Equity Risk Premium Prediction: Return Decomposition and Noise Shrinkage

Abstract

We propose a novel decomposition of stock returns into a fundamental component (FC) and an unexpected capital gain component (UC). The FC, driven by firm’s valuation ratios, reflects long-term growth and exhibits high persistence, while the UC, influenced by market trading, reflects short-term fluctuations and is more random. To predict the UC, we use a predictive regression model with an L multiplier to shrink noise for mitigating estimation errors. Among the 41 monthly predictors examined by Goyal, Welch and Zafirov (2024), we find 33 of them significantly outperform the historical average forecast, compared to only 5 with their method. Aggregating information across the predictors, we re-affirm the predictability of the equity risk premium. Furthermore, our analysis shows that the stock market remains predictable post-2008, even when accounting for publication bias.

Keywords: Return Decomposition, Stock Return Predictability, Fundamental Component (FC), Unexpected Capital Gain (UC), Noise Shrinkage