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.

Bio:
Markus Pelger is an Assistant Professor of Management Science & Engineering at Stanford University and a Reid and Polly Anderson Faculty Fellow. 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: statistical learning in high-dimensional financial data sets, stochastic financial modeling, and high-frequency statistics. His most recent work focuses on developing machine learning solutions to big-data problems in empirical asset pricing.
Markus' work has appeared in the Journal of Finance, Review of Financial Studies, Management Science, Journal of Econometrics  and Journal of Applied Probability. He is an Associate Editor of Management Science, Digital Finance and Data Science in Science. His research has been recognized with several awards, including the Utah Winter Finance Conference Best Paper Award, the Best Paper in Asset Pricing Award at the SFS Cavalcade, the Dennis Aigner Award of the Journal of Econometrics, the International Center for Pension Management Research Award, the CAFM Best Paper Award and the IQAM Research Award. He has been invited to speak at hundreds of world-renowned universities, conferences and investment and technology firms.
Markus received his Ph.D. in Economics from the University of California, Berkeley. He has two Diplomas in Mathematics and in Economics, both with highest distinction, from the University of Bonn in Germany. 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 and the Graduate Teaching Award at Stanford University. Markus is a founding organizer of the AI & Big Data in Finance Research Forum and the Advanced Financial Technology Laboratories at Stanford University.

Zoom ：
https://us02web.zoom.us/j/84604498297?pwd=YzVOaGdBVFQvaTNNSDB2blo4YW83Zz09

ID：846 0449 8297
PW：406541