Credit Risk Of Companies Research Articles

Relationship between COVID-19 and the credit risk: a case study for EuroStoxx 50 companies

In this paper, we explore the impact of the COVID-19 pandemic on the credit risk of large European companies. We selected corporations belonged to the EuroStoxx 50 Index and whose CDS (Credit Default Swap) may be found in the iTraxx Europe Index. Then we applied the methodology of event studies to our database of companies, chosen as the event, the day of the declaration of pandemic by the WHO. The results indicate that the significance levels of the CAR (Cummulative Abnormal Default) show that the impact on the credit risk of the companies, as measured by the change in the spread of CDS, is important and depending on the sector in which the corporation is included.

Empirical Research on Credit Evaluation of Real Estate Industry Based on Multi-source Data Fusion

In this paper, based on the evaluation of corporate credit risk using traditional financial indicators, we will combine with non-financial data, define whether the listed company has ST in its abbreviation as a default fact, and establish a logistic model to quantitatively analyze the credit risk. In this paper, 123 listed companies in the real estate industry are used as samples, and the financial data, number of employees, percentage of shareholding of the largest shareholder, and CEO's education disclosed in the 2019 and 2020 annual reports are obtained through the Guotaian and CNRDS databases. Using principal component analysis to extract principal component factors from 21 financial data and combining non-financial indicators, a logistic model based on financial indicators and a logistic model based on financial and non-financial data were constructed to study the impact of non-financial data on company credit risk. It is found that non-financial indicators reflecting company size as well as corporate governance ability have a certain degree of influence on the credit risk of the company, i.e., the prediction results of the logistic model based on financial and non-financial data are more effective.

Early Warning of Companies' Credit Risk Based on Machine Learning

With the advent of the big data era, information barriers are gradually being broken down and credit has become a key factor of company operations. The lack of company credit has greatly and negatively impacted the social economy, which has triggered considerable research on company credit. In this article, a credit risk warning model based on the XGBoost-SHAP algorithm is proposed that can accurately assess the credit risk of a company. The degree of influence of the characteristics of a company's credit risk and the warning threshold of important characteristics are obtained based on the model output. Finally, a comparison with several other machine learning algorithms showed that the XGBoost-SHAP model achieved the highest early warning accuracy and the most comprehensive explanatory output results. The experimental results show that the method can effectively provide a warning of the credit risk of a company based on the historical performance of the company's historical characteristics data. This method provides positive guidance for companies and financial institutions.

Carbon Costs and Credit Risk in a Resource-Based Economy: Carbon Cost Impact on the Z-Score of Canadian TSX 260 Companies

Climate risks and climate risk-related policies on carbon threaten the ability of economies to thrive and will impact the credit risk of many sectors, primarily high-emitting sectors. Higher credit risks will also affect lenders if their credit portfolios are exposed to climate change risks. The introduction of carbon pricing policies will exacerbate this threat in resource-based economies such as Canada. While some research exists on climate exposure and risks to lending portfolios, there is a knowledge gap on how carbon pricing impacts individual commercial credit risk. Consequently, this study analyzes the effect of different carbon pricing scenarios on Altman’s z-score. Using the Canadian TSX 260 data between 2010 and 2020 as a sample, this paper applied the costs of different carbon prices using the Canadian Government’s carbon price regime of $0 to $170 to analyze Altman’s z-score variables until 2030. The results suggest that carbon price will significantly impact the credit risk of companies in high-emitting industries, such as the energy sector. We conclude that climate policy exposure in the form of carbon costs will have a real impact on credit risk and that lenders must consider carbon emissions as part of their credit risk assessment.

Machine Learning for Credit Risk in the Reactive Peru Program: A Comparison of the Lasso and Ridge Regression Models

COVID-19 has caused an economic crisis in the business world, leaving limitations in the continuity of the payment chain, with companies resorting to credit access. This study aimed to determine the optimal machine learning predictive model for the credit risk of companies under the Reactiva Peru Program because of COVID-19. A multivariate regression analysis was applied with four regressor variables (economic sector, granting entity, amount covered, and department) and one predictor (risk level), with a population of 501,298 companies benefiting from the program, under the CRISP-DM methodology oriented especially for data mining projects, with artificial intelligence techniques under the machine learning Lasso and Ridge regression models, with econometric algebraic mathematical verification to compare and validate the predictive models using SPSS, Jamovi, R Studio, and MATLAB software. The results revealed a better Lasso regression model (λ60 = 0.00038; RMSE = 0.3573685) that optimally predicted the level of risk compared to the Ridge regression model (λ100 = 0.00910; RMSE = 0.3573812) and the least squares model with algebraic mathematics, which corroborates that the Lasso regression model is the best predictive model to detect the level of credit risk of the Reactiva Peru Program. The best predictive model for detecting the level of corporate credit risk is the Lasso regression model.

