TAILIEUCHUNG - Correlating Financial Time Series with Micro-Blogging Activity

As the volume of data from online social networks increases, sci- entists are trying to find ways to understand and extract knowledge from this data. In this paper we study how the activity in a popular micro-blogging platform (Twitter) is correlated to time series from the financial domain, specifically stock prices and traded volume. We compute a large number of features extracted from postings (“tweets”) related to certain publicly-traded companies. Our goal is to find out which of these features are more correlated with changes in the stock of the companies. We start by carefully creating filters to select the relevant tweets for a company. We study various filtering approaches. | Correlating Financial Time Series with Micro-Blogging Activity Eduardo J. Ruiz Vagelis Hristidis Department of Computer Science Engineering University of California at Riverside Riverside California USA eruiz009 vagelis @ Carlos Castillo Aristides Gionis Alejandro Jaimes Yahoo Research Barcelona Barcelona Spain chato gionis ajaimes @ ABSTRACT 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 and the number of nodes of the interaction graph. The correlation is stronger with the traded volume than with the price of the stock. However by using a simulator we show that even relatively small correlations between price and micro-blogging features can be exploited to drive a stock trading strategy that outperforms other baseline strategies. Categories and Subject Descriptors Information Systems Applications-Systemsand Software Information networks Social and Behavioral Sciences Economics General Terms Algorithms Experimentation Keywords Social Networks Financial Time Series Micro-Blogging Permission to make digital or hard .

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