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									Analysis of the Relationship Between Bank Risk, Diversification, and
                       Group Network Density

                                Chuang-Min Chao, Lunchi Yuan
                           Institute of Commerce Automation and Management,
     National Taipei University of Technology, 1, Sec 3, Chung-Hsiao E. Rd, Taipei City 10608, Taiwan
                                      ROC    t5488033@ntut.edu.tw


     This study investigates whether the activities of diversification conducted by
banks influence their risk taking. But not only banks or finance institute take
diversification activities, the business conglomerates which combine a bank and
nonblank firms do so. With the viewpoint of corporate governance, we further analyze
the impact of bank’s board density and group network density on bank itself. We find
the close relationship between a bank and other related firms within the same group.
 Key-Words: bank risk, diversification, group network density, z-score


     Before the 1990s, the government strongly restricted bank’s operating ability in
Taiwan. Setting up new banks, opening new branches and engage in multiple
financial activities were not easily allowed. It was a closed financial system which
slowed down bank’s development, but in this stable environment banks were often
with steady cash flows. The shape and structure of Taiwan banking has changed
drastically since financial deregulation in the end of the1980s. Banks may now branch
more freely and many new banks were built quickly.1 The amount of banks increased
rapidly during the last decade but most of these banks were very small and very local.
It’s no longer easy to struggled surviving in this more and more competitive and
global environment, especially to those small banks with less capital. These banks
were often with simple cash flows and can’t resist the violent change related to their
main earning resource. It’s the way with taking diversification activities to broader
revenue earning resources to reduce environment influence. Firms can benefit from
diversification through the creation of internal capital markets (Williamson, 1970),
economies of scope (Teece, 1980).With financial deregulations of security, insurance

  The total amount of Taiwan financial institutes was growing from 3,955 to 6,310 with increasing rate
59.54% during 1990 to 2002. Hsu Cheng-Ming(2004) Nowadays and the perspective of Taiwan
financial industry.
and other financial services issued in the following years, banks now provide more
financial activities in different operating sector.
     Diversification activities were taken by many banks in recent years. The reason
might be the underlying synergy, which banks can gather extensive customer
information and reuse the information not only to benefit a single business but also to
other affiliated businesses. As suggested by the study of Diamond (1991), Rajan
(1992), Saunders and Walter (1994), and Stein (2002), banks acquire customer
information during the process of making loans that may facilitate the efficient
provision of other financial services, including the underwriting of securities.
similarly, securities and insurance underwriting, brokerage and mutual fund services,
and other activities may produce information that improves loan making (Laeven and
Levine, 2005).
     Another reason we mentioned above is that the financial service industry has
experienced a dramatic change in recent years. Globalization and the deregulation of
the banking industry have made the financial service industry become more dynamic
and unpredictable. Theoretically, diversified banks are able to diversify their revenue
sources among various business areas, such as private financial management services,
saling of insurance policies and mutual funds, trading bills that can reduce the
operation risk under the volatile business environment. Although the diversification of
banks seems to be the major trend nowadays, literature regarding the “diversification
discounts” finds the opposite conclusion. Diversification may increase bank risk of
more losses. The study of Demsetz and Strahan (1997) discuss that large banks today,
while clearly better diversified, are not safer than small banks with less operating item
because they tend to hold riskier loans and finance themselves with less equity that
leads to higher leverage. The diversification discount may be caused by that too many
operating items make the banks lose their focus on specialized field. The bad debt of
credit cards and cash cards of Taiwan banks is such an example. The huge loss even
forced some banks into bankruptcy. Another reason may cause the diversification
discounts including the inefficient internal resource allocation (Lamont 1997;
Scharfstein 1997), the informational asymmetries between head office and divisional
managers (Harris, Kriebel, Raviv, 1992).
     Not only financial institute but also other business conglomerates which combine
a bank and non-bank firms take diversification activities to expand their business
scope. That makes banks within group been them more involved in the firm’s
financial transactions, and they generally receive more detail information about the
firm’s operating activities (Amsden and Hikino, 1991; Sheard, 1994; Hoshi, 1994; Cai
Cheung, Coyal, 1999). Banks have more chances and capabilities to be involved in
the firms’ decision-making, and they can be considered as a corporate governance
mechanism during the process of strategy management (Gorton and Schmid, 2000).
The costs of financial distress are lower for those firms that maintain close
relationship with banks (Hoshi, Kashyap, Scharfstein, 1990; Kester, 1991).
Consequently, the bank could benefit, through its holding, from the improved
investment decisions of the firm.
      But in the Bank’s point of view. This situation makes banks become a dual role
as creditors and shareholders may cause the agency problems (Laeven, et al. 2005) if
a bank is hold by a conglomerates combining other non-bank companies. The agency
problem has become an important issue after several financial frauds in the world. In
Taiwan., there are such financial scandals as cases of the Taichung Bank(1997), the
Chung-Shing Bank(2000), and the Chinese Bank(2007). When taking a closer look at
these governance failures, the unusual cash flows between the banks and their holding
groups owned by some business tycoons can be seen. Because of the role of
shareholders, banks may undertake the high risk project from their related non-bank
      La Porta, López-de-Silanes, and Zamarripa (2002) have such the alternative view
that close relationship between banks and borrowers allow insiders to divert resources
from depositors and minority shareholders to themselves. This view is related to the
idea of looting (Akerlof and Romer 1993). Looting can take several forms. If that
country’s banking system is protected by deposit insurance, the manager of a bank
can take excessive risk or make loans to their related companies on non-market terms,
that the government will bear the costs of bad loans of related firms. Even without
deposit insurance, the controllers of a bank have a strong incentive to divert funds to
companies, as long as their share of profits in their related companies within same
large conglomerates is greater than their share of profits in the bank. Furthermore, the
controllers of conglomerates can easily over-lending to their related companies which
may have weak financial situation or just move money to themselves in worst. The
general implication is that related lending is very attractive to the borrower, but may
higher the default risk to bankrupt the lender. Literatures call this pessimistic
assessment of related lending the “looting view.”
     Therefore, it is important to examine the network system among banks and their
holding groups and if the strong network system may influence the banks’ loan
decision to higher their bankruptcy risk.

