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

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  • Chang-Jin Kim

    () (University of Washington and Korea University)

  • Cheolbeom Park

    () (Department of Economics, Korea University, Seoul, Republic of Korea)

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.

Suggested Citation

  • Chang-Jin Kim & Cheolbeom Park, 2012. "Disappearing Dividends: Implications for the Dividend-Price Ratio and Return Predictability," Discussion Paper Series 1205, Institute of Economic Research, Korea University.
  • Handle: RePEc:iek:wpaper:1205
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    File URL: http://econ.korea.ac.kr/~ri/WorkingPapers/w1205.pdf
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    References listed on IDEAS

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    1. Park, Joon Y. & Hahn, Sang B., 1999. "Cointegrating Regressions With Time Varying Coefficients," Econometric Theory, Cambridge University Press, vol. 15(05), pages 664-703, October.
    2. John Y. Campbell & John H. Cochrane, 1994. "By force of habit: a consumption-based explanation of aggregate stock market behavior," Working Papers 94-17, Federal Reserve Bank of Philadelphia.
    3. Gallant, A. Ronald, 1981. "On the bias in flexible functional forms and an essentially unbiased form : The fourier flexible form," Journal of Econometrics, Elsevier, vol. 15(2), pages 211-245, February.
    4. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    5. Bossaerts, Peter & Hillion, Pierre, 1999. "Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?," Review of Financial Studies, Society for Financial Studies, vol. 12(2), pages 405-428.
    6. Donald Robertson & Stephen Wright, 2006. "Dividends, Total Cash Flow to Shareholders, and Predictive Return Regressions," The Review of Economics and Statistics, MIT Press, vol. 88(1), pages 91-99, February.
    7. Bierens, Herman J. & Martins, Luis F., 2010. "Time-Varying Cointegration," Econometric Theory, Cambridge University Press, vol. 26(05), pages 1453-1490, October.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.

    More about this item

    Keywords

    Stock Return Predictability; Adjusted Dividend-price ratio; Disappearing; Dividends; Time-Varying Cointegration Vector;

    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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