用动量指标和新损失函数提升股票推荐的排名与收益表现
Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization
- 融合动量线指标与列表式排序损失，同时优化趋势预测与排名
- 在三个中国股市数据集上超越现有方法，排名与收益率双提升
- 适合关注量化选股与多任务学习的金融工程研究者
股票推荐在金融科技中至关重要，需结合价格序列与替代信息预测未来表现。传统时间序列训练难以同时捕捉股票趋势与排名，而这两者对投资者尤为关键。为此，本文提出多任务学习框架 MiM-StocR，通过引入动量线指标增强短期趋势感知，并设计自适应k近似NDCG列表级排序损失以优化头部股票识别与投资分配。针对股市波动导致的过拟合问题，提出基于收敛性的四平衡（CQB）方法。在SEE50、CSI 100与CSI 300三个基准数据集上进行实验，结果表明MiM-StocR在排名与盈利能力评估中均优于当前最优多任务基线。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。