My research focuses primarily on applications of artificial intelligence and machine learning in empirical asset pricing, with broader interests in sustainable finance, mutual funds, and cryptocurrency markets. My current work examines return and risk predictability in ESG mutual funds and the extent to which risks from established financial markets are reflected in cryptocurrency returns.
Status: Available upon request.
This paper examines whether machine-learning methods can predict the future returns and realized volatility of ESG mutual funds and whether the predictive information comes from fund characteristics, holdings-based stock characteristics, or ESG information. The analysis uses quarterly data from 2006 to 2024 and combines CRSP mutual fund data, Refinitiv ESG scores, ESG fund classifications, portfolio holdings, and stock-level characteristics.
The results show that future fund volatility is substantially more predictable than returns, with holdings-based stock characteristics providing the strongest and most consistent predictive information. Although return forecasts have limited accuracy in levels, portfolio sorts indicate that the predictions contain economically meaningful cross-sectional information about subsequent fund outcomes. ESG information makes a more limited and model-dependent contribution than fund characteristics and holdings-based information.
Scheduled presentations: 3rd AI in Finance Conference, 2026; Climate Finance & Sustainability Conference, 2026.
This paper examines whether exposures to equity-market returns and volatility command risk premia in the cross-section of cryptocurrency returns. Using a large cryptocurrency universe, we estimate time-varying sensitivities to equity returns and market volatility and test whether these exposures explain subsequent differences in cryptocurrency returns. The findings indicate that cryptocurrency pricing partly reflects risks originating in equity markets.
Presented at: FMA Annual Meeting, 2024; FMA PhD Poster Session, 2025; SWFA Annual Meeting, 2025; World Finance Conference, 2026.
Complementing the preceding paper’s focus on risk pricing, this paper examines cryptocurrency exposure to a broader set of systematic factors from equity, currency, and commodity markets. The results show that cryptocurrency returns are connected to several established asset classes, with the strongest and most consistent evidence coming from equity-market factors.
Presented at: FMA Emerging Scholars Initiative, 2023.