用深度学习动态预测公司股票回购，揭示长期低估与现金流突增的触发机制。
Dynamic Forecasting and Temporal Feature Evolution of Stock Repurchases in Listed Companies Using Attention-Based Deep Temporal Networks
- 融合TCN与注意力LSTM捕捉财务变化的长短周期模式。
- 在2014-2024年A股数据上显著优于逻辑回归与XGBoost等静态模型。
- 揭示回购决策受长期低估驱动、短期现金流激增触发，适合量化投资研究者。
准确预测股票回购对量化投资与风险管理至关重要，但传统静态模型难以捕捉企业财务状况的复杂时序依赖。本文提出一个融合经济理论与深度时序网络的动态预警系统，基于2014–2024年中国A股面板数据，采用混合时序卷积网络（TCN）与注意力机制LSTM，捕捉财务演变的长短期模式。滚动窗口交叉验证显示，该模型显著优于逻辑回归与XGBoost等静态基线。进一步通过可解释AI（XAI）分析发现：持续存在的“低估”是长期动因，而“现金流”骤升则是决定性短期触发因素。本研究为金融预测提供了稳健的深度学习范式，并为经典公司金融假说提供了动态实证支持。
原文摘要 · Abstract (English)
Accurately predicting stock repurchases is crucial for quantitative investment and risk management, yet traditional static models fail to capture the complex temporal dependencies of corporate financial conditions. This paper proposes a dynamic early warning system integrating economic theory with deep temporal networks. Using Chinese A-share panel data (2014-2024), we employ a hybrid Temporal Convolutional Network (TCN) and Attention-based LSTM to capture long- and short-term financial evolutionary patterns. Rolling-window cross-validation demonstrates our model significantly outperforms static baselines like Logistic Regression and XGBoost. Furthermore, utilizing Explainable AI (XAI), we reveal the temporal dynamics of repurchase decisions: prolonged "undervaluation" serves as the long-term underlying motive, while a sharp increase in "cash flow" acts as the decisive short-term trigger. This study provides a robust deep learning paradigm for financial forecasting and offers dynamic empirical support for classic corporate finance hypotheses.
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