用记忆门控注意力机制提升股市投资组合的收益与稳定性
MIGT: Memory Instance Gated Transformer Framework for Financial Portfolio Management
- 引入门控实例注意力模块，结合Transformer与归一化技术
- 在道琼斯30指数上实现至少9.75%的累计收益提升
- 适合关注量化交易与强化学习的金融研究者
深度强化学习（DRL）被用于改善动态市场中的投资组合管理。然而，股票市场受全球事件和投资者情绪等多重因素影响，波动性更高，构建具备强收益能力、稳定训练过程及良好泛化性能的DRL框架仍具挑战。本文提出基于记忆实例门控Transformer（MIGT）的新框架，通过创新的门控实例注意力模块——融合Transformer变体、实例归一化与轻量门控单元——以最大化投资回报，同时保障学习稳定性并降低异常值影响。在道琼斯工业平均指数30只成分股上的测试表明，该框架在关键财务指标上优于15种基准策略：累计收益率至少提升9.75%，风险-收益比率（夏普、索提诺、欧米茄）至少提高2.36%，显著推动了DRL在投资组合管理中的应用进展。
原文摘要 · Abstract (English)
Deep reinforcement learning (DRL) has been applied in financial portfolio management to improve returns in changing market conditions. However, unlike most fields where DRL is widely used, the stock market is more volatile and dynamic as it is affected by several factors such as global events and investor sentiment. Therefore, it remains a challenge to construct a DRL-based portfolio management framework with strong return capability, stable training, and generalization ability. This study introduces a new framework utilizing the Memory Instance Gated Transformer (MIGT) for effective portfolio management. By incorporating a novel Gated Instance Attention module, which combines a transformer variant, instance normalization, and a Lite Gate Unit, our approach aims to maximize investment returns while ensuring the learning process's stability and reducing outlier impacts. Tested on the Dow Jones Industrial Average 30, our framework's performance is evaluated against fifteen other strategies using key financial metrics like the cumulative return and risk-return ratios (Sharpe, Sortino, and Omega ratios). The results highlight MIGT's advantage, showcasing at least a 9.75% improvement in cumulative returns and a minimum 2.36% increase in risk-return ratios over competing strategies, marking a significant advancement in DRL for portfolio management.
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