Analysis of Credit Ratings for Small - IA de Investigación especializada en el MIT

Analysis of Credit Ratings for Small
and Medium-Sized Enterprises:
Evidence from Asia
∗
NAOYUKI YOSHINO AND FARHAD TAGHIZADEH-HESARY

In Asia, small and medium-sized enterprises (SMEs) account for the major share
of employment and dominate domestic economies, yet providing these compa-
nies with access to ﬁnance is a challenge across the region. Asian economies are
often characterized as having bank-dominated ﬁnancial systems and underde-
veloped capital markets, in particular with regard to venture capital. Como resultado,
banks are the main source of ﬁnancing for SMEs. It is crucial for banks to be
able to distinguish healthy from risky companies. If they can do this, lending
and ﬁnancing SMEs through banks will be easier. en este documento, we explain the
importance of SMEs in Asia. Entonces, we provide a scheme for assigning credit
ratings to SMEs by employing two statistical analysis techniques—principal
component analysis and cluster analysis—applying 11 ﬁnancial ratios of 1,363
SMEs in Asia. If used by the ﬁnancial institutions, this comprehensive and efﬁ-
cient method could enable banks and other lending agencies around the world,
and especially in Asia, to group SME customers based on ﬁnancial health and
adjust interest rates on loans and set lending ceilings for each group.

Palabras clave: Asian economies, SME credit rating, SME ﬁnancing
JEL codes: G21, G24, G32

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I. Introducción

Small and medium-sized enterprises (SMEs) are the backbone of Asian
economías, accounting on average for 98% of all enterprises, 66% of the national
labor force, y 38% of gross domestic product (PIB) during 2007–2012 (ADB
2014). Over the same period, SMEs accounted for an average of more than 30% de
total export value. In the People’s Republic of China (PRC) en 2012, SMEs accounted
para 41.5% of total export value, arriba 6.8% year-on-year (y-o-y), while in Thailand

∗Naoyuki Yoshino: Dean of the Asian Development Bank Institute (ADBI) and Professor Emeritus of Keio University.
Correo electrónico: nyoshino@adbi.org. Farhad Taghizadeh-Hesary (Autor correspondiente): Assistant Professor of Economics
at Keio University and Research Assistant to the Dean of ADBI. Correo electrónico: farhadth@gmail.com. This paper is the
revised version of a paper presented at the SME Finance Forum hosted by the International Finance Corporation
in Washington, DC in 2014. The authors would like to thank the anonymous referees, the Managing Editor, el
participants at the Paciﬁc Economic Cooperation Council conference held in Singapore on 27 Febrero 2015, y
the commentators at the ADBI–OECD Roundtable on Capital Market and Financial Reform in Asia held in Tokyo
on 11–12 March 2015 for their comments and suggestions. The usual disclaimer applies.

Asian Development Review, volumen. 32, No. 2, páginas. 18–37

C(cid:3) 2015 Asian Development Bank
and Asian Development Bank Institute

CREDIT RATINGS OF ASIAN SMES 19

they made up 28.8% of total export value, con 3.7% y-o-y growth. SMEs that are
part of global supply chains have the potential to promote international trade and
mobilize domestic demand.

Because of the signiﬁcance of SMEs to Asian national economies, es
important that ways be found to provide them with stable access to ﬁnance. asiático
economies are often characterized as having bank-dominated ﬁnancial systems and
capital markets, in particular venture capital markets, that are not well-developed.
This means banks are the main source of ﬁnancing. Although the soundness of
banking systems has improved signiﬁcantly since the 1997/98 Asian ﬁnancial crisis,
banks have been cautious about lending to SMEs, even though such enterprises
account for a large share of economic activity. Start-up companies, En particular,
are ﬁnding it increasingly difﬁcult to borrow money from banks because of strict
Basel III capital requirements (Yoshino and Hirano 2011, 2013). Riskier SMEs also
face difﬁculty in borrowing money from banks (Yoshino 2012).1 Por eso, an efﬁcient
credit rating scheme that rates SMEs based on their ﬁnancial health would help banks
to lend money to SMEs in a more rational way while at that same time reducing the
risk to banks.

Various credit rating indexes such as Standard and Poor’s (S&PAG) rate large
enterprises. By looking at a large enterprise’s credit rating, banks can decide to
lend them up to a certain amount. For SMEs, the issue is more complicated as
there are no comparable ratings. Sin embargo, there is a useful model in Japan. en un
government-supported project, 52 credit guarantee corporations collected data from
Japanese SMEs. These data are now stored at a private corporation called Credit
Risk Database (CRD). If similar systems could be established in other parts of Asia
to accumulate and analyze credit risk data, and to measure each SME’s credit risk
accurately, banks and other ﬁnancial institutions could use it to categorize their SME
customers based on their ﬁnancial health. SMEs would also beneﬁt as they could
both raise funds from the banks more easily and gain access to the debt market by
securitizing their claims.

