Markus Pelger

Associate Professor of Finance (by courtesy)
Associate Professor of Management Science and Engineering, School of Engineering
Academic Area:
Finance
Personal Website
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Markus Pelger

Bio

Markus Pelger is an associate professor of management science and engineering at Stanford University, a Chambers Faculty Scholar in the School of Engineering and the director of the Stanford Advanced Financial Technologies Laboratory (AFTLab). He is also an associate professor of finance (by courtesy) at Stanford Graduate School of Business and a research associate at the National Bureau of Economic Research.

His research on machine learning and statistical methods in finance has appeared in the Journal of Finance, the Review of Financial Studies, the Journal of Financial Economics, Management Science, and the Journal of Econometrics. Pelger’s research has been recognized with the Best Paper Award at the Utah Winter Finance Conference, the Best Paper in Asset Pricing Award at the SFS Cavalcade, the Dennis J. Aigner Award of the Journal of Econometrics, the Bates-White Prize of the Society for Financial Econometrics and the Engle Prize of the Journal of Financial Econometrics. He is an associate editor of Management Science, the Review of Finance, the Journal of Econometrics, the Journal of Financial Econometrics and Operations Research, and a co-founder and co-organizer of the AI & Big Data in Finance Research Forum. He has presented his research at more than 200 seminars and conferences and has been a consultant to investment institutions, the Federal Reserve Banks of Dallas and San Francisco and the International Monetary Fund.

Pelger received his PhD in Economics from the University of California, Berkeley, and Diplom degrees in Mathematics and in Economics, both with distinction, from the University of Bonn in Germany. He joined the Stanford faculty in 2015.

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Research Interests

  • Asset pricing
  • AI and machine learning in finance
  • Financial econometrics
  • Fixed income
  • Financial technology

Sites & Social

Academic Degrees

  • PhD in Economics, University of California, Berkeley, 2015
  • Diplom in Mathematics, University of Bonn (Germany), 2012
  • Diplom in Economics, University of Bonn (Germany), 2009

Academic Appointments

  • Associate Professor of Finance (by courtesy), Stanford Graduate School of Business, 2026–present
  • Associate Professor of Management Science and Engineering (with tenure), Stanford University, 2024–present
  • Assistant Professor of Management Science and Engineering, Stanford University, 2015–2024
  • Research Associate, National Bureau of Economic Research (Asset Pricing)

Professional Experience

  • Consultant, International Monetary Fund
  • Consultant, Federal Reserve Bank of Dallas
  • Consultant, Federal Reserve Bank of San Francisco
  • Consultant, AllianceBernstein
  • Consultant, Migdal
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Research Statement

Markus Pelger's research focuses on understanding and managing financial risk. It has three streams. The first builds machine learning solutions to big-data problems in empirical asset pricing. Examples are deep learning models that price individual stocks, identify skilled fund managers and build statistical arbitrage strategies, and robust methods that estimate yield curves and the risk premia of Treasury bonds. The second develops statistical theory for high-dimensional data, including latent factor models, missing data and causal inference in large panels. The third covers stochastic financial modeling, with applications such as contingent capital for banks.

Teaching Statement

Pelger teaches quantitative investment, financial statistics, and AI and machine learning in finance at the undergraduate, master's, PhD, and professional levels. He has advised or co-advised fourteen PhD students to completion and has served on dissertation committees across Stanford, including those of finance PhD students at Stanford GSB. He received the Graduate Teaching Award of the Department of Management Science and Engineering in 2019.

Teaching Materials

Course descriptions and syllabi

Videos and Podcasts

  • Shrinking the Term Structure

    Risk Seminar, Consortium for Data Analytics in Risk, University of California, Berkeley, April 2024.

    Shrinking the Term Structure
    01:17:29

    Shrinking the Term Structure

  • Stripping the Discount Curve: A Robust Machine Learning Approach
    Stripping the Discount Curve: A Robust Machine Learning Approach
    00:51:25

    Stripping the Discount Curve: A Robust Machine Learning Approach

  • Missing Financial Data
    Missing Financial Data
    00:21:13

    Missing Financial Data

  • Machine-Learning the Skill of Mutual Fund Managers
    Machine-Learning the Skill of Mutual Fund Managers
    00:28:56

    Machine-Learning the Skill of Mutual Fund Managers

  • Deep Learning Statistical Arbitrage

    Risk Seminar, Consortium for Data Analytics in Risk, University of California, Berkeley, September 2021.

    Deep Learning Statistical Arbitrage
    01:24:07

    Deep Learning Statistical Arbitrage

  • Deep Learning in Asset Pricing

    Utah Winter Finance Conference, 2020.

    Deep Learning in Asset Pricing
    00:27:47

    Deep Learning in Asset Pricing

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Documents, Presentations, and Multimedia