Construction of Early Warning Model for Financial Economic Cycle Using Genetic Algorithm

Abstract

Economic crises occur when the economy experiences drastic fluctuations, which in turn cause heavy losses to society and the economy. Economic crises happen when there are significant losses to society and the economy as a result of the economy experiencing abrupt changes or deep recessions. The term “economic cycle early warning mechanism” refers to a collection of theories and techniques for keeping track of, assessing, forecasting, and choosing policies regarding finances in light of the particular economic phenomenon known as economic cycle fluctuations. The traditional early warning mechanism has been unable to keep up with the demands of economic cycle early warning due to the growth of economic globalisation, and the phenomenon of incomplete early warning and poor early warning accuracy frequently occurs. A genetic algorithm simulates the natural selection and genetic mechanisms of Darwin’s theory of biological evolution. It is a computational model of the biological evolution process. It is a technique for looking for the best answer by simulating the course of natural evolution. Genetic algorithms have advanced incredibly quickly in recent years. It has been widely applied in machine learning, neural networks, control system optimization, and the social sciences as an effective, useful, and reliable optimization technique. In order to optimize the early warning mechanism and increase the thoroughness and accuracy of early warning, this paper investigates the early warning mechanism of financial and economic cycles by combining genetic algorithms. A mathematical model of the economic cycle was built during the experiment and tested in accordance with the genetic algorithm’s basic operating principle. The findings demonstrate that the genetic algorithm-based early warning system for the financial economic cycle is more complete and accurate, with a 6.4% increase in accuracy.