In Section II, we describe the characteristics of Asian economies, En particular
the important role played by SMEs. In Section III, we explain the advantages of
preparing a complete SME database in each country. In Section IV, we propose a
way of establishing SME credit ratings using statistical techniques and ﬁnancial
ratios. This captures all the characteristics of SMEs, including leverage, liquidity,
proﬁtability, coverage, and activity. This method can be used by banks all around the
world, especially in Asia, to group SMEs based on their ﬁnancial health, and adjust
loan interest rates and set lending ceilings accordingly for each group. Section V
contains concluding remarks.

1For more information on SME ﬁnancing constraints, see Vermoesen, Deloof, and Laveren (2013).

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20 ASIAN DEVELOPMENT REVIEW

Cifra 1. Financial Markets in Asia
(Equity markets, bond markets, and bank loans as % of total)

PRC = People’s Republic of China.
Fuente: Kashiwagi, S. 2011. Presentation at FSA Financial Research Center International Conference. Tokio. 3
Febrero.

II. Characteristics of Asian Economies

High Potential Growth

Asian economies have had relatively high economic growth rates over the
pasado 2 decades and further strong growth is expected over the next few years, driven
by the region’s expanding middle class. Populations are young in most of Asia. Si
Asian economies continue to expand, the rates of return on investments in the region
will be higher than those in other regions. De este modo, there is huge potential for growth
and ﬁnancial investment in Asia (Yoshino 2012).

Bank-Dominated Financial Systems and the Economic Importance of Small
and Medium-Sized Enterprises

Cifra 1 shows the size of the equity and bond markets in comparison to bank

loans in Asia.

Cifra 2, Mesa 1, y figura 3 show the shares of SMEs in the economies of
Japón, the PRC, and Indonesia. SMEs dominate the domestic economy, in terms of
the number of ﬁrms and the share of employment, in all three countries.

As shown in Figure 2, más que 99% of all businesses in Japan are SMEs;
they also employ most of the working population and account for a large proportion
of economic output.

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CREDIT RATINGS OF ASIAN SMES 21

Cifra 2. Small and Medium-Sized Enterprises in Japan

SME = small and medium-sized enterprise.
Fuente: Government of Japan, Ministry of Economy, Trade and Industry. 2011. White Paper on Small and Medium
Enterprises in Japan. Tokio.

Mesa 1. Small and Medium-Sized Enterprises in the People’s Republic of China

Item

2007

2008

2009

2010

2011

2012

. . .
. . .

4,152
57.6

4,773
57.9

4,303
58.6

68,671
77.7

60,521
76.8

67,877
76.9

72,369
75.8

59,357
64.7

333,858
99.1

334,321
97.3

431,110
99.3

422,925
99.3

449,130
99.2

316,498
97.2

Number of SMEs
SMEs (number)
SMEs to total (%)
Employment by SMEs
SME employees (’000)
SMEs to total (%)
SME Exports
SME exports (CNY billion)
SMEs to total exports (%)
. . . = data not available, SME = small and medium-sized enterprise.
Notas: The data cover industrial enterprises above a certain operational scale. For 2007–2010, “above operational
scale” refers to all industrial enterprises that generated a minimum annual income of CNY5 million from their core
negocio. For 2011–2012, it refers to all industrial enterprises that generated a minimum annual income of CNY20
million from their core business. The industry sector includes mining; manufacturing; and electricity, gas, and water
production and supply industries. Data for 2007–2010 are based on the following criteria: number of employees
fewer than 2,000, sales of CNY300 million or less, or total assets of CNY400 million or less. Medium-sized
enterprises must have more than 300 employees, sales of more than CNY30 million, and total assets of CNY40
million or more; the rest are small businesses. Data for 2011–2012 are based on 2011 SME classiﬁcation criteria.
Industrial micro, pequeño, and medium-sized enterprises are deﬁned as enterprises that employ fewer than 1,000
persons or whose annual turnover does not exceed CNY400 million. A medium-sized enterprise is deﬁned as an
enterprise that employs more than 300 persons and whose annual turnover exceeds CNY20 million. A small enterprise
is deﬁned as one that employs more than 20 persons and whose annual turnover exceeds CNY3 million. A micro
enterprise is deﬁned as an enterprise that employs fewer than 20 persons or whose annual turnover does not exceed
CNY3 million. Data on micro enterprises in 2011–2012 are not available.
Fuente: ADB. 2014. Asia SME Finance Monitor 2013. Manila.

4,919
54.7

4,142
41.6

4,423
41.5

In the PRC, the number of SMEs has expanded steadily since the government
introduced the Reform and Opening-up Policy in 1978. As can be seen from Table 1,
SMEs have played a crucial role in boosting the economy, increasing employment
opportunities, and creating industries. According to the Ministry of Commerce,
Había 12.5 million enterprises (most of which were SMEs) registered with
the State Administration for Industry and Commerce, y 37.6 million privately or
individually owned businesses at the end of 2011. SMEs contributed 50% of tax

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22 ASIAN DEVELOPMENT REVIEW

Cifra 3. Micro, Pequeño, and Medium-Sized Enterprise Contribution to Gross Domestic
Product in Indonesia

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GDP = gross domestic product, MSME = micro, pequeño, and medium-sized enterprise.
Nota: Data include micro enterprises.
Fuente: ADB 2014.

revenues and 60% of GDP. They also provided 80% of urban job opportunities,
introducido 75% of new products, and accounted for 65% of patents and inventions
in the PRC (ADB 2014).

