Forecasting stock returns with cycle-decomposed predictors

Yongsheng Yi, Feng Ma, Yaojie Zhang and Dengshi Huang

International Review of Financial Analysis, 2019, vol. 64, issue C, 250-261

Abstract: We find that predictors can consistently provide more predictive information after each of them is decomposed into a long-cycle mean component and a short-term deviation component. Better predictive ability is achieved by the decomposed predictors, many of which significantly outperform the benchmark of the historical average. From the perspective of multivariate strategies, the set of predictors can consistently promote their aggregate forecasting performance with the implementation of the decomposition approach. Our results are robust to various robustness tests and extensions.

Keywords: Stock returns; Predictability; Predictive regressions; Predictor decomposition; Statistical performance; Economic performance (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:64:y:2019:i:c:p:250-261

DOI: 10.1016/j.irfa.2019.05.009

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