A neural network model to forecast and describe bond ratings

Neural Network;Bond Ratings;accountancy

Application of Neural Networks to House Pricing and Bond Rating

Feed forward neural networks receive a growing attention as a data modelling tool in economic classification problems. It is well-known that controlling the design of a neural network can be cumbersome. Inaccuracies may lead to a manifold of problems in the application such as higher errors due to local optima, overfitting and ill-conditioning of the network, especially when the number of observations is small. In this paper we provide a method to overcome these difficulties by regulating the flexibility of the network and by rendering measures for validating the final network. In particular a method is proposed to equilibrate the number of hidden neurons and the value of the weight decay parameter based on 5 and 10-fold cross-validation. In the validation process the performance of the neural network is compared with a linear model with the same input variables. The degree of monotonicity with respect to each explanatory variable is calculated by numerical differentiation. The outcomes of this analysis is compared to what is expected from economic theory. Furthermore we propose a scheme for the application of monotonic neural networks to problems where monotonicity with respect to the explanatory variables is known a priori. The methods are illustrated in two case studies: predicting the price of housing in Boston metropolitan area and the classification of bond ratings.Classification;error estimation;monotonicity;finance;neural-network models

Does Financial Performance Influence Credit Ratings? An analysis of Korean KRX Firms

Credit rating agencies offer information about default risk. Previous literature suggests that firm’s credit ratings are influenced by various metrics, specifically, numerous risk considerations such as size, leverage and growth. However, there is limited evidence to support the relationship between credit ratings and financial performance. Our research is motivated by this caveat. The purpose of this paper is to discover if financial performance measures can be included as an indicator for default risk since the relation between financial performance and default risk/credit rating is a question left unanswered in a South Korean context. In this paper, we empirically test if financial performance measures can provide additional information about credit ratings and credit rating changes. We perform a battery of tests to establish if the following financial performance measures: EPS, CPS, ROA, ROE, and ROS have any explanatory power in explaining credit ratings levels and credit rating changes. Using a sample from 2002 to 2013, we find that EPS and CPS has a statistically positive relation to credit ratings, suggesting that firms with higher credit ratings have higher levels of EPS and CPS compared to firms with lower credit ratings. Moreover, we find that firms with positive performance measured by EPS and CPS in period t have the potential to experience a credit ratings change in period t+1. However, in South Korea, the majority of firms do not experience a credit ratings change. When we estimate the financial performance of the firms that do not experience a credit ratings change, we find a statistically significant relation between credit rating and financial performance for EPS and CPS. The results suggest that credit ratings for firms with positive financial performance remain stable Finally, we examine the relation between performance in period t and credit ratings increase and decrease in period t+1. The results suggest that the credit ratings of firms with high level of financial performances increase or remain the same. We do not find a relation between financial performance and credit rating decreases; this result may be due to our small sample size. The previous literature has largely ignored the association between credit ratings and performance. Taken together, our results suggests that EPS and CPS can be used as financial performance measures by investors, government agencies and debt issuers as additional information about a firms credit rating levels, and subsequent changes. We contribute to the literature by providing empirical evidence of a relationship between performance metrics and credit ratings, specifically the link between EPS

Corporate Credit Rating: A Survey

Corporate credit rating (CCR) plays a very important role in the process of contemporary economic and social development. How to use credit rating methods for enterprises has always been a problem worthy of discussion. Through reading and studying the relevant literature at home and abroad, this paper makes a systematic survey of CCR. This paper combs the context of the development of CCR methods from the three levels: statistical models, machine learning models and neural network models, summarizes the common databases of CCR, and deeply compares the advantages and disadvantages of the models. Finally, this paper summarizes the problems existing in the current research and prospects the future of CCR. Compared with the existing review of CCR, this paper expounds and analyzes the progress of neural network model in this field in recent years.Comment: 11 page

Application of Neural Networks to House Pricing and Bond Rating

Feed forward neural networks receive a growing attention as a data modelling tool in economic classification problems. It is well-known that controlling the design of a neural network can be cumbersome. Inaccuracies may lead to a manifold of problems in the application such as higher errors due to local optima, overfitting and ill-conditioning of the network, especially when the number of observations is small. In this paper we provide a method to overcome these difficulties by regulating the flexibility of the network and by rendering measures for validating the final network. In particular a method is proposed to equilibrate the number of hidden neurons and the value of the weight decay parameter based on 5 and 10-fold cross-validation. In the validation process the performance of the neural network is compared with a linear model with the same input variables. The degree of monotonicity with respect to each explanatory variable is calculated by numerical differentiation. The outcomes of this analysis is compared to what is expected from economic theory. Furthermore we propose a scheme for the application of monotonic neural networks to problems where monotonicity with respect to the explanatory variables is known a priori. The methods are illustrated in two case studies: predicting the price of housing in Boston metropolitan area and the classification of bond ratings

A Multi-Layered Framework for Developing a Web-Based Intelligent Corporate Bond Rating Agent

This paper describes a Web-based Intelligent Corporate Bond Rating Agent. It is designed to include a real- time dimension in a corporate bond rating information system. By incorporating qualitative variables, the agent also provides a means of supplementing conventional rating approaches. Timely information capabilities of the Web-based Intelligent Corporate Bond Rating Agent would be a benefit for the capital market considering the nonmonotonic nature of rating information systems