The Indonesian economy has grown resiliently amid rapid changes in the
global economy, backed by strong domestic demand and driven by the micro, pequeño,
and medium-sized enterprise (MSME) sector. In Indonesia, Había 56.5 mil-
lion MSMEs, accounting for 99.9% of total enterprises in 2012. The MSME sector
has regularly recorded about 2% y-o-y growth in terms of number of enterprises,
even during and after the 2008/09 global ﬁnancial crisis. Primary industries such
as agriculture, forestry, and ﬁsheries accounted for about 50% of MSMEs in 2011,
followed by wholesale and retail trade and the hotel and restaurant sector with a
combined share of 28.8%. The MSME sector employed about 97% of the total
workforce, accounting for 107.7 million employees in 2012 en 5.8% y-o-y growth.
The sectors employing the greatest number of MSME workers in 2011 were primary
industries (42.4% of all MSME employees), followed by trade (21.7%), manufac-
turing (11.7%), and services (10.5%). These sectors have underpinned the national
economía, regularly contributing about 60% of GDP (Cifra 3); the trade sector
contributes the most at 26.7% of MSMEs’ GDP contribution in 2011. Indonesian
MSMEs accounted for 14.1% of total export value in 2012. Small-scale, export-
oriented manufacturers, such as handicrafts and wooden furniture industries, existir
across Indonesia and often organize in clusters, which helps to make their production

CREDIT RATINGS OF ASIAN SMES 23

Cifra 4. Access to Finance—Small and Medium-Sized Enterprise and Large
Firms in Japan

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CY = commercial year, DI = diffusion index.
Nota: The diffusion index is a method of summarizing the common tendency of a group of statistical series.
Fuente: Bank of Japan. 2014. Financial System Report October 2014. Tokio.

processes more efﬁcient. MSME exports were directly affected by the 2008/09 global
ﬁnancial crisis, registering a sharp decrease of 8.9% en 2009. Although the busi-
ness environment has gradually recovered since then, the growth of MSME exports
remains volatile, as evidenced by the 11.1% y-o-y decrease in 2012 (ADB 2014).

Small and Medium-Sized Enterprises’ Difﬁculties in Raising Money

Cifra 4 shows the level of difﬁculty in raising money depending on ﬁrm
tamaño: the thick line shows the difﬁculties faced by SMEs, and the thin line shows the
relative ease for large enterprises. Data points below zero indicate that companies
are ﬁnding it difﬁcult to raise money from either banks or the capital market. SMEs
appear to face a more difﬁcult situation in raising money when compared with large
ﬁrms.2

2There are also nonbank ﬁnancial institutions that can ﬁnance SMEs. Por ejemplo, the coauthor of this
paper, Naoyuki Yoshino, proposed the creation of Hometown Investment Trust Funds (HITs). HITs are new forms of
ﬁnancial intermediation that have been adopted as a national strategy in Japan. For more information on HITs, ver
Yoshino (2013) and Yoshino and Taghizadeh-Hesary (2014a, 2014b, y 2015).

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24 ASIAN DEVELOPMENT REVIEW

Cifra 5. Credit Risk Database of Small and Medium-Sized Enterprises

CRD = Credit Risk Database; SME = small and medium-sized enterprise.
Fuente: Authors and CRD website.3

III. Small and Medium-Sized Enterprise Database

Considering the importance of SMEs to many dimensions of Asian economic
actividad, further efforts need to be made to offer them access to ﬁnance. Their ﬁnancial
and nonﬁnancial accounts are often difﬁcult to assess, but the Credit Risk Database
(CRD) in Japan shows how SMEs can be rated based on ﬁnancial and nonﬁnancial
datos. The CRD includes a huge amount of data that can be used to rate SMEs through
statistical analysis.

Database Provided by the CRD Association

The CRD Association was established in 2001 as an initiative of the Japanese
Ministry of Economy, Trade and Industry and the Small and Medium Enterprise
Agencia. The initial membership was 52 credit guarantee corporations as well as
ﬁnancial and nonﬁnancial institutions. Its aim was to facilitate fundraising for SMEs
and to improve their operational efﬁciency. The association’s membership increased
de 73 institutions at the end of March 2002 a 175 por 1 Julio 2015.

