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Article
Publication date: 28 January 2014

Fernando Castagnolo and Gustavo Ferro

The purpose of this paper is to assess and compare the forecast ability of existing credit risk models, answering three questions: Can these methods adequately predict…

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Abstract

Purpose

The purpose of this paper is to assess and compare the forecast ability of existing credit risk models, answering three questions: Can these methods adequately predict default events? Are there dominant methods? Is it safer to rely on a mix of methodologies?

Design/methodology/approach

The authors examine four existing models: O-score, Z-score, Campbell, and Merton distance to default model (MDDM). The authors compare their ability to forecast defaults using three techniques: intra-cohort analysis, power curves and discrete hazard rate models.

Findings

The authors conclude that better predictions demand a mix of models containing accounting and market information. The authors found evidence of the O-score's outperformance relative to the other models. The MDDM alone in the sample is not a sufficient default predictor. But discrete hazard rate models suggest that combining both should enhance default prediction models.

Research limitations/implications

The analysed methods alone cannot adequately predict defaults. The authors found no dominant methods. Instead, it would be advisable to rely on a mix of methodologies, which use complementary information.

Practical implications

Better forecasts demand a mix of models containing both accounting and market information.

Originality/value

The findings suggest that more precise default prediction models can be built by combining information from different sources in reduced-form models and combining default prediction models that can analyze said information.

Details

The Journal of Risk Finance, vol. 15 no. 1
Type: Research Article
ISSN: 1526-5943

Keywords

Article
Publication date: 17 October 2018

Linda Gabbianelli

The purpose of this paper is to test whether the qualitative variables regarding the territory and the firm–territory relationship can improve the accuracy rates of small…

Abstract

Purpose

The purpose of this paper is to test whether the qualitative variables regarding the territory and the firm–territory relationship can improve the accuracy rates of small business default prediction models.

Design/methodology/approach

The authors apply a logistic regression to a sample of 141 small Italian enterprises located in the Marche region, and the authors build two different default prediction models: one using only financial ratios and one using jointly financial ratios and variables related to the relationship between firm and territory.

Findings

Including variables regarding the relationships between firms and their territory, the accuracy rates of the default prediction model are significantly improved.

Research limitations/implications

The qualitative variables data collected are affected by subjective judgments of respondents of the firms studied. In addition, neither other qualitative variables (such as those regarding competitive strategies, or managerial skills) are included nor those variables regarding the relationships between firms and financial institutions are included.

Practical implications

The study suggests that financial institutions should include territory qualitative variables, and, above all, qualitative variables regarding the firm–territory relationship, when constructing business default prediction models. Including this type of variables, it could be able to reduce the tendency to place unnecessary restrictions on credit.

Originality/value

The field of business failure prediction modeling using variables regarding the relationship between firm–territory is a unexplored area as it count of a very few studies.

Details

Studies in Economics and Finance, vol. 35 no. 4
Type: Research Article
ISSN: 1086-7376

Keywords

Article
Publication date: 20 November 2017

Andreas Behr and Jurij Weinblat

The purpose of this paper is to do a performance comparison of three different data mining techniques.

Abstract

Purpose

The purpose of this paper is to do a performance comparison of three different data mining techniques.

Design/methodology/approach

Logit model, decision tree and random forest are applied in this study on British, French, German, Italian, Portuguese and Spanish balance sheet data from 2006 to 2012, which covers 446,464 firms. Because of the strong imbalance with regard to the solvency status, classification trees and random forests are modified to adapt to this imbalance. All three model specifications are optimized extensively using resampling techniques, relying on the training sample only. Model performance is assessed, strictly, based on out-of-sample predictions.

Findings

Random forest is found to strongly outperform the classification tree and the logit model in almost all considered years and countries, according to the quality measure in this study.

Originality/value

Obtaining reliable estimates of default propensity scores is of immense importance for potential credit grantors, portfolio managers and regulatory authorities. As the overwhelming majority of firms are not listed on stock exchanges, annual balance sheets still provide the most important source of information. The obtained ranking of the three models according to their predictive performance is relatively robust, due to the consideration of several countries and a relatively long time period.

Details

The Journal of Risk Finance, vol. 18 no. 5
Type: Research Article
ISSN: 1526-5943

Keywords

Article
Publication date: 27 February 2009

J. Samuel Baixauli and Susana Alvarez

The purpose of this paper is to critically analyze the common assumption, made by many credit risk models such as the Moody's KMV Loss‐Calc model, of a β distribution for…

732

Abstract

Purpose

The purpose of this paper is to critically analyze the common assumption, made by many credit risk models such as the Moody's KMV Loss‐Calc model, of a β distribution for the loss‐given default (LGD). The paper shows that this assumption does not perform well in constructing analytic prediction intervals for LGD.

Design/methodology/approach

Simulation experiments were conducted to highlight the potential problems associated with this distributional assumption in constructing prediction intervals for LGD.

