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Autocorrelation, structural breaks and the predictive ability of dividend yield

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  • An-Sing Chen
  • Tai-Wei Zhang

Abstract

We study whether dividend yield (DY) can predict aggregate stock returns while controlling for the effects of structural breaks in the parameters and bias induced by autocorrelation in the predictor variable. To do so we apply the Bai and Perron (BP) (1998, 2000) methodology to test for structural breaks and the bias-adjusted predictability test of Lewellen (2004). We show that although DY predicts market returns during the period 1946 to 1989, there exist 'natural' subsamples bounded by statistically detectable structural breaks that can last for long periods of time (up to 11 years in duration) when DY does not show significant forecasting power. This has important implications in that even if in the long-run DY actually provides strong predictive ability, investors should be mentally prepared for long dry spells of unpredictability with respect to DY.

Suggested Citation

  • An-Sing Chen & Tai-Wei Zhang, 2007. "Autocorrelation, structural breaks and the predictive ability of dividend yield," Applied Economics, Taylor & Francis Journals, vol. 39(5), pages 645-652.
    • Handle: RePEc:taf:applec:v:39:y:2007:i:5:p:645-652
    DOI: 10.1080/00036840500447708
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    Cited by:

    1. Ruey-Shii Chen & Tai-Wei Zhang, 2018. "Dividend cuts and predictability," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 42(2), pages 249-267, April.
    2. David G. McMillan, 2014. "Modelling Time‐Variation in the Stock Return‐Dividend Yield Predictive Equation," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 23(5), pages 273-302, December.

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