Bio

Markus Pelger is an Associate Professor of Management Science & Engineering at Stanford University and a Reid and Polly Anderson Faculty Fellow. He is also a faculty research fellow at the National Bureau of Economic Research. 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: machine learning solutions to big-data problems in empirical asset pricing, statistical theory for high-dimensional data and stochastic financial modeling.
Markus' work has appeared in the Journal of Finance, Review of Financial Studies, Journal of Financial Economics, Management Science, Journal of Econometrics and Journal of Applied Probability. He is an Associate Editor of Management Science, Operations Research, 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 Bates-White Prize for the Best Paper at the Society for Financial Econometrics Conference, the Crowell Memorial Prize, 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. He has been a consultant or advisor to investment institutions, governmental agencies and supranational organizations.
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 Advanced Financial Technology Laboratories and the AI & Big Data in Finance Research Forum. He is affiliated with the Stanford Institute for Computational and Mathematical Engineering, the Stanford Institute for Human-Centered Artificial Intelligence and the Stanford Woods Institute for the Environment.

Academic Appointments

Honors & Awards

  • Bates-White Prize, Society for Financial Econometrics (SoFiE) (2023)
  • Crowell Memorial Prize, PanAgora (2022)
  • Best Paper IQAM Research Award, IQAM Institute (2022)
  • ICPM Research Award, International Center for Pension Management (2022)
  • Best Paper Award, Hong Kong Conference for Fintech, AI and Big Data in Business (2022)
  • Dennis Aigner Award, Journal of Econometrics (2021)
  • AQR Capital Insight Award Finalist, AQR (2021)
  • Best Paper in Asset Pricing Award, SFS Cavalcade (2020)
  • Best Paper Award, Utah Winter Finance Conference (2020)
  • Best Paper Award, Asia-Pacific Financial Markets Conference (2020)
  • CQA Academic Paper Competition, Chicago Quantitative Alliance (2020)
  • Graduate Teaching Award, Stanford University (2019)
  • Reid and Polly Anderson Faculty Fellow, Stanford University (2015)
  • Eliot J. Swan Prize, Department of Economics, UC Berkeley (2012)
  • Outstanding Graduate Student Instructor Award, UC Berkeley (2011)
  • Institute for New Economic Thinking (INET) Prize in Economic History, UC Berkeley (2011)
  • Scholarship of the German Academic Exchange Service, DAAD (2009)
  • Fulbright Scholarship, Institute of International Education (2007)
  • Scholarship of the German National Academic Foundation, Studienstiftung (2004-2009)

Professional Education

  • Ph.D., UC Berkeley, Economics (2015)
  • Diplom, University of Bonn, Mathematics (2012)
  • Diplom, University of Bonn, Economics (2009)

Contact

  • Academic
    mpelger@stanford.edu
    University - Faculty Department: Management Science and Engineering Position: Associate Professor

Additional Info

  • Mail Code: 4026

Links

Current Research and Scholarly Interests

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: machine learning solutions to big-data problems in empirical asset pricing, statistical theory for high-dimensional data and stochastic financial modeling.

2024-25 Courses

Stanford Advisees

All Publications