Date of Award

5-2026

Degree Type

Dissertation

Degree Name

Ph.D.

Degree Program

Financial Economics

Department

Economics and Finance

Major Professor

Atsuyuki Naka

Second Advisor

Luca Pezzo

Third Advisor

Mohammad K Hassan

Fourth Advisor

Walter Lane

Abstract

The first essay examines whether sentiment, ambiguity, time-varying risk aversion, and preferences for lottery-like stocks predict future crash risk using a large panel of U.S. stocks from 2000-2023. Both survey-based sentiment and market-based proxies, including the option-to-stock volume ratio, predict crash risk. A one-standard deviation increase in sentiment raises crash probabilities by 1 to 1.6%, and a 1% increase in the option-to-stock volume ratio raises crash odds by 7 to 8%. Aggregate risk aversion and market-level ambiguity significantly increase crash probabilities. This effect is particularly pronounced during periods of high ambiguity, as measured by the VVIX and economic uncertainty indices. Preference-based channels also matter: lottery demand and nominal price salience are associated with downside risk, consistent with behavior-driven crashes. A central finding is that nominal prices are associated with greater downside volatility relative to upside volatility, contrasting with the symmetric volatility relation reported by Shue and Townsend (2021).

The second essay investigates whether sentiment, liquidity risk, and heterogeneous informed trading shape idiosyncratic volatility (IVOL) using a large panel of U.S. firms from 1990-2023. The option-to-stock volume ratio, a proxy for informed sentiment, along with survey-based and market-based sentiment measures, is positively associated with IVOL. A one-standard deviation increase in the option-to-stock volume ratio raises IVOL by about 3% relative to its mean. To address endogeneity, we exploit the introduction of the CBOE Penny Pilot Program as an exogenous shock to options market trading and confirm the causal effect of informed option trading on IVOL. Risk preferences and uncertainty also matter: higher risk aversion and ambiguity increase IVOL, while sentiment effects are stronger when risk aversion and ambiguity are low. Exposure to aggregate liquidity shocks also contributes to IVOL, as liquidity risk measured by the liquidity beta is positively related to IVOL, consistent with limits-to-arbitrage mechanisms. Finally, machine-learning measures of informed trading reveal heterogeneous effects. Activist, patient, and impatient informed trading are negatively related to IVOL, whereas insider and short-selling trading activities increase IVOL, particularly during periods of high sentiment and investor disagreement.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

Download

Available for download on Saturday, May 31, 2031

Share

COinS