Out-of-Sample Predictability of the Equity Risk Premium

Daniel de Almeida, Ana-Maria Fuertes and Luiz Hotta
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Daniel de Almeida: Department of Statistics, Universidad Carlos III de Madrid, 28903 Getafe, Spain
Ana-Maria Fuertes: Bayes Business School, City University of London, London EC1Y 8TZ, UK

Mathematics, 2025, vol. 13, issue 2, 1-23

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.

Keywords: business cycles; forecast combination; technical indicators; gradient boosting; random forest (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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