Asset prices aggregate information and reflect market expectations about real outcomes. In this dissertation, I examine the informational content of prices and investigate the implications for forecasting returns, volatility, and the successful completion of corporate events with applications for popular hedge fund trading strategies.
The first chapter introduces a structural model for stock and option pricing in mergers and acquisitions. I show theoretically and empirically that option prices contain significant content for forecasting deal outcomes. Additionally, I employ my model to study the risks and returns of merger arbitrage strategies. Consistent with the data, my model predicts that merger arbitrage exhibits low volatility and large Sharpe ratios when deals are likely to succeed. To implement this observation, I construct the returns from a buy and hold strategy that overweights deals with a high implied probability of success. The high probability strategy nearly doubles the monthly Sharpe ratio of an equal weighted strategy that invests in all of the active deals in the economy.
The second chapter, which incorporates material from a joint paper with Yacine Aït-Sahalia and Jiangmin Xu, examines the relationship between high frequency machine-readable news and asset prices. Within the trading day, I show that positive news sentiment forecasts high returns and low volatility, and that large quantities of news forecast high volatility and high volumes. In an application of these observations, I use intraday news sentiment to improve the performance of contrarian trading strategies. Additionally, I demonstrate that intraday patterns in the arrival of news are contemporaneous with patterns in realized volatility and volume, and I document examples of large price movements that lead and lag the news.
The third chapter concludes by proposing a new test of dynamic asset pricing models whose expected returns satisfy a conditional beta relationship. The test applies recent developments from the financial econometrics literature to estimate time varying betas with high frequency data thereby providing a nonparametric alternative to traditional asset pricing tests. Empirically, I find the conditional CAPM is rejected by the data.
|Commitee:||Brunnermeier, Markus, Jurek, Jakub|
|School Location:||United States -- New Jersey|
|Source:||DAI-A 77/02(E), Dissertation Abstracts International|
|Subjects:||Statistics, Economics, Finance|
|Keywords:||Asset pricing, Financial econometrics, Financial economics, Forecasting, High-frequency, Option pricing|
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