RWP 20-20, December 2020; updated November 2022
This paper introduces novel news-based measures for tracking global energy markets. These measures compress thousands of news articles into a parsimonious set of real-time indicators and are successful in-sample forecasters of oil spot, futures, and energy company stock returns, and of changes in oil volatility, production, and inventories, complementing and extending traditional (non-text) predictors. In out-of-sample tests, text-based measures predict oil futures returns and changes in oil spot prices better than traditional predictors, although the latter are more useful for forecasting changes in oil volatility.
JEL Classification: C52, G10, G14, G17, Q47
Article Citation
- Calomiris, Charles W., Nida Çakır Melek, and Harry Mamaysky. 2020. “Big Data Meets the Turbulent 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 September 2021 was titled "Predicting the Oil Market"
The views expressed are those of the authors and do not necessarily reflect the positions of the Federal Reserve Bank of Kansas City or the Federal Reserve System.