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Disappearing Dividends: Implications for the Dividend-Price Ratio and Return Predictability

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  • CHANG-JIN KIM
  • CHEOLBEOM PARK

Abstract

The conventional dividend-price ratio is highly persistent, and the literature reports mixed evidence on its role in predicting stock returns. In particular, its predictive power seems to be sensitive to the choice of the sample period. We argue that the decreasing number of firms with traditional dividend-payout policy is responsible for these results, and develop a model in which the long-run relationship between the dividends and stock price is time-varying. An adjusted dividend-price ratio that accounts for the time-varying long-run relationship is stationary with considerably less persistence than the conventional dividend-price ratio. Furthermore, the predictive regression model that employs the adjusted dividend-price ratio as a regressor outperforms the random-walk model in terms of long-horizon out-of-sample predictability. These results are robust with respect to the firm size.
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  • Chang-Jin Kim & Cheolbeom Park, 2013. "Disappearing Dividends: Implications for the Dividend-Price Ratio and Return Predictability," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(5), pages 933-952, August.
    • Handle: RePEc:mcb:jmoncb:v:45:y:2013:i:5:p:933-952
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    1. Martin Lettau & Stijn Van Nieuwerburgh, 2008. "Reconciling the Return Predictability Evidence," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1607-1652, July.
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    Cited by:

    1. Heejoon Han & Na Kyeong Lee, 2018. "Modeling the Dynamics between Stock Price and Dividend: An Endogenous Regime Switching Approach," Korean Economic Review, Korean Economic Association, vol. 34, pages 213-235.
    2. Chang-Jin Kim & Cheolbeom Park, 2013. "Disappearing Dividends: Implications for the Dividend-Price Ratio and Return Predictability," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(5), pages 933-952, August.
    3. 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.
    4. Helmut Herwartz & Malte Rengel & Fang Xu, 2016. "Local Trends in Price‐to‐Dividend Ratios—Assessment, Predictive Value, and Determinants," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(8), pages 1655-1690, December.
    5. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea," Energy Economics, Elsevier, vol. 46(C), pages 334-347.
    6. Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand," Working Papers 1409, Department of Economics, University of Missouri.
    7. Helmut Herwartz & Malte Rengel, 2018. "Size-corrected inference in fiscal policy reaction functions: a three country assessment," Empirical Economics, Springer, vol. 55(2), pages 391-416, September.

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    More about this item

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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