Accepted at the Journal of Financial Research (September 2021)
(with Gregory Cohen, Melanie Friedrichs, Kamran Gupta, William Hayes, Seung Jung Lee, Blake Marsh, Nathan Mislang, and Martin Sicilian)
Abstract: We introduce a new software package for determining linkages between datasets without common identifiers. We apply these methods to three datasets commonly used in academic research on syndicated lending: Refinitiv LPC DealScan, the Shared National Credit Database, and S&P Global Market Intelligence Compustat. We benchmark the results of our match using results from the literature and previously matched files that are publicly available. We find that the company level matching is enhanced by careful cleaning of the data and considering hierarchical relationships. For loan level matching, a tailored approach based on a good understanding of the data can be better in certain dimensions than a more pure machine learning approach.
Revise and Resubmit at the Journal of Finance
Abstract: I show that household investment decisions depend on the manner in which information is displayed by exploiting a regulatory change which prohibited the display of past returns for any period shorter than twelve months. In this setting, the information displayed was altered but the information households could access remained the same. Using a differences-in-differences design, I find that the shock to information display caused a reduction in the sensitivity of fund flows to short-term returns, a decline in overall trade volume, and increased asset allocation toward riskier funds. These results are consistent with models of limited attention and myopic loss aversion. To further explore the concept of salience, I propose a distinction between relative and absolute salience and find evidence consistent with the latter. Overall, my findings indicate that small changes in the manner in which past performance information is displayed can have large effects on household investment behavior and potentially influence households’ accumulated wealth at retirement.
(with Inessa Liskovich)
Abstract: Technological innovation facilitated households’ access to information about their cost of credit. We exploit a quasi-natural experiment in an online consumer credit market to identify which households take advantage of informative markets. We find that when a platform switched from personalized loan prices to prices by credit grade - less experienced individuals immediately and disproportionately exit the market, especially among riskier borrowers. We conclude that less experienced borrowers sort into markets offering personalized information. Additional analysis confirms that their behavior is consistent with learning from personalized prices. Our results highlight the important, yet overlooked, informative role of the growing fintech sector.
This package is a byproduct of an effort by Federal Reserve staff to merge various datasets with information about the US corporate lending market.