Investigating The Unexpected Price Plummet and Volatility Rise In Energy Market: A Comparative Study of Machine Learning Approaches
| dc.contributor.author | Ojugo, Arnold Adimabua | |
| dc.contributor.author | Otakore, Oghenevwede Debby | |
| dc.date.accessioned | 2024-04-29T11:36:34Z | |
| dc.date.available | 2024-04-29T11:36:34Z | |
| dc.date.issued | 2020 | |
| dc.description | Journal Article | |
| dc.description.abstract | Energy market aims to manage risks associated with prices and volatility of oil asset. It is a capital-intensive market, rippled with chaos and complex interactions among its demand-supply derivatives. Models help users to forecast such interactions and give insight with empirical evidence of price direction to investors. Our study sought to investigate 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). | |
| dc.identifier.issn | 2722-6247 | |
| dc.identifier.uri | https://doi.org/10.35877/454RI.qems12119 | |
| dc.identifier.uri | https://repository.fupre.edu.ng/handle/123456789/84 | |
| dc.language.iso | en | |
| dc.title | Investigating The Unexpected Price Plummet and Volatility Rise In Energy Market: A Comparative Study of Machine Learning Approaches | |
| dc.type | Article |
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