InfoGAT: A Superior Stock Recommendation Framework Combining Informer Time-Series Modeling with Graph Attention Networks

InfoGAT: A Superior Stock Recommendation Framework Combining Informer Time-Series Modeling with Graph Attention Networks | 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 InfoGAT: A Superior Stock Recommendation Framework Combining Informer Time-Series Modeling with Graph Attention Networks Siqi Wang, Yuxue Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7704670/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract The growing significance of equities in portfolio allocation has intensified the demand for advanced predictive models capable of handling noisy, high-dimensional, and highly nonlinear financial data. Traditional approaches often fall short in jointly modeling the structural dependencies among stocks and the long-term temporal dynamics inherent in financial markets. To address these challenges, we propose InfoGAT, a novel multi-scale prediction framework that integrates Informer-based time-series modeling with Graph Attention Networks (GAT). InfoGAT incorporates three key innovations. First, the GAT module dynamically encodes inter-stock structural relationships, capturing both strong intra-industry correlations and weak cross-industry dependencies. Second, the Informer model, with its probabilistic sparse attention mechanism, efficiently learns long-term market dynamics and periodic fluctuations. Third, a multi-scale graph reconstruction with hierarchical regularization enables dynamic correction, mitigating over-concentration, preserving prediction diversity, and preventing degradation during inference. An end-to-end multi-task learning architecture further allows InfoGAT to simultaneously forecast future returns and price trends, while a Top-K selection mechanism translates predictions into actionable investment strategies. Extensive experiments on the CSI-300 and S&P 500 datasets demonstrate that InfoGAT consistently outperforms four state-of-the-art baselines in terms of annualized return, Sharpe ratio, and information coefficient, highlighting its strong cross-market generalization and practical relevance for quantitative investment. Stock Recommendation Financial Time Series Deep Learning Graph Attention Networks Informer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers invited by journal 05 Oct, 2025 Editor assigned by journal 28 Sep, 2025 Submission checks completed at journal 26 Sep, 2025 First submitted to journal 24 Sep, 2025 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-7704670","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530238281,"identity":"a95950a5-991e-43ed-9463-63e3ca789101","order_by":0,"name":"Siqi Wang","email":"","orcid":"","institution":"Northeast Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Wang","suffix":""},{"id":530238283,"identity":"8c3a3e3f-2328-4eab-985c-c9b9e44a8c4b","order_by":1,"name":"Yuxue 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