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We show how text from news articles can be used to predict intraday pricemovements of financial assets using support vector machines. Multiple kernellearning is used to combine equity returns with text as predictive features toincrease classification performance and we develop an analytic center cuttingplane method to solve the kernel learning problem efficiently. We observe thatwhile the direction of returns is not predictable using either text or returns,their size is, with text features producing significantly better performancethan historical returns alone.

Predicting Abnormal Returns From News Using Text Classification