{"ID":2886227,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10461","arxiv_id":"2509.10461","title":"Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization","abstract":"Stock recommendation is critical in Fintech applications, which leverage price series and alternative information to estimate future stock performance. Traditional time-series forecasting training often fails to capture stock trends and rankings simultaneously, which are essential factors for investors. To tackle this issue, we introduce a Multi-Task Learning (MTL) framework for stock recommendation, \\textbf{M}omentum-\\textbf{i}ntegrated \\textbf{M}ulti-task \\textbf{Stoc}k \\textbf{R}ecommendation with Converge-based Optimization (\\textbf{MiM-StocR}). To improve the model's ability to capture short-term trends, we incorporate a momentum line indicator in model training. To prioritize top-performing stocks and optimize investment allocation, we propose a listwise ranking loss function called Adaptive-k ApproxNDCG. Moreover, due to the volatility and uncertainty of the stock market, existing MTL frameworks face overfitting issues when applied to stock time series. To mitigate this issue, we introduce the Converge-based Quad-Balancing (CQB) method. We conducted extensive experiments on three stock benchmarks: SEE50, CSI 100, and CSI 300. MiM-StocR outperforms state-of-the-art MTL baselines across both ranking and profitability evaluations.","short_abstract":"Stock recommendation is critical in Fintech applications, which leverage price series and alternative information to estimate future stock performance. Traditional time-series forecasting training often fails to capture stock trends and rankings simultaneously, which are essential factors for investors. To tackle this...","url_abs":"https://arxiv.org/abs/2509.10461","url_pdf":"https://arxiv.org/pdf/2509.10461v2","authors":"[\"Hao Wang\",\"Jingshu Peng\",\"Yanyan Shen\",\"Xujia Li\",\"Quanqing Xu\",\"Chuanhui Yang\",\"Lei Chen\"]","published":"2025-08-05T09:04:38Z","proceeding":"q-fin.ST","tasks":"[\"q-fin.ST\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}