COVID-19 Pandemic and Global Corporate CDS Spreads

We examine the impact of the COVID-19 pandemic on the credit risk of companies around the world. We find that increased infection rates affect firms more adversely as reflected by the wider increase in their credit default swap (CDS) spreads if they are larger, more leveraged, closer to default, have worse governance and more limited stakeholder engagement, and operate in more highly exposed industries. We observe that country-level determinants such as GDP, political stability, foreign direct investment, and commitment to crisis management (income support, health and lockdown policies) also affect the sensitivity of CDS spreads to COVID-19 infection rates. A negative amplification effect exists for firms with high default probability in countries with fiscal constraints. A direct comparison between global CDS and stock markets reveals that the CDS market prices in a distinct set of corporate traits and government policies in pandemic times.

A Logistic Regression Model for Credit Risk of Companies in the Service Sector

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.

The Contagion of Associated Credit Risk Based on the Real Estate Companies Network

This paper discusses the structural characteristics of the real estate company’s related network and the contagion law of the associated credit risk by constructing a model of associated credit risk contagion based on the real estate enterprise network.The study showed that the real estate companies network has the power-law characteristics of a scale-free network, the network density of parent companies is relatively high, local subsidiaries are isolated and the path becomes longer. At the same time, leading companies with different associated structures have significant differences in the impact on the rest of the companies after default. Furthermore, the credit risk of companies with complex relational structures is more difficult to affect individuals in the network.

Application of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies

The purpose is to avert the systematic financial risks from the Internet financial bubble and improve the efficiency of legal service companies’ credit risk assessment ability. Firstly, this study analyzes the commonly used classification model, Support Vector Machine (SVM), and linear regression model, Logistic model, and then puts forward the integrated SVM-Logistic + Fuzzy Multicriteria Decision-Making (FMCDM) to evaluate and analyze the credit risk level of listed companies. In the proposed integrated model, the SVM model classifies the data sample from listed companies, and the Logistic model is used for regression analysis on the credit risk assessment. Based on the credit risk indexes and weight uncertain factors of sample companies, FMCDM based on fuzzy set is applied to obtain the evaluation indexes. Then, the Analytic Hierarchy Process (AHP) is used to obtain the weight of key indexes. Finally, the fit analysis is carried out according to the existing risk status of the sample company and the risk status results of the proposed integrated model. The results show that the integrated SVM-Logistic model is complementary and has high intensive evaluation. According to the fitness value obtained by FMCDM, the company's credit risk status can be accurately evaluated, and the intermediate threshold of corporate credit default risk measurement is 0.56152; if Fit is lower than the threshold, the company’s credit is low, and if Fit is higher than the threshold, the company’s credit is high. Therefore, the data mining technology based on integrated SVM-Logistic model + FMCDM has high precision and feasible application in the credit risk assessment from legal service companies. This study creates a new method model for legal service companies in the field of corporate credit risk assessment and can provide references and ideas for corporate credit risk assessment.

Towards an improved credit scoring system: the Greek case

During the development of credit risk assessment models, it is very important to find variables that allow the evaluation of a company's credit risk accurately, as the classification results depend on the appropriate characteristics for a selected data set. In this paper, new credit risk models tested on real data, which evaluate credit risk of Greek companies are introduced. These models use a combination of financial and credit behaviour data. The credit risk models, which are introduced in this paper, do have some important additional advantages: 1) they contain a relatively small number of variables; 2) their stability is tested on samples after the time-period of the time period of data-collection; 3) the characterisation of 'good' and 'bad' credit behaviour is strictly defined.

Optimization on the Credit Risk of Companies in Malaysia With Data Envelopment Analysis Model

Financial institutions provide financial services to their clients or retail customers where money is managed. Credit risk has been identified as one of the dominant risks that affect the performance of a company. A firm’s efficiency with different credit risk management practices is still unknown. This research aims to evaluate the credit risk management and efficiency of the financial institutions that are publicly listed in Bursa Malaysia from year 2013-2016 with the Data Envelopment Analysis (DEA) model. Based on the financial ratios, the DEA model allows the relative efficiency of a set of companies to be assessed by solving a linear programming model. The results show that ALLIANZ, APEX, BURSA, ECM, LPI and TAKAFUL are efficient in terms of their credit risk management. This study identifies the efficient and inefficient financial companies in Malaysia.

