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