Jin-Mo Kim

Local Institutional Investors, Information Asymmetries, and Equity Returns

We examine the informational role of geographically proximate institutions in stock markets. We find that both the level of and change in local institutional ownership predict future stock returns, particularly for firms with high information asymmetry; in contrast, such predictive abilities are relatively weak for nonlocal institutional ownership. The local advantage is especially evident for local investment advisors, high local ownership institutions, and high local turnover institutions. We also find that the stocks that local institutional investors hold (trade) earn higher excess returns around future earnings announcements than those that nonlocal institutional investors hold (trade). & 2010 Elsevier B.V. All rights reserved. All rights reserved. providing us with rm) data and Russ ted returns. We are

Is Firm‐Specific Return Variation a Measure of Information Efficiency?

The issue of whether firm‐specific return variation measures the private information reflected in stock returns or trading noise is controversial. Using a firms geographic proximity to its investors as a proxy for a firms private information, we investigate the relation between firm‐specific return variation and price informativeness. We find that firms located in metropolitan areas experience higher firm‐specific return variation and that holdings and trading by local institutional investors positively affect firm‐specific return variation. These findings suggest that higher firm‐specific return variation is indicative of more informative stock prices.

Local Twitter Activity and Stock Returns

In this study, we use geographic proximity as a measure of private information and examine the informational role of social media in stock markets. Using a large sample of individual messages (tweets) collected from Twitter during the period July 2011–March 2012, we find that local Twitter users are more likely to tweet about firms with high information asymmetry, and their Twitter activity in turn increases the trading volume of local stocks. More importantly, we find that the negative tone of local tweets predicts future stock returns and subsequent earnings announcement returns, while nonlocal tweets present no such predictability. These results suggest that local social media activity reflects new information, contributing to price discovery. We also find that the negative tone of local tweets is associated with higher bid-ask spreads and lower market depths. This finding suggests that social media – in contrast to traditional news media, which reduce firms’ information asymmetry – serve to share information with an audience in their networks and therefore increase information asymmetry among investors. Overall, our findings suggest that local social media contain value-relevant information and affect firms’ information environments.