Credit Default Modeling: a Logit Approach

VIDYA - A JOURNAL OF GUJARAT UNIVERSITY

Credit risk, also known as default risk, is the likelihood of a corporation losing money if a business partner defaults. If the liabilities are not met under the terms of the contract, the firm may default, resulting in the loss of the company. There is no clear way to distinguish between organizations that will default and those that will not prior to default. We can only make probabilistic estimations of the risk of default at best. There are two types of credit risk default models in this regard: structural and reduced form models. Structural models are used to calculate the likelihood of a company defaulting based on its assets and liabilities. If the market worth of a company's assets is less than the debt it owes, it will default. Reduced form models often assume an external cause of default, such as a Poisson jump process, which is driven by a stochastic process. They model default as a random event with no regard for the balance sheet of the company. This paper provides ...

2011

This paper is focused on estimating the credit scoring models for companies operating in the Republic of Croatia. According to level of economic and legal development, especially in the area of bankruptcy regulation as well as business ethics in the Republic of Croatia, the models derived can be applied in wider region particularly in South-eastern European countries that twenty years ago transferred from state directed to free market economy. The purpose of this paper is to emphasize the relevance and possibilities of particular financial ratios in estimating the creditworthiness of business entities what was realized by performing the research among 110 companies. Along most commonly used research methods of description, analysis and synthesis, induction, deduction and surveys, the mathematical and statistical logistic regression method took the central part in this research. The designed sample of 110 business entities represented the structure of firms operating in Republic of C...

International Research in Economics and Finance

Credit risk prediction is a vital issue in empirical studies as it has attracted the interests of many researchers. In the current study, a logistic regression model is used to evaluate determinants of payment default risks of companies in the service sector.Data, which consist of six financial variables and two macro-economic variables, have been collected from the Tunisian Central Bank and World Development Indicators.The obtained results show that debt, solvency and profitability ratios and a loan amount are the key firm-specific factor determining credit risk. Moreover, we further find that high level of inflation and the decrease of GDP growth rate are able to increase corporate credit risk.

International Journal of Economics and Finance, 2016

This paper aims to develop models for foreseeing default risk of small and medium enterprises (SMEs) for one Tunisian commercial bank using two different methodologies (logistic regression and discriminant analysis). We used a database that consists of 195 credit files granted to Tunisian SMEs which are divided into five sectors “industry, agriculture, tourism, trade and services” for a period from 2012 to 2014. The empirical results that we found support the idea that these two scoring techniques have a statistically significant power in predicting default risk of enterprises. Logistic discrimination classifies enterprises correctly in their original groups with a rate of 76.7% against 76.4% in case of linear discrimination giving so a slight superiority to the first method.

Industrija, 2016

In this paper a quantitative PD model development has been excercised according to the Basel Capital Accord standards. The modeling dataset is based on the financial statements information from the Republic of Serbia. The goal of the paper is to develop a credit scoring model capable of producing PD estimate with high predictive power on the sample of corporate entities. The modeling is based on 5 years of end-of-year financial statements data of available Serbian corporate entities. Weight of evidence (WOE) approach has been applied to quantitatively transform and prepare financial ratios. Correlation analysis has been utilized to reduce long list of variables and to remove highly interdependent variables from training and validation datasets. According to the best banking practice and academic literature, the final model is provided by using adjusted stepwise Logistic regression. The finally proposed model and its financial ratio constituents have been discussed and benchmarked against examples from relevant academic literature.

Iranian Journal of Management Studies (IJMS) , 2008

Credit risk estimation is a key determinant for the success of financial institutions. The aim of this paper is presenting a new hybrid model for estimating the probability of default of corporate customers in a commercial bank. This hybrid model is developed as a combination of Logit model and Neural Network to benefit from the advantages of both linear and non-linear models. For model verification, this study uses an experimental dataset collected from the companies listed in Tehran Stock Exchange for the period of 2008-2014. The estimation sample included 175 companies, 50 of which were considered for model testing. Stepwise and Swapwise least square methods were used for variable selection. Experimental results demonstrate that the proposed hybrid model for credit rating classification outperform the Logit model and Neural Network. Considering the available literature review, the significant variables were gross profit to sale, retained earnings to total asset, fixed asset to total asset and interest to total debt, gross profit to asset, operational profit to sale, and EBIT to sale.

The present paper aims at empirically comparing the performance of the different scoring techniques in detecting corporate default on a corporate loan portfolio of 151 big size companies hold by one of the biggest banks in Tunisia. Our objective is to identify the most performing internal credit scoring model for Tunisian banks which aim at improving their current predictive power of financial risk factors. Our results show that on our sample of Tunisian corporate loans the neural networks outperform all the other scoring techniques and that the most statistically relevant financial ratios for predicting loan default for the Tunisian bank are linked to the investment policy, the importance of the debt service, the short term liquidities and the firm's competitiveness. We thus underline the whole reasoning process behind the screening of loan applications and stress that borrowers should pay attention to reduced number of financial ratios when managing their business.

The issue of bankruptcy is very discussed in the modern theory of the company. There are currently many companies that must deal with this issue. They obtain useful information and also use the appropriate tools in order to avoid bankruptcy. Therefore financial analysts are still looking for appropriate ways to predict the bankruptcy of the company. The practical part of the following contribution consists of three steps. At the beginning we randomly selected five Slovak companies. Next we chose four predictive models which are calculated for the last one year. Then we chose the method of Economic Value Added as a method by which we can measure the value of the company. We calculate the Economic Value Added for the last one year in selected companies. Finally, we compare the results of predictive models with the results of Economic Value Added to evaluate the risk of bankruptcy in selected companies. The aim of this paper will be captured the dependence between selected predictive models and Economic Value Added and based on these calculation capture credit risk of these companies.

2022

The search for standards that contribute to the prediction of risk is growing in organizations. The use of credit scoring models seeks to assist the credit analyst in making decisions. This work aims to develop methodological procedures, to structure and improve credit scoring models aimed at the analysis of small and medium-sized companies. With the use of the statistical technique of logistic regression, through the improvements developed in the methodological procedures, such as division of the database into classes according to the companies' framework, it was possible to develop 5 credit scoring models, one model for each class of companies and another for the general database. The models were directed to entities that promote and grant credit to small and medium-sized companies. The accuracy of the models showed significant percentages for the database with non-accounting and nonauditable variables, reaching satisfactory percentages.

Journal of Banking & Finance, 2020

This paper examines the predictive power of the main default-risk measures used by both academics and practitioners, including accounting measures, market-price-based measures and the credit rating. Given that some measures are unavailable for some firm types, pair wise comparisons are made between the various measures, using same-size samples in every case. The results show the superiority of market-based measures, although their accuracy depends on the prediction horizon and the type of default events considered. Furthermore, examination shows that the effect of withinsample firm characteristics varies across measures. The overall finding is of poorer goodness of fit for accurate default prediction in samples characterised by high book-to-market ratios and/or high asset intangibility, both of which suggest pricing difficulty. In the case of large-firm samples, goodness of fit is in general negatively related to size, possibly because of the "too-big-to-fail" effect.