Predicting the Oil Market

Charles Calomiris, Nida Cakir Melek and Harry Mamaysky

No 29379, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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-codes: C52 G10 G12 G14 G17 Q47 (search for similar items in EconPapers)
Date: 2021-10
New Economics Papers: this item is included in nep-cwa and nep-ene
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