Deep Learning Statistical Arbitrage

doi:10.1287/mnsc.2022.03132

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Deep Learning Statistical Arbitrage

2025-12-01

PRE

Jorge Guijarro-Ordóñez

Markus Pelger *

Greg Zanotti

DOI：10.1287/mnsc.2022.03132

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Abstract

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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.

Keywords:

statistical arbitrage

pairs trading

machine learning

deep learning

big data

stock returns

convolutional neural network

transformer

attention

factor model

market efficiency