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Investor sentiment and energy futures predictability: Evidence from Feasible Quasi Generalized Least Squares

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  • Fasanya, Ismail
  • Adekoya, Oluwasegun
  • Oyewole, Oluwatomisin
  • Adegboyega, Soliu

Abstract

Contributing to the budding literature on how emotional and sentimental actions impact the performance of financial markets, this study examines the predictability of energy futures prices with investors’ sentiments. In particular, we examine which of the three (neutral, bear and bull) investors’ sentiments offer accurate forecast information on four energy futures prices. Using the predictability test proposed by Westerlund and Narayan (2015), we discover that all the forms of investors’ sentiments are significant predictors of the movements in energy futures prices. However, the bear sentiments outshine other variants in the forecast of crude oil futures prices, while the bull sentiments provide the most accurate forecast information for the remaining energy futures prices, namely heating oil, gasoline and natural gas. We also find this evidence consistent even when asymmetries are considered in the predictability models. Among other implications of these findings, investors in energy futures and portfolio managers are expected to consider often emotional perceptions in their portfolio constructions and the predictability of future gains.

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  • Fasanya, Ismail & Adekoya, Oluwasegun & Oyewole, Oluwatomisin & Adegboyega, Soliu, 2022. "Investor sentiment and energy futures predictability: Evidence from Feasible Quasi Generalized Least Squares," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
  • Handle: RePEc:eee:ecofin:v:63:y:2022:i:c:s1062940822001656
    DOI: 10.1016/j.najef.2022.101830
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    More about this item

    Keywords

    Energy futures; Investors sentiment; Forecast evaluation; Asymmetries; Behavioural finance;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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