| Since the 1990 s,financial crisis prediction has been a topic of concern.The financial crisis of many well-known companies have been exposed frequently,which has damaged investor confidence,jeopardized the regular operation of the capital market,and produced significant negatives.After more than thirty years of development in our country 's securities market,the scale of market has been continuously expanded,and it has occupied a very critical position in the national economic system.While some enterprises continue to grow,the financial part also hides enormous risks.If you can judge the current financial situation of the company based on the existing business data and discover the financial crisis early,not only the enterprise managers can find and take corresponding measures in time,but investors can also adjust the investment strategy to avoid investing in companies that are at risk of the financial crisis.Reduce your losses.Hence,it is of considerable significance to establish a financial crisis early warning model for listed companies.The financial status of listed companies are affected by many factors.This article selects 20 indicators based on many influencing factors to build a financial early warning indicator system for listed companies.This study collected useful financial data for a total of 663 listed companies from 2008 to 2017,divided the company types into ST companies,regular companies,and cancelled ST companies,and used principal component analysis to reduce the dimension of the collected financial data In order to predict further the financial crisis of the enterprise,an economic forecast model based on K-means ++ and improved RBF neural network is proposed and compared with the calculation results of BP neural network and RBF neural network.The empirical results show that the use of K-means +-improved RBF neural network combination method helps to predict corporate financial crises better and is useful for financial management risk control. |
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