Abstract: This paper studies subjective risk perceptions by constructing and examining a novel dataset linking corporate bond analysts' recommendations to their textual comments. Two stylized facts emerge: (i) bond analysts favor higher-yielding, riskier bonds only in higher-rated categories, even when fundamentals are comparable; (ii) perceived credit risk extracted from analysts' comments predicts subsequent credit deterioration, but is not reflected in their recommendations for higher-rated bonds. These patterns hold across investor types and client and non-client issuers, suggesting that catering to investors, or catering to clients do not fully explain the results. Rather, the evidence supports categorical thinking: analysts infer from credit rating categories instead of rational Bayesian updating, leading them to underweight their perceived bond-specific risks for purportedly safe bonds. Analyst recommendations earn positive alphas only in lower-rated bonds.
Selected Presentations: Purdue University (2025), FMA Ph.D. poster (2025, scheduled), Sydney Banking and Financial Stability Conference (2025, scheduled), 19th International Behavioural Finance Conference (2025, scheduled)(with Umang Ketan, Jetson Leder-Luis, Jialan Wang)
Revise and Resubmit: AEJ: Economic Policy
Abstract: We study fraud in the unemployment insurance system using a dataset of 35 million debit card transactions. We apply machine learning techniques to cluster cards corresponding to varying levels of potentially fraudulent activity. We then conduct a difference-in-differences analysis based on the staggered adoption of statelevel identity verification systems between 2020 and 2021 to assess the effectiveness of screening technologies for reducing fraud. Our findings suggest that identity verification reduced payouts to suspicious cards by 27%, while leaving non-suspicious cards largely unaffected. Our results indicate that screening may be an effective mechanism for mitigating fraud in large public benefits programs.
Selected Presentation: NBER Public Economics (2025), MFA (2024), Purdue Brownbag (2024), AEA (2024), NBER Innovative Data in Household Finance (2023), MIT Rising Scholars Conference (2023), FMA (2023), Georgetown University(with Huseyin Gulen, Yan Liu, Nan Yang)
Abstract: We investigate the extent to which expectations of both individual stock returns and stock index returns implied from option prices are extrapolative. We use option-implied bounds as our proxy for option traders' expectations for future stock returns. We find that the option-implied expectation bounds actually load negatively on past returns and that this behavior is in line with future stock return realizations, suggesting that the behavior of investors trading in the option markets is consistent with rational expectations. We also document that approximately 60% of mutual fund managers trade in the same direction as the option-implied expected returns.
Selected Presentations: 2023 Purdue University