The Research On Financial Crisis Early-warning Model Of Listed Companies With Non-financial Information

Enterprises are threated by financial crisis in the fierce stockmarket now. If enterprises can't take precaution against the financial crisis in time, they will get into trouble. But enterprises are not powerless in the face of such a predicament; the financial crisis can be predicted in advance. Thus, essential analysis based on setting up the financial crisis early warning index system which is disrelated and covered amount of information, and building up a financial crisis early warning model, has become the important aspect of stabilifying the development of stockmarket, national economy, and society.Financial distress is a cosmopolitan problem. Since 1930 studies on corporate failure prediction had prevailed both in the U.S.A and European countries, and researches on this subject have been conducted in our country recent years. Most of prior researches were concerned with the establishment of corporate failure prediction models based on only financial ratios. This paper makes use of the financial statements of listed companies in China Stock Exchange and non-financial information to build a successful model for corporate failure discrimination with Logit method and Fisher Discrimination Analysis. Based on financial factors, we adds some non-financial variables into the model, suah as equity structure, directorate information and aduit advice.Firstly, this paper discusses the concept of financial distress, then it reviews the important literature on financial distress at home and abroad, and then it introduces some innovations and development trend in predict methodology with non-financial variables over the past two decades especially. By the way, it discusses the main progressin this study. Secondly, we analysis some non-financial factors, such as equity structure, directorate information and aduit advice.Thirdly, research method was discussed. We introduce the empirical method to selecte the variables and samples, and the empirical methods and empirical models. The fourth part builds up the financial distress early warning model by Logistic model and Fisher Discrimination Analysis after extracting 10 principal component factors. At last, we summarize the empirical results, the shortcomings of our study and suggestment for the future study.The main characteristic and innovation of this paper lie in three aspects. Firstly, we apply new matching method to select sample, and the applicability of the model increase evidently. Secondly, the index system is more integrate for including non-financial indexes. Thirdly, we build up a financial distress early warning model including non-financial indexes. The empirical results indicate that the models have satisfactory predicting accuracy, and the models include nonfinancial variables is more accurate.