PDFDownload paper RWP 20-20, December 2020
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.
JEL Classification: C52, G10, G14, G17, Q47
- Calomiris, Charles W., Nida Çakır Melek, and Harry Mamaysky. 2020. “Mining for Oil Forecasts.” Federal Reserve Bank of Kansas City, Research Working Paper no. 20-20, December. Available at External Linkhttps://doi.org/10.18651/RWP2020-20