Research On Forewarning Of The ST Companies' Financial Distress Under The New Normal

Under the new normal,the situation of China's economic development has occurred great changes.The risk of enterprises' operational crisis and even bankruptcy liquidation has increased greatly because financial distress.ST companies are "special treatment" listed companies which have occurred operational crisis,and its future financial position is the focus of the parties concerned.Constructing a financial forewarning model which combines F-Score model,decision tree C5.0 algorithm and the Microsoft time series analysis method,and took 26 ST listed companies' 2012?2016 financial statement panel data as the research object to carry out empirical research.Through analyzing and testing the actual data the results showed that:the volatility of some key financial indicators such as total assets growth rate,asset liability ratio,inventory turnover and fixed asset turnover will greatly affect and decide the financial distress degree of ST companies.Applying 2013-2015 financial statement panel data to prove the model has good judgement in the financial distress degree of ST companies.Then applying Microsoft time series analytical method to forecast the key financial indicators of sample ST companies in fourth quarter of 2016.Combining the results with the decision tree C5.0's judgment rules to judge the ST companies'financial distress degree and comparing with the realities in fourth quarter of 2016.As a result,we can obtain the accuracy rate of the forewarning model.The empirical result showed the accuracy rate(84.6%)of the model is satisfied.By applying this warning model,ST companies' managers can take some targeted measures to prevent and deal with financial distress,and then make the companies adapt to the "new normal" of economy,maintain the stability and prosperity of China's capital market.ST stock investors can dynamically grasp the changes of the financial distress degree of ST companies,and avoid the risk of delisting.They can also select the promising ST stocks according to the key financial indicators,and optimize the portfolio and decision-making behaviors.