About

Robert Korajczyk is the Harry G. Guthmann Professor of Finance at the Kellogg School of Management and has been a member of the faculty since 1982. He serves as the Senior Associate Dean for Faculty and Research and is co-director of the Center for Financial Institutions and Markets. His research interests are in investments and empirical asset pricing, with a focus on applying insights to finance. Korajczyk has held numerous academic positions, including chair of the Department of Finance and director of the Zell Center for Risk Research, and has held visiting appointments at several international institutions. He has received multiple awards for his research and teaching, including the 2024 Invesco Factor Investing Prize and the 2022 Harry Markowitz Special Distinction Award. Korajczyk has also served as an associate editor for several prominent finance journals and has contributed to the academic community through editorial roles and advisory positions.

Research topics

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

Selected publications

• The Intra-Day Stock Return Periodicity Puzzle

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Large Sample Estimators of the Stochastic Discount Factor

  Journal of Financial Econometrics · 2024-01-01

  articleSenior author

  Abstract We propose estimators of the stochastic discount factor using large cross-sections of individual stocks. We introduce a short time-block structure on a large N, T panel to exploit unbalanced panels of individual stock returns and suggest a novel bias correction to achieve the consistency of our estimators. Our estimators can accommodate pre-specified traded and nontraded factors, and latent factors. The estimators perform well in simulations. We apply our estimators to return data for U.S. individual stocks over a 50-year sample period and identify those factors in popular asset pricing models that command significant premia. A number of proposed nontraded factors have insignificant risk premia. Contrary to many studies, we find the market factor has a significant premium, as do profitability, value, and momentum factors.

  PublisherDOI

• Nonstandard Errors

  The Journal of Finance · 2024 · 85 citations

  • Computer Science
  • Econometrics
  • Statistics

  ABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

  DOI

• An Intangibles-Adjusted Profitability Factor

  National Bureau of Economic Research · 2023-03-01 · 8 citations

  reportOpen access
  PublisherDOI

• An Intangibles-Adjusted Profitability Factor

  SSRN Electronic Journal · 2023-01-01 · 2 citations

  articleOpen access
  PublisherDOI

• An Intangibles-Adjusted Profitability Factor

  SSRN Electronic Journal · 2023-01-01 · 1 citations

  articleOpen access
  PublisherDOI

• Semi-Strong Factors in Asset Returns

  Journal of Financial Econometrics · 2022 · 13 citations

  Senior authorCorresponding
  • Computer Science
  • Econometrics
  • Statistics

  Abstract We refine the approximate factor model of asset returns by distinguishing between strong factors, whose sum of squared factor betas grow at the same rate as the number of assets, and semi-strong factors, whose sum of squared factor betas grow to infinity, but at a slower rate. We develop a test statistic for strength of factors based on the cross-sectional mean-square of regression-estimated betas. We also describe an adjusted version of the test statistic to differentiate semi-strong factors from strong factors. We apply the methodology to daily equity returns to characterize some pre-specified factors as strong or semi-strong.

  PublisherDOI

• Characteristic-Based Returns: Alpha or Smart Beta?

  SSRN Electronic Journal · 2021-07-05 · 1 citations

  articleOpen access

  Abstract We propose new methodology to construct arbitrage portfolios by utilizing information contained in firm characteristics for both abnormal returns and betas (and, therefore, smart-beta risk premiums). Our methodology gives maximal weight to risk-based interpretations of characteristics' predictive power before any attribution to abnormal returns. The method allows the explanatory power of a characteristic for both alpha and beta to ebb and flow. This feature is particularly important when we expect that profit opportunities may be arbitraged away by investors. We apply the methodology to a large panel of U.S. stock returns from 1965–2018. Empirically, characteristics have time-varying explanatory power for both factor betas and alpha. We find the arbitrage portfolio has (statistically and economically) significant alpha and annualized Sharpe ratios ranging from 1.31 to 1.66.

  Publisher

• Arbitrage Portfolios

  Review of Financial Studies · 2020 · 94 citations

  • Econometrics
  • Economics
  • Financial economics

  Abstract We propose a new methodology for forming arbitrage portfolios that utilizes the information contained in firm characteristics for both abnormal returns and factor loadings. The methodology gives maximal weight to risk-based interpretations of characteristics’ predictive power before any attribution is made to abnormal returns. We apply the methodology to simulated economies and to a large panel of U.S. stock returns. The methodology works well in our simulation and when applied to stocks. Empirically, we find the arbitrage portfolio has (statistically and economically) significant alphas relative to several popular asset pricing models and annualized Sharpe ratios ranging from 1.31 to 1.66.

  PublisherDOI

• Do High-Frequency Traders Improve your Implementation Shortfall?

  SSRN Electronic Journal · 2019-01-01 · 2 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Ronnie Sadka

  119 shared

• Steven L. Heston

  University of Maryland, College Park

  106 shared

• Gregory Connor

  31 shared

• Ravi Jagannathan

  17 shared

• Deborah Lucas

  Massachusetts Institute of Technology

  12 shared

• Robert L. McDonald

  Kellogg's (Canada)

  6 shared

• Xiaoxia Lou

  Second Hospital of Shandong University

  6 shared

• Avraham Kamara

  University of Washington

  6 shared

Education

• Ph.D., Graduate School of Business

  University of Chicago

  1983

• MBA, Graduate School of Business

  University of Chicago

  1977

• BA, College

  University of Chicago

  1976

Awards & honors

• 2024 Invesco Factor Investing Prize
• 2022 Journal of Investment Management Harry Markowitz Specia…
• 2009 Crowell Prize for best paper in the field of quantitati…
• Alumni Choice Faculty Award 2000
• Core Teaching Award 1998 and 2000