Energy Economics, Financial Time-Series Analysis","lastPublishedDoi":"10.21203/rs.3.rs-9072475/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9072475/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid expansion of the Egyptian renewable energy sector has created a critical need for high-precision financial forecasting tools to guide institutional investment and policy stability. This study presents a comparative benchmarking of five computational paradigms, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), Random Forest, and XGBoost to predict the stock price evolution of six major solar energy entities: ACWA Power, JA Solar, Jinko Solar, Longi, TAQA, and Trina. Utilizing a daily historical dataset spanning from 2019 to early 2026, we transformed raw price sequences into stationary log returns to mitigate the impact of market noise and heteroscedasticity. Our empirical findings reveal a distinct performance hierarchy based on the asset's volatility regime. The LSTM model emerged as the most robust architecture for stable, trend-driven entities like JA Solar and Trina, achieving\u0026nbsp; R^2\u0026nbsp; scores above 0.95 and MAPE values below 2%. Conversely, all models exhibited a predictive ceiling when applied to ACWA Power, where sudden \"step-shifts\" and regime changes led to negative\u0026nbsp; R^2\u0026nbsp; values, highlighting the limitations of pure historical price action in capturing fundamental market shocks. By performing an inverse log transformation to reconstruct actual price levels, this research provides a practical framework for identifying which algorithmic structures are best suited for different market conditions. 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