Deep Learning Statistical Arbitrage

Jorge Guijarro-Ordonez, Markus Pelger and Greg Zanotti

Papers from arXiv.org

Abstract: Statistical arbitrage exploits temporal price differences between similar assets. We develop a unifying 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, that maximizes risk-adjusted returns under constraints. Our comprehensive empirical study on daily US equities shows a high compensation for arbitrageurs to enforce the law of one price. Our arbitrage strategies obtain consistently high out-of-sample mean returns and Sharpe ratios, and substantially outperform all benchmark approaches.

Date: 2021-06, Revised 2022-10
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ore
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