About

Xu Han is an Assistant Research Professor in the Department of Biomedical Engineering at Penn State University. He is affiliated with the Biomedical Engineering program and is based in the Millennium Science Complex. His research focuses on biomedical engineering, integrating traditional engineering principles with medicine and technology to improve human health and society. His work involves areas such as biomaterials and drug delivery, biomechanics and mechanobiology, biomedical devices, biomedical imaging, computational modeling of biological systems, and regenerative medicine. As part of his role, he contributes to advancing biomedical engineering research and education at Penn State.

Research topics

• Financial economics
• Economics
• Econometrics
• Political Science
• Monetary economics
• Computer Science
• Data Mining
• Actuarial science
• Accounting
• Statistics
• Mathematics
• Finance

Selected publications

• Persistence of investor sentiment and market mispricing

  Financial Review · 2022 · 33 citations

  1st authorCorresponding
  • Political Science
  • Economics
  • Econometrics

  Abstract We investigate changes in US market sentiment using structural break analysis over a period of five decades. We show that investor sentiment was trending and nonstationary from 1965 to 2001, a period associated with numerous crashes. Since 2001, sentiment has been substantially more mean reverting, implying the diminished effect of noise investors and their associated mispricing. We illustrate how these changes in sentiment persistence affect equity anomalies and assess the predictive power of sentiment on short‐run returns when regime changes are considered. Our findings suggest that the presence of sentiment‐driven investors and their market impact is significantly time‐variant.

  DOI

• Man versus Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases

  Review of Financial Studies · 2022 · 86 citations

  • Computer Science
  • Econometrics
  • Data Mining

  Abstract We introduce a real-time measure of conditional biases to firms’ earnings forecasts. The measure is defined as the difference between analysts’ expectations and a statistically optimal unbiased machine-learning benchmark. Analysts’ conditional expectations are, on average, biased upward, a bias that increases in the forecast horizon. These biases are associated with negative cross-sectional return predictability, and the short legs of many anomalies contain firms with excessively optimistic earnings forecasts. Further, managers of companies with the greatest upward-biased earnings forecasts are more likely to issue stocks. Commonly used linear earnings models do not work out-of-sample and are inferior to those analysts provide. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

  PublisherOA PDFDOI

• Predicting the equity premium with the implied volatility spread

  Journal of Financial Markets · 2020 · 26 citations

  Senior authorCorresponding
  • Economics
  • Financial economics
  • Econometrics
  DOI

Frequent coauthors

• Jules H. van Binsbergen

  6 shared

• Charles Cao

  Knoxville College

  3 shared

• Timothy T. Simin

  Pennsylvania State University

  3 shared

• Joseph J. Henry

  2 shared

• Alejandro Lopez-Lira

  University of Florida

  2 shared

• David Gempesaw

  2 shared

• Nikolai Roussanov

  University of Pennsylvania

  2 shared

• Arman Eshraghi

  1 shared

Education

• Ph.D., Finance

  Pennsylvania State University

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