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The Myth of Long-Horizon Predictability

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  • Jacob Boudoukh
  • Matthew Richardson
  • Robert Whitelaw
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    Abstract

    The prevailing view in finance is that the evidence for long-horizon stock return predictability is significantly stronger than that for short horizons. We show that for persistent regressors, a characteristic of most of the predictive variables used in the literature, the estimators are almost perfectly correlated across horizons under the null hypothesis of no predictability. For example, for the persistence levels of dividend yields, the analytical correlation is 99% between the 1- and 2-year horizon estimators and 94% between the 1- and 5-year horizons, due to the combined effects of overlapping returns and the persistence of the predictive variable. Common sampling error across equations leads to ordinary least squares coefficient estimates and R2s that are roughly proportional to the horizon under the null hypothesis. This is the precise pattern found in the data. The asymptotic theory is corroborated, and the analysis extended by extensive simulation evidence. We perform joint tests across horizons for a variety of explanatory variables, and provide an alternative view of the existing evidence.

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    Bibliographic Info

    Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 11841.

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    Date of creation: Dec 2005
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    Publication status: published as Boudoukh, Jacob, Matthew Richardson, and Robert F. Whitelaw. "The Myth of Long-Horizon Predictability." Review of Financial Studies 21, 4 (July 2008): 1576-1605.
    Handle: RePEc:nbr:nberwo:11841

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    3. Lettau, Martin & Ludvigson, Sydney, 1999. "Consumption, Aggregate Wealth and Expected Stock Returns," CEPR Discussion Papers 2223, C.E.P.R. Discussion Papers.
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    Citations

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    Cited by:
    1. Ferson, Wayne E. & Sarkissian, Sergei & Simin, Timothy, 2008. "Asset Pricing Models with Conditional Betas and Alphas: The Effects of Data Snooping and Spurious Regression," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 43(02), pages 331-353, June.
    2. Larrain, Borja & Yogo, Motohiro, 2008. "Does firm value move too much to be justified by subsequent changes in cash flow," Journal of Financial Economics, Elsevier, vol. 87(1), pages 200-226, January.
    3. Martin Lettau & Stijn Van Nieuwerburgh, 2008. "Reconciling the Return Predictability Evidence," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1607-1652, July.
    4. John H. Cochrane, 2008. "The Dog That Did Not Bark: A Defense of Return Predictability," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1533-1575, July.
    5. Won-Gi Kim & Noh-Sun Kwark, 2012. "Leading Behavior of Interest Rate Term Spreads and Credit Risk Spreads in Korea," Working Papers 1203, Research Institute for Market Economy, Sogang University.
    6. Erik Hjalmarsson, 2006. "New methods for inference in long-run predictive regressions," International Finance Discussion Papers 853, Board of Governors of the Federal Reserve System (U.S.).
    7. Bandi, Federico M. & Perron, Benoît, 2008. "Long-run risk-return trade-offs," Journal of Econometrics, Elsevier, vol. 143(2), pages 349-374, April.

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