Deep Learning Statistical Arbitrage

• 成果类型: Article; Early Access
• 署名作者: Guijarro-Ordonez, Jorge; Pelger, Markus; Zanotti, Greg
• 署名单位: Stanford University; Stanford University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.03132
• 发表日期: 2025
• 关键词: statistical arbitrage; pairs trading; Machine Learning; Deep learning; big data; stock returns; convolutional neural network; transformer; attention; factor model; market efficiency; INVESTMENT

摘要

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.