Findings

The simulation experiments show that, when starting from a different assumption concerning the shape of the population distribution, the beta distribution does not perform well in constructing prediction intervals for LGD.

Originality/value

The analysis performed in this study addresses a relevant subject. Indeed, a correct estimate of a credit exposure LGD is particularly relevant not only for internal risk management and management purposes, but also for regulatory reasons within the context of the internal ratings based approach of the recently approved capital regulation framework (Basel II).

Details

The Journal of Risk Finance, vol. 10 no. 2
Type: Research Article
ISSN: 1526-5943

Keywords

Article
Publication date: 16 June 2016

Khushbu Agrawal and Yogesh Maheshwari

– The purpose of this paper is to assess the significance of the Merton distance-to-default (DD) in predicting defaults for a sample of listed Indian firms.

1274

Abstract

Purpose

The purpose of this paper is to assess the significance of the Merton distance-to-default (DD) in predicting defaults for a sample of listed Indian firms.

Design/methodology/approach

The study uses a matched pair sample of defaulting and non-defaulting listed Indian firms. It employs two alternative statistical techniques, namely, logistic regression and multiple discriminant analysis.

Findings

The option-based DD is found to be statistically significant in predicting defaults and has a significantly negative relationship with the probability of default. The DD retains its significance even after the addition of Altman’s Z-score. This further establishes its robustness as a significant predictor of default.

Originality/value

The study re-establishes the utility of the Merton model in India using a simplified version of the Merton model that can be easily operationalized by practitioners, reasonably larger sample size and is done in a more recent period covering the post global financial crisis period. The findings could be valuable to banks, financial institutions, investors and managers.

Details

South Asian Journal of Global Business Research, vol. 5 no. 2
Type: Research Article
ISSN: 2045-4457

Keywords

Article
Publication date: 1 May 2006

Arindam Bandyopadhyay

This paper aims at developing an early warning signal model for predicting corporate default in emerging market economy like India. At the same time, it also aims to…

5519

Abstract

Purpose

This paper aims at developing an early warning signal model for predicting corporate default in emerging market economy like India. At the same time, it also aims to present methods for directly estimating corporate probability of default (PD) using financial as well as non‐financial variables.

Design/methodology/approach

Multiple Discriminate Analysis (MAD) is used for developing Z‐score models for predicting corporate bond default in India. Logistic regression model is employed to directly estimate the probability of default.

Findings

The new Z‐score model developed in this paper depicted not only a high classification power on the estimated sample, but also exhibited a high predictive power in terms of its ability to detect bad firms in the holdout sample. The model clearly outperforms the other two contesting models comprising of Altman's original and emerging market set of ratios respectively in the Indian context. In the logit analysis, the empirical results reveal that inclusion of financial and non‐financial parameters would be useful in more accurately describing default risk.

Originality/value

Using the new Z‐score model of this paper, banks, as well as investors in emerging market like India can get early warning signals about the firm's solvency status and might reassess the magnitude of the default premium they require on low‐grade securities. The default probability estimate (PD) from the logistic analysis would help banks for estimation of credit risk capital (CRC) and setting corporate pricing on a risk adjusted return basis.

Details

The Journal of Risk Finance, vol. 7 no. 3
Type: Research Article
ISSN: 1526-5943

Keywords

Article
Publication date: 13 July 2012

Ana Paula Matias Gama and Helena Susana Amaral Geraldes

The purpose of this paper is to develop a credit‐scoring model as an aggregate valuation procedure that integrates various financial and non‐financial factors and thereby…

2973

Abstract

Purpose

The purpose of this paper is to develop a credit‐scoring model as an aggregate valuation procedure that integrates various financial and non‐financial factors and thereby improves small to medium‐sized enterprises' (SMEs) knowledge about their default risk.

Design/methodology/approach

Using panel data from a representative sample of Portuguese SMEs operating in the food or beverage manufacturing sector, this paper develops a logit scoring model to estimate one‐year predictions of default.

Findings

The probability of non‐default in the next year is an increasing function of profitability, liquidity, coverage, and activity and a decreasing function of leverage. Smaller firms and those with just one bank relationship have a higher probability of default. The findings suggest that a main bank has incentives to engage in hold up by increasing margins that ex post are too high.

Practical implications

Because SMEs differ from large corporations in their credit risk (e.g., riskier, lower asset correlations), this study has implications for both banks and supervisory actors. Banks should consider qualitative variables when setting internal systems and procedures to manage credit risk. Supervisory institutions should claim mixed credit ratings to determine regulatory capital requirements.

Originality/value

This paper offers a new model, focused specifically on SMEs, and explores the role of financial and non‐financial factors in determining internal credit risks.

