  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 to
increase classification performance and we develop an analytic center cutting
plane method to solve the kernel learning problem efficiently. We observe that
while the direction of returns is not predictable using either text or returns,
their size is, with text features producing significantly better performance
than historical returns alone.
