An Empirical Study On Financial Early-warning For Listed Petrochemical Companies

Financial crisis will not occur immediately, it is gradual change in process. From many cases in which the companies occurs financial crises we can conclude that: the company's financial situation trend towards the brink of bankruptcy from the normal status is a gradual process, in this process people can capture the signals from some crisis. In order to avoid the occurrence of financial crisis, the company's financial managers should pay attention to those crises signal, using the company's financial data analysis to identify the important financial indicators then construct a crisis early warning model, once the abnormal financial question happen, the model will be able to give timely warning signal, so that the company's managers can quickly examine the warning information, timely fill gaps and lack to avoid the occurrence of financial crises. This thesis choose Listed companies as a study object for the following reasons: First, the Shanghai and Shenzhen Stock Exchange establish in the 1990, they has approved about thousands of companies in Shanghai and Shenzhen Stock Exchange listing and financing, the capital market gradually move toward a mature status. Second, the financial reports of listed companies disclosure on the basis of objective and fair after audited by the certified public accountants, it has high credibility, so it will easily choice financial reports with includes high information content of financial indicators to build a reliable early-warning model. Third, large numbers of listed companies are widely distributed in different industry. Because different industries have different characteristics of production and operation, if regardless the differences in industry to choose same indicators to build up a warning model, it will be lower effectively. To make research more accurate and targeted, this paper chooses petrochemical listed companies as study object. As the foundation of the industry, the petrochemical industry is an important raw material industry. The upper, middle and lower level company have high correlation between the high technical requirements and higher economic output. This paper through the petrochemical listed company's financial annual reports, find the important indicators of petrochemical to build a financial early-warning model.Combination in production and management characteristics of petrochemical listed companies, the thesis finally decide to use Logistic regression analysis to build petrochemical early-warning model. Primarily, I choose 23 indicators, through the significance test, then through relevance test, ultimately choose seven indicators to build the model. According to taking financial data into the model, the conclusions as following: The petrochemical model can judge ST status correctly up to 72.2% before 3-year; the forecast accuracy up to 91.7% before 2 years; forecast accuracy rate as high as 94.4% before 1 year. The model more close to the ST status has the higher accuracy. Demonstration shows that use of Logistic regressive analysis to build the model is feasible and effective, it has good predictive capability.Finally, this paper sums up study's results, include innovation and limitations.