An Evaluation of Equity Premium Prediction Using Multiple Kernel Learning with Financial Features

Abstract

This paper introduces and extensively explores a forecasting procedure based on multivariate dynamic kernels to re-examine—under a non-linear, kernel methods framework—the experimental tests reported by Welch and Goyal (Rev Financ Stud 21(4):1455–1508, 2008) showing that several variables proposed in the finance literature are of no use as exogenous information to predict the equity premium under linear regressions. For this new approach to equity premium forecasting, kernel functions for time series are used with multiple kernel learning (MKL) in order to represent the relative importance of each of the variables. We find that, in general, the predictive capabilities of the MKL models do not improve consistently with the use of some or all of the variables, nor does the predictability by single kernels, as determined by different resampling procedures that we implement and compare. This fact tends to corroborate the instability already observed by Welch and Goyal for the predictive power of exogenous variables, now in a non-linear modelling framework.