Prof. Dr. Markus Pelger, Assistant Professor at Stanford University, will speak about Deep Learning in Asset Pricing in the "Diginomics Research Talks meet BREMEN.AI Data Lounge" event series co-organized with the Diginomics Research Group of the University of Bremen and BREMEN.AI Data Lounge.
KEYNOTE: DEEP LEARNING IN ASSET PRICING
Markus Pelger and his co-authors use deep neural networks to estimate an asset pricing model 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. The key innovations are to use the fundamental no-arbitrage condition as criterion function, to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Their asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors and identifies the key factors that drive asset prices.
The background of this talk is given by the lacking abilities of many companies to deploy AI technologies to generate real customer value and eventually, economic impact. The problem derives from the entrenched habit to either using a firm-level AI strategy or a technology-based approach by developing new models respectively algorithms. However, a data science team does not benefit from this in either way to maximize any short to medium-term profits.
ABOUT THE SPEAKER
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 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.
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REGISTRATION
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YouTube.