A game-theoretic model for the classification of selected oil companies' price changes

PeerJ Comput Sci. 2023 Jan 25:9:e1215. doi: 10.7717/peerj-cs.1215. eCollection 2023.

Abstract

One of the essential properties of a machine learning model is to be able to capture nuanced connections within data. This ability can be enhanced by considering alternative solution concepts, such as those offered by game theory. In this article, the Nash equilibrium is used as a solution concept to estimate probit parameters for the binary classification problem. A non-cooperative game is proposed in which data variables are players that attempt to maximize their marginal contribution to the log-likelihood function. A differential evolution algorithm is adapted to solve the proposed game. The new method is used to study the price changes of the Romanian oil company, OMV Petrom SA Romania, relative to the price of oil (crude and Brent) and the evolution of two other major oil companies with influence in the region. Results show that the proposed method outperforms the baseline probit and classical classification approaches in predicting price changes.

Keywords: Binary classification; Nash equilibrium; Oil data.

Grants and funding

This work was supported by a grant from the Romanian Ministry of Education and Research, CNCS—UEFISCDI, project number PN-III-P4-ID-PCE-2020-2360, within PNCDI III. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.