The Empirical Study On Financial Distress Prediction In Listed Companies With The Introduction Of Corporate Governance Characteristics

With the fast development of the securities market in our country and its increasing expending scale, the listed companies play an increasing role in such aspects as promoting the development of the national economy, establishing modern corporate system, optimizing the allocation of resources, raising funds and promoting the core competitiveness of the companies and so on. With the fast development of securities market and the continuous improvement of the overall business performance of listed companies, part of the listed companies, under the high market competition, make a slip in the management strategies and investment decision-making, manage disorderly in production and operation, which lead to annual declination in operating performance, and get the company trapped in financial distress, because of the structural imbalance of corporate governance and poor management and so on. The financial distress of listed companies is a deteriorating process with the features of gradualness and predictability etc.. Before companies are trapped in the trouble, the obvious signs will appear which are much more apparent through the financial data. Therefore, such problems become the common and important ones to the listed companies'authorities, investors, claimants and other stakeholders as how to use the financial indicators and the non-financial indicators, e.g. the governance construction to predict the degree of the possible financial distress though establishment of mathematical model.Based on these theoretical analysis of the basic characteristics of financial distress in listed companies and its causes, and of the relationship between the corporate governance and financial distress, this thesis, taking as the study sample the listed companies of the manufacturing sector in financial distress, financial health and financial sub-health during the period of 1998 and 2007, respectively establishes financial variables, and multivariate and multiclass Logistic return model for financial distress prediction with the introduction of non-financial variables such as the corporate governance characteristics. Combining the normative analysis and empirical analysis, the qualitative analysis and quantitative research together, and using the comparative analysis, this thesis does empirical study on financial distress prediction in listed companies. There are five main innovations in this thesis as follow: first, analyzing the relationship between corporate governance and financial distress in terms of ownership structure, the board system and managerial incentive systemically and theoretically; second, based on the financial variables and the results derived from the first point stated above, introducing the non-financial variables such as corporate governance and taking it as study variables; third, diving the company's financial status into such three types as financial distress, financial health and financial sub-health and then establishing model for financial distress prediction; fourth, respectively establishing the financial variables model and the Logistic return model for multivariate and multiclass financial distress prediction with the introduction of non-financial variables such as corporate governance characteristics; and finally, testing the prediction accuracy and the external usefulness of the early-warning model for financial distress with modeling samples and texting samples.This thesis is divided into five parts as follows.Chapter One is the introduction part and puts forward the basic research ideas, methods, and the main innovations of this thesis after the explanation of the backgrounds, the purpose and significance of research, the summary and comment on the research at home and abroad.Chapter Two is a theoretical analysis of an study on financial distress prediction, and it is the theoretical basis of the empirical study on financial distress prediction, which covers concrete definitions of company's financial distress; normative theory interpretation based on the basic characteristic analysis of financial distress; theoretical analysis of factors causing financial distress of Chinese listed companies from the macroscopic and microscopic perspectives; and systematic analysis of the relationship between ownership structure, board system and managerial incentive etc. and financial distress.Chapter Three covers the selection of study sample of prediction model for financial distress and of study variables. This thesis divided the financial status in listed companies into such three kinds as financial distress, financial health and financial sub-health. Firstly, the thesis composes modeling samples and testing samples including selected financial distress, financial health and financial sub-health companies. Secondly, it initially selects the research variables including both financial variables and non-financial variables (corporate governance, etc.) and calculates those initial variables in terms of data of sample companies such as financial statements. Thirdly, it further selects the research variables, that is, in accordance with normality test of the above calculation results, it selects the financial and non-financial variables (corporate governance, etc.) with remarkable differences among financial distress companies, financial health companies and financial sub-health companies to carry through correlation tests, and removes highly correlated variables to carry through multicollinearity tests and then gets the final research variables.Chapter Four is a determination of multiclass logistic return model for financial distress prediction. On the basis of logistic return model identification, in terms of data of T-2, T-3 and T-4 years in modeling samples, it respectively builds multivariate and multiclass logistic return prediction models of financial variables as well as of non-financial variables (corporate governance, etc.) and tests model prediction accuracy and external validity by modeling samples and testing samples, and finally analyzes model empirical study results.Chapter Five covers conclusion and further consideration. It notes several issues in the practical application of this multiclass logistic return model for financial distress prediction and puts forward some matters to be further improved in the following study from the perspective of empirical study on financial distress prediction.The main conclusions can be drawn as follows:(1)With the approaching of financial distress, the numbers of financial variables in obvious differences among financial distress, financial health and financial sub-health companies are gradually becoming remarkable, which shows that the financial distress is a deteriorating process. In different years, the final variables are remarkably different among financial distress, financial health and financial sub-health companies, which shows that the publicly-announced financial information of the listed companies has enough information content and strong timeliness, and that it is applicable to construct the multiclass logistic return model for financial distress prediction through the financial variables.(2) In the financial variables, the net profit ratio of asset reflecting the profitability, with strong stability, can strongly predict the financial distress in long or short term. While non-recurring profit and loss ratio can only predict in short term. The variable of other receivables turnover reflecting the operating capacity includes information content which forecasts the financial distress in short term, and the variable of total asset turnover ratio is better in long-term prediction. Besides, such variables are powerful in short-term the financial distress prediction as the net profit growth and hedging and proliferating ratio reflecting the development and the accounts receivable in variable of current assets ratio reflecting the financial structure of the accounts. While the variables reflecting the cash flow and other accounts receivable in variable of current assets ratio are better in long-term prediction.(3) In the multiclass Logistic return prediction model for financial variables, on the one hand, if the value of such financial variables is greater as net profit ratio of asset, other receivables turnover ratio, total asset turnover ratio, net profit growth, hedging and proliferating ratio and cash re-investment ratio etc., the probability of financial distress is smaller and the probability of financial health and sub-health greater. On the other hand, if the value of financial variables is greater, like non-recurring profit and loss ratio, the accounts receivable in current assets ratio and the other accounts receivable in current assets ratio, the probability of financial distress is greater and the financial health and sub-health is smaller.(4) Information content of distress financial prediction is gained when the non-financial variables reflecting company ownership concentration are significantly different among financial distress, financial health and financial sub-health companies. On the contrary, information content of distress financial prediction is not gained without the significant differences among non-financial variables reflecting board administration, managerial incentive and the annual financial report audit of financial distress, financial health and financial sub-health companies.(5) Among the multiclass Logistic return models with the introduction of non-financial variables, the non-financial variables, such as the absolute control of the share by the largest shareholders and the share ratio of the institutional investors, can strongly predict financial distress in short term. The more absolutely the largest shareholders hold the shares and the larger the share ratio of institutional investors have, the more possible the company's financial health or sub-health is and the less possible the financial distress is. And the share ratio of the largest shareholder can strongly predict the financial distress in long term. If the ratio is greater, the probability of financial health and sub-health of the company is greater and the possibility of financial distress is smaller.(6) It can promote the accuracy of the prediction of the model to some extent to introduce non-financial variables into the multiclass logistic return model for financial distress prediction, which shows that non-financial variables contain enough information and have some capability to predict the company's financial position.