Forecasting the European Monetary Union equity risk premium with regression trees

David Cortés and Pilar Soriano

Abstract

This paper investigates whether classification and regression trees ensemble algorithms such as bagging, random forests and boosting improve on traditional parametric models for forecasting the equity risk premium. In particular, we work with European Monetary Union (EMU) data for the period from its foundation in 2000 to 2020. The paper first compares the monthly out-of-sample forecasting ability of multiple economic and technical variables using univariate linear regression models and regression tree techniques. The results obtained suggest that regression trees do not show better forecasting ability than a first-order autoregressive benchmark model and univariate linear regressions. The paper then analyses asset allocation strategies with regression trees and checks whether these can select the best economic predictors to form dynamic portfolios composed of two assets: a risk-free asset and an equity index. The results indicate that trading strategies built with two or three economic predictors selected with boosting and random forest algorithms can generate economic value for a risk-averse investor with a quadratic utility function.