Essays on Empirical Asset Pricing

Abstract

In this dissertation, I investigate important questions related to the impact of non-fundamentally driven demand shocks on asset prices (first chapter) and crowding by institutional investors (second chapter). The first chapter is single-authored, and the second chapter is a joint work with Ludwig Chincarini and Fabio Moneta. In the first chapter, I revisit the stock price fragility measure of Greenwood and Thesmar (2011) and propose a different approach to estimate it using exchange-traded funds (ETFs) data rather than mutual funds data as in the original paper. Previous literature employs equity mutual fund flows to measure a stock's exposure to non-fundamental demand risk - stock price fragility. However, this approach may be biased by confounding fundamental information, potentially leading to underestimation of risk exposure. Moreover, I document a significant decrease in the forecasting power of the mutual fund-based fragility for future stock return volatility in the most recent sample period. I propose an alternative approach that incorporates readily available primary market data from ETFs. This approach significantly enhances the predictive power of fragility in forecasting stock return volatility. Moreover, this approach captures the influence of increased ETF activeness while partially capturing the effect of non-fundamental institutional demand on return volatility. In the second chapter, we investigate the relation between crowded trades, those in which many investors hold the same stocks possibly exhausting their liquidity provision, and future stock returns on a set of well-known stock market anomalies. We find that anomaly risk-adjusted returns appear to be concentrated among the most (least) crowded stocks for the long-leg (short-leg) portfolio. Moreover, we find that our results remain significant after publication dates. We hypothesize that crowded equity positions in anomaly stocks increase institutional investor’s exposure to crash risk. Our findings are consistent with this hypothesis and suggest that crowding adds a new consideration to the limits of arbitrage.