Energy finance risk warning model based on GABP algorithm

Abstract

Energy finance is the product of the close combination of the energy industry and the financial industry, and the two affect each other. The energy crisis may lead to a financial crisis, and the financial crisis may also lead to a energy crisis. Early risk warning for the energy financial crisis can effectively mitigate and reduce risks. This article used the GABP (Genetic Algorithm Back Propagation) algorithm model to systematically analyze and predict the risks of energy financial crises. After establishing indicators for energy finance risk warning, this article collected relevant data from 150 energy companies and 210 financial companies, and compared them with the GABP algorithm model and manual analysis model. The error value of the model is determined by the numerical expansion in the positive and negative directions based on zero scale values. The closer the zero scale value is, the smaller the error; the farther it is from the zero scale value, the greater the error. The results show that the average accuracy of the GABP model for energy finance risk warning is 85.2%, and the minimum error value is −0.23. The average accuracy of using manual analysis models for energy finance risk warning is 75.8%, with a minimum error value of 1.89. The GABP algorithm has advantages in constructing energy finance risk warning models.