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When does the dividend-price ratio predict stock returns?

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  • Park, Cheolbeom

Abstract

If the dividend-price ratio becomes I(1) while stock returns are I(0), the unbalanced predictive regression makes the predictability test more likely to indicate that the dividend-price ratio has no predictive power. This might explain why the dividend-price ratio evidences strong predictive power during one period, while it exhibits weak or no predictive power at other times. Using international data, this paper demonstrates that the dividend-price ratio generally has predictive power for stock returns when both are I(0). However, this paper also shows that the dividend-price ratio loses its predictive power when it becomes I(1). The results are shown to be robust across countries.

Suggested Citation

  • Park, Cheolbeom, 2010. "When does the dividend-price ratio predict stock returns?," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 81-101, January.
  • Handle: RePEc:eee:empfin:v:17:y:2010:i:1:p:81-101
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    5. Rengel, Malte & Herwartz, Helmut & Xu, Fang, 2013. "Persistence in the price-to-dividend ratio and its macroeconomic fundamentals," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79860, Verein für Socialpolitik / German Economic Association.
    6. Mohitosh Kejriwal & Xuewen Yu & Pierre Perron, 2020. "Bootstrap procedures for detecting multiple persistence shifts in heteroskedastic time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(5), pages 676-690, September.
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    9. Chen, Xiaoyu & Chiang, Thomas C., 2016. "Stock returns and economic forces—An empirical investigation of Chinese markets," Global Finance Journal, Elsevier, vol. 30(C), pages 45-65.
    10. Lawrenz, Jochen & Zorn, Josef, 2017. "Predicting international stock returns with conditional price-to-fundamental ratios," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 159-184.
    11. Cerqueti, Roy & Costantini, Mauro, 2011. "Testing for rational bubbles in the presence of structural breaks: Evidence from nonstationary panels," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2598-2605, October.
    12. David G. McMillan, 2021. "Forecasting sector stock market returns," Journal of Asset Management, Palgrave Macmillan, vol. 22(4), pages 291-300, July.
    13. Devpura, Neluka & Narayan, Paresh Kumar & Sharma, Susan Sunila, 2018. "Is stock return predictability time-varying?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 152-172.
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    15. Cheolbeom Park & Dong-hun Shin, 2014. "Stock Market Predictability: Global Evidence and an Explanation," Discussion Paper Series 1405, Institute of Economic Research, Korea University.
    16. 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.
    17. 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.
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    19. McMillan, David G., 2019. "Stock return predictability: Using the cyclical component of the price ratio," Research in International Business and Finance, Elsevier, vol. 48(C), pages 228-242.
    20. 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.
    21. McMillan, David G., 2019. "Predicting firm level stock returns: Implications for asset pricing and economic links," The British Accounting Review, Elsevier, vol. 51(4), pages 333-351.
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