Forecasting stock market returns by combining sum-of-the-parts and ensemble empirical mode decomposition

Zhifeng Dai and Huan Zhu

Applied Economics, 2020, vol. 52, issue 21, 2309-2323

Abstract: In this article, we combine the sum-of-the-parts (SOP) method with Ensemble Empirical Mode Decomposition (EEMD) to forecast stock market returns. We obtain very significant stock return predictability both in statistical and economic terms. Interestingly, the strongest performance is achieved by the extended SOPEEMD method to forecast stock market returns when the price-earnings multiple growth is forecasted using the dividend yield as predictor ($$R_{oos}^2$$Roos2of 21.25%) with monthly data and the book-to-market ratio as predictor achieves $$R_{oos}^2$$Roos2 of 20.05% with monthly data. The highest monthly CER gains for the extended SOPEEMD method are for book-to-market ratio reach 14.11%. Furthermore, the evidence based on robust check supports the feasibility of our forecasting strategy.

Date: 2020
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DOI: 10.1080/00036846.2019.1688244

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