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.

Bio: Markus Pelger is an Assistant Professor of Management Science & Engineering at Stanford University. His research focuses on understanding and managing financial risk. He develops mathematical financial models and statistical methods, analyzes financial data and engineers computational techniques. His research is divided into three streams: stochastic financial modeling, high-frequency statistics and statistical learning in high-dimensional financial data sets. His most recent work includes developing machine learning solutions to big-data problems in empirical risk management and asset pricing.

Markus' work has appeared in the Journal of Finance, Review of Financial Studies, Journal of Applied Probability and Journal of Econometrics. He is an Associate Editor of Management Science and also referees for several journals in the fields of statistics, econometrics, finance and management. Markus received his Ph.D. in Economics from the University of California, Berkeley. He is a scholar of the German National Merit Foundation and he was awarded a Fulbright Scholarship, the Institute for New Economic Thinking Prize, the Eliot J. Swan Prize, the Graduate Teaching Award at Stanford University, the Utah Winter Finance Conference Best Paper Award and the Best Paper in Asset Pricing Award at the SFS Cavalcade. He has two Diplomas in Mathematics and in Economics, both with highest distinction, from the University of Bonn in Germany.