Economic cycles may lead to changes in corporate bond credit ratings. This paper utilizes the Markov model to describe transition probability matrixes of economic states for the separation of economic cycles. We develop a new model, which we term the Markov rating shopping dual reputation model, incorporating two reputation effects. This model is well suited to analyze the conditions of the dual rating incentive regulation and the constraint regulation for preventing rating inflation in collusion among credit rating agencies. Then, we apply the Markov regime switching-vector auto-regression (MS-VAR) to estimate the transition probability matrixes of America, England, Japan and China. Based on the numerical analysis and the simulations, the results show that a dual rating regulation can prevent the collusion of inflated ratings, as well as increased rating fees with the separation of economic cycles; additionally, when separating the economic cycles, a constraint regulation is more effective at reducing the risk of rating inflation in collusion and regulatory cost.

This article adds to the existing literature on global rating agencies (GRAs, i.e., the S&P, Moody’s, and Fitch) and domestic rating agencies (DRAs). In our research, we introduce the reputation, rating cost and rating accuracy of rating agencies to improve the Hotelling model. According to the theoretical analysis and empirical tests, the results show that the open policy of the Chinese rating industry contributes to higher rating quality in the domestic bond market. This open policy leads to rating convergence between DRAs and GRAs from in the long term.

Since the outbreak of credit risk, researching on corporate credit rating has been brought into investors, the government and scholars focus. This paper constructs an optimal K-means clustering Group Method of Data Handling model can effectively improve the accuracy of rating results, reduce the computational complexity, and this paper proves the model under the least squares estimation can get the optimal results. This article uses Chinese corporate credit rating and financial indexes to study, comparing its results with the concequences of Hidden Markov GMDH model and other traditional neural network models. The empirical outcomes show that the K-means clustering GMDH model is better than Hidden Markov GMDH model and the remaining four neural network models, indicating that the method can effectively improve the accuracy of corporate credit rating assessment and reduce the cost of rating.

We developed a dual-reputational rating shopping model to introduce public and institutional reputations. Investor’s and regulator’s penalty rates are described as public and institutional reputations, respectively. We achieved the available conditions of single-rating and dual-rating regulations to prevent rating inflation in this model. To examine the regulatory effects of different types of regulations on Chinese corporate bond ratings, we utilize panel ordered logit models. Theoretical analysis and empirical tests show that, when the reputation effect is low, the single-rating regulation is better at improving rating quality, and when the reputation effect is high, the dual-rating regulation induces rating agencies to provide more accurate ratings. Compared to the regulatory effects of the single-rating and the multi-rating regulations, the dual-rating regulation most effectively improves the rating quality of corporate bonds and prevents rating inflation.

This paper, starting from the effects of financial regulatory policies, considers the interaction between Chengxin_Moody and Lianhe_Fitch with the dual rating system and the multi-rating system, constructs a new ordered Logit model, and attempts to explore the impact of the Notice, the dual rating system and the multiple rating system on the probability of Chinese corporate bond defaults, rating upgrades, rating downgrades, and the magnitude of credit rating migrations. This study compares the effectiveness of different rating regulatory systems. Using nine thousand two hundred and sixty-two data of Chinese corporate bonds as the research samples. Empirical analysis and robustness test reveal the following findings: (1) The issuance of the Notice has a significant positive effect on the implementation of both the dual rating system and the multiple rating system, with a greater impact on the implementation of the multiple rating system; (2) The issuance of the Notice, along with the dual rating system and the multiple rating system, can all reduce the probability of corporate bond defaults, with the multiple rating system showing the best preventive effect against corporate bond defaults; (3) The dual rating system is more effective in promoting rating agencies to adjust rating behaviors, accurately correcting corporate bond ratings and effectively alleviating the issue of rating inflation. Competition among rating agencies intensifies rating shopping; (4) Under the dual rating system, rating agencies are more likely to expand the magnitude of credit rating upgrades and downgrades, enhancing the differentiation between high-quality corporate bonds and junk corporate bonds; (5) The selection of rating information from Chengxin_Moody and Lianhe_Fitch can appropriately adjust the degree of flexibility or tightening in response to rating regulatory systems. Chengxin_Moody demonstrates a more sensitive reaction compared to Lianhe_Fitch regarding the rating regulatory systems. This study provides valuable references for the formulation and evaluation of the effectiveness of Chinese corporate bond rating regulatory policies.