The CRD covers SMEs exclusively (Cifra 5). As of March 31, 2015 it in-
cluded 2,210,000 incorporated SMEs and 1,099,000 sole-proprietor SMEs, y eso
is by far the largest SME database in Japan. The database for enterprises in default
covered 500,000 incorporated and sole-proprietor SMEs. The CRD Association re-
ceives active support from both the private and public sectors, which has contributed

3www.crd-ofﬁce.net

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CREDIT RATINGS OF ASIAN SMES 25

to its success. Por ejemplo, the Small and Medium Enterprise Agency nominates
representatives of the CRD Association to government councils, which gives the
association an opportunity to promote its activities and increase its membership.
Credit guarantee corporations and private ﬁnancial institutions use the CRD when
they create a joint guarantee scheme.4 Before the CRD was formally established, el
government invested (cid:2)1.3 billion from the supplementary budgets for ﬁscal years
1999 y 2000 to ﬁnance the setting up of the CRD’s computer system and other
operational costs. The association provides sample data and statistical information,
and scoring services.

Member ﬁnancial institutions use scoring models to evaluate creditworthi-
ness, check the validity of internal rating systems, and align loan pricing with credit
riesgo. Además, the CRD Association provides consulting services to support the
management of SMEs on the assumption that if SMEs are better managed, este
will reduce the credit risk for member ﬁnancial institutions and strengthen SME
business operations. Consulting services have also been offered to member ﬁnancial
institutions to help them promote implementation of Basel II.

If such systems could be established in other parts of Asia to accumulate and
analyze credit risk data, and to measure each SME’s credit risk accurately, SMEs
would not only be able to raise funds from the banking sector, they could also gain
access to the debt market by securitizing their claims.

IV. Analysis of Small and Medium-Sized Enterprise Credit Ratings

Using Asian Data

Credit ratings are opinions expressed in terms of ordinal measures, reﬂecting
the current ﬁnancial creditworthiness of issuers such as governments, ﬁrms, y
ﬁnancial institutions. These ratings are conferred by rating agencies—such as Fitch
Ratings, Moody’s, and S&P—and may be regarded as a comprehensive evaluation
of an issuer’s ability to meet their ﬁnancial obligations in full and on time. Por eso,
they play a crucial role by providing participants in ﬁnancial markets with useful in-
formation for ﬁnancial planning. To conduct rating assessments of large corporates,
agencies resort to a broad range of ﬁnancial and nonﬁnancial pieces of information,
including domain experts’ expectations. Rating agencies usually provide general

4A credit guarantee system would make it easier for banks to lend money to SMEs. Por ejemplo, En el caso de
an SME default, a percentage of the losses would be met by the credit guarantee corporation, which is a governmental
organización. Por ejemplo, assuming a credit guarantee corporation sets 80% as the guarantee ratio, if an SME went
into bankruptcy, a bank could recover 80% of its loan. If there were no credit guarantee system in place and an SME
went into bankruptcy, the bank would lose its entire loan. Research is needed into the optimal level of partial credit
guarantees; eso es, the percentage at which a credit guarantee corporation can encourage lending yet ensure that
banks have an incentive to carefully assess the creditworthiness of borrowers. Arr´aiz, Mel´endez, and Stucchi (2014)
have provided a framework for a partial credit guarantee system.

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26 ASIAN DEVELOPMENT REVIEW

guidelines on their rating decision-making process, but detailed descriptions of the
rating criteria and the determinants of banks’ ratings are generally not provided
(Orsenigo and Vercellis 2013). In search of more objective assessments of the
creditworthiness of large corporate and ﬁnancial institutions, there has been a
growing body of research into the development of reliable quantitative methods
for automatic classiﬁcation according to their ﬁnancial strength.

Extensive empirical research devoted to analyzing the stability and soundness
of large corporates dates back to the 1960s. Ravi Kumar and Ravi (2007) provided
a comprehensive survey of the application of statistical and intelligent techniques
to predicting the likelihood of default among banks and ﬁrms. Despite its obvious
relevance, sin embargo, the development of reliable quantitative methods for the
prediction of large corporates’ credit ratings has only recently begun to attract
strong interest. These studies are mainly conducted within two broad research
strands focusing on statistical and machine learning techniques, and may address
both feature selection and classiﬁcation. Poon, Firth, and Fung (1999) desarrollado
logistic regression models for predicting ﬁnancial strength ratings assigned by
Moody’s, using bank-speciﬁc accounting variables and ﬁnancial data. Factor
analysis was applied to reduce the number of independent variables and retain
the most relevant explanatory factors. The authors showed that loan provision
información, and risk and proﬁtability indicators added the greatest predictive
value in explaining Moody’s ratings. Huang et al. (2004) compared support vector
machines and back-propagation neural networks to forecast the rating of ﬁnancial
institutions operating in the United States and Taipei,Porcelana, respectivamente. In each
caso, ﬁve rating categories were considered based on information released by S&PAG
and TRC, respectivamente. The analysis of variance was used to discard noninformative
características. en este estudio, support vector machines and neural networks achieved
comparable classiﬁcation results. Sin embargo, the authors found that the relative
importance of the ﬁnancial variables used as inputs by the optimal models were
quite different between the two markets.

In a more recent study, Yoshino, Taghizadeh-Hesary, and Nili (2015) usado
two statistical analysis techniques on various ﬁnancial variables taken from bank
statements for the classiﬁcation and credit rating of 32 Iranian banks. El
underlying logic of both techniques—principal component analysis (PCA) y
cluster analysis—is dimension reduction; eso es, summarizing information on
numerous variables in just a few variables. While the two techniques achieved
this in different ways, their results both classiﬁed 32 banks into two groups and
sorted them based on their credit ratings.

