The Empirical Study Of Corporation's Credit Risk Based On The Nature Of The Ownership

Credit risk, as one of the most pronounced appearance for financial risk, has become an important research topic. Corporations are economic entities, whose objective is to make profit. They are the representatives of the modern enterprise system. The extent of their credit risk will have a direct impact on the whole society's credit system. In this paper, we detail studied the current status of corporation's credit risk(CCR) , its forming mechanism, its accounting result, the market reaction and the establishment of prediction scheme, basing on the different level of credit risk from the different nature of the corporation ownership.This paper adopts the standard empirical research paradigms with the process of theoretical analysis– hypothesis proposition– data selection– empirical test– result getting. The four parts in the empirical test are developed gradually. First, we compared the current status of credit risk between the state-owned enterprises (SOEs) and private-owned enterprises (POEs), and found each of their own distinct credit risk characteristics. Upon verifying these distinct characteristics between SOEs and POEs, we studied the effect on CCR from the following factors: the forming mechanism of credit risk, corporation's financial situation, governance structure and growth ability, the macro-economy environment and the cost of debt financing. We then analyzed CCR's economic consequence, i.e. the accounting result and market reaction from the different level of CCR. At the last part, we gave the CCR prediction model, which can be utilized as a pre-warning support system to decrease CCR level and reduce damage caused by credit risk.In the first section, we categorized the normal elements that generate CCR. After comparing the current credit risk status between SOEs and POEs, the results show that the difference from the property rights and its protection mechanism, the degree of information asymmetry and expectation are the main reasons for their dissimilar credit risk levels. The average credit risk level for POEs is always greater than that for SOEs. This conclusion was proved by using Altman's Z-score, liquidity ratio and cash liquidity debt ratio (CLDR) as the measurements for CCR.In the second section, we set out dissecting the CCR formation, then we did a thorough theoretical and empirical study by considering all the impact factors on CCR. As the result, we found that corporation's financial situation has a negative correlation with its credit risk level. Corporation's stock share structure, board commission, growth ability, macro-economy environment and the cost of debt financing, all have impact on its credit risk level, besides its financial situation.In the third section, we examined the economical consequence of the CCR, and performed empirical test on CCR's accounting result and market reaction. We reach the conclusion that the CCR's accounting result has crucial influence on corporation's short-term and long-term finance ability and its trade credit. The higher the CCR level, the lower the short-term and long-term finance ability and trade credit. In the meantime, market reaction also corresponds to corporation's credit risk level. Corporations with low credit risk have much higher average capital adequacy ratio (ACAR) than corporations with high credit risk.At the final section, we conducted both theoretical and empirical study to establish CCR prediction model, which includes prediction indication and method selection. This prediction scheme produces superior pre-warning performance by adding corporation's governance structure, growth ability, the cost of debt financing and some macro-economy environment variables into the model. We also conducted back to the judge on the test sample data. The first category error is on the high end, but the second category error is very low, which means that this scheme has a very conservative prediction performance.Collectively, this paper provided the entire framework to conduct research on CCR by stepping through current status– forming mechanism– economic consequence– prediction model, and reviewed the developing process and final results of CCR.This paper has the following original contributions comparing to all the current research in this field:1.For the sample selection, the majority of the current researches made their sample selection targeting listed companies of all or particular industry, and based on the standard whether the companies have been ST and PT or not as the definition and judgment on credit risk. All these were severely departed from the basic definition of credit risk, so we selected samples from SOEs and POEs with Chinese characteristics, while didn't use ST and PT as judgment standard. Instead, Z-score, liquidity ratio and CLDR were chosen as the sample selection judgment standards, and the differentiation among these three standards was compared at the same time. The various properties of credit risk were extracted from the sample group of high credit risk corporations and the sample group of low credit risk corporations.2.From research perspectives, most current studies conducted credit risk assessment and measurement from credit risk recipient's point of view, and paid little attention to credit risk creator's role in the features and generation of credit risk. An example is the credit risk assessment and management study with bank as the creditor. Corporations as the most important market entity, their credit risk level dictates the establishment of the whole society's credit system. Therefore, corporation, the credit risk creator, was picked as research objectives in this paper. Further, in order to reduce the CCR damage to every beneficial entity, we explored CCR forming mechanism and its economical consequence, and built a real-time CCR prediction model.3.As for the research object, there was no current CCR study considering the diversification of the stock share characteristic