Towards an improved credit scoring system: the Greek case

During the development of credit risk assessment models, it is very important to find variables that allow the evaluation of a company's credit risk accurately, as the classification results depend on the appropriate characteristics for a selected data set. In this paper, new credit risk models tested on real data, which evaluate credit risk of Greek companies are introduced. These models use a combination of financial and credit behaviour data. The credit risk models, which are introduced in this paper, do have some important additional advantages: 1) they contain a relatively small number of variables; 2) their stability is tested on samples after the time-period of the time period of data-collection; 3) the characterisation of 'good' and 'bad' credit behaviour is strictly defined.

Logit models for predicting bankruptcy

Going into bankruptcy represents main driver of company credit risk, because of which the monitoring of doing business is considered justified. Models that forecast company's going into bankruptcy are efficient tool with whose application risk of doing business can be reduced. In the paper, the first models built on the foundations of logistic regression are presented. These models also have the biggest application in practice. Advantages and disadvantages of logit model for forecasting of bankruptcy are highlighted. Short display of logit model is incurred for concrete markets with special characteristics among which is market of Republic of Serbia too.

Relation between Financial Flexibility and Probability of Default: KMV Model

Background/Objectives: Investment sources are allocated to economic activities through financial markets. One important topic is studying and evaluation of credit risks that is breach probability for reimbursement of granted facilities by customers. Methods/Statistical Analysis: This research evaluated the effects of financial flexibility independent variable on the probability of default dependent variable. This was investigated by gathering data from 101 companies receiving bank facilities from 2009 to 2013. To measure credit risk of companies, KMV Model has been used. The study hypotheses tested using combinational data, and for data analyzing and evaluating multi-variable regression model and study’s hypotheses test, we used E-views software. Findings: This research identifies some of the effective factors related to financial flexibility, as one of the modern financial patterns and strategies, affecting probability of default. The results show that there is a reverse and significant relation between financial flexibility and default probability. Also, there is a reverse and significant linear relation between flexibility and default probability of companies with high and low financial leverages, too with small and large sizes. Thus, relation rate varies in different levels of firm sizes and financial leverages, which indicates the effect of company size and financial leverage variables on relations between independent and dependent variables. Application/Improvements: Regarding to the results, it is recommended that banks andfinancial institutions and companies that sell credits notice financial flexibility, financial leverage, company sizes, and the effects of these variables on debt default when granting facilities.

Wykorzystanie metody DEA w ocenie ryzyka kredytowego przedsiębiorstw

The subject of the present article is a new procedure forecasting the credit risk of companies in the Polish economy environment. The suggestion is strongly supported by the fact that so far the DEA method has not been applied to estimate the credit risk of companies within the framework of credit-scoring. The research described in the article has been conducted on the basis of a comparison of the suggested DEA method with currently used procedures, namely the point method, discriminative analysis and linear regression. Considering the research, it can be concluded that the DEA method facilitates forecasting financial problems, including the bankruptcy of companies in Polish economic conditions, and its effectiveness is comparable or even greater than any approaches implemented so far. keywords: credit-scoring, credit risk, credit rating, Data Envelopment Analysis, technical effectiveness Implementing the dea method in credit risk management The methodology of credit risk assessment with the use of the DEA1 method suggested below was prepared on the basis of literature studies (Emel et al. 2003: 103–123; Simak 2000: 1–189; Gospodarowicz 2004: 119–129) as well as the author’s own research (Feruś 2006a: 44–59; Feruś 2006b: 245–253; Feruś 2006c: 263–269; Feruś 2007d: 225–233; Feruś 2007e: 144–154; Feruś 2008f: 196–215; Feruś 2008g: 153–160; Feruś 2008h: 109–118; Feruś 2009i: 221–231). It consists of five stages, as presented in Figure 1. * The article presents results of the research conducted within research project # H02B 015 30 financed with educational sources. ** dr Anna Feruś, Politechnika Rzeszowska, Zaklad Finansow i Bankowości, al. Powstancow Warszawy 10, 35-959 Rzeszow, e-mail: aferus@prz.edu.pl. 1 The DEA method was first introduced in 1978 by American economists Charnes, Cooper and Rhodes. Relying on a productivity concept, formulated by G. Debreu (1951) and M.J. Farrel (1957), which defined effectiveness measured as quotient of a singular effect and singular set-up, they used it for a multidimensional situation in which there was more than one set-up as well as more than one effect. Using the DEA method, the effectiveness of an object is calculated in relation to other objects from a particular group. Effective objects within a particular group make the so-called effectiveness curve. The effectiveness of remaining objects are calculated in relation to the curve defined through solving the issue of linear programming (using the DEA method). ZESZYTY NAUKOWE UNIWERSYTETU SZCZECIŃSKIEGO nr 855 Finanse, Rynki Finansowe, Ubezpieczenia nr 74, t. 1 (2015) DOI: 10.18276/frfu.2015.74/1-42 s. 493–500

