Big Data Meets the Turbulent Oil Market

Nida Cakir Melek, Charles Calomiris and Harry Mamaysky ()

No RWP 20-20, Research Working Paper from Federal Reserve Bank of Kansas City

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 (search for similar items in EconPapers)
JEL-codes: C52 G10 G14 G17 Q47 (search for similar items in EconPapers)
Pages: 59
Date: 2020-12-23, Revised 2022-11
New Economics Papers: this item is included in nep-cwa, nep-ene and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedkrw:89532

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DOI: 10.18651/RWP2020-20

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