Deep Learning Statistical Arbitrage

Abstract

Statistical arbitrage identifies and exploits temporal price differences between similar assets. We propose a unifying conceptual framework for statistical arbitrage and develop a novel deep learning solution, which finds commonality and time-series patterns from large panels in a data-driven and flexible way. First, we construct arbitrage portfolios of similar assets as residual portfolios from conditional latent asset pricing factors. Second, we extract the time series signals of these residual portfolios with one of the most powerful machine learning time-series solutions, a convolutional transformer. Last, we use these signals to form an optimal trading policy, that maximizes risk-adjusted returns under constraints. We conduct a comprehensive empirical comparison study with daily large cap U.S. stocks. Our optimal trading strategy obtains a consistently high out-of-sample Sharpe ratio and substantially outperforms all benchmark approaches. It is orthogonal to common risk factors, and exploits asymmetric local trend and reversion patterns. Our strategies remain profitable after taking into account trading frictions and costs. Our findings suggest a high compensation for arbitrageurs to enforce the law of one price.

Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization

“…Modern machine learning methods are also explored in , where the authors propose the use of deep neural networks, gradient-boosted trees, and random forests for finding stat-arb portfolios. Another recent study of deep-learning stat-arb finding is (Guijarro-Ordonez et al, 2021). Earlier work on using machine learning for finding stat-arbs includes, e.g., (Dixon et al, 2015;Moritz and Zimmermann, 2014;Takeuchi and Lee, 2013;Huck, 2010Huck, , 2009.…”

“…With the copula approaches of (De Moura et al, 2016;Stübinger et al, 2018;Krauss and Stübinger, 2017), the trading strategy is based on deviations from confidence intervals. The machine learning methods of (Sarmento and Horta, 2020;Guijarro-Ordonez et al, 2021;Dixon et al, 2015;Moritz and Zimmermann, 2014;Takeuchi and Lee, 2013;Huck, 2010Huck, , 2009 also include trading strategies.…”

A Diversification Framework for Multiple Pairs Trading Strategies

“…Additionally, there are methods that utilize copulas (Liew and Wu 2013;Xie et al 2016), Principal Component Analysis (PCA) (Avellaneda and Lee 2010), and machine learning (Guijarro-Ordonez et al 2021). In recent years, new optimization algorithms have been proposed to generate spreads with maximum in-sample mean reversion.…”