Abstract
With the continuous improvement of China’s market economy, many listed companies enjoy the unlimited development opportunities brought by the market economy environment but are also threatened by various potential risks. They may be labeled “ST” at any time due to financial risks. The label may even end up in danger of delisting. Most companies encountered serious financial crises or even bankruptcies in the later period because they did not pay enough attention to the financial problems that occurred in the early stage and did not take effective measures to deal with the crisis in a timely manner. This is extremely detrimental to the subsequent development of the company. Therefore, more and more attention has been paid to the research on the financial risk status of enterprises. Therefore, on the basis of analyzing the financial information of listed companies, this article extracts the characteristics of listed companies and images them and uses convolutional neural networks to construct a financial risk prediction model to improve the accuracy of risk prediction. Specifically, this article also compares and analyzes the financial risk prediction models of different types of listed companies, optimizes the index system, and uses the convolutional neural network method to construct a targeted financial risk prediction model with data characteristics. The actual operation data and actual risk data of the listed companies are verified, proving that it has strong adaptive ability to face different types of data, strong operability, and high prediction accuracy.
Highlights
With the continuous improvement of financial markets, the impact of financial conditions on the healthy development of enterprises has become significant
Erefore, in order to effectively prevent the occurrence of corporate financial risks, this article takes listed companies as the research object, starting from multiple angles that affect the occurrence of corporate financial risks, constructs a comprehensive and effective financial risk prediction index system for listed companies, and uses some artificial intelligence related algorithms to construct an effective financial risk prediction model, which can effectively enhance the enterprise’s risk management capabilities and improve the enterprise’s risk prevention mechanism, and successfully apply it to the actual management of the enterprise to enhance enterprise risk management mechanism to promote the sustainable development of enterprises
Financial ratio imaging is performed on the financial statement index data of listed companies, and the convolutional neural network is used to build a model for the bankruptcy risk assessment research of listed companies, and methods such as Z-score, SVM, and Multilayer Perceptron (MLP) are compared and analyzed. e empirical results show that the new method has greatly improved the prediction accuracy rate compared with the traditional method
Summary
With the continuous improvement of financial markets, the impact of financial conditions on the healthy development of enterprises has become significant. It needs to use the back-propagation algorithm to guide the machine to self-learn by changing the internal variables and explore the deeper content contained in the data sample This method of using back-propagation or hierarchical models to expand corresponding learning has been used in media such as images, videos, text, and audio. Erefore, in order to effectively prevent the occurrence of corporate financial risks, this article takes listed companies as the research object, starting from multiple angles that affect the occurrence of corporate financial risks, constructs a comprehensive and effective financial risk prediction index system for listed companies, and uses some artificial intelligence related algorithms to construct an effective financial risk prediction model, which can effectively enhance the enterprise’s risk management capabilities and improve the enterprise’s risk prevention mechanism, and successfully apply it to the actual management of the enterprise to enhance enterprise risk management mechanism to promote the sustainable development of enterprises
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