Mining for Oil Forecasts

Nida Cakir Melek, Charles Calomiris and Harry Mamaysky ()

No RWP 20-20, Research Working Paper from Federal Reserve Bank of Kansas City

Abstract: 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.

Keywords: Asset Pricing; Commodity Markets; Energy Forecasting; Model Validation (search for similar items in EconPapers)
JEL-codes: C52 G14 G17 G18 Q47 (search for similar items in EconPapers)
Pages: 29
Date: 2020-12-23
New Economics Papers: this item is included in nep-cwa, nep-ene and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedkrw:89532

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DOI: 10.18651/RWP2020-20

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