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Investing for the Long Run when Returns Are Predictable

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  • Nicholas Barberis

    (Graduate School of Business, University of Chicago)

Abstract

We examine how the evidence of predictability in asset returns affects optimal portfolio choice for investors with long horizons. Particular attention is paid to estimation risk, or uncertainty about the true values of model parameters. We find that even after incorporating parameter uncertainty, there is enough predictability in returns to make investors allocate substantially more to stocks, the longer their horizon. Moreover, the weak statistical significance of the evidence for predictability makes it important to take estimation risk into account; a long-horizon investor who ignores it may overallocate to stocks by a sizeable amount. Copyright The American Finance Association 2000.

Suggested Citation

  • Nicholas Barberis, 2000. "Investing for the Long Run when Returns Are Predictable," Journal of Finance, American Finance Association, vol. 55(1), pages 225-264, February.
  • Handle: RePEc:bla:jfinan:v:55:y:2000:i:1:p:225-264
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