Deep Learning Statistical Arbitrage
构建统计套利概念框架，用卷积变换器提取时间序列信号，形成最优交易策略，在美国股票数据上获得高夏普比率，为套利者执行一价定律提供补偿。
Statistical arbitrage exploits temporal price differences between similar assets. We develop a comprehensive conceptual framework for statistical arbitrage and a novel data-driven solution. First, we construct arbitrage portfolios of similar assets as residual portfolios from conditional latent asset pricing factors. Second, we extract their time-series signals with a powerful machine learning time-series solution, a convolutional transformer. Lastly, we use these signals to form an optimal trading policy, which maximizes risk-adjusted returns under constraints. Our comprehensive empirical study on daily U.S. equities shows a high compensation for arbitrageurs to enforce the law of one price. Our arbitrage strategies obtain considerable out-of-sample mean returns and Sharpe ratios, and outperform all benchmark approaches. This paper was accepted by Agostino Capponi, finance. Funding: The authors thank MSCI for generous research support.