Construction of Financial Management Early Warning Model Based on Improved Ant Colony Neural Network.

Meiluan Wang

doi:10.1155/2021/6748920

Abstract

With the advent of the era of economic globalization, the world capital market is also facing financial risks. It is necessary to have a corresponding financial management early warning model to reduce economic losses. This paper uses the combination of ant colony algorithm and neural network algorithm to build a neural network improved by ant colony algorithm model. By setting relevant assumptions, the financial statements and annual report texts are predicted and analyzed and compared with the original static data forecasting model. Compared with traditional methods, the time series sequencing analysis used in this paper makes the result prediction more accurate. This allows one year's data to be used to predict the data for the next two years. This research can provide a corresponding reference for the optimization of financial management early warning system.

Highlights

Altman put forward a landmark new practical model, namely, five-variable Z-score model, which opened the era of using multivariate statistical methods to predict financial crisis [3]. e Z-score model selects 22 financial index data and makes a comparative analysis on the prediction variable “whether the company has bankruptcy risk,” which has achieved good results and has been widely used in Europe and the United States
Based on the previous literature review, this paper determines the purpose and method of this study, that is, using the data of financial statement indicators and the body of the annual report to predict the problem of “whether the listed company is specially treated” and using the neural network improved by ant colony algorithm to predict and analyze it, respectively
Is paper is divided into five parts. e first part is the research background. e second part is the literature review, analyzing the research results of the problem. e third part is the introduction of neural network improved by ant colony algorithm. e fourth part is the specific experimental analysis. is part expounds the application of the improved neural network ant colony algorithm in financial management early warning. e fifth part is the conclusion of the article

Summary

Related Work

Around 1980, the trend of using machine learning method for classification prediction came, and some people began to apply the early machine learning algorithms such as logistic regression, support vector machine, and artificial neural network to the research of financial crisis early warning, and the sample size of the model was further expanded. Lv et al established the financial early warning model of listed companies by using BP neural network model and conducted empirical analysis with 30 training samples and 8 test samples [16]. Is paper uses neural network improved by ant colony algorithm to study financial management early warning. Based on ant colony algorithm, the construction of financial management early warning model of neural network is improved [27]. Irdly, the ant colony system adopts the local update rule, which constructs the path and updates the pheromone concentration at the same time. E financial situation of listed companies generally has two aspects: one is the poor operation of the company; the other is the falsification of the company’s financial statements. rough the summary of previous empirical research conclusions and theoretical models, we can divide the indicators to predict the financial crisis into six dimensions: solvency, profitability, operating ability, development ability, cash flow analysis, and risk level

Results and Discussion

Conclusion