Testing for Predictability in Financial Returns Using Statistical Learning Procedures

This article examines the ability of recently developed statistical learning procedures, such as random forests or support vector machines, for forecasting the first two moments of stock market daily returns. These tools present the advantage of the flexibility of the considered nonlinear regression functions even in the presence of many potential predictors. We consider two cases: where the agent's information set only includes the past of the return series, and where this set includes past values of relevant economic series, such as interest rates, commodities prices or exchange rates. Even though these procedures seem to be of no much use for predicting returns, it appears that there is real potential for some of these procedures, especially support vector machines, to improve over the standard GARCH(1,1) model the out-of-sample forecasting ability for squared returns. The researcher has to be cautious on the number of predictors employed and on the specific implementation of the procedures since using many predictors and the default settings of standard computing packages leads to overfitted models and to larger standard errors.