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

Abstract: Financial data vendors collect, aggregate, and process data on clients' behalf. I show that data vendors' coverage decisions affect institutional investor demand. I focus on Standard & Poor's ('S&P') Compustat database as an empirical setting. Compustat provides subscribers with decades of 10-K and 10-Q data; however, it does not cover every public firm in every period. I show that institutional investment in firms with no Compustat coverage is over 36% below its unconditional mean, even controlling for other firm characteristics. A novel quasi-natural experiment establishes a plausibly causal connection: a technology improvement at S&P in the 1990s causes a discrete reduction in missing data. This change in data coverage is followed by a significant increase in institutional investment for treated firms relative to control firms. I then show that missing Compustat data is associated with lower informational efficiency of equity prices. I conclude that data vendors' actions can exert a material influence on 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, scheduled), 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. This “Sharpe ratio puzzle” is mitigated by accounting for the fact that historical data influences factor selection. We show that common approaches to measuring Sharpe ratios for data-instigated models are subject to substantial optimistic bias. Using alternative empirical approaches that mitigate this bias, we find that multifactor model Sharpe ratio improvements relative to the capital asset pricing model fall dramatically. Our analysis illustrates the importance of accounting for data-instigated model selection effects in evaluating and comparing factor models. 

• 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

Companion to Job Market Paper

Abstract: A comprehensive discussion of when and why data is missing in Compustat, including suggestions for addressing missing data.

Abstract: A brief description of data availability in CRSP.