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Forecasting stock returns with cycle-decomposed predictors

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  • Yi, Yongsheng
  • Ma, Feng
  • Zhang, Yaojie
  • Huang, Dengshi

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.

Suggested Citation

  • Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2019. "Forecasting stock returns with cycle-decomposed predictors," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 250-261.
    • Handle: RePEc:eee:finana:v:64:y:2019:i:c:p:250-261
    DOI: 10.1016/j.irfa.2019.05.009
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