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Markus Pelger — Deep Learning in Asset Pricing

July 26, 2019 @ 2:00 pm - 3:00 pm

Abstract: We propose a novel approach to estimate asset pricing models for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. Our general non-linear asset pricing model is estimated with deep neural networks applied to all U.S. equity data combined with a substantial set of macroeconomic and firm-specific information. We estimate the stochastic discount factor that explains all asset returns from the conditional moment constraints implied by no-arbitrage. Our asset pricing model outperforms out-of-sample all other benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors. We trace its superior performance to including the no-arbitrage constraint in the estimation and to accounting for macroeconomic conditions and non-linear interactions between firm-specific characteristics. Our generative ad- versarial network enforces no-arbitrage by identifying the portfolio strategies with the most pricing information. Our recurrent Long-Short-Term-Memory network finds a small set of hid- den economic state processes. A feedforward network captures the non-linear effects of the conditioning variables. Our model allows us to identify the key factors that drive asset prices and generate profitable investment strategies. This is based on a joint work with Luyang Chen and Jason Zhu from Stanford University.

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: 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 and Eliot J. Swan Prize. He has two Diplomas in Mathematics and in Economics, both with highest distinction, from the University of Bonn in Germany.

 

 

 

Details

Date:
July 26, 2019
Time:
2:00 pm - 3:00 pm
Event Tags:
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Organizer

Berkeley IEOR
Website:
https://ieor.berkeley.edu

Venue

George B. Dantzig Auditorium – 1174 Etcheverry Hall
Etcheverry Hall
Berkeley, CA 94720 United States
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Phone:
510-642-6222