While the aforementioned examples are for credit ratings of large corporate
and ﬁnancial institutions, the story is different for SMEs because of the lack of
datos. In Japan and other Asian economies, rating SMEs is regarded as a difﬁcult
action when compared to rating large corporates; data is available for large corporates

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CREDIT RATINGS OF ASIAN SMES 27

because of ofﬁcial auditing, while for SMEs, there are no such auditing requirements.
As mentioned earlier, the CRD Association started to compile a database on SMEs,
which made it much easier to evaluate SMEs since the huge datasets tell us the
normal distribution of SME data. In Japan, SMEs have been categorized since 2012
into one of ﬁve rating classiﬁcations based on the CRD.

En esta sección, we present an efﬁcient and comprehensive scheme for rating
the creditworthiness of SMEs. Primero, we examine various ﬁnancial ratios that de-
scribe the characteristics of SMEs and which enable banks to categorize their SME
customers into different groups based on their ﬁnancial health. The data for this
statistical analysis were provided by an Asian bank for 1,363 SMEs.

Selection of the Variables

A large number of possible ratios have been identiﬁed as useful in predicting
a ﬁrm’s likelihood of default. Chen and Shimerda (1981) show that out of more
than 100 ﬁnancial ratios, almost 50% were found useful in at least one empirical
estudiar. Some have argued that quantitative variables are not sufﬁcient to predict SME
defaults and that including qualitative variables—such as the legal form of the busi-
ness, the region where the main business is carried out, and industry type—improves
a model’s predictive power (Lehmann 2003; Grunert, Norden, and Weber 2004).
Sin embargo, the data used here are based on ﬁrms’ ﬁnancial statements, which do not
contain such qualitative variables.

We have followed Altman and Sabato (2007) and Yoshino and Taghizadeh-
Hesary (2014b) who proposed ﬁve categories to describe a company’s ﬁnancial
proﬁle: (i) liquidity, (ii) proﬁtability, (iii) leverage, (iv) coverage, y (v) actividad.
For each of these categories, they created a number of ﬁnancial ratios identiﬁed in
the literature. Mesa 2 shows the ﬁnancial ratios selected for this survey.

The ﬁrms considered as being unsound in this study are those whose risk-

weighted assets are greater than their shareholders’ equity.

In the next stage, two statistical techniques are used: PCA and cluster analysis.
The underlying logic of both techniques is dimension reduction—summarizing
information on multiple variables into just a few variables—but they achieve this in
different ways. PCA reduces the number of variables into components (or factors).
Cluster analysis reduces the number of SMEs by placing them in small clusters. En
this survey, we use components (factores) that are the result of PCA and then run the
cluster analysis in order to group the SMEs.

Principal Component Analysis

PCA is a standard data-reduction technique that extracts data, removes redun-
dant information, highlights hidden features, and visualizes the main relationships

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28 ASIAN DEVELOPMENT REVIEW

No.

Symbol

Definición

Mesa 2. Examined Variable

1
2

3
4
5

6
7
8

10
11

Equity_TL
TL_Tassets

Cash_Tassets
WoC_Tassets
Cash_Sales

EBIT_Sales
Rinc_Tassets
Ninc_Sales

EBIT_IE

AP_Sales
AR_TL

Equity (book value)/total liabilities
Total liabilities/total assets

Cash/total assets
Working capital/total assets
Cash/net sales

Ebit/sales
Retained earnings/total assets
Net income/sales

Ebit/interest expenses

Account payable/sales
Account receivable/total liabilities

Category

Leverage

Liquidity

Proﬁtability

Coverage

Activity

Notas: Retained earnings refers to the percentage of net earnings not paid out as dividends,
but retained by the company to be reinvested in its core business or to pay debt; it is recorded
under shareholders’ equity in the balance sheet. Ebit refers to earnings before interest and taxes.
Account payable refers to an accounting entry that represents an entity’s obligation to pay off
a short-term debt to its creditors; the accounts payable entry is found on a balance sheet under
current liabilities. Account receivable refers to money owed by customers (individuals or
corporations) to another entity in exchange for goods or services that have been delivered or
usado, but not yet paid for; receivables usually come in the form of operating lines of credit and
are usually due within a relatively short time period, ranging from a few days to 1 año.
Fuente: Authors’ description.

that exist between observations.5 PCA is a technique for simplifying a dataset, por
reducing multidimensional datasets to lower dimensions for analysis. Unlike other
linear transformation methods, PCA does not have a ﬁxed set of basis vectors. Its
basis vectors depend on the dataset, and PCA has the additional advantage of indi-
cating what is similar and different about the various models created (Bruce-Ho and
Dash-Wu 2009). Through this method, we reduce the 11 variables listed in Table 2 a
determine the minimum number of components that can account for the correlated
variance among SMEs.

