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Valuation ratios and long-horizon stock price predictability

Author

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  • Mark E. Wohar

    (Department of Economics, University of Nebraska at Omaha, USA)

  • David E. Rapach

    (Department of Economics, Saint Louis University, USA)

Abstract

Using annual data for 1872-1997, this paper re-examines the predictability of real stock prices based on price-dividend and price-earnings ratios. In line with the extant literature, we find significant evidence of increased long-horizon predictability; that is, the hypothesis that the current value of a valuation ratio is uncorrelated with future stock price changes cannot be rejected at short horizons but can be rejected at longer horizons based on bootstrapped critical values constructed from linear representations of the data. While increased statistical power at long horizons in finite samples provides a possible explanation for the pattern of predictability in the data, we find via Monte Carlo simulations that the power to detect predictability in finite samples does not increase at long horizons in a linear framework. An alternative explanation for the pattern of predictability in the data is nonlinearities in the underlying data-generating process. We consider exponential smooth-transition autoregressive models of the price-dividend and price-earnings ratios and their ability to explain the pattern of stock price predictability in the data. Copyright © 2005 John Wiley & Sons, Ltd.

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  • Mark E. Wohar & David E. Rapach, 2005. "Valuation ratios and long-horizon stock price predictability," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 327-344.
    • Handle: RePEc:jae:japmet:v:20:y:2005:i:3:p:327-344
    DOI: 10.1002/jae.774
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    1. De Long, J Bradford, et al, 1990. "Positive Feedback Investment Strategies and Destabilizing Rational Speculation," Journal of Finance, American Finance Association, vol. 45(2), pages 379-395, June.
    2. Kilian, Lutz, 1999. "Exchange Rates and Monetary Fundamentals: What Do We Learn from Long-Horizon Regressions?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 491-510, Sept.-Oct.
    3. Campbell, John Y., 2001. "Why long horizons? A study of power against persistent alternatives," Journal of Empirical Finance, Elsevier, vol. 8(5), pages 459-491, December.
    4. Campbell, J.Y. & Shiller, R.J., 1988. "Stock Prices, Earnings And Expected Dividends," Papers 334, Princeton, Department of Economics - Econometric Research Program.
    5. Jeremy Berkowitz & Lorenzo Giorgianni, 2001. "Long-Horizon Exchange Rate Predictability?," The Review of Economics and Statistics, MIT Press, vol. 83(1), pages 81-91, February.
    6. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
    7. Kothari, S. P. & Shanken, Jay, 1997. "Book-to-market, dividend yield, and expected market returns: A time-series analysis," Journal of Financial Economics, Elsevier, vol. 44(2), pages 169-203, May.
    8. John Y. Campbell, 2000. "Asset Pricing at the Millennium," Journal of Finance, American Finance Association, vol. 55(4), pages 1515-1567, August.
    9. Kilian, Lutz & Taylor, Mark P., 2003. "Why is it so difficult to beat the random walk forecast of exchange rates?," Journal of International Economics, Elsevier, vol. 60(1), pages 85-107, May.
    10. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    11. Sarno,Lucio & Taylor,Mark P., 2003. "The Economics of Exchange Rates," Cambridge Books, Cambridge University Press, number 9780521485845.
    12. Campbell, John Y., 1999. "Asset prices, consumption, and the business cycle," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 19, pages 1231-1303, Elsevier.
    13. Berben, R-P. & van Dijk, D.J.C., 1998. "Does the absence of cointegration explain the typical findings in long horizon regressions?," Econometric Institute Research Papers EI 9814, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    15. Shleifer, Andrei, 2000. "Inefficient Markets: An Introduction to Behavioral Finance," OUP Catalogue, Oxford University Press, number 9780198292272, Decembrie.
    16. Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 60(4), pages 953-966, July.
    17. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
    18. Stambaugh, Robert F., 1999. "Predictive regressions," Journal of Financial Economics, Elsevier, vol. 54(3), pages 375-421, December.
    19. Taylor, Mark P. & Peel, David A., 2000. "Nonlinear adjustment, long-run equilibrium and exchange rate fundamentals," Journal of International Money and Finance, Elsevier, vol. 19(1), pages 33-53, February.
    20. Kirby, Chris, 1997. "Measuring the Predictable Variation in Stock and Bond Returns," Review of Financial Studies, Society for Financial Studies, vol. 10(3), pages 579-630.
