Abstract/Summary

This thesis contributes to the growing literature on the textual analysis of news and the cross-section of stock returns throughout three main chapters based on the Dow Jones Newswire Archive. First, I consider in an empirical setting how the presence/absence of news coverage affects lottery-type stocks, measured by the maximum daily returns (MAX) in the prior month. I find an augmented negative relationship between MAX stocks without news and expected returns, whereby MAX with news coverage generates return momentum. The differing future return relationships between MAX stocks with and without news appears to be best explained by information uncertainty mitigation upon news arrival. Overall, this finding suggests that news plays a role in resolving information uncertainty in the stock market. Next, by applying a “co-coverage” concept, I propose to identify each firm’s news-based peers (NBPs) and thereby construct a time-varying firm-centric grouping aiming to augment existing industry classification schemes. The advantage of NBP-augmented schemes over traditional industry classifications is to better capture the up-to-date relative importance of the economic links between a base firm and its peers. I show that the base firm’s share price responds more favourably to the return shocks of its NBPs than of its traditional industry peers that are not NBPs. The response persists for several months, suggesting that the up-to-date relative importance of the economic links is not immediately clear to investors. Additional empirical tests show that the persistent response, referred to as NBP momentum, can partially unify several lead-lag return momentums previously documented and is consistent with the investor attention hypothesis. Taken together, these results suggest that monitoring news co-coverage is a key to understanding the lead-lag return momentums documented in the literature. Finally, I distinguish news coverage as being either slowly or quickly incorporated into contemporaneous stock prices. The return spread between stocks classified according to these two types of news yields a statistically significant profit of 139 basis points per month. This abnormal return cannot be explained by other well-known risk factors and is robust to allow for trading costs. Overall, this research refines the role of news regarding information dissemination in the financial markets.