Investigating The Unexpected Price Plummet And Volatility Rise In Energy Market: A Comparative Study of Machine Learning Approaches

  • Arnold Adimabua Ojugo Federal University of Petroleum Resources Effurun (NG)
  • Oghenevwede Debby Otakore Department of Computer Science, Federal University of Petroleum Resources, Effurun 32001, Delta State, Nigeria (NG)
Keywords: Energy market, deep neural network, machine learning, price volatility, futures price, oil, spot price

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The energy market aims to manage risks associated with prices and volatility of oil asset. It is a capital-intensive market that is rippled with chaos and complex interactions among its demand-supply derivatives. Models help users forecast such interactions, to provide investors with empirical evidence of price direction. Our study sought to investigate the reasons for the unexpected plummet in price of the energy market using evolutionary modeling – which seeks to analyze input data and yield an optimal, complete solution that are tractable, robust and low-cost with tolerance of ambiguity, uncertainty and noise. We adopt the Gabillon’s model to: (a) predict spots/futures prices, (b) investigate why previous predictions failed as to why price plummet, and (c) seek to critically evaluate values reached by both proposed deep learning model and the memetic algorithm by Ojugo and Allenotor (2017).


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How to Cite
Ojugo, A. A., & Otakore, O. D. (2020). Investigating The Unexpected Price Plummet And Volatility Rise In Energy Market: A Comparative Study of Machine Learning Approaches. Quantitative Economics and Management Studies, 1(3), 219-229.