Out-of-sample equity premium predictability: An EMD-denoising based model

Haohua Li, Yuhe Mei, Xianfeng Hao and Zhuo Chen

Pacific-Basin Finance Journal, 2024, vol. 88, issue C

Abstract: The poor out-of-sample forecasting performance of the stock returns of various predictors has been widely confirmed in the literature, which casts doubt on the reliability of stock-return predictability. However, the reliability of return predictability is closely related to the noise contained in the data. In this study, we design a new method to address the noise in the framework of empirical mode decomposition. The EMD method provides an efficient return decomposition, and based on which we selectively remove high-frequency components that are more likely to be contaminated by outliers. Our new model delivers statistically and economically significant out-of-sample gains relative to the historical average. The predictive ability mainly originates from the business-cycle risk and survives a series of robustness tests.

Keywords: Out-of-sample forecasting; EMD decomposition; Denoising method; Return predictability (search for similar items in EconPapers)
JEL-codes: C58 G11 G12 G14 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:88:y:2024:i:c:s0927538x24002889

DOI: 10.1016/j.pacfin.2024.102536

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