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The illusion of oil return predictability: The choice of data matters!

Abstract : Previous studies document statistically significant evidence of crude oil return predictability by several forecasting variables. We suggest that this evidence is misleading and follows from the common use of within-month averages of daily oil prices in calculating returns used in predictive regressions. Averaging introduces a bias in the estimates of the first-order autocorrelation coefficient and variance of returns. Consequently, estimates of regression coefficients are inefficient and associated t-statistics are overstated, leading to false inference about the true extent of in-sample and out-of-sample return predictability. On the contrary, using end-of-month data, we do not find convincing evidence for the predictability of oil returns. Our results highlight and provide a cautionary tale on how the choice of data could influence hypothesis testing for return predictability.
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Submitted on : Monday, January 10, 2022 - 4:57:22 PM
Last modification on : Tuesday, January 11, 2022 - 3:01:49 AM

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Thomas Conlon, John Cotter, Emmanuel Eyiah-Donkor. The illusion of oil return predictability: The choice of data matters!. Journal of Banking and Finance, Elsevier, 2022, 134, pp.106331. ⟨10.1016/j.jbankfin.2021.106331⟩. ⟨hal-03519860⟩

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