Weblog analysis for predicting correlations in stock price evolutions

Abstract

We use data extracted from many weblogs to identify the underlying relations of a set of companies in the Standard and Poor (S&P) 500 index. We define a pairwise similarity measure for the companies based on the weblog articles and then apply a graph clustering procedure. We show that it is possible to capture some interesting relations between companies using this method. As an application of this clustering procedure we propose a cluster-based portfolio selection method which combines information from the weblog data and historical stock prices. Through simulation experiments, we show that our method performs better (in terms of risk measures) than cluster-based portfolio strategies based on company sectors or historical stock prices. This suggests that the methodology has the potential to identify groups of companies whose stock prices are more likely to be correlated in the future. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.