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Predicting Stock Returns — The Information Content Of Predictors Across Horizons

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  • KAIHUA DENG

    (Department of Economics, University of Washington, Seattle, WA 98195-3330, USA†Hanqing Advanced Institute of Economics and Finance, Renmin University of China, Beijing 100872, China)

  • CHANG-JIN KIM

    (#x2021;Department of Economics, University of Washington, Seattle, WA 98195-3330, USADepartment of Economics, Korea University, Seoul, South Korea)

Abstract

We evaluate and compare the information contents of dividend-price ratio and consumption-wealth ratio (cay) for predicting stock returns at different horizons. To do this, we conduct a canonical correlation analysis of wavelet-decomposed stock returns and a selected group of predictors. We show that predictive information is often wasted due to a weak signal problem: The highly predictive component is met with very low variation. Nevertheless, we find that cay contains valuable information about the long run and that, after allowing for structural breaks, dividend-price ratio becomes very informative about short-to-medium-horizon returns and outperforms cay in terms of in-sample R2.

Suggested Citation

  • Kaihua Deng & Chang-Jin Kim, 2015. "Predicting Stock Returns — The Information Content Of Predictors Across Horizons," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-27, December.
  • Handle: RePEc:wsi:afexxx:v:10:y:2015:i:02:n:s201049521550013x
    DOI: 10.1142/S201049521550013X
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    References listed on IDEAS

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