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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

Article Citation

  • 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 Link

Note: A previous version of this RWP from December 2020 was titled "Mining for Oil Forecasts"


Nida Çakır Melek

Senior Economist

Nida Çakır Melek is a senior economist in the Economic Research Department of the Federal Reserve Bank of Kansas City. She joined the Bank in August 2013 after receiving her Ph.D…