Big Data Meets the Turbulent Oil Market

60 Pages Posted: 28 Dec 2020 Last revised: 22 Nov 2022

See all articles by Charles W. Calomiris

Charles W. Calomiris

Columbia University - Columbia Business School; National Bureau of Economic Research (NBER)

Nida Çakır Melek

Federal Reserve Bank of Kansas City

Harry Mamaysky

Columbia University - Columbia Business School

Multiple version iconThere are 2 versions of this paper

Date Written: November 21, 2022

Abstract

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.

Keywords: Asset Pricing, Commodity Markets, Energy Forecasting, Model Validation

JEL Classification: C52, G10, G14, G17, Q47

Suggested Citation

Calomiris, Charles W. and Çakır Melek, Nida and Mamaysky, Harry, Big Data Meets the Turbulent Oil Market (November 21, 2022). Federal Reserve Bank of Kansas City Working Paper No. 20-20, Available at SSRN: https://ssrn.com/abstract=3755487 or http://dx.doi.org/10.2139/ssrn.3755487

Charles W. Calomiris

Columbia University - Columbia Business School ( email )

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National Bureau of Economic Research (NBER)

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Nida Çakır Melek (Contact Author)

Federal Reserve Bank of Kansas City ( email )

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Kansas City, MO 64198
United States

Harry Mamaysky

Columbia University - Columbia Business School ( email )

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New York, NY 10027
United States

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