City Research Online

Abstract

A large set of macroeconomic variables have been suggested as equity risk premium predictors in the literature. Acknowledging the different predictability of the equity premium in expansions and recessions, this paper proposes an approach that combines equity premium forecasts from two-state regression models using an agreement technical indicator as the observable state variable. A comprehensive out-of-sample forecast evaluation exercise based on statistical and economic loss functions demonstrates the superiority of the proposed approach versus combined forecasts from linear models or Markov switching models and forecasts from machine learning methods such as random forests and gradient boosting. The parsimonious state-dependent aspect of risk premium forecasts delivers large improvements in forecast accuracy. The results are robust to sub-period analyses and different investors’ risk aversion levels.

Publication Type:	Article
Additional Information:	© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Publisher Keywords:	business cycles, forecast combination, technical indicators, gradient boosting, random forest
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management H Social Sciences > HG Finance Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	Bayes Business School Bayes Business School > Finance
SWORD Depositor:	Symplectic Administrator

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