Financial Communities

Abstract

Whereas many studies in finance have examined and established a strong link between stock returns and information, the physical mechanics of this link have been relatively unexplored. With the advent of stock message boards, it has become feasible to look more closely at the group process by which information impacts prices and vice versa. This paper utilizes a large universe of messages posted to stock market discussion forums to understand how opinions are linked across tickers during small investor discussion. We define a collective information unit, the financial community. These are clusters of tickers sharing and accessing the same information generators. Graph theoretic techniques are used to detect financial communities and to summarize their properties. Community stocks display connectedness, and we find that the greater the connectedness in a financial community, the greater the covariance of returns within the community as opposed to that amongst stocks that are not part of a major financial community. Highly connected stocks, on average, have lower return variance and higher mean returns. Using eigenvector techniques, we detect stocks that are hubs for information flow, using a measure known as centrality. We find that stocks with high centrality scores tend to have greater average covariance with other stocks than those with low scores. Our analysis of connectedness and centrality establishes a link between one arena of the information generation process and stock return correlations.

Keywords: Graph theory, connectedness, centrality

JEL Classification: C63, G10