Abstract
This paper mainly analyzes the theories related to the financial risk of the company and combines the principles of principal component analysis, particle swarm optimization algorithm, and artificial neural network to derive the financial risk index system of the company. To improve the accuracy of financial risk prediction, principal component analysis and particle swarm algorithm are applied to optimize the BP neural network model, the input data of the prediction model is improved, and the optimal initial weights and thresholds are given to the BP neural network by using particle swarm algorithm search, whereby the financial risk prediction model of particle swarm optimization BP neural network is constructed. The empirical results show that the model constructed by BP neural network not only has a high accuracy rate for static financial risk evaluation but also has a better prediction effect. After training and testing, the BP neural network-based enterprise financial risk evaluation model can accurately determine the existing financial situation of enterprise financial management and has a good prediction effect. Our research method is a fusion of the processing of the two methods, which belongs to the first integration of results.
Highlights
Related WorkScholars’ research on financial risk early warning is mainly based on two aspects, the focus of some scholars is the research on issues related to the enterprise financial risk early-warning index system, and the other scholars focus on the hot spot is the research on the method of enterprise financial risk early-warning model construction [7]
Businesses are an important force to be reckoned with in today’s economic market, driving the country’s economic development and accelerating the national plan for a stronger network
By comparing the advantages and disadvantages of various financial risk evaluation methods, this paper selects the BP neural network method to evaluate the financial risk of Internet enterprises, diagnoses, and analyses; optimizes the company’s financial risk prediction model based on BP neural network; integrates the particle swarm optimization algorithm and neural network; and introduces the particle swarm optimization algorithm with smaller prediction error, faster convergence speed, and simpler implementation into the neural network
Summary
Scholars’ research on financial risk early warning is mainly based on two aspects, the focus of some scholars is the research on issues related to the enterprise financial risk early-warning index system, and the other scholars focus on the hot spot is the research on the method of enterprise financial risk early-warning model construction [7]. Li analyzed the enterprise financial risk influencing factors and evaluation indexes to get seven principal component variables by principal component analysis and established a financial risk evaluation model based on BP neural network to test on sample enterprises, and the research results show that this indicates that the evaluation accuracy of BP neural network will improve with the approach of time [13]. Total purchasing power available to consumers is lower than the total value of social products e economic cycle theory describes the causes and processes of corporate financial risk and their interrelationships in terms of monetary factors, investment cycles, and underconsumption. Before conducting a significance analysis of financial indicators, the test is first performed on the study sample to determine the distribution of the sample. e K-S test is a method to determine whether the sample data obey a particular distribution, so K-S is often
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