In order to examine the suitability of these data for factor analysis, el
Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity were performed.
KMO is a measure of sampling adequacy that indicates the proportion of common
variance that might be caused by underlying factors. High KMO values (mayor que
0.6) generally indicate that factor analysis may be useful, which is the case in this
study as the KMO value is 0.71. If the KMO value is less than 0.5, factor analysis
will not be useful. Bartlett’s test of sphericity indicates whether the correlation ma-
trix is an identity matrix, indicating that variables are unrelated. A signiﬁcance level
less than 0.05 indicates that there are signiﬁcant relationships among the variables,
which is the case in this study as the signiﬁcance of Bartlett’s test is less than 0.001.

5PCA can be also called the Karhunen–Lo`eve transform (KLT), named after Kari Karhunen and Michel

Lo`eve.

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CREDIT RATINGS OF ASIAN SMES 29

Mesa 3. Total Variance Explained

Component

Eigenvalues % of Variance Cumulative Variance %

Z1
Z2
Z3
Z4
Z5
Z6
Z7
Z8
Z9
Z10
Z11

3.30
2.19
1.25
1.08
0.94
0.75
0.56
0.48
0.32
0.13
0.09

30.00
19.90
11.38
9.78
8.56
6.79
5.09
4.36
2.87
1.14
0.13

Fuente: Authors’ calculations.

30.00
49.90
61.28
71.06
79.62
86.41
91.50
95.86
98.73
99.87
100.00

Mesa 4. Factor Loadings of Financial Variables after Direct
Oblimin Rotation

Variables
(Financial Ratios)

Equity_TL
TL_Tassets
Cash_Tassets
WoC_Tassets
Cash_Sales
EBIT_Sales
Rinc_Tassets
Ninc_Sales
EBIT_IE
AP_Sales
AR_TL

Component

0.009
−0,032
−0.034
−0.05
−0.937
0.962
0.014
0.971
0.035
−0.731
0.009

0.068
−0.878
−0,061
0.762
0.021
0.008
0.877
−0.012
0.045
−0,017
−0,041

0.113
0.069
0.811
0.044
0.083
0.024
0.015
0.015
0.766
−0.037
−0.104

0.705
−0.034
0.098
0.179
0.009
−0,004
−0.178
0.014
−0.098
−0,016
0.725

Notas: The extraction method was principal component analysis. The rotation method
was direct oblimin with Kaiser normalization.
Fuente: Authors’ calculations.

Próximo, we determine how many factors to use in our analysis. Mesa 3 reports
the estimated factors and their eigenvalues. Only those factors accounting for more
than 10% of the variance (eigenvalues >1) are kept in the analysis. Como resultado, solo
the ﬁrst four factors were ﬁnally retained. Tomados juntos, Z1 through Z4 explain
71% of the total variance of the ﬁnancial ratios.

In running the PCA, we used direct oblimin rotation. Direct oblimin is the
standard method to obtain a non-orthogonal (oblique) solution—that is, one in
which the factors are allowed to be correlated. In order to interpret the revealed
PCA information, the pattern matrix must then be studied. Mesa 4 presenta el
pattern matrix of factor loadings by the use of the direct oblimin rotation method,
where variables with large loadings, absolute value (>0.5) for a given factor, son
highlighted in bold.

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30 ASIAN DEVELOPMENT REVIEW

Mesa 5. Component Correlation Matrix

Component

Z1
Z2
Z3
Z4

1
0.037
−0,031
−0.005

Z2
0.037
1
0.106
0.102

Z3
−0,031
0.106
1
0.033

Z4
−0.005
0.102
0.033
1

Nota: The extraction method is principal component analysis. El
rotation method is direct oblimin with Kaiser normalization.
Fuente: Authors’ calculations.

Como se puede observar en la tabla 4, the ﬁrst component, Z1, has four variables with an
absolute value (>0.5), of which two are positive (ebit/sales and net income/sales)
and two are negative (cash/net sales and account payable/sales). For Z1, the variables
with large loadings are mainly net income and earnings. Por eso, Z1 generally reﬂects
the net income of an SME. As this factor explains the most variance in the data, es
the most informative indicator of an SME’s overall ﬁnancial health. Z2 reﬂects short-
term assets. This component has three major loading variables: (i) liabilities/total
assets, which is negative, meaning that an SME has few liabilities and mainly relies
on its own assets; (ii) working capital/total assets, which is positive, meaning an SME
has short-term assets; (iii) retained earnings/total assets, which is positive, significado
an SME has some earnings that it keeps with the company or in the bank. These three
variables indicate an SME whose reliance on borrowings is small and which is rich in
working capital and retained earnings, and therefore has plenty of short-term assets.
Z3 reﬂects the liquidity of SMEs. This factor has two variables with large loadings
(cash/total assets and ebit/interest expenses), both with positive values, which shows
an SME that is cash-rich and has high earnings. Por eso, it mainly reﬂects an SME’s
liquidity. The last factor, Z4, reﬂects capital. This factor has two variables with large
loadings, both with positive values: equity (book value)/total liabilities and accounts
receivable/total liabilities, meaning an SME with few liabilities that is rich in equity.
Mesa 5 shows the correlation matrix of the components and shows there is
no correlation between these four components. This means we could have used a
regular orthogonal rotation approach to force an orthogonal rotation, although in
this survey, we used an oblique rotation method, which still provided basically an
orthogonal rotation factor solution because these four components are not correlated
with each other and are distinct entities.

