Essays in financial economics

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, September, 2020Cataloged from student-submitted PDF version of thesis.Includes bibliographical references.Chapter 1 constructs a theoretical model of an ETF. Conventional wisdom warns that exchange-traded funds (ETFs) harm stock price discovery, either by ``stealing'' single-stock liquidity or forcing stock prices to co-move. Contra this belief, I develop a theoretical model that investors with stock-specific information trade both single stocks and ETFs. While the ETF is payoff-redundant, asymmetric information and a position limit for informed traders combine to make the ETF non-redundant. Single-stock investors can access ETF liquidity by means of this tandem trading, and stock prices can flexibly adjust to ETF price movements. Effects are strongest when an individual stock has a large weight in the ETF and a large stock-specific informational asymmetry. I conclude that ETFs can provide single-stock price discovery. Chapter 2 empirically tests the predictions of the ETF model. Using high-resolution data on SPDR and the Sector SPDR ETFs, I exploit exchange latencies in order to show that investors place simultaneous, same-direction trades in both a stock and ETF. Consistent with my model predictions, effects are strongest when an individual stock has a large weight in the ETF and a large stock-specific informational asymmetry. Chapter 3 models how risk-averse investors trade when they are uncertain about the quality of their signal. I show that when traders are risk-averse, traders can submit demands which are non-monotone in their signal. While their expected value for the asset may rise with stronger signals, so does the risk that the signal is noise. This leads to short-term behavior which is herding-like. Unlike herding, investors maintain a positive expected value for the asset, but it is their risk aversion leads them to take smaller positions, which has a similar slowing effect on price discovery.by Thomas Ernst.Ph. D.Ph.D. Massachusetts Institute of Technology, Sloan School of Managemen