AI-Enhanced Portfolio Management in China’s A-Share Market: A Dynamic Factor Integration Framework

AI-Enhanced Portfolio Management in China’s A-Share Market: A Dynamic Factor Integration Framework | 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 AI-Enhanced Portfolio Management in China’s A-Share Market: A Dynamic Factor Integration Framework Ng Zhe Xin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8555979/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 This study develops and validates an integrated AI-driven asset management framework tailored for the Chinese A-share market. By synergistically combining Gradient Boosting Decision Trees (GBDT) for return forecasting, Reinforcement Learning (RL) for dynamic portfolio allocation, and Graph Neural Networks (GNNs) for cross-asset relationship modeling, we construct a multi-factor strategy that adapts to evolving market regimes. Rigorous event-study analysis is employed to quantify and exploit stock price reactions to corporate announcements, capturing both short-term anomalies and medium-term trends. Our backtesting results (2021–2025) demonstrate that this integrated framework achieves a Sharpe ratio of 1.34 and a cumulative return of + 901.7%, significantly outperforming static benchmarks like the CSI 300. The findings underscore the importance of adaptive factor timing, integration of alternative data sources, and event-driven insights for generating persistent alpha in complex and volatile equity markets. These results suggest that AI-driven multi-factor frameworks can enhance investment decision-making, improve portfolio robustness, and offer practical guidance for asset managers seeking systematic and data-driven approaches in emerging market environments. Asset Management Portfolio Optimization Artificial Intelligence Reinforcement Learning Graph Neural Networks China A-Share Market 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-8555979","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":574016065,"identity":"66028351-5c27-4faa-a490-3edfc248ac86","order_by":0,"name":"Ng Zhe 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