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On the statistical and economic performance of stock return predictive regression models: an international perspective

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  • Pierre Giot
  • Mikael Petitjean

Abstract

The predictability of stock returns is assessed in 10 countries using the linear predictive regression framework. We use recently developed out-of-sample statistical tests and include both valuation ratios and interest rates as predictive variables. Contrary to previous studies, we explicitly address the issue of the small-sample bias, deal with trading profitability, and employ several risk-adjusted metrics. When statistical forecastability is found, it cannot be exploited to consistently deliver abnormal returns across countries and investment horizons. We hold the view that returns are predictable to some extent, but show that such forecasts are not useful for portfolio advice.

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  • Pierre Giot & Mikael Petitjean, 2011. "On the statistical and economic performance of stock return predictive regression models: an international perspective," Quantitative Finance, Taylor & Francis Journals, vol. 11(2), pages 175-193.
    • Handle: RePEc:taf:quantf:v:11:y:2011:i:2:p:175-193
    DOI: 10.1080/14697680903468971
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    Cited by:

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    4. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2017. "Forecasting market returns: bagging or combining?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 102-120.
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    8. Tissaoui, Kais & Azibi, Jamel, 2019. "International implied volatility risk indexes and Saudi stock return-volatility predictabilities," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 65-84.

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