2021/06/08 by Guijarro-Ordonez, Jorge, Pelger, Markus, Zanotti, Greg · 3 citations
#FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Portfolio Management (q-fin.PM)
paper · doi:10.48550/arxiv.2106.04028
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.