Recommended by Mark Bebbington We show how it is possible to construct efficient duration dependent semi-Markov reliability models by considering recurrence time processes. We define generalized reliability indexes and we show how it is possible to compute them. Finally, we describe a possible application in the study of credit rating dynamics by considering the credit rating migration as a rel...

Most market observers attribute the recent credit crunch to a confluence of factors: excess leverage, opacity, improperly estimated correlation between bundled assets, lax screening by mortgage originators, and market-distorting regulations. It was the job of the credit rating agencies to create transparency, to provide the basis for risk-management regulation, and to discipline mortgage lender...

Small and medium-sized logistics companies are playing an increasingly important part in social life. We selected the data of small and medium-sized logistical companies in Beijing, Shanghai and Guangzhou, reformulated ordinal regression support vector machine method so that different input points could make different contributions to decide hyper plane, to analyze multi-classification of credi...

This paper examines the quality of credit ratings issued by the three major credit rating agencies Moody’s, Standard and Poor’s and Fitch. If credit ratings are informative, then prices of underlying credit instruments such as fixed-income securities and credit default insurance should change to reflect the new credit risk information. Using data on 246 different major fixed-income securities i...

Kernel classifiers (such as support vector machines) have been successfully applied in numerous areas, and have demonstrated excellent performance. However, due to the high dimensionality and nonlinear distribution of financial input data in credit rating forecasting, finding a suitable low dimensional subspace by nonlinear dimensionality reductions is a key step to improve classifier performan...

Corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has been one of the attractive research topics in the literature. In recent years, multiclass classification models such as artificial neural network (ANN) or multiclass support vector machine (MSVM) have become a very appealing machine learning approaches due to their good performance. However, mos...

The purpose of this work is to examine the relationship various financial and non-financial (qualitative) factors performance companies their credit ratings.
 We developed scoring model which was based on methodologies international Russian rating agencies. modelled ratings for 2018–2020 were compared with actual assigned by agencies discrepancies explained. sample includes from retail, pr...

By the end of 2011, agricultural population in accounted for 48.73% in China. Also, farmers in China are dispersed and their financial information is incomplete, which leads to credit risk evaluation system of farmers in China is not sound at all. Most Chinese banks even have not established the rating system, so farmer credit risk rating system pressed for solution. This study proposes an inde...

Credit ratings affect firms’ access to capital and investment choices. We show that the identity of the credit analysts covering a firm significantly affects the firm’s rating, comparing ratings for the same firm at the same time across agencies. Analyst effects account for 30% of the within variation in ratings. Moreover, the rating biases of analysts carry through to credit spreads on the rat...