Correlating financial time series with micro-blogging activity

We study the problem of correlating micro-blogging activity with stock-market events, defined as changes in the price and traded volume of stocks. Specifically, we collect messages related to a number of companies, and we search for correlations between stock-market events for those companies and features extracted from the microblogging messages. The features we extract can be categorized in two groups. Features in the first group measure the overall activity in the micro-blogging platform, such as number of posts, number of re-posts, and so on. Features in the second group measure properties of an induced interaction graph, for instance, the number of connected components, statistics on the degree distribution, and other graph-based properties. We present detailed experimental results measuring the correlation of the stock market events with these features, using Twitter as a data source. Our results show that the most correlated features are the number of connected components an...

• Type: Journal
• Year: 2012
• Where: WSDM
• Authors: Eduardo J. Ruiz, Vagelis Hristidis, Carlos Castillo, Aristides Gionis, Alejandro Jaimes
• Keywords: Data Mining, Financial Time Series, Interaction Graph, Stock Trading Strategy, WSDM 2012