Information Asymmetry and Expectations about Returns 1073 views

With the advent of the internet and social media, we now have real time opinions about future asset price changes by large numbers of people. In the first chapter, opinionated tweets about the Euro/dollar exchange rate are used to illustrate how information can be extracted from social media. We develop a detailed lexicon used by FX traders to translate verbal tweets into opinions that are ranked positive, negative and neutral. The methodologically novel aspect of our approach is the use of model with a precise information structure to interpret the data from opinionated FX tweets. The parameters related to the information structure are quite precisely estimated and the model is able to match a wide variety of moments involving Twitter Sentiment and the exchange rate. Based on the estimated model we are able to use daily Twitter Sentiment to predict exchange rates and compute Sharpe ratios for trading strategies. We are able to significantly outperform related results for interest differentials, which are the foundation of the large carry-trade industry. The second chapter introduces a new measure of stock market investor sentiment based on the opinions shared on Twitter. The main advantage of the proposed index over existing measures of sentiment is the possibility of using the number of followers as a proxy for the quality of private signals. Moreover, the data allows for gauging sentiment directly with high frequency data. The index is used to test the implications of theories in asset pricing. The results show that (1) the follower-weighted sentiment index predicts the same day return of the stock market index, but the equal-weighted index has no predictive power for daily returns, (2) dispersion of expectations about future returns predicts volatility of the stock market returns, (3) information asymmetry is positively related to return volatility, and (4) the density of information arrival measured by the number of opinionated tweets is positively correlated with volatility and trading volume of the stock market index.

PHD (Doctor of Philosophy)

English

All rights reserved (no additional license for public reuse)

2016-11-27