Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization
Stock recommendation is critical in Fintech applications, which use price series and alternative information to estimate future stock performance. Although deep...
Abstract
Stock recommendation is critical in Fintech applications, which use price series and alternative information to estimate future stock performance. Although deep learning models are prevalent in stock recommendation systems, traditional time-series forecasting training often fails to capture stock trends and rankings simultaneously, which are essential consideration 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 novelly invoke a momentum line indicator in model training. To prioritize top-performing stocks and optimize investment allocation, we propose a list-wise 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 profitable evaluations.
Access Full Paper
This research paper is available on arXiv, an open-access archive for academic preprints.
Citation
Hao Wang. "Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization." arXiv preprint. 2025-08-05. http://arxiv.org/abs/2509.10461v1
About arXiv
arXiv is a free distribution service and open-access archive for scholarly articles in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering, systems science, and economics.
Disclaimer: This article is for informational purposes only and does not constitute financial advice. Consult a licensed financial advisor before making investment decisions.
Related Articles
Disclaimer
This article is for informational purposes only and does not constitute financial, legal, or tax advice. SwissFinanceAI is not a licensed financial services provider. Always consult a qualified professional before making financial decisions.
References
- [1]ResearchCredibility: 9/10Hao Wang. "Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization." arXiv.org. August 5, 2025. Accessed November 18, 2025.
Transparency Notice: This article may contain AI-assisted content. All citations link to verified sources. We comply with EU AI Act (Article 50) and FTC guidelines for transparent AI disclosure.
Original Source
This article is based on Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization (arXiv.org)