Cifra 6 shows the distribution of the four components (Z1, Z2, Z3, y
Z4) for Group A, which comprises ﬁnancially sound SMEs, and Group B, cual
comprises unsound SMEs.

It is clear from all six graphs in this ﬁgure that Group A SMEs can generally
be found in the positive areas of the graphs and Group B SMEs in the negative areas
in most cases. This is evidence that these four deﬁned components (Z1, Z2, Z3,
and Z4) are able to separate SMEs, suggesting they represent a good measure for
showing the ﬁnancial soundness of SMEs.

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CREDIT RATINGS OF ASIAN SMES 31

Cifra 6. Distribution of Factors for SME Groups A and B

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SME = small and medium-sized enterprise.
Notas: Group A = sound SMEs, group B = unsound SMEs. The ﬁrms considered to be unsound in this study have
risk-weighted assets greater than their shareholders’ equity.
Fuente: Authors’ calculations.

Cluster Analysis

In this section we take the four components that were used in the previous
section and identify those SMEs that have similar traits. We then generate clusters
and place the SMEs in distinct groups. Para hacer esto, we employ cluster analysis, cual
organizes a set of data into groups so that observations from a group with similar
characteristics can be compared with those from a different group (Martinez and
Martinez 2005). The result of the cluster analysis tells us how much each individual
SME is close to others and it looks at the distance between two companies based on
their ﬁnancial statements. If they are close to each other in the cluster analysis, él

32 ASIAN DEVELOPMENT REVIEW

Cifra 7. Dendrogram Using Average Linkage

SME = small and medium-sized enterprise.
Fuente: Authors’ calculations.

means their ﬁnancial statements are similar; if two SMEs are different, it means their
ﬁnancial statements are completely different. De este modo, the similarities and differences
between two companies are statistically analyzed.

En este caso, SMEs were organized into distinct groups according to the four
components derived from the PCA used in the previous section. Cluster analy-
sis techniques can themselves be broadly grouped into three classes: hierarchical
clustering, optimization clustering, and model-based clustering.6 We used the most
prevalent method of these in the literature, hierarchical clustering. This produced
a nested sequence of partitions by merging (or dividing) grupos. At each stage of
the sequence, a new partition is optimally merged (or divided) from the previous
partition according to some adequacy criterion. The sequence of partitions ranges
from a single cluster containing all the individuals to a number of clusters (norte) estafa-
taining a single individual. The series can be described by a tree display called the
dendrogram (Cifra 7). Agglomerative hierarchical clustering proceeds by a series
of successive fusions of the n objects into groups. Por el contrario, divisive hierarchical
methods divide the n individuals into progressively ﬁner groups. Divisive methods
are not commonly used because of the computational problems they pose (Everitt,
Landau, and Leese 2001; Landau and Chis Ster 2010). Abajo, we use the average
linkage method, which is a hierarchical clustering technique.

6The main difference between the hierarchical and optimization techniques is that in hierarchical clustering
the number of clusters is not known beforehand. The process consists of a sequence of steps where two groups are
either merged (aglomerativo) or divided (divisive) according to the level of similarity. Eventualmente, each cluster can be
subsumed as a member of a larger cluster at a higher level of similarity. The hierarchical merging process is repeated
until all subgroups are fused into a single cluster (Martinez and Martinez 2005). Optimization methods on the other
hand do not necessarily form hierarchical classiﬁcations of the data as they produce a partition of the data into a
speciﬁed or predetermined number of groups by either minimizing or maximizing some numerical criterion (Feger
and Asafu-Adjaye 2014).

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CREDIT RATINGS OF ASIAN SMES 33

The Average Linkage Method

The average linkage method deﬁnes the distance between clusters as the
average distance from all observations in one cluster to all points in another cluster.
En otras palabras, it is the average distance between pairs of observations, where one is
from one cluster and one is from the other. The average linkage method is relatively
robust and also takes the cluster structure into account (Martinez and Martinez
2005, Feger and Asafu-Adjaye 2014, and Yoshino and Taghizadeh-Hesary 2014b,
2014C). The basic algorithm for the average linkage method can be summarized in
the following manner:

• N observations start out as N separate groups. The distance matrix D = (dij) es

searched to ﬁnd the closest observations, Por ejemplo, Y and Z.

• The two closest observations are merged into one group to form a cluster (YZ),
producing N − 1 total groups. This process continues until all observations are
merged into one large group.

Cifra 7 shows the dendrogram that results from this hierarchical clustering.

