Network analysis on stock BBS: Extract excess return from BBS postings

Abstract

This study attempts to uncover underlying information in stock message board (hereafter BBS) by using network analysis. It is said that many of postings on BBS could be noise. Therefore the overall sentiment of BBS often carries little useful content for future stock investment return. Moreover, under efficient market hypothesis (EMH), it is unlikely that investors (hereafter poster(s)) disseminate valuable information without compensation. However, some empirical research in the United States shows that there are a few posters in the community who post valuable information on BBS. The problem is how to extract such informed posters systematically from the BBS community. In this study, I utilize network analysis to solve this problem. Results of the empirical study on Yahoo! Stock BBS show that neither number of posting nor degree centrality could extract informed poster. However clique could extract informed poster. Return of informed posters is both statistically and economically significant even after risk adjusted. This indicates that the network analysis approach is valuable in screening out noise in BBS posting.