Credit rating is a significant area of financial management which is of major interest to practitioners, financial and credit analysts. The credit/financial analysts have to investigate an enormous volume of financial and non-financial data of firms, estimate the corresponding credit rating and finally make crucial decisions regarding the financing of firms. Considerable attention has been devoted in this field from the theoretical and academic points of view during the last three decades.
Financial and operational researchers have tried to relate the characteristics of a firm (financial ratios and strategic variables) to its credit rating. According to this relationship, the components of credit rating are identified and decision models are developed to assess credit rating and the corresponding creditworthiness of firms as accurately as possible.
Although many studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective to solve a specific classification problem. Alternatively, it has been suggested that a better approach to classification problem might be to integrate various learning techniques. Intelligent combining of several learning algorithms and their synergistic use may lead to improving predictive ability.
Recently, a number of studies have demonstrated that a hybrid model integrating artificial intelligence approaches such as Artificial Neural Networks, Rule-based system and Case-based Reasoning with other feature selection algorithms can be alternative methodologies for business classification problems.
In this article, we propose a hybrid approach using rough set theory as an alternative methodology to select appropriate attributes for case-based reasoning. We use rough set theory to extract knowledge that can guide effective retrievals of useful cases. Our specific interest lies in the stable combining of both rough set theory and case-based reasoning in the problem of corporate credit rating.
This thesis is organized as follows:
Chapter 1 explains motivations and academic backgrounds of applying integrated model in the field of corporate credit rating. Chapter 2 provides a brief description of various credit rating methodologies. Chapter 3 describes the framework of credit rating models and data set used in the experiments. Chapter 4 elaborates on the process of applying our hybrid approach to the real data set. Finally, Chapter 5 reports the results of corporate credit rating application and discusses the conclusions with future research issues.
