Date Available

4-26-2019

Year of Publication

2019

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Business and Economics

Department/School/Program

Finance and Quantitative Methods

First Advisor

Dr. Kristine Hankins

Abstract

The first chapter studies mutual funds. I model intraquarter trading and use a genetic algorithm to estimate the trade pattern that is most consistent with the fund's daily reported returns. I validate the model empirically on a sample of institutional trades from Ancerno and I confirm that the method more accurately predicts daily holdings when compared to existing naive assumptions. Further, my method is substantially more accurate in classifying a fund's tendency to supply liquidity, and this increased precision has important implications for identifying superior performing funds. Specifically, a long-short strategy based on the model's liquidity provision measures earns significant abnormal returns, while a similar strategy that relies on quarterly holdings does not exhibit any outperformance. The second chapter studies investment research. We find evidence that crowdsourced investment research facilitates informed trading by retail investors and improves firm liquidity. Specifically, retail order imbalances are strongly correlated with the sentiment of Seeking Alpha articles, and the ability of retail order imbalances to predict returns is roughly twice as large on research article days. In addition, firms with exogenous reductions in Seeking Alpha coverage experience increases in bid-ask spreads and price impact, with the effect being stronger for firms with high retail ownership. Our findings suggest that technological innovations have helped democratize access to investment research with important implications for firm liquidity.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2019.149

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