This paper introduces a forecasting procedure based on multivariate dynamic kernels to re-examine –under a non linear framework– the experimental tests reported by Welch and Goyal showing that several variables proposed in the academic literature are of no use to predict the equity premium under linear regressions. For this approach kernel functions for time series are used with multiple kernel learning in order to represent the relative importance of each of these variables.
CITATION STYLE
Fábregues, L., Arratia, A., & Belanche, L. A. (2017). Forecasting financial time series with multiple kernel learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10306 LNCS, pp. 176–187). Springer Verlag. https://doi.org/10.1007/978-3-319-59147-6_16
Mendeley helps you to discover research relevant for your work.