Out-of-Sample Predictability of the Equity Risk Premium
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 regress...
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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| author | Daniel de Almeida Ana-Maria Fuertes Luiz Koodi Hotta |
| author_facet | Daniel de Almeida Ana-Maria Fuertes Luiz Koodi Hotta |
| author_sort | Daniel de Almeida |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-838309e425714c8bbd549359d1bc4c62 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-838309e425714c8bbd549359d1bc4c622025-01-24T13:39:55ZengMDPI AGMathematics2227-73902025-01-0113225710.3390/math13020257Out-of-Sample Predictability of the Equity Risk PremiumDaniel de Almeida0Ana-Maria Fuertes1Luiz Koodi Hotta2Department of Statistics, Universidad Carlos III de Madrid, 28903 Getafe, SpainBayes Business School, City University of London, London EC1Y 8TZ, UKDepartment of Statistics, Universidade Estadual de Campinas (UNICAMP), Campinas 13083-859, BrazilA 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.https://www.mdpi.com/2227-7390/13/2/257business cyclesforecast combinationtechnical indicatorsgradient boostingrandom forest |
| spellingShingle | Daniel de Almeida Ana-Maria Fuertes Luiz Koodi Hotta Out-of-Sample Predictability of the Equity Risk Premium Mathematics business cycles forecast combination technical indicators gradient boosting random forest |
| title | Out-of-Sample Predictability of the Equity Risk Premium |
| title_full | Out-of-Sample Predictability of the Equity Risk Premium |
| title_fullStr | Out-of-Sample Predictability of the Equity Risk Premium |
| title_full_unstemmed | Out-of-Sample Predictability of the Equity Risk Premium |
| title_short | Out-of-Sample Predictability of the Equity Risk Premium |
| title_sort | out of sample predictability of the equity risk premium |
| topic | business cycles forecast combination technical indicators gradient boosting random forest |
| url | https://www.mdpi.com/2227-7390/13/2/257 |
| work_keys_str_mv | AT danieldealmeida outofsamplepredictabilityoftheequityriskpremium AT anamariafuertes outofsamplepredictabilityoftheequityriskpremium AT luizkoodihotta outofsamplepredictabilityoftheequityriskpremium |