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Short-term returns and the predictability of Finnish stock returns

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  • Mika Vaihekoski

    (Swedish School of Economics and Business Administration, Helsinki)

Abstract

The predictability of Finnish stock returns is studied using the framework of Ferson and Harvey (1993). We employ a conditional asset pricing model where risk premia and risk sensitivities are conditioned on a range of financial information variables. In particular, we study the effect of the return interval on the predictability of short-term stock returns. Using daily, weekly, and monthly returns on Finnish size and industry-sorted portfolios, we find that the predictability of returns increases with the length of the return interval, but so does the power of the conditional pricing model to explain the predictability. Consistent with the earlier results, we report that the time variation in risk premium accounts for most of the predictability. However, the results also show a sizable positive interaction between the beta and the risk premium which seems to increase for smaller companies.

Suggested Citation

  • Mika Vaihekoski, 1998. "Short-term returns and the predictability of Finnish stock returns," Finnish Economic Papers, Finnish Economic Association, vol. 11(1), pages 19-36, Spring.
  • Handle: RePEc:fep:journl:v:11:y:1998:i:1:p:19-36
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    References listed on IDEAS

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    Cited by:

    1. Mika Vaihekoski, 2000. "Unconditional international asset pricing models: empirical tests," Finnish Economic Papers, Finnish Economic Association, vol. 13(2), pages 71-88, Autumn.
    2. Mika Vaihekoski, 2007. "Global Market and Currency Risk in Finnish Stock Market," Finnish Economic Papers, Finnish Economic Association, vol. 20(1), pages 72-88, Spring.

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    More about this item

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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