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On Predicting Stock Returns with Nearly Integrated Explanatory Variables

Author

Listed:
  • Walter Torous

    (UCLA)

  • Rossen Valkanov

    (UCLA)

  • Shu Yan

    (The University of Arizona)

Abstract

Statistical inference in predictive regressions depends critically on the stochastic properties of the posited explanatory variable, in particular, its order of integration. Confidence intervals computed for the largest autoregressive root of many explanatory variables commonly used in predictive regressions, including the dividend yield, the book-to-market ratio, the short-term rate of interest, and the term and default spreads, confirm uncertainty surrounding these variables' order of integration. We investigate the effects of this uncertainty on inferences drawn in predictive regressions. Once this uncertainty is accounted for, contrary to previous evidence, we find reliable evidence of predictability at shorter rather than at longer horizons.

Suggested Citation

  • Walter Torous & Rossen Valkanov & Shu Yan, 2004. "On Predicting Stock Returns with Nearly Integrated Explanatory Variables," The Journal of Business, University of Chicago Press, vol. 77(4), pages 937-966, October.
  • Handle: RePEc:ucp:jnlbus:v:77:y:2004:i:4:p:937-966
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