Topics: Asset pricing, Institutional Investors, Behavioral Finance
Methods: Structural Estimation, Demand Systems, Panel Data Econometrics, Causal Inference, Machine learning, Optimization
Software: Python (expert), Stata (expert), R (advanced), HTC (parallel processing)
Job Market Paper
Abstract:
This paper investigates how institutional investors incorporate anomaly signals into their trading once these signals become publicly available. To address this question, I combine institutional holdings data (FactSet Ownership) with 150 well-documented cross-sectional stock return anomalies from the literature and estimate a structural demand system to capture time-varying demand elasticities to anomaly returns. This approach enables me to identify shifts in investor demand following the initial publication of academic research on stock return anomalies. The findings reveal strong heterogeneity across anomalies and investor types. On average, demand for anomalies declines after both SSRN and journal publication, while evidence of investor learning is observed for 36% of anomalies at the anomaly level. Moreover, these learning patterns are closely linked to the historical performance of anomalies after accounting for trading costs.
Keywords: Demand System Asset Pricing, Stock Return Anomalies, Institutional Investor Behavior, Information Diffusion.
Under Review at Journal of Banking and Finance
Presented: 2024 FMA Annual Meeting (New Idea Sessions)- 2025 FMA Annual Meeting (accepted)- 2024 MBAA International Conference
Abstract:
We integrate a well-established measure of price elasticity into the asset demand system to examine whether investors value sustainability and environmental, social, and governance (ESG) factors. Our study focuses on U.S. mutual funds from 2010 to 2023, ranking them based on MSCI ESG ratings. On average, higher-rated ESG mutual funds charge higher fees, prompting the critical question: Do these funds deliver on what their ESG labels imply? Our findings show that on average high-rated ESG mutual funds show lower elasticity toward high ESG stocks and are more price sensitive toward low-rated ESG stocks. We also show that high-rated ESG mutual funds tilt their portfolio toward greener stocks, and with one standard deviation increase in environmental score, they increase their holdings by about 3.56%. As expected, Low-rated ESG mutual funds prefer browner stocks with the coefficient of -14.29%.
Keywords: ESG Investing, Demand System Asset Pricing, Price Elasticity of Demand, Cross-Sectional Regression.
With Candace Jens
