Research

Job Market Paper:

Do Investors Have Data Blind Spots? The Role of Data Vendors in Capital Markets

• 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. 

• Awards: Best Paper in Empirical Finance (SFA 2024)

• 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

Published and Forthcoming Papers:

Taking Over the Size Effect: Asset Pricing Implications of Merger Activity

with Jeffry Netter, Bradley Paye, and Michael Stegemoller

Journal of Financial and Quantitative Analysis, March 2024, vol. 59, no. 2, pp.690-726.

• 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

Working Papers:

High (on) Sharpe Ratios: Assessing Data-Instigated Factor Models 

• • with Bradley Paye

  • Abstract: Estimates of maximum Sharpe ratios for multifactor asset pricing models seem too large to be consistent with risk-based explanations. We argue that this “Sharpe ratio puzzle” can be resolved by accounting for the fact that historical data influences the selection of factors. We show that common approaches to measuring Sharpe ratios for data-instigated models are subject to substantial optimistic bias. Upon adopting estimation approaches that mitigate this bias, we find that multifactor model Sharpe ratio improvements relative to the capital asset pricing model fall dramatically. Our results challenge the perception that rich factor models are vastly superior to classic benchmarks.

  • 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

Works In Progress:

Why is Data Missing in CRSP and Compustat?

• Companion to Job Market Paper

• Abstract: CRSP and Compustat are two of the most widely used databases in finance and accounting research. However, neither database provides complete information for all NYSE, AMEX, or NASDAQ firms at any point in time. I describe when data is missing in both databases for over 50 variables, which are used to construct a comprehensive selection of firm characteristics. Importantly, I identify why data is missing. Researchers employing either CRSP or Compustat should be wary of missing data. Whether missing data impacts empirical analyses relying on these databases depends critically on what data is used, and when and why that information is missing.