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Stock Return Predictability and Oil Prices

Author

Listed:
  • Jaime Casassus
  • Freddy Higuera
Abstract
This paper shows that oil price changes, measured as short-term futures returns, are a strong predictor of excess stock returns at short horizons. Ours is a leading variable for the business cycle and exhibits low persistence which avoids the ctitious long-horizon predictability associated to other predictors used in the literature. We compare our variable with the most popular predictors in a sample period that includes the recent nancial crisis. Our results suggest that oil price changes are the only variable with forecasting power for stock returns. This signi cant predictive ability is robust against the inclusion of other variables and out-of-sample tests. We also study the cross-section of expected stock returns in a conditional CAPM framework based on oil price shocks. Our model displays high statistical signi cance and a better t than all the conditional and unconditional models considered including the Fama French three-factor model. From a practical perspective, ours is a high-frequency, observable variable that has the advantage of being readily available to market-timing investors.

Suggested Citation

  • Jaime Casassus & Freddy Higuera, 2011. "Stock Return Predictability and Oil Prices," Documentos de Trabajo 406, Instituto de Economia. Pontificia Universidad Católica de Chile..
    • Handle: RePEc:ioe:doctra:406
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    File URL: https://www.economia.uc.cl/docs/doctra/dt-406.pdf
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    References listed on IDEAS

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    Cited by:

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    2. Lombardi, Marco J. & Ravazzolo, Francesco, 2016. "On the correlation between commodity and equity returns: Implications for portfolio allocation," Journal of Commodity Markets, Elsevier, vol. 2(1), pages 45-57.
    3. Jaime Casassus & Freddy Higuera, 2013. "The Economic Impact of Oil on Industry Portfolios," Documentos de Trabajo 433, Instituto de Economia. Pontificia Universidad Católica de Chile..
    4. Thomadakis, Apostolos, 2016. "Do Combination Forecasts Outperform the Historical Average? Economic and Statistical Evidence," MPRA Paper 71589, University Library of Munich, Germany.
    5. Sohag, Kazi & Shams, S.M. Riad & Gainetdinova, Anna & Nappo, Fabio, 2023. "Frequency connectedness and cross-quantile dependence among medicare, medicine prices and health-tech equity," Technovation, Elsevier, vol. 120(C).

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    More about this item

    Keywords

    Return predictability; business cycle; crude oil; futures prices; asset pricing; conditional CAPM;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • 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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