Probabilidades de default de las empresas españolas en época de crisis

This work focuses on obtaining default probabilities for the Spanish case, in order to study the impact of the current economic situation on company credit risk. Using Merton's model (1974) we estimate the likelihood that outstanding non-financial companies listed on the Spanish stock exchange market would have to default on its debt obligations over the period from January 2002 to December 2011. Based upon the number of shares of each company, the observable price of a share and its volatility, and the short and long term debt face value, we computed company values and volatility. These data are used to determine the monthly time series of default probabilities for selected companies over the period analyzed. The analysis carried out reveals a negative impact of the economic crisis over these probabilities, especially in the sector of Financial Services and Real Estate.

Barriers to financing small and medium business enterprises in Poland

The subject of the present article is a new procedure forecasting credit risk of companies in Polish economic environment. What favors the suggested approach is the fact that in Poland, unlike in western countries, DEA method has not yet been implemented in order to assess credit risk that companies face. The research described in the article has been conducted on the basis of comparison of suggested DEA method with currently used procedures, namely point method, discriminative analysis and linear regression. In order to verify and compare the efficiency of various methods of company credit risk estimation the efficiency of classification of companies has also been examined. The study has involved an analyzed sample (a teaching sample) as well as a test sample which was not taken in model building. Considering the research, it can be concluded that DEA method facilitates forecasting financial problems, including bankruptcy of companies in Polish economic conditions, and its efficiency is comparable or even greater than approaches implemented so far. The DEA methodology was found to be successful within the credit evaluation process, however it might not be used as a stand alone tool for this purpose, but it can offer valuable insight to the loan officer or the analyst facing the credit approval decision.

The Application of DEA Method in Evaluating Credit Risk of Companies

The Application of DEA Method in Evaluating Credit Risk of Companies

The DEA Method in Managing the Credit Risk of Companies

The subject of the present paper is a new procedure of forecasting credit risk to companies in the Polish economic environment. What favors the suggested approach is the fact that in Poland, unlike in western countries, the DEA method has not yet been implemented in assessing credit risk faced by companies. The research described in the paper has been conducted on the basis of comparing the proposed DEA method with the currently used procedures, namely the point method, discriminative analysis and linear regression. To verify and compare the efficiency of various methods of company credit risk estimation, the efficiency of the classification of companies has also been examined. The study has involved an analysed sample (a teaching sample) as well as a test sample which was not taken in model building. To conclude, the DEA method facilitates forecasting financial problems, including bankruptcy of companies, in Polish economic conditions, and its efficiency is comparable to or even greater than that of the approaches implemented so far.

Modelo de classificação de risco de crédito de empresas

O processo de gerenciamento de risco de crédito em instituições financeiras vem passando por uma revisão ao longo dos últimos anos. Nesse contexto, diversas novas técnicas de mensuração de risco de crédito e tomadores têm sido desenvolvidas e implementadas por grandes Bancos. O objetivo desta pesquisa é desenvolver um modelo de classificação de risco para avaliar o risco de crédito de empresas no mercado brasileiro. O modelo foi construído com base em uma amostra de empresas de capital aberto classificadas como solventes ou insolventes no período entre 1994 e 2004. A técnica estatística utilizada no desenvolvimento do modelo foi a regressão logística. As variáveis independentes são índices financeiros calculados a partir das demonstrações contábeis e utilizados para representar a situação econômico-financeira das empresas. A validação do modelo foi efetuada utilizando o método Jackknife e uma Curva ROC. Os resultados do estudo indicam que o modelo de classificação de risco desenvolvido prevê eventos de default com um ano de antecedência com bom nível de acurácia. Os resultados, também, indicam que as demonstrações contábeis contêm informações que possibilitam a classificação das empresas como prováveis solventes ou prováveis insolventes.