Research

Working Papers

Mispriced Equity, Default Likelihood, and Credit Rating Accuracy (with Alexei Zhdanov and Samuel Bonsall IV)

• Abstract:  We investigate the real effects of equity mispricing on default probability and its implications for credit ratings accuracy. We find that equity overpricing significantly reduces the probability of default, primarily through increased investment and equity issuance. Examining methodological differences between Egan Jones Ratings (EJR) and Standard & Poor’s (S&P), we show that EJR’s quantitative approach makes its ratings more sensitive to equity mispricing compared to S&P’s holistic method, particularly for financially distressed firms. Importantly, EJR’s ratings demonstrate greater accuracy for both overpriced and underpriced firms. Our findings suggest that credit rating agencies employing quantitative models may better capture the effects of equity mispricing on default risk, thus improving rating accuracy. This study also highlights a previously unexplored real effect of equity mispricing on corporate defaults.

• Presentations:  PSU Seminar(2024)

A Tale of Two Anomalies: Value, Momentum, and Risk Sentiment (with Christian Lundblad, Mihail Velikov, and Alexei Zhdanov)

• Abstract:  We uncover a fundamental divide in how asset pricing anomalies respond to shifts in investor risk sentiment: build-up anomalies thrive in risk-off periods while resolution anomalies collapse. Using momentum and value as representative cases, we show that value stocks and past losers experience sharp underperformance precisely when risk appetite deteriorates. Trading data offer an additional perspective: during risk-off episodes, retail investors flee value stocks, while short sellers double down, intensifying the drawdown. In contrast, momentum stocks evade similar selling pressure, reinforcing their resilience. Specifically, we distinguish between build-up and resolution anomalies, providing new evidence on the resilience of some anomalies in the face of deteriorating risk sentiment while others unravel. These differences in both returns and investor flows between anomaly types are difficult to coherently reconcile with a comprehensive theoretical explanation.

• Presentations: PSU Brownbag* (2024); 19th International Behaviour Finance Conference (2025)

Work in Progress

Market Feedback and Managerial Learning: The Role of Forward-Looking Disclosure  

• Abstract:  I identify what information managers extract from stock market reactions to their disclosures. Using textual analysis of 10-K forward-looking statements and machine learning, I show that managers learn about their growth plans' viability relative to product peers. Investment-q sensitivity increases with firm-specific forward-looking disclosure, driven by enhanced market feedback about relative growth prospects. I develop a measure capturing relative growth probability from disclosure text that predicts post-filing returns and analyst revisions. Corporate investment subsequently responds to peer-adjusted returns, with stronger responses following more specific disclosures. My findings reveal that managers strategically disclose detailed plans to elicit market feedback about competitive dynamics they cannot assess internally.

• Presentations:  2026 AFA PhD Poster Session (Scheduled), 2025 FMA (Scheduled), 2025 FMA Doctorial Consortia (Scheduled), PSU Colloquium (2025)

• Grants & Awards: Smeal Small Research Grant ($1500)