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Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?

  • Bossaerts, Peter
  • Hillion, Pierre

Statistical model selection criteria provide an informed choice of the model with best external (i.e., out-of-sample) validity. Therefore they guard against overfitting ('data snooping'). We implement several model selection criteria in order to verify recent evidence of predictability in excess stock returns and to determine which variables are valuable predictors. We confirm the presence of in-sample predictability in an international stock market dataset, but discover that even the best prediction models have no out-of-sample forecasting power. The failure to detect out-of-sample predictability is not due to lack of power. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

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Article provided by Society for Financial Studies in its journal Review of Financial Studies.

Volume (Year): 12 (1999)
Issue (Month): 2 ()
Pages: 405-28

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Handle: RePEc:oup:rfinst:v:12:y:1999:i:2:p:405-28
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  1. Fama, Eugene F, 1991. " Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-617, December.
  2. Brock, W. & Lakonishok, J. & Lebaron, B., 1991. "Simple Technical Trading Rules And The Stochastic Properties Of Stock Returns," Working papers 90-22, Wisconsin Madison - Social Systems.
  3. Campbell, John, 1987. "Stock Returns and the Term Structure," Scholarly Articles 3207699, Harvard University Department of Economics.
  4. Foster, F Douglas & Smith, Tom & Whaley, Robert E, 1997. " Assessing Goodness-of-Fit of Asset Pricing Models: The Distribution of the Maximal R-Squared," Journal of Finance, American Finance Association, vol. 52(2), pages 591-607, June.
  5. Solnik, Bruno, 1993. "The performance of international asset allocation strategies using conditioning information," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 33-55, June.
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