A Novel Sentiment Correlation-based Method with Dual Transformer Model for Stock Price Prediction

A Novel Sentiment Correlation-based Method with Dual Transformer Model for Stock Price 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 Novel Sentiment Correlation-based Method with Dual Transformer Model for Stock Price Prediction Qizhao Chen, Hiroaki Kawashima This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6479946/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Dec, 2025 Read the published version in International Journal of Data Science and Analytics → Version 2 posted 7 You are reading this latest preprint version Show more versions Abstract In real-world financial markets, a company's stock performance is often influenced by its competitors, partners, and other connected firms. However, traditional sentiment-based approaches typically focus only on the target company's sentiment, overlooking the broader network of influence. To address this limitation, this paper proposes a Dual Transformer architecture that integrates sentiment signals from connected companies to enhance prediction accuracy. This approach aims to bridge a critical gap in financial sentiment analysis by capturing inter-company relationships and their impact on stock price movements. The Dual Transformer comprises two transformer structures, the Enhancement Transformer and the Forecast Transformer. The Enhancement Transformer is used to enhance the correlation strengths between related companies, while the Forecast Transformer is used for stock price forecasting. Eight companies from various industries and financial markets are selected for analysis. The model utilizes the polarity scores of these related companies, combined with historical closing prices from 2015 to 2024, to forecast the next day's closing price. Prediction performance is evaluated using the Mean Squared Error (MSE). Experimental results demonstrate that incorporating the news sentiment of related companies improves the prediction of the target company’s stock price when using the proposed Dual Transformer model. In addition, the Dual Transformer model can outperform existing models such as Temporal Fusion Transformer, N-Beats, Informer and LSTM by achieving consistently lower MSE values across all eight companies. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Dec, 2025 Read the published version in International Journal of Data Science and Analytics → Version 2 posted Editorial decision: Accepted 16 Sep, 2025 Reviews received at journal 06 Aug, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Submission checks completed at journal 09 Jul, 2025 First submitted to journal 08 Jul, 2025 You are reading this latest preprint version Show more versions 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. 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