用注意力机制挖掘可套利资产，提升交易收益
Attention Factors for Statistical Arbitrage
- 通过因子嵌入学习动态关联资产，捕捉复杂交互
- 24年美股数据验证，夏普比率超4，净交易成本后仍达2.3
- 适合量化交易、金融工程研究者参考
统计套利利用相似资产间的时序价格差异获利。我们提出一个联合框架：通过因子识别相似资产、发现定价偏差，并制定最大化风险调整后收益（扣除交易成本）的交易策略。提出的注意力因子是条件隐变量因子，对套利交易最具价值，由企业特征嵌入学习得到，能建模复杂交互关系。我们使用通用序列模型从因子残差组合中提取时间序列信号。联合估计因子与套利策略至关重要，可最大化扣除交易成本后的盈利。在覆盖美国最大规模股票的24年全面实证研究中，注意力因子模型实现样本外夏普比率高于4；一步式解决方案在扣除交易成本后仍取得2.3的前所未有的夏普比率。研究还表明，弱因子在套利中同样重要。
原文摘要 · Abstract (English)
Statistical arbitrage exploits temporal price differences between similar assets. We develop a framework to jointly identify similar assets through factors, identify mispricing and form a trading policy that maximizes risk-adjusted performance after trading costs. Our Attention Factors are conditional latent factors that are the most useful for arbitrage trading. They are learned from firm characteristic embeddings that allow for complex interactions. We identify time-series signals from the residual portfolios of our factors with a general sequence model. Estimating factors and the arbitrage trading strategy jointly is crucial to maximize profitability after trading costs. In a comprehensive empirical study we show that our Attention Factor model achieves an out-of-sample Sharpe ratio above 4 on the largest U.S. equities over a 24-year period. Our one-step solution yields an unprecedented Sharpe ratio of 2.3 net of transaction costs. We show that weak factors are important for arbitrage trading.
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