Markus Pelger
Associate Professor of Management Science and Engineering
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.
Honors & Awards
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Bates-White Prize, Society for Financial Econometrics (SoFiE) (2023)
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Crowell Memorial Prize, PanAgora (2022)
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Best Paper IQAM Research Award, IQAM Institute (2022)
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ICPM Research Award, International Center for Pension Management (2022)
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Best Paper Award, Hong Kong Conference for Fintech, AI and Big Data in Business (2022)
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Dennis Aigner Award, Journal of Econometrics (2021)
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AQR Capital Insight Award Finalist, AQR (2021)
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Best Paper in Asset Pricing Award, SFS Cavalcade (2020)
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Best Paper Award, Utah Winter Finance Conference (2020)
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Best Paper Award, Asia-Pacific Financial Markets Conference (2020)
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CQA Academic Paper Competition, Chicago Quantitative Alliance (2020)
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Graduate Teaching Award, Stanford University (2019)
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Reid and Polly Anderson Faculty Fellow, Stanford University (2015)
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Eliot J. Swan Prize, Department of Economics, UC Berkeley (2012)
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Outstanding Graduate Student Instructor Award, UC Berkeley (2011)
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Institute for New Economic Thinking (INET) Prize in Economic History, UC Berkeley (2011)
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Scholarship of the German Academic Exchange Service, DAAD (2009)
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Fulbright Scholarship, Institute of International Education (2007)
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Scholarship of the German National Academic Foundation, Studienstiftung (2004-2009)
Professional Education
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Ph.D., UC Berkeley, Economics (2015)
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Diplom, University of Bonn, Mathematics (2012)
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Diplom, University of Bonn, Economics (2009)
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
- Blockchain Technologies & Entrepreneurship
MS&E 447 (Win) - Financial Statistics
MS&E 349 (Spr) - Investment Science
MS&E 245A (Aut) - Senior Project
MS&E 108 (Win) -
Independent Studies (5)
- Curricular Practical Training
CME 390 (Aut, Win, Spr, Sum) - Directed Reading and Research
MS&E 408 (Aut, Win, Spr, Sum) - Master's Research
CME 291 (Aut, Win, Spr, Sum) - Ph.D. Research
CME 400 (Aut, Win, Spr, Sum) - Ph.D. Research Rotation
CME 391 (Aut, Win, Spr, Sum)
- Curricular Practical Training
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Prior Year Courses
2023-24 Courses
- Blockchain Technologies & Entrepreneurship
MS&E 447 (Spr) - Financial Statistics
MS&E 349 (Spr) - Investment Science
MS&E 245A (Aut) - Senior Project
MS&E 108 (Win)
2022-23 Courses
- Financial Statistics
MS&E 349 (Win) - Investment Science
MS&E 245A (Aut) - Senior Project
MS&E 108 (Win)
2021-22 Courses
- Investment Science
MS&E 245A (Aut) - Senior Project
MS&E 108 (Win)
- Blockchain Technologies & Entrepreneurship
Stanford Advisees
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Doctoral Dissertation Reader (AC)
Kasper Johansson -
Doctoral Dissertation Advisor (AC)
Enrica Archetti, Junting Duan, Aldis Elfarsdottir, Yang Fan, Greg Zanotti -
Master's Program Advisor
Karthick Arunachalam, Amélie Buc, Natalie Cao, Bocheng Dai, Ramsey Gordon, Christian Guallpa, Sandra Ha, Cole Kastner, Lei Liu, Ricky Liu, Haoxuan Lu, Gavin McDonell, Alex Patel, Luis Varela Eleta, Rose Wang, Yuteng Zhuang, yanling li -
Doctoral (Program)
Andrew Caosun, I-han Lai, Julien Maire, Xueye Ping, Rose Wang
All Publications
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Machine-learning the skill of mutual fund managers
JOURNAL OF FINANCIAL ECONOMICS
2023; 150 (1): 94-138
View details for DOI 10.1016/j.jfineco.2023.07.004
View details for Web of Science ID 001148406400001
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Large dimensional latent factor modeling with missing observations and applications to causal inference?
JOURNAL OF ECONOMETRICS
2023; 233 (1): 271-301
View details for DOI 10.1016/j.jeconom.2022.04.005
View details for Web of Science ID 000952500700001
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Deep Learning in Asset Pricing
MANAGEMENT SCIENCE
2023
View details for DOI 10.1287/mnsc.2023.4695
View details for Web of Science ID 000936046900001
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Discussion of "Text Selection" by Bryan Kelly, Asaf Manela, and Alan Moreira
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
2021; 39 (4): 880-882
View details for DOI 10.1080/07350015.2021.1948420
View details for Web of Science ID 000702725800002
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Interpretable Sparse Proximate Factors for Large Dimensions
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
2021
View details for DOI 10.1080/07350015.2021.1961786
View details for Web of Science ID 000691501400001
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State-Varying Factor Models of Large Dimensions
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
2021
View details for DOI 10.1080/07350015.2021.1927744
View details for Web of Science ID 000662789600001
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TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search
ASSOC COMPUTING MACHINERY. 2021: 2848-2857
View details for DOI 10.1145/3442381.3449842
View details for Web of Science ID 000733621802076
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Estimating latent asset-pricing factors
JOURNAL OF ECONOMETRICS
2020; 218 (1): 1–31
View details for DOI 10.1016/j.jeconom.2019.08.012
View details for Web of Science ID 000541723600001
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Understanding Systematic Risk: A High-Frequency Approach
JOURNAL OF FINANCE
2020
View details for DOI 10.1111/jofi.12898
View details for Web of Science ID 000531023800001
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Factors That Fit the Time Series and Cross-Section of Stock Returns
REVIEW OF FINANCIAL STUDIES
2020; 33 (5): 2274–2325
View details for DOI 10.1093/rfs/hhaa020
View details for Web of Science ID 000536040400009
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ON THE EXISTENCE OF SURE PROFITS VIA FLASH STRATEGIES
JOURNAL OF APPLIED PROBABILITY
2019; 56 (2): 384–97
View details for DOI 10.1017/jpr.2019.32
View details for Web of Science ID 000477856800003
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Large-dimensional factor modeling based on high-frequency observations
ELSEVIER SCIENCE SA. 2019: 23–42
View details for DOI 10.1016/j.jeconom.2018.09.004
View details for Web of Science ID 000454377800003
- Large-dimensional factor modeling based on high-frequency observations Journal of Econometrics 2018
- Factors that Fit the Time-Series and Cross-Section of Stock Returns Working paper 2018
- State-Varying Factor Models of Large Dimensions Working paper 2018
- Interpretable Sparse Proximate Factors for Large Dimensions Working paper 2018
- Change-Point Testing and Estimation for Risk Measures in Time Series Working paper 2018
- Contingent Capital, Tail Risk, and Debt-Induced Collapse Review of Financial Studies 2017
- Optimal Stock Option Schemes for Managers Review of Managerial Science 2013
- New Performance-Vested Stock Option Schemes Applied Financial Economics 2013
- Contingent Convertible Bonds: Pricing, Dilution Costs and Efficient Regulation Working paper 2012