Research

Abstract: We show that merger announcement returns account for virtually all of the measured size premium. An empirical proxy for ex ante takeover exposure positively and robustly relates to cross-sectional expected returns. The relation between size and expected returns becomes positive or insignificant, rather than negative, conditional on this takeover characteristic. Asset pricing models that include a factor based on the takeover characteristic outperform otherwise similar models that include the conventional size factor. We conclude that the takeover factor should replace the conventional size factor in benchmark asset pricing models.

Presentations: Aarhus University, University of Washington Summer Finance Conference (2022), Virginia Tech, Washington and Lee University

Online Appendix: Link

Abstract: Financial data vendors’ data coverage decisions affect institutional investor demand. Using Standard & Poor’s (‘S&P’) Compustat database, I show that institutional investment in firms with missing data is over 36% below the mean. A quasi-natural experiment confirms a plausibly causal connection: A technology improvement at S&P causes a discrete reduction in missing data, leading to a rise in institutional investment for the subset of treated firms. Missing Compustat data ultimately relates to lower price efficiency via its impact on institutional demand. I conclude that data vendors’ actions can materially impact capital markets because they affect firms’ access to institutional capital.

Presentations: SFS Cavalcade North America (2024), Northern Finance Association Conference (2024), Financial Management Association Conference (2024), Southern Finance Association Conference (2024), Eastern Finance Association Conference (2025), Arizona State University, Georgia Tech, Iowa State University, Louisiana State University, Miami University, Texas Christian University, Tulane University, University of Arizona, University of Missouri, University of Oklahoma, Virginia Tech

Previously Titled: Information Intermediaries and the Distorting Effect of Incomplete Data

• Abstract: Estimates of maximum Sharpe ratios for popular multifactor asset pricing models seem too large to be consistent with risk-based explanations. We show that this “Sharpe ratio puzzle” can be explained by accounting for the influence of historical data on factor selection. From a methodological perspective, we demonstrate that commonly used out-of-sample model assessment approaches are prone to optimistic bias because evaluation samples overlap with data analyzed in prior return anomaly studies. We instead measure model performance using validation samples that plausibly did not influence model specification. Empirically, this approach substantially reduces estimates of maximum Sharpe ratios across a wide range of multifactor models, thereby resolving the Sharpe ratio puzzle. 

• Presentations: Financial Management Association Conference (2023), Portuguese Finance Network Conference (2023), Financial Management Association European Conference (2023), University of North Carolina at Charlotte, University of North Texas, University of Virginia, Virginia Tech

A brief description of data availability in CRSP.