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Equity premium forecasting with reliability-screened forward-looking signals.

Jeonggyu Huh1, Jaegi Jeon2, Seungwon Jeong3

  • 1Department of Mathematics, Sungkyunkwan University, Suwon, Republic of Korea.

Plos One
|May 15, 2026
PubMed
Summary

Forecasting the equity risk premium is challenging due to unstable predictive relationships. A new two-stage framework selectively admits reliable forward-looking signals, improving forecast accuracy and economic value, especially during market downturns.

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Area of Science:

  • Quantitative Finance
  • Econometrics
  • Machine Learning

Background:

  • Forecasting the equity risk premium (ERP) is crucial for investment decisions but hampered by unstable predictive relationships out-of-sample.
  • Traditional models often struggle with the dynamic and non-linear nature of financial markets, leading to unreliable predictions.

Purpose of the Study:

  • To develop and evaluate a novel two-stage forecasting framework for the equity risk premium.
  • To enhance out-of-sample accuracy and economic value of ERP forecasts by selectively admitting forward-looking signals based on reliability criteria.

Main Methods:

  • A two-stage framework generating forward-looking signals from macro-financial predictors.
  • Stage 1: Forecasting predictors to obtain expected movement and uncertainty.
  • Stage 2: Using random forests with selective signal admission (optionally with SHAP) to predict excess returns.

Main Results:

  • Selective admission of forward-looking signals significantly improves out-of-sample accuracy compared to benchmarks.
  • Economically meaningful gains in risk-adjusted performance and drawdown control were observed in selected specifications.
  • Performance improvements are concentrated in downside market states, indicating enhanced robustness during market stress.

Conclusions:

  • The proposed framework offers a more reliable approach to equity risk premium forecasting.
  • Selective admission based on predictor-level reliability is key to unlocking the practical value of forward-looking information.
  • The method provides robust and downside-sensitive forecasts, particularly valuable for risk management.