Abstract

We study the usefulness of a large number of traditional variables and novel text-based measures for in-sample and out-of-sample forecasting of oil spot and futures returns, energy company stock returns, oil 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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Citation

Calomiris, Charles, Nida Cakir Melek, and Harry Mamaysky. "Mining for Oil Forecasts." Columbia Business School, December 26, 2020.

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