among the listed companies. However, this unique diversified share characteristic and structure in Chinese stock market were the roots of some of the distinct characters for company's credit risk and governance structure, so we selected SOEs and POEs with Chinese characteristics as the research objects. After studying the CCR of listed companies, it was clear that SOEs and POEs were facing different kinds of CCR. The underline logic was studied for this difference, and the importance of the regulatory environment to CCR was emphasized.4 . In research content , credit risk assessment, measurement and management were the main current research topics. Although there are studies that touched on the forming mechanism and impacting factor of CCR, no systemic study has been done till this paper. Here, we seek the underline logic (i.e. credit risk forming mechanism and related impact factor) of CCR formation, rather than just summarizing the different CCR characteristics from the credit risk study of various ownership property companies. Accounting result and market reaction were also investigated. Based on all these studies, a CCR prediction model was built and some rigorous tests were performed to verify the effectiveness of the model. A systemic and detailed study on CCR was realized for the first time.Through these four parts empirical study, the economical consequence of CCR due to various impact factors, was demonstrated. It is necessary to establish a CCR prediction model, in order to reduce the damage caused by CCR. The principal conclusions from this study were:1.By using Z-score, liquidity ratio and CLDR analyzing the current status of CCR, the CCR level of POEs was always higher than that of SOEs, which validated our conclusion about the relationship between the ownership property of the company and its credit risk performance. To further confirm our conclusion, three different indictors were adopted simultaneously in the analysis. Even when all three indictors were the same for SOEs and POEs, there was notable difference for the CCR level between them. It is clear that without accounting for the various ownership properties of enterprise entities (SOEs and POEs) in our unique regulatory environment, inaccurate conclusions has been drawn from previous studies, which was unsuitable to be used in policy making, was harmful to the healthy development of our stock market and would point false directions to the investors.2.During the investigation of impact factors on CCR, we found that company's financial situation has a negative correlation with its credit risk level. All sampled enterprises, doesn't matter if it is SOEs or POEs, their quick liquidity ratio (QLR), liquidity asset ratio (LAR) and cash liquidity debt ratio (CLDR), all have a positive correlation with Z-score, i.e. have a negative correlation with CCR level, while asset debt ratio (ADR) has a negative correlation with Z-score, i.e. has a positive correlation with CCR level. Company's share structure had the following relationships with its credit risk level: it had a positive correlation with the control percentage of the controlling shareholders; it had a positive correlation with the share control balance power; and it had a negative correlation with the degree of share concentration. And, company's board structure had the following relationships with its credit risk level: it had a positive correlation with the same person acting as the chairman of the board and CEO; it had a positive correlation with the scale of the board; and it had a negative correlation with the numbers of the independent board directors. Additionally, company's growth trend had a negative correlation with its CCR level; regional market process had a negative correlation with CCR level; and the cost of debt financing had a positive correlation with CCR level.3.While investigating the economical consequence induced by CCR, we noticed that credit risk could depress the enterprise value, could also trigger the behavior change of the management, bank and suppliers, which eventually would translate into the finance indicators change of the enterprise. The empirical test results show, low credit risk companies had greater short-term and long-term borrowing abilities and better trade credit. The results derived from accretion revenue method (ARM) indicated that the group with low credit risk had higher ACAR than that of the group with high credit risk for POEs, SOEs and all sampled enterprises. Also, the results derived from regression analysis show that CCR level had a negative correlation with accretion revenue (AR). All together, we demonstrated that the market would have a dramatic negative reaction to CCR.4.We chose multiple linear regression model and logistic regression model as CCR model, when conducting CCR pre-warning scheme. The results from SectionTwo were utilized to the selection of the indicator variables. Empirical test results show that both multiple linear regression model and logistic regression model gave conservative prediction indications, i.e. the second category error was lower and the first category error was higher than it. Even though there is a discrepancy with the pervious research results, we got a better prediction accuracy ratio from multiple linear regression model than from logistic regression model, in general, both prediction models demonstrated an accurate performance.In summary, this paper explained the different CCR appearance corresponding to various ownership properties, gave the underline logic for this appearance, and described accounting result and market reaction from CCR. The CCR prediction model demonstrated good pre-warning performance by adding governance structure factor, macro-economy environment factor, growth ability factor and the cost of debt financing factor. This paper provided an important cornerstone for future studies in CCR field, also provided a valuable decision support system to securities regulatory departments, investors and all interest related parties.