MICRO CREDIT RISK METRICS: A COMPREHENSIVE REVIEW
DOI | http://doi.org/10.1002/isaf.1344 |
Published date | 01 October 2013 |
Date | 01 October 2013 |
MICRO CREDIT RISK METRICS: A COMPREHENSIVE REVIEW
ŞABAN ÇELIK*
Department of International Trade and Finance, Yaşar University,İzmir, Turkey
SUMMARY
Default modelling is a general term used for several interrelated fields of risk management. Bond defaults, credit
(loan) defaults, firm defaults and country defaults are examples of this kind. The scope and reason for existence
of this study is to focus mainly on firm default. The purpose of this review is to shed light on the development
and evaluation of the models proposed for predicting bankruptcy in terms of conceptualization, country
distribution, sector specification, time dimension, variables used and findings reported. The current review includes
firm default studies published in business fields such as accounting, economics, finance and management science.
This review is distinct in that it seeks (i) to give a comprehensive examination of the models, (ii) to compare and
contrast the features of the models and (iii) to show with a solid argument where future research should be focused.
Copyright © 2013 John Wiley & Sons, Ltd.
Keywords: default modelling; firm default; credit risk; bankruptcy; review
1. INTRODUCTION
Default modelling is a general term used for several interrelated fields of risk management. Bond
defaults, credit (loan) defaults, firm defaults and country defaults are examples of this kind. The scope
and reason for existence of this study is to focus mainly on firm default. Default modelling is more
specifically used as credit risk modelling. Therefore, both terms will be used interchangeably.
Credit risk modelling has become an important field of research since the 1960s, whereas the
importance of evaluating firm creditability dates back to the beginning of trading. Academic literature
shows thatthe late 1960s can be a structuralbreak between quantitativeand qualitative researchin the field
of credit risk modelling. Despite the methodological differences, the basic purpose of evaluating firm
credit worthiness and default probability remains the same. The role of credit risk modelling becomes a
critical stage in the risk management systems at financial institutions (Lopez and Saidenberg, 2000: 152).
In the contemporary financial environment, rating the bonds, firms or countries plays a vital role for
firms’executives, investors, politicians, regulators, fund providers, financial institutions and intermedi-
ates. In such an important field, there are some rating firms actively providing financial advice for their
creditworthiness. Standard & Poor’s Rating Services, Moody’s Investor Services and Fitch Ratings are
those well-known institutions in this area. Credit ratings and the changes in these rates are paid
attention and watched carefully (Chan et al., 2010: 3478). The reason for having such importance in
rating is that corporate governance advice constitutes a considerably high market value. Daines et al.
(2010: 439) stated that
RiskMetrics/InstitutionalShareholder Services(ISS), the largest advisor,claims over 1,700institutional clientsman-
aging $26 trillionin assets, including 24 of the top 25 mutual funds, 25 of thetop 25 asset managers, and 17 of the
*Correspondenceto: Ş. Çelik, Department of International Tradeand Finance, Yaşar University, İzmir,Turkey.
E-mail: saban.celik@yasar.edu.tr
Copyright © 2013 John Wiley & Sons, Ltd.
INTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE AND MANAGEMENT
Intell. Sys. Acc. Fin. Mgmt. 20, 233–272 (2013)
Published online 6 September 2013 in Wiley Online Library (wileyonlinelibrary.com)DOI: 10.1002/isaf.1344
top 25 publicpension funds. ISS wassold in 2007 to RiskMetrics,a firm that has since gone public, for an estimated
$550 million. Governance Metrics International (GMI)advises clients managing $15 trillion.
Corporate executives have numerous tasks to accomplish, whereas the task of maintaining a firm’s
operation and solvency is one of the most critical ones. Platt and Platt (2011: 1139) pointed out that this
role becomes morecrucial following 2008 and countsfinancial crisis which left so many companieseither
petitioning bankruptcy courts for protection orforcing the sell-off of significant assets to repay creditors.
Detecting firm default and developing early warning systems of impending financial crisis are
important not only to sector players in developed countries but also in developing countries. Altman
(1984: 171) underlined the fact that even noncapitalist nations are obliged to consider individual firm
performance assessment. In addition to this obligatory situation, smaller nations are more vulnerable
to financial panics coming out from defaults of individual enterprises.
Default (failure) is defined in many different contexts depending upon the specific interest or
condition of the firms. A general definition is stated that ‘failure is the situation that a firm cannot
pay lenders, preferred stock shareholders, suppliers, etc., or a bill is overdrawn, or the firm is bankrupt
according to law’(Dimitrias et al., 1996: 487). The way of defining default may vary, whereas this does
not change the reality that the firms no longer continue their operations. Another related concept is
default risk, which refers to ‘a probability that counterparty’s intrinsic credit quality deteriorates such
that contractual agreements cannot be honored within a given time horizon’(Baestaens, 1999: 233).
