Impact of Dynamic Corporate News Networks on Assets Return and Volatility
Social Computing (SocialCom), 2013 ASE/IEEE International Conference on , pp.809
6 Pages Posted: 5 Jan 2013 Last revised: 19 Jul 2017
Date Written: September 14, 2013
This paper analyzes the relationship between assets return, volatility and the centrality indicators of a corporate news network. We build a sequence of daily corporate news network for the period 2005-2011 using companies of the STOXX 50 index as nodes; the weights of the edges are the sum of the number of news items with the same topic by every pair of companies identified by the topic model methodology. The STOXX 50 includes the top 50 European companies by level of capitalization.
We conducted two studies to evaluate the impact of corporate news network in the assets return dynamic. In the first study we conducted a longitudinal network analysis using the stochastic actor oriented model with daily return and news for March 2009. We found that there was a 0.55 correlation between the rate of change of the news network and the STOXX 50 index. In the second study we extended our longitudinal analysis of networks using a sequence of daily corporate news networks for the period 2005-2011. We performed the Granger causality test and the Brownian distance covariance test of independence among several measures of centrality, return and volatility. We found that the average eigenvector centrality of the corporate news networks at different points of time has an impact on return and volatility of the STOXX 50 index. Likewise, return and volatility of the STOXX 50 index also has an effect on average eigenvector centrality. These results are more significant during the most important period of the recent financial crisis (January2008-March 2009). The same results hold when we examine this relationship at the level of individual companies. So, we observe that there is a dynamic process that affects and is affected by return, volatility, and centrality. The causality tests suggest it is possible to improve the prediction of return and volatility by extracting and analyzing a network based on the common topics of news stories.
Keywords: Computational finance, social networks, common topics, text analysis, link mining, financial forecasting
JEL Classification: C53, C63, G12, G14, F30
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