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In this dissertation, I consider a range of topics related to the role played by information in modern asset pricing theory. The primary research focus is twofold. First, I synthesize existing research in insider trading and seek to stimulate an expansion of the literature at the intersection of work in the insider trading and financial economics areas. Second, I present the case for using Peter Bossaerts's (2004) Efficiently Learning Markets (ELM) methodology to empirically test asset pricing models.
The first chapter traces the development of domestic and international insider trading regulations and explores the legal issues surrounding the proprietary nature of information in financial markets. I argue that, practically, the reinvigoration of the insider trading debate is unfortunate because, in spite of seemingly unending efforts to settle the debate, we are no closer to answering whether insider trading is even harmful, much less worthy of legal action. In doing so, I challenge the conventional wisdom of framing insider trading research as a quest for resolution to the debate. By adopting an agnostic perspective on the desirability of insider trading regulations, I am able to clearly identify nine issues in this area that are fruitful topics for future research.
The second chapter studies prices and returns for movie-specific Arrow-Debreu securities traded on the Iowa Electronic Markets. The payoffs to these securities are based on the movies' initial 4-week U.S. box office receipts. We employ a unique data set for which we have traders' pre-opening forecasts to provide the first direct test of Bossaerts's (2004) ELM hypothesis. We supplement the forecasts with estimated convergence rates to examine whether the prior forecast errors affect market price convergence. Our results support the ELM hypothesis. While significant deviations between initial forecasts and actual box-office outcomes exist, prices nonetheless evolve in accordance with efficient updating. Further, convergence rates appear independent of both the average initial forecast error and the level of disagreement in forecasts.
Lastly, the third chapter revisits the theoretical justifications for Bossaerts's (2004) ELM, with the goal of providing clear, intuitive proofs of the key results underlying the methodology. The seemingly biggest hurdle to garnering more widespread adoption of the ELM methodology is the confusion that surrounds the use of weighted modified returns when testing for rational asset pricing restrictions. I attack this hurdle by offering a transparent justification for this approach. I then establish how and why Bossaerts's results extend from the case of digital options to the more practically relevant class of all limited-liability securities, including equities. I conclude by showing that the ELM restrictions naturally lend themselves to estimation and testing of asset pricing models, using weighted modified returns, in a Generalized Method of Moments (GMM) framework.