RWP 20-20, December 2020; updated September 2021
In this paper, we study the performance of many traditional and novel, text-based variables for in-sample and out-of-sample forecasting of oil spot, futures, and energy company stock returns, and changes in oil volatility, production, and inventories. After controlling for small-sample biases, we find evidence of in-sample predictability. Our text measures, derived using energy news articles, hold their own against traditional variables. While we cannot identify ex-ante rules for selecting successful out-of-sample forecasters, an analysis of all possible two-variable models reveals out-of-sample performance above that expected under random variation. Our findings provide new directions for identifying robust forecasting models for oil markets, and beyond.
JEL Classification: C52, G10, G14, G17, Q47
- Calomiris, Charles W., Nida Çakır Melek, and Harry Mamaysky. 2020. “Predicting the Oil Market.” Federal Reserve Bank of Kansas City, Research Working Paper no. 20-20, December. Available at External Linkhttps://doi.org/10.18651/RWP2020-20
Note: A previous version of this RWP from December 2020 was titled "Mining for Oil Forecasts"