A Primer on Machine Learning Methods for Credit Rating Modeling

Using machine learning methods, this chapter studies features that are important to predict corporate bond ratings. There is a growing literature of predicting credit ratings via machine learning methods. However, there have been less empirical studies using ensemble methods, which refer to the technique of combining the prediction of multiple classifiers. This chapter compares six machine learning models: ordered logit model (OL), neural network (NN), support vector machine (SVM), bagged decision trees (BDT), random forest (RF), and gradient boosted machines (GBMs). By providing an intuitive description for each employed method, this chapter may also serve as a primer for empirical researchers who want to learn machine learning methods. Moody’s ratings were employed, with data collected from 2001 to 2017. Three broad categories of features, including financial ratios, equity risk, and bond issuer’s cross-ownership relation with the credit rating agencies, were explored in the modeling phase, performed with the data prior to 2016. These models were tested on an evaluation phase, using the most recent data after 2016

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

Predicting Bankruptcy with Support Vector Machines

The purpose of this work is to introduce one of the most promising among recently developed statistical techniques – the support vector machine (SVM) – to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors as financial ratios. A method of adapting it to default probability estimation is proposed. A survey of practically applied methods is given. This work shows that support vector machines are capable of extracting useful information from financial data, although extensive data sets are required in order to fully utilize their classification power.support vector machine, classification method, statistical learning theory, electric load prediction, optical character recognition, predicting bankruptcy, risk classification

Accounting for the determinants of banks’ credit ratings

The contribution of the banking industry to the recent financial crisis 2007/8 has raised public concerns about the excessive involvement of banks in risky activities. In addition there have been public concerns about the ability of credit rating agencies to evaluate these risks in advance. In this context, this study uses an ordered logit analysis to examine the determinants of banks’ credit ratings using a sample of US and UK banks’ accounting data from 1994 to 2009. Our intention is to examine to what extent banks’ ratings reflect banks’ risks. Our analysis shows that a small number of accounting variables, namely: bank size, liquidity, efficiency and profitability are able to correctly assign credit rating for approximately 74% to 78% the sample banks. Surprisingly, the association between banks’ credit ratings and each of leverage asset quality and capital is not robust, suggesting that the rating agency’s models did not pick them up despite their importance in the crisis. In addition, the relationship between banks’ credit ratings and liquidity is the reverse of that which an adequate early warning system would require. As banks benefit from higher credit ratings they will have addressed their determinants rather than taking care of systemic factors that affect underlying risk. Policy makers therefore need to intervene to address this market failure.This study was financially supported by the Institute of Chartered Accountants of Scotland (ICAS)

Rating Companies with Support Vector Machines

The goal of this work is to introduce one of the most successful among recently developed statistical techniques - the support vector machine (SVM) - to the field of corporate bankruptcy analysis. The main emphasis is done on implementing SVMs for analysing predictors in the form of financial ratios. A method is proposed of adapting SVMs to default probability estimation. A survey of practically and commercially applied methods is given. This work proves that support vector machines are capable of extracting useful information from financial data although extensive data sets are required in order to fully utilise their classification power.Support vector machines; Company rating; Default probability estimation

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