| Every day investors, financial professionals and the academia the world over look to one credit risk predicting mechanism that is authoritative, objective and credible. For instance, while issuing credit rating, Standard & Poor's, a credit rating, indices, investment research and data service provider, also pays much attention to key business and financial elements, public debt default and recovery of a rated firm. Nowadays, more and more public investors invest in listed companies through the security market. If the financial distress appears in a listed company, investors, without knowing relevant information in time, will face enormous risk, which will probably result in personal financial losses and even influence the stability of the whole society. In 2001, the amount of decrease of A-share markets of Shanghai and Shenzhen was once up to 20% and 30% respectively. A-share market value of circulation of two cities was 1,550,600 million yuan at the end of 2000, yet shrank to 1,245,400 million yuan (deducting the newly-issued stocks) by the end of 2001, which means 300 billion yuan A-share market value of circulation had vaporized. But up to August of 2007, the total market value of Shanghai and Shenzhen has already topped 23,000 billion, the total number of stock owners exceeds 110 million, the daily turnover of Shanghai is close to 160 billion. After moving up to 5000, the Shanghai Composite Index keeps challenging its record high. The authorities, throughstrengthening the macro-control policy, keep requiring higher bank reserve and issuing special treasure bonds aiming to ease liquidity and prevent the economy from overheating. At this moment, all participants in the security market should not only bear in mind the historical lesson but also exercise extreme caution while choosing investment targets, and furthermore, they are in dire need of a risk-free and reliable financial distress predicting mechanism of listed companies to avoid possible financial disaster.In recent years, the examples that the listed companies are classified as ST or *ST Companies because of financial distress, poor management or harsh competition from outside are of common occurrence. But any financial distress has its cause and sign, it's is very essential and necessary to establish an accurate financial distress predicting mechanism to forecast emergency in time. The CEO can take the corresponding measure early at the embryonic stage appearing in financial distress and prevent from failing; investors can deal with their investment in time after getting the financial distress prediction of the listed companies and avoid greater losses; financial institutions can utilize this kind of prediction to make loan decision and control the loan procedure; relevant enterprises can make the best use of the predicting mechanism to manage the credit policy and the account receivable effectively; with the help of prediction, those who are ambitious to expand can easily locate the targets for back-door listing and reorganization; certified accountants can utilize such early warning information to confirm their auditing procedure and the prospect of any listed company. Therefore, the research meaning of this thesis lies in regarding the financial distress of listed company as a relatively independent dynamic system, probing into prerequisite and foundation of financial distress prediction, analyzing the cause taking place in financial distress, and constructing the financial distress predicting model.This research consists of six chapters. Chapter one which is an introduction part mainly focuses on the basis of the selected title and related definition, introduces relevant concepts, ways of thinking and method studied, and last but not least recommends the thesis structure and its possible innovation. Chapter two is literature review and comment. The main representative figures of the relevant fields of the thesis and their research results are reviewed on a chronological basis, including several following respects: the cause of financial distress (financial and non-financial factors), agency problem and corporate governance, empirical studies on financial distress predicting models and related comparison and commentary. Chapter three is the research approach demonstration, which is composed of the definition of financial distress, sample choosing procedure, and the operational definition of variables. The construction of statistical models and the establishment of hypotheses are illustrated in Chapter four. While building models, besides financial ratio variables, I add on corporate governance variables, macro-economic variables and business operation efficiency variables sequentially and progressively to establish synthesized models. After the models are constructed, on the basis of those research and literature reviewed, I establish a series of research hypotheses accordingly as the starting point for upcoming empirical analysis. Chapter five is the results and analysis of empirical study. The related data of 126 ST companies and 252 non-ST matching companies listed as A-Shares in China, time span from the year 2002 to 2005, is processed to help construct models through statistical measures accordingly. First, by employing DEA model, Logit model and neural network model, I apply the data progressively yet employ the models respectively. Then, I compare the empirical results of different models and hypotheses. An optimum comprehensive financial distress predicting model is constructed and confirmed through above mentioned process. Finally, the optimum Logit Model IV is further verified to be robust in predicting financial distress by applying the data of 1379 A-share listed companies (insurance and banking companies are excluded) in the year of 2006 as testing sample. By employing the specific synthesized Logit model, I can locate 53.03% ST companies from 0-10% sample segment, and I can even illustrate 90.15% ST companies in the first 50% sample segment in the year of 2006. Chapter six is the conclusion and follow-up study suggestion. In this chapter, I summarize the whole thesis, point out the deficiency and limitation, draw a constructive conclusion and last but not least offer my suggestion for future study on this specific topic.
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