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Mining for Oil Forecasts

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In this paper, we study the usefulness of a large number of traditional determinants and novel text-based variables for in-sample and out-of-sample forecasting of oil spot and futures returns, energy company stock returns, oil price volatility, oil production, and oil inventories. After carefully controlling for small-sample biases, we find compelling evidence of in-sample predictability. Our text measures hold their own against traditional variables for oil forecasting. However, none of this translates to out-of-sample predictability until we data mine our set of predictive variables. Our study highlights that it is difficult to forecast oil market outcomes robustly.

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  • Nida Çakır Melek & Charles W. Calomiris & Harry Mamaysky, 2020. "Mining for Oil Forecasts," Research Working Paper RWP 20-20, Federal Reserve Bank of Kansas City.
    • Handle: RePEc:fip:fedkrw:89532
    DOI: 10.18651/RWP2020-20
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    More about this item

    Keywords

    Asset Pricing; Commodity Markets; Energy Forecasting; Model Validation;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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