About

Huseyin Gulen is a Professor of Finance and the Department Head of Finance at the Daniels School of Business, Purdue University. His research primarily focuses on empirical asset pricing, corporate finance, and market microstructure. Gulen has made significant contributions to understanding stock return predictability, the role of intangible capital in factor models, and the impact of policy uncertainty on corporate investment and mergers and acquisitions. His work often employs advanced econometric techniques and explores the interaction between market prices and investor beliefs, as well as the effects of government regulation and corporate governance on financial markets. Throughout his career, Gulen has published extensively in leading academic journals such as the Journal of Financial Economics, Review of Financial Studies, Management Science, and the Journal of Finance. His research has been recognized with several awards, including the Jack Treynor Prize and best paper awards from various finance associations. Gulen continues to advance the field through ongoing research on topics such as expectation formation, market misvaluation, and the influence of artificial intelligence on stock return forecasts.

Research topics

• Economics
• Econometrics
• Financial economics
• Mathematics
• Monetary economics
• Finance

Selected publications

• Balancing External vs. Internal Validity: An Application of Causal Forest in Finance

  Management Science · 2025-08-22 · 1 citations

  article1st authorCorresponding

  Answering causal questions with generalizable results is challenging. Estimators requiring pseudorandomization provide estimates with no bias (i.e., strong internal validity) but limited generalizability (i.e., weak external validity). Theoretically, causal forest, a nonparametric, machine learning–based matching estimator, can provide low-to-no-bias, generalizable estimates even when treatment is endogenous. We empirically compare the performance of ordinary least squares (OLS), regression discontinuity design (RDD), and causal forest at recovering estimates in simulated observational panel data and show the robustness of causal forest estimates to many sources of bias. We revisit a popular RDD setting, debt covenant default, to show how extendable, heterogeneous causal forest estimates can enhance inferences. This paper was accepted by Tomasz Piskorski, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00109 .

  PublisherDOI

• Price-Path Convexity and Short-Horizon Return Predictability

  Journal of Financial and Quantitative Analysis · 2025-06-20 · 1 citations

  articleOpen access1st author

  Abstract We document a strong, negative relation between the curvature of stock price paths (i.e., price-path convexity) and future short-horizon returns at both the aggregate and firm levels. This relation obtains regardless of the cumulative return during the convexity estimation period. At the aggregate level, convexity is a better predictor of future returns than many commonly used predictors. At the firm level, this effect is not explained by known return predictors, microstructure frictions, or illiquidity. Using survey-based expectations of short-horizon returns, we show that the negative relation between convexity and future returns is driven in part by overextrapolation of past returns.

  PublisherDOI

• Price-Path Convexity and Short-Horizon Return Predictability – ERRATUM

  Journal of Financial and Quantitative Analysis · 2025-10-27

  erratumOpen access1st author
  PublisherDOI

• What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts

  arXiv (Cornell University) · 2024-09-17 · 4 citations

  preprintOpen access

  We examine how large language models (LLMs) interpret historical stock returns and compare their forecasts with estimates from a crowd-sourced platform for ranking stocks. While stock returns exhibit short-term reversals, LLM forecasts over-extrapolate, placing excessive weight on recent performance similar to humans. LLM forecasts appear optimistic relative to historical and future realized returns. When prompted for 80% confidence interval predictions, LLM responses are better calibrated than survey evidence but are pessimistic about outliers, leading to skewed forecast distributions. The findings suggest LLMs manifest common behavioral biases when forecasting expected returns but are better at gauging risks than humans.

  PublisherOA PDFDOI

• Credit Cycles, Expectations, and Corporate Investment

  Review of Financial Studies · 2024-09-05 · 9 citations

  article1st authorCorresponding

  Abstract We provide a systematic empirical assessment of the Minsky hypothesis that business fluctuations stem from irrational swings in expectations. Using predictable firm-level forecast errors, we build an aggregate index of irrational expectations and use it to provide three sets of results. First, we show that our index predicts aggregate credit cycles. Next, we show that these predictable credit cycles drive cycles in firm-level debt issuance and investment and similar cycles between financially constrained and unconstrained firms, as Minsky predicts. Finally, we show more pronounced cycles in firm-level financing and investment for firms with ex ante more optimistic expectations. (JEL G31, G32, G40, E32, E44)

  PublisherDOI

• The Selective Enforcement of Government Regulations: Battleground States, State Regulators, and the Environmental Protection Agency

  The Journal of Law and Economics · 2024-02-01 · 9 citations

  article1st authorCorresponding

  The Electoral College creates incentives for politicians and regulators to direct policy favors toward battleground or swing states. We examine whether this affects regulatory enforcement and find that facilities in battleground states are less likely to be found in violation of the Clean Water Act, partially because the permit limits for facilities in these states are less restrictive. Identification is obtained by analyzing violation rates of similar facilities located along the border between battleground and nonbattleground states.

  PublisherDOI

• What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts

  SSRN Electronic Journal · 2024-01-01 · 5 citations

  preprintOpen access
  PublisherDOI

• Intangible Capital in Factor Models

  Management Science · 2024-05-21 · 13 citations

  article1st authorCorresponding

  The transition from a traditional manufacturing-based economy to a knowledge- and service-based economy over recent decades resulted in a considerable rise in intangible capital, most of which is not reported on companies’ balance sheets. As a result, balance sheet-based valuation ratios, investment measures, and other firm characteristics that do not incorporate off-balance sheet (OBS) intangible capital suffer from significant measurement error problems. We incorporate a new measure of OBS intangible capital into firm characteristics, such as book to market, investment, and profitability, to address these measurement errors. These OBS intangible adjustments improve the performance of the Fama–French three- and five-factor models and the q-factor model, especially during recent decades. We further find that the value factor is no longer redundant in these empirical factor models. This paper was accepted by Lukas Schmid, finance. Funding: The authors are grateful for financial support from the 2019 EDHEC Scientific Beta “Advanced ESG & Factor Investing” Research Chair. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01261 .

  PublisherDOI

• Extracting extrapolative beliefs from market prices: An augmented present-value approach

  Journal of Financial Economics · 2024-12-26 · 9 citations

  articleOpen accessCorresponding
  PublisherOA PDFDOI

• The use of asset growth in empirical asset pricing models

  Journal of Financial Economics · 2023 · 27 citations

  • Economics
  • Financial economics
  • Econometrics
  PublisherDOI

Frequent coauthors

• Michael J. Cooper

  MIT Lincoln Laboratory

  26 shared

• Yuhang Xing

  Rice University

  18 shared

• Lu Zhang

  University of Cambridge

  18 shared

• P. Raghavendra Rau

  University of Cambridge

  16 shared

• Stefano Cassella

  12 shared

• Mihai Ion

  University of Arizona

  8 shared

• Stewart Mayhew

  6 shared

• Benjamin Golez

  University of Notre Dame

  6 shared

Education

• Ph.D., Finance

  University of Texas at Austin

  1999

• M.S., Finance

  University of Texas at Austin

  1995

• B.S., Business Administration

  Middle East Technical University

  1993

Awards & honors

• Winner of the Jack Treynor Prize – Q Group (2018)
• Third Prize in the Q-Group’s 2007 Rodger F. Murray Prize Com…
• Best paper award Midwest Finance Association (2009)
• Winner of the Q Group Grant (2003)
• Winner of “ Best Paper in Finance” Award , FMARC Conference…