Abstract
The financial crisis of listed companies will bring huge losses to investors, so it is very important to establish a financial early warning model for investors and other stakeholders. The forward neural network model of particle swarm optimization is used to model and analyze the financial early warning of listed companies. In terms of data selection, earnings management indicators are substituted into the model for the common phenomenon of earnings management in listed companies. The results show that the accuracy of the model considering earnings management factors is improved from 65% to 70%. In the process of modeling, this paper uses the logistic regression model to further modify the model. The empirical results show that the accuracy of the model can be improved from 70% to 75%. When using the forward neural network model based on particle swarm optimization to make an empirical analysis of financial early warning of listed companies, adding quantitative indicators of earnings management can improve the accuracy of the model. In the demonstration, the correction of logistic regression model can also improve the accuracy of the particle swarm neural network financial early warning model. This greatly reduces the risk that companies with poor financial conditions will face bankruptcy and liquidation.
Highlights
In recent years, with the rapid development of the financial market, the securities market has become the central hub of financing and investment, and listed companies, as the cornerstone of the whole capital market, play an increasingly important role [1]
Financial crisis can be predicted in advance [6]
E innovation of this paper is to use the forward neural network model based on particle swarm optimization to make an empirical analysis of financial early warning of listed companies. e forward neural network model of particle swarm optimization is used to model and analyze
Summary
With the rapid development of the financial market, the securities market has become the central hub of financing and investment, and listed companies, as the cornerstone of the whole capital market, play an increasingly important role [1]. By establishing an effective and accurate financial early warning mechanism, listed companies can predict the real financial situation in advance and correct the company’s strategy in time through internal rectification, so as to avoid further deterioration [7]. E innovation of this paper is to use the forward neural network model based on particle swarm optimization to make an empirical analysis of financial early warning of listed companies. The modification of logistic regression model can improve the accuracy of the particle swarm optimization neural network financial early warning model. Chapter 4: empirical analysis in MATLAB software is done, quantitative factors of earnings management is added, and the logical regression model to modify the forward neural network model based on particle swarm optimization is implemented.
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