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Predicting the equity market risk premium: A model selection approach

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  • Ciner, Cetin

Abstract

We present a novel approach to investigate US stock return predictability. Our analysis utilizes the best subset method to construct a single predictive regression from a set of fundamental factors and hence, it is robust to data snooping. We consider models with non-Gaussian distributions as a first in the literature. We find that our selected predictive regression is Student’s t-distributed and has both in sample and out of sample forecasting power, with a high degree of economic significance.

Suggested Citation

  • Ciner, Cetin, 2022. "Predicting the equity market risk premium: A model selection approach," Economics Letters, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:ecolet:v:215:y:2022:i:c:s0165176522000970
    DOI: 10.1016/j.econlet.2022.110448
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    References listed on IDEAS

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    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
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    10. Atsushi Inoue & Lutz Kilian, 2005. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," Econometric Reviews, Taylor & Francis Journals, vol. 23(4), pages 371-402.
    11. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.
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    Cited by:

    1. Ciner, Cetin & Kosedag, Arman & Lucey, Brian, 2023. "Predictors of clean energy stock returns: An analysis with best subset regressions," Finance Research Letters, Elsevier, vol. 55(PA).

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