Predicting Abnormal Returns From News Using Text Classification

We show how text from news articles can be used to predict intraday price movements of financial assets using support vector machines. Multiple kernel learning is used to combine equity returns with text as predictive features in order to increase classification performance and we develop an analytic center cutting plane method to solve the kernel learning problem efficiently. This method exhibits linear convergence but requires very few gradient evaluations (each of them a support vector machine classification problem), making it particularly efficient on the large sample sizes considered in this application.