Research

PUBLICATION

Finance or Gambling? Prediction Markets After Loper Bright and the Administrative Record

Forthcoming at the American Business Law Journal (2027),  Volume 64, Issue 1

(with Nizan Geslevich Packin, Elior Sulem, and Sharon Rabinovitz)

Abstract: After Loper Bright displaced Chevron deference, how an agency constructs and defends the administrative record became a central question of administrative law, and prediction markets have become one of its most contested settings. Reframing the finance-versus-gambling classification dispute as a problem of administrative law, this Article examines how it manifests in the administrative record itself. Applying computational text and sentiment analysis to 812 public comments on the CFTC's 2024 Event Contracts proposed rulemaking, we find a pronounced asymmetry: 87.2% of submissions framed prediction markets in economic, financial, or regulatory terms, while only 5.8% emphasized gambling risks. Professional and organized stakeholders, though a numerical minority, dominate the record through the length and density of their submissions. In a post-Loper Bright environment of independent judicial scrutiny, the record's rhetorical construction can prove decisive, suggesting the legal status of prediction markets may turn less on their inherent characteristics than on the discursive strategies through which interested parties present them to regulators and courts.

Inside the Odds

Forthcoming at the Vanderbilt Journal of Entertainment and Technology Law (Forthcoming 2027)

(with Nizan Geslevich Packin, Elior Sulem, and Sharon Rabinovitz)

Abstract: Prediction markets have rapidly moved from regulatory fringe to consequential financial infrastructure, yet the doctrinal and empirical foundations for governing insider trading in these markets remain underdeveloped. This Article argues that the mainstreaming of prediction markets has produced insider trading risks that neither securities law nor gambling law is equipped to address. It presents the first empirical study of insider trading risk recognition in prediction markets, examining public comments to the CFTC's 2024 Notice of Proposed Rulemaking using LLM analysis, computational text analysis, and multi-model validation. The findings show that neither regulators nor commenters meaningfully engaged with insider trading risks before they became controversial, revealing a systematic failure of regulatory processes to anticipate informational abuse. Building on these findings, the Article develops a typology of insider trading-like misconduct, including trading on privileged institutional access, temporal informational advantages, manipulation of event probabilities, exploitation of platform design features, and self-referential trading, and proposes a governance framework integrating doctrinal clarification, regulatory oversight, and platform-level safeguards. 

The U.S. Syndicated Loan Market: Matching data

Journal of Financial Research, 44(4), 695–723 (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.

WORKING PAPERS 

The Display of Information and Household Investment Behavior

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.

Media: Capital Ideas; Chicago Booth Review; Forefront; Harvard Business Review;  Il Sole 24 Ore

Appears in: Misbehaving: The Making of Behavioral Economics; The Smarter Screen: Surprising Ways to Influence and Improve Online Behavior

Selection into Informative Consumer Credit Markets

Submitted

(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.

Relationship Banking and Credit Scores: Evidence from a Natural Experiment

(with Tali Bank and Nimrod Segev) 

Media: Calcalist (in Hebrew)

Abstract: We show the effect of exogenous introduction of credit scoring data on pricing of loans by banks. Utilizing a novel dataset of the universe of loans in Israel, we nd that a decline in information asymmetry, following credit scores' introduction, led to a decrease in loan prices for households with strong relationship banking. Prior to that, when banks held a monopoly on potential borrowers' credit history, they charged higher interest rates all else equal, as predicted by theoretical models. We then show that these informational rents signicantly decrease once credit scores are introduced, and document the resulting decline in the hold-up problem. To the best of our knowledge, this paper is the first to show the causal impact of credit scoring on households' loan pricing. Our results highlight the importance of information sharing in consumer credit markets and have important public policy implications.

Programs

Fedmatch R Package

This package is a byproduct of an effort by Federal Reserve staff to merge various datasets with information about the US corporate lending market.

See the fedmatch.pdf and examples/match_template.R for more information on the functionality of the package.