The Research Of Financial Distress Worning Module In Short Term Of List Company

Loss coverage and amount of Chinese listed company is in a steadily rising trend, some even had severe financial crisis. For investors, potential market risk is larger. How to evaluate objectively on the listing company's financial situation, especially if estimation based on public financial data could cause financial distress, is very instructive in investors' decision. In addition, to predict future tend of listing company's finance is important for supervising team to identify those sightless financing company, for investing bank to discover effectively potential customers, for loan bank, suppliers and other related company to adjust custom relations, also for management team in company to improve self running.It takes time to make things happen. Company in trouble will show some sign in previous time, which is obvious in financial data. So how to use accounting data and adopt suitable financial index and set up math module to make prediction has been an important research. Some documents showed many researchers set up financial trouble warning module according to company annual report. Then, frequent change can be occurred in company running within a year. How can the annual report be fit to mid-year or quarter financial data? This is doubtable. If annual module is not suitable to financial data at other period, this module cannot produce effective warning. So this paper aims to set up a short term warning module system according to company financial data at each time, which can help to make effective warning continuously within a year.This paper makes thorough analysis on financial short term warning theoretically and practically. In this research, definition of financial trouble short term warning is raised; its system is set up as well. This system includes 21 modules in three types and three periods. In single variables accurate rate of total asset rewarding rate module, logistic regress module and Fisher'sdifferentiate module is 92.26%, 92.59%, 90.36%. As for total accurate rate of sample prediction, they are 92.26%, 94.05%}1l 92.31%. Three types of module achieved high accuracy on prediction and can be totally put into practice and can solve problem of financial trouble continuous warning.