Deep Learning Statistical Arbitrage

2021/06/08 by Jorge Guijarro-Ordonez, Markus Pelger, Guijarro-Ordonez, Jorge +3 · 5 citations
Computer Science · Economics, Econometrics and Finance · #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Portfolio Management (q-fin.PM) #cs.LG #q-fin.PM

paper · pdf · doi:10.48550/arxiv.2106.04028

arxiv created 2022/10/07 · arxiv updated 2022/10/11

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.

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