The resultant dendrogram (hierarchical average linkage cluster tree) proporciona
a basis for determining the number of clusters by sight. In the dendrograms shown
En figura 7, the horizontal axis shows 1,363 SMEs. Because of the large number of
SMEs in this survey, they have not been identiﬁed by number in the dendrogram,
although this is how they are identiﬁed in this survey. Bastante, the dendrogram
categorizes the SMEs in three main clusters (Groups 1, 2, y 3), but it does not
show which of these three clusters contains the ﬁnancially healthy SMEs, cual
contains unhealthy SMEs, and which contains intermediate SMEs. Por eso, hay
one more step to go.

Cifra 7 shows the 1,363 SMEs categorized into three major clusters. Usando
their components, which were derived from the PCA described in Section IV.B,
we can plot the distribution of factors for each member of the three major clusters.
Cifra 8 shows the distribution of Z1–Z2 for these three cluster members separately.7
As it is clear in Figure 8, Group 1 comprises the healthiest SMEs, Group 3
the least healthy SMEs, and Group 2 the in-between SMEs. Curiosamente, cuando nosotros
do this grouping using the other components (Z1–Z3, Z1–Z4, Z2–Z4, Z2–Z3, y
Z3–Z4), the grouping is similar in most cases, which implies that this analysis is an
effective way of grouping SMEs.

7The dendrogram shows us the major and minor clusters. One useful feature of this tree is that it identiﬁes a
representative SME of most of the minor groups, which has the average traits of the other members of the group. Para
simpliﬁcation, En figura 8, we have only used data from these representative SMEs, which explains the whole group’s
traits. This is why the total number of observations in Figure 8 is lower than the 1,363 observations in this survey.

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34 ASIAN DEVELOPMENT REVIEW

Cifra 8. Grouping Based on Principal Component Analysis (Z1–Z2) and Cluster Analysis

Notas: Group 1 comprises the healthiest SMEs. Group 2 represents the in-between SMEs. Group 3 represents the
least healthy SMEs.
Fuente: Authors’ calculations.

Mesa 6. Average of Financial Ratios for Each Group
of SMEs

SME Groups

Variables
(Financial Ratios)

Group 1
1.11
0.56
0.08
0.15
0.06
0.24
0.28
0.20
22.88
0.49
0.61

Group 2
0.77
0.62
0.03
0.11
0.05
0.26
0.17
0.25
7.74
0.50
0.44

Group 3
0.33
0.78
0.05
0.04
0.05
0.13
0.06
0.18
2.04
0.60
0.41

Equity_TL
TL_Tassets
Cash_Tassets
WoC_Tassets
Cash_Sales
EBIT_Sales
Rinc_Tassets
Ninc_Sales
EBIT_IE
AP_Sales
AR_TL
SME = small and medium-sized enterprise.
Notas: Group 1 comprises the healthiest SMEs. Group 2 represents the
in-between SMEs. Group 3 represents the least healthy SMEs. Para el
deﬁnition of each variable (ﬁnancial ratios) ver tabla 2.
Fuente: Authors’ calculations.

For a robustness check of classﬁcations based on the aformentioned method,

we have done one more step and the results are summarized in Table 6.

Mesa 6 shows the average of the 11 ﬁnancial ratios based on our classiﬁ-
cations, which categorized 1,363 SMEs into three groups. The healthiest group of
SMEs (Group 1) in all ratios had a relatively better performance in comparison with

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CREDIT RATINGS OF ASIAN SMES 35

the two other groups. The performance of the in-between SMEs (Group 2) in most
cases was better than the least healthy SMEs (Group 3). Por otro lado, 59% de
ﬁrms in Group 3 are unsound ﬁrms, which means they have risk-weighted assets
greater than their shareholders’ equity. This percentage is higher than the share of
unsound SMEs in either Group 1 or Group 2, demonstrating that the rationale of our
method is acceptable and we can retain the results.

V. Concluding Remarks

SMEs play a signiﬁcant role in all Asian economies. They are responsible
for very high shares of employment and output. Sin embargo, they ﬁnd it difﬁcult to
borrow money from banks and other ﬁnancial institutions. Using accumulated data
on SMEs, we can carry out statistical analysis on their quality in a way that can
facilitate bank ﬁnancing for SMEs.

We applied 11 ﬁnancial variables of 1,363 SMEs who are customers of Asian
banks and subjected them to PCA and cluster analysis. The results showed that four
variables (net income, short-term assets, liquidity, and capital) are the most important
for describing the general characteristics of SMEs. Three groups of SMEs were then
differentiated based on ﬁnancial health.

The policy implications of this research are that if Asian governments can
provide a comprehensive SME database—such as the CRD in Japan—and apply
analytical techniques similar to those presented in this paper, then a comprehensive
and efﬁcient credit rating system for SMEs can be created. Respectivamente, ﬁnancially
healthy SMEs could borrow more money from banks at lower interest rates because
of their lower default risk, while SMEs in poor ﬁnancial health would have to pay
higher interest rates and have a lower borrowing ceiling. By using such a credit
rating mechanism, banks could reduce the amount of nonperforming loans made
to SMEs, which would improve the creditworthiness of the ﬁnancial system and
help healthy SMEs to raise money more easily from banks while contributing to
economic growth.

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