The term ‘intrinsic’implies the presence of credit enhancement in the form of collateral or guarantees.
Defaults constitute high costs to all stakeholders. Beaver (1968: 179) demonstrated that the stock
market price of the firm decreases as it approaches bankruptcy. Therefore, prediction of default (bank-
ruptcy) is inevitable to prevent possible costs occurring as a result of default. In the last two decades the
corporate world has witnessed some major bankruptcies, such as WorldCom, Enron and LTCM (Long
Term Capital Management). All of these defaults produce significant losses and bring high costs to all
related parties. Basel II and other related regulations are aiming to minimize credit risk for this reason.
Predicting bankruptcy is a long-standing research interest in the financial literature. The models that
are intended to predict bankruptcy play an important role in two ways:
1. To predict bankruptcy and work as an ‘early warning system’. In this case, the decisions regarding to
mergerand acquisition,liquidation or reorganizationare some aims to workfor (Casey et al., 1986: 150).
2. To evaluate firm at the investment point of view (Dimitrias et al., 1996: 488).
Maclachian (1999: 92) pointed out the benefits of improved credit risk modelling as follows:
1. Traditional bank products contain many covenants that mirrorembedded credit derivatives.Modelling
the value of the credit derivative improvesunderstanding of bank risks and the efficient design of debt
contracts.
2. Bank regulators wish to move towards an internal models approach forallocating regulatory capital to
credit risk. A prerequisite of value at risk credit portfolio models is the ability to accurately price credit risk.
3. Improved modelling of credit risk has significant benefits in related fields of finance,such as the
measurement of interest rate duration on default-risky instruments,and improved modelling of
optimal capital structures in the presence of bankruptcy costs.
The models developed in the research area of predicting bankruptcy can be divided into two main
categories. The first category contains a model that has some theoretical background and implications.
This category is defined as a theory-based model in the context of the study. The second category, on
234 Ş. ÇELIK
Copyright © 2013 John Wiley & Sons, Ltd. Intell. Sys. Acc. Fin. Mgmt. 20, 233–272 (2013)
DOI: 10.1002/isaf
the other hand, includes a statistical justificationof selecting and/or classifying. Thiscategory is defined as
non-theory-based models. The second category can also be divided into two subcategories: statistical-
based models and artificial intelligence models.
Researchers have used different sets of variables in predicting bankruptcy. Financial ratios are the
oldest and most applied variables in this manner. The early studies used financial ratios extensively.
In addition, trend variables, statistical variables and dummy variables are employed to increase
efficiency of predictions. The primary aim of the models developed in the literature is to predict the
overall performance of the model and so-called type 1 and type 2 errors. The overall performances
show the models’ability to differentiate bankrupt and non-bankrupt firms. A type 1 error, also called
a credit mistake, shows instances whereby a credit was granted to a counterparty that subsequently
defaulted. A type 2 error, also called commercial mistake, shows instances whereby a credit was
refused to a counterparty that subsequently survived (Baestaens, 1999: 225). Researchers claimed that
type 1 errors are more costly than type 2 errors are. Therefore, models that provide a low type 1 error
are more appealing than the others.
The purpose of this review is to shed light on development and evaluation of the models proposed for
predicting the bankruptcy in terms of conceptualization, country distribution, sector specification, time
dimension, variables used and findings reported. The current review includes firm default studies
published in business fields such as accounting, economics, finance and management science. This review
is distinct in that it seeks (i) to give a comprehensive examination of the models, (ii) to compare and contrast
the features of the models and (iii) to show with a solid argument where future research should be focused.
2. LITERATURE REVIEW
The literature review is conducted on those studies that have a specific or general purpose in dealing
with credit risk metrics. This study classifies articles within five categories:
1. those that propose a theoretical model about credit risk metrics;
2. those that propose a statistical model;
3. those that propose an artificially intelligent model;
4. those that review the related literature; and
5. those that do not belong to the first four categories but deal with the details or a part of the discussion
regarding credit risk metrics.
Studies other than academic articles constitute a different source of knowledge. Therefore, it was not
intended to cite lecture notes, working papers, and so on.
The method of conducting the literature review has both structural and non-structural ways of
selecting the appropriate articles to interpret in the context of the study. Regarding the structural
literature review, by this it is meant that article selection applies some systematic path. The systematic
path followed here can be summarized as follows:
•journal-based selection –the article should be published in a journal that should be indexed by the
Social Science Citation Index (SSCI) or Science Citation Index (SCI);
•database-based selection –the article should be published in a journal that should be available in the
databases covering the field of business, economics and finance;
•scope-based selection (firm default) –the article should be mentioning a firm’s default rather than
credit (loan) default, bond default or country default.
MICRO CREDIT RISK METRICS: A COMPREHENSIVE REVIEW 235
Copyright © 2013 John Wiley & Sons, Ltd. Intell. Sys. Acc. Fin. Mgmt. 20, 233–272 (2013)
DOI: 10.1002/isaf
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