    21. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
    22. Gallagher, Liam A & Taylor, Mark P, 2001. "Risky Arbitrage, Limits of Arbitrage, and Nonlinear Adjustment in the Dividend-Price Ratio," Economic Inquiry, Western Economic Association International, vol. 39(4), pages 524-536, October.
    23. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    24. Campbell, John Y & Shiller, Robert J, 1988. " Stock Prices, Earnings, and Expected Dividends," Journal of Finance, American Finance Association, vol. 43(3), pages 661-676, July.
    25. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    26. Qi, Min, 1999. "Nonlinear Predictability of Stock Returns Using Financial and Economic Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 419-429, October.
    27. Nelson, Charles R & Kim, Myung J, 1993. "Predictable Stock Returns: The Role of Small Sample Bias," Journal of Finance, American Finance Association, vol. 48(2), pages 641-661, June.
    28. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
    29. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    30. McMillan, David G., 2001. "Nonlinear predictability of stock market returns: Evidence from nonparametric and threshold models," International Review of Economics & Finance, Elsevier, vol. 10(4), pages 353-368, December.
    31. Mark, Nelson C, 1995. "Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability," American Economic Review, American Economic Association, vol. 85(1), pages 201-218, March.
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    6. Lee, King Fuei, 2011. "Demographics and the Long-Horizon Returns of Dividend-Yield Strategies in the US," MPRA Paper 46350, University Library of Munich, Germany.
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    8. Erik Hjalmarsson, 2006. "Inference in Long-Horizon Regressions," International Finance Discussion Papers 853, Board of Governors of the Federal Reserve System (U.S.).
    9. Yung, Julieta, 2021. "Can interest rate factors explain exchange rate fluctuations?," Journal of Empirical Finance, Elsevier, vol. 61(C), pages 34-56.
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    11. David G. McMillan & Mark E. Wohar, 2010. "Stock return predictability and dividend-price ratio: a nonlinear approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 351-365.
    12. McMillan, David G., 2009. "Revisiting dividend yield dynamics and returns predictability: Evidence from a time-varying ESTR model," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(3), pages 870-883, August.
    13. McMillan, David G., 2013. "Consumption and stock prices: Evidence from a small international panel," Journal of Macroeconomics, Elsevier, vol. 36(C), pages 76-88.
    14. Nan-Kuang Chen & Han-Liang Cheng, 2017. "House price to income ratio and fundamentals: Evidence on long-horizon forecastability," Pacific Economic Review, Wiley Blackwell, vol. 22(3), pages 293-311, August.
    15. McMillan, David G., 2007. "Bubbles in the dividend-price ratio? Evidence from an asymmetric exponential smooth-transition model," Journal of Banking & Finance, Elsevier, vol. 31(3), pages 787-804, March.
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    18. Algaba, Andres & Boudt, Kris, 2017. "Generalized financial ratios to predict the equity premium," Economic Modelling, Elsevier, vol. 66(C), pages 244-257.
    19. Goodness C. Aye & Frederick W. Deale & Rangan Gupta, 2016. "Does Debt Ceiling and Government Shutdown Help in Forecasting the US Equity Risk Premium?," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 63(3), pages 273-291, June.
    20. Ralf Becker & Junsoo Lee & Benton Gup, 2012. "An empirical analysis of mean reversion of the S&P 500’s P/E ratios," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 36(3), pages 675-690, July.
    21. Huong Dang & Michael Jolly, 2016. "IPOs in New Zealand: Valuation Multiples and Benchmark Adjusted Performance," Working Papers in Economics 16/32, University of Canterbury, Department of Economics and Finance.
    22. Hjalmarsson, Erik, 2012. "Some curious power properties of long-horizon tests," Finance Research Letters, Elsevier, vol. 9(2), pages 81-91.
    23. Lee, King Fuei, 2013. "Demographics and the long-horizon returns of dividend-yield strategies," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(2), pages 202-218.
    24. Snaith, Stuart & Coakley, Jerry & Kellard, Neil, 2013. "Does the forward premium puzzle disappear over the horizon?," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3681-3693.
    25. Nikolaos Mitianoudis & Theologos Dergiades, 2016. "Stock Prices Predictability at Long-horizons: Two Tales from the Time-Frequency Domain," Discussion Paper Series 2016_04, Department of Economics, University of Macedonia, revised Dec 2016.

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