Details

Management Research Review, vol. 35 no. 8
Type: Research Article
ISSN: 2040-8269

Keywords

Article
Publication date: 11 November 2014

Khushbu Agrawal and Yogesh Maheshwari

This paper aims to find out significant macroeconomic variables, incorporated as sensitivity variables (macroeconomic sensitivities), affecting financial distress for a…

1000

Abstract

Purpose

This paper aims to find out significant macroeconomic variables, incorporated as sensitivity variables (macroeconomic sensitivities), affecting financial distress for a sample of listed Indian firms.

Design/methodology/approach

The study uses a matched pair sample of defaulting and non-defaulting listed Indian firms. It uses two alternative statistical techniques, viz., logistic regression and multiple discriminant analysis. The macroeconomic sensitivities are estimated by regressing the monthly stock return of the individual firm on the monthly changes in each macroeconomic variable.

Findings

Sensitivity to changes in the stock market (stock market sensitivity) and sensitivity to changes in inflation [Consumer Price Index (CPI) sensitivity] have a significant impact on the default probability of a firm. Stock market sensitivity has a significant positive relationship with the probability of default, and CPI sensitivity has a significant negative relationship with the probability of default.

Originality/value

The study links the developments in the external environment to the firm’s susceptibility to default. Furthermore, it highlights the significance of sensitivity of a firm to uncertainties in the macroeconomic environment and its impact on default risk. This establishes the fact that each firm is uniquely affected by the changes in the overall macroeconomic environment. The findings could be valuable to lenders such as banks and financial institutions, investors and policymakers.

Details

Journal of Indian Business Research, vol. 6 no. 4
Type: Research Article
ISSN: 1755-4195

Keywords

Article
Publication date: 9 January 2007

Arindam Bandyopadhyay

The purpose of this paper is to develop a hybrid logistic model by using the inputs obtained from BSM equity‐based option model described in the companion paper, “Mapping…

1264

Abstract

Purpose

The purpose of this paper is to develop a hybrid logistic model by using the inputs obtained from BSM equity‐based option model described in the companion paper, “Mapping corporate drift towards default – Part 1: a market‐based approach” that can more accurately predict corporate default.

Design/methodology/approach

In a set of logistic regressions, the ability of the market value of assets, asset volatility and firm's leverage structure measures to predict future default is investigated. Next, a check is made as to whether accounting variables and other firm specific characteristics can provide additional significant information in assessing the real world credit quality of a firm in a multifactor model

Findings

From analysis of 150 publicly‐traded Indian corporates over the year 1998 to 2005 it was found that in a volatile equity market like India, one needs to enhance the BSM model with other accounting information from financial statements and develop hybrid models. The results in this paper indicate that a mix of asset volatility, market value of asset and firm's leverage structure along with other financial and non financial factors can give us a more accurate prediction of corporate default than the ratio‐based reduced form model.

Originality/value

The hybrid model developed in this paper allows us to integrate information from the structural model as well as profitability of firms, liquidity risk, other firm specific supplementary information and macroeconomic factors to predict real world corporate distress potential through a multivariate analysis.

Details

The Journal of Risk Finance, vol. 8 no. 1
Type: Research Article
ISSN: 1526-5943

Keywords

Article
Publication date: 23 October 2019

Senthil Arasu Balasubramanian, Radhakrishna G.S., Sridevi P. and Thamaraiselvan Natarajan

This paper aims to develop a corporate financial distress model for Indian listed companies using financial and non-financial parameters by using a conditional logit…

1681

Abstract

Purpose

This paper aims to develop a corporate financial distress model for Indian listed companies using financial and non-financial parameters by using a conditional logit regression technique.

Design/methodology/approach

This study used a sample of 96 companies, of which 48 were declared sick between 2014 and 2016. The sample was divided into a training sample and a testing sample. The variables for the study included nine financial variables and four non-financial variables. The models were developed using financial variables alone as well as combining financial and non-financial variables. The performance of the test sample was measured with confusion matrix, sensitivity, specificity, precision, F-measure, Types 1 and 2 error.

Findings

The results show that models with financial variables had a prediction accuracy of 85.19 and 86.11 per cent, whereas models with a combination of financial and non-financial variables predict with comparatively better accuracy of 89.81 and 91.67 per cent. Net asset value, long-term debt–equity ratio, return on investment, retention ratio, age, promoters holdings pledged and institutional holdings are the critical financial and non-financial predictors of financial distress.

Originality/value

This study contributes to the financial distress prediction literature in different ways. First, there have been, until now, few studies in the area of financial distress prediction in the Indian context. Second, business failure studies in the past have used only financial variables. The authors have combined financial and non-financial variables in their model to increase predictive ability. Thirdly, in most earlier studies, variable institutional holdings were found to affect financial distress negatively. In contrast, the authors found this parameter to be positively significant to the financial distress of the company. Finally, there have hitherto been few studies that have used promoter holdings pledged (PHP) or pledge ratio. The authors found this variable to influence business failure positively.

Details

International Journal of Law and Management, vol. 61 no. 3/4
Type: Research Article
ISSN: 1754-243X

Keywords

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