Markus Pelger

About

Markus Pelger is an Associate Professor of Management Science & Engineering at Stanford University and a Chambers Faculty Scholar in the School of Engineering. He is also a Research Associate at the National Bureau of Economic Research. His research focuses on understanding and managing financial risk through the development of mathematical financial models, statistical methods, analysis of financial data, and engineering of computational techniques. His work is divided into three streams: machine learning solutions to big-data problems in empirical asset pricing, statistical theory for high-dimensional data, and stochastic financial modeling. Pelger's research has been published in prominent journals such as the Journal of Finance, Review of Financial Studies, Journal of Financial Economics, Management Science, Journal of Econometrics, and Journal of Applied Probability. He serves as an Associate Editor for several academic journals including Management Science, Operations Research, the Journal of Econometrics, Digital Finance, and Data Science in Science. Recognized for his contributions, he has received numerous awards, including the Utah Winter Finance Conference Best Paper Award, the Best Paper in Asset Pricing Award at the SFS Cavalcade, and the Dennis Aigner Award of the Journal of Econometrics, among others. Pelger has been invited to speak at hundreds of universities, conferences, and industry firms worldwide, and has acted as a consultant or advisor to investment…

Research topics

• Computer Science
• Economics
• Econometrics
• Financial economics
• Mathematics
• Statistics
• Engineering

Selected publications

• Deep Learning in Asset Pricing

  Management Science · 2023 · 401 citations

  We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, keeps a fully flexible form, and accounts 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. Our asset pricing model outperfo…

  PublisherDOI

• Factors That Fit the Time Series and Cross-Section of Stock Returns

  Review of Financial Studies · 2020 · 331 citations

  Senior authorCorresponding

  Abstract We propose a new method for estimating latent asset pricing factors that fit the time series and cross-section of expected returns. Our estimator generalizes principal component analysis (PCA) by including a penalty on the pricing error in expected returns. Our approach finds weak factors with high Sharpe ratios that PCA cannot detect. We discover five factors with economic meaning that explain well the cross-section and time series of characteristic-sorted portfolio returns. The out-of…

  PublisherDOI

• Estimating latent asset-pricing factors

  Journal of Econometrics · 2020 · 177 citations

  Senior authorCorresponding
  PublisherDOI

• Machine-learning the skill of mutual fund managers

  Journal of Financial Economics · 2023-08-11 · 106 citations

  articleOpen access

  We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, before and after fees. The outperformance persists for more than three years. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, while characteristics of the stocks that funds hold are not predictive. Returns of predictive long-short portfolios are higher following a period of high sentiment. Our estimation with neur…

  PublisherDOI

• Missing Financial Data

  Review of Financial Studies · 2024-07-02 · 23 citations

  articleSenior authorCorresponding

  Abstract We document the widespread nature and structure of missing observations of firm fundamentals and show how to systematically handle them. Missing financial data affects more than 70% of firms that represent about half of the total market cap. Firm fundamentals have complex systematic missing patterns, invalidating traditional approaches to imputation. We propose a novel imputation method to obtain a fully observed panel of firm fundamentals that exploits both time-series and cross-sectio…

  PublisherDOI

Frequent coauthors

• Martin Lettau

  40 shared

• Ruoxuan Xiong

  18 shared

• Svetlana Bryzgalova

  London Business School

  15 shared

• Damir Filipović

  12 shared

• Stijn Van Nieuwerburgh

  Graduate School USA

  9 shared

• Eckhard Platen

  8 shared

• Jason Zhu

  Stanford University

  7 shared

• Ron Kaniel

  University of Rochester

  7 shared

Education

• Ph.D., Management Science and Engineering

  Stanford University

  2006

• M.S., Management Science and Engineering

  Stanford University

  2002

• B.S., Computer Science

  University of Karlsruhe (TH)

  1999

Awards & honors

• Utah Winter Finance Conference Best Paper Award
• Best Paper in Asset Pricing Award at the SFS Cavalcade
• Dennis Aigner Award of the Journal of Econometrics
• Bates-White Prize for the Best Paper at the Society for Fina…
• Crowell Memorial Prize

Similar researchers at Stanford University

• Amin Saberih-53
• Ashish Goelh-52
• Benjamin Van Royh-51