2. Empirical analyses
     In this study we evaluate the effect of diversification index and group network
density on the bank risk. We adopt volatility of earnings and z-scores as the proxy
variables of bank risk .They respond to the concept of diversification with their
measurement of volatility.

     The calculation of the group network density applies the model developed by
Borgatti, Everett and Freeman (1992) and the software UCINET. The Analysis
attempt to find th relationship between the bank risk, diversification index, and group
network density.

2.1. Sample selection and data

   We use financial data from Taiwan Economic Journal, and Market Observation
Post System database of regional banks in Taiwan. It is covering two periods from
fiscal 1995 through fiscal 1999 and fiscal 2002 through fiscal 2006. During 1990s,
because the deregulation of the financial service industry, it was the first time that
financial institute increased their earning resource and expanded business scope. The
second period is after 2001, the year that the lawsuit of financial holding company
was issued. Financial holding company’ establishing and bank merging activities was
frequency during this time than before.

2.2 Variables

volatility of earnings(EAR)
     Smooth earnings or assets return rate information could be an important financial
indicator for outside investors and the government regulator such as the Financial
Supervisory Commission’s (Smith, Stulz 1985; Moses, 1987). The earliest studies
established that not all nonbank activities would reduce the risk of banking firms.
Johnson and Meinster (1974), Heggestad (1975), Wall and Eisenbeis (1984), and
Litan (1985) used IRS data to compare the aggregate earnings streams of the banking
industry to the earnings streams of other financial industries (e.g., insurance,
securities, leasing). Earnings in the banking industry were more volatile than some of
the non-bank industries, but less volatile than others. More importantly, banking
industry earnings were positively correlated to the earnings of other financial
industries, but negatively correlated to other iindustry. Constructing a risk-minimizing
portfolio of banking and nonbanking activities may be difficult (Young, Roland,
1999), and volatility of earnings could be meaningful to banks’ risk while they have
various earnings resources.
     To calculate the volatility of earnings at bank i, we begin by calculating the
percent change in its quarter-to-quarter revenues for each quarter t: ()
                  i ,t
     % i  i ,t 1
                  i ,t
and volatility of earnings can be calculated as following equation:
                                                                r                           
                                                                      n                        2
                                                                                         ri
     EARi  standard deviation of % i                              t 1         it


For convenience, we will express the percentage change in earning as a number
between zero and one.

     The regulator may not care about the actual level of risk but put their eyes on a
bank’s distance to default. It’s a potential criticism of an asset return volatility risk
which is usually used to measure a bank’s bankruptcy probability (Bouwman, 2004).
How closer to failure is generally measured by an index which is called Z-score,
introduced by Altman (1968). The Z-score estimates the number of standard
deviations below the mean by which profits would have to fall before equity becomes
negative. Boyd and Graham (1986) adopt the following equation to examine the risk
of failure at large BHCs that diversified into non-banking activities during the 1970s
and early 1980s,

     Zi     2
               j 1   j                
                          / Aj  Aj 1  / n     E  E
                                                    j 1   j   j 1    / A   j                   
                                                                                    Aj 1  / n / Sr   
where π is net income after taxes, A is total assets, E is total equity, Sr is the standard
deviation of π/A, and the subscript j denotes the time period. High values of Z-score
are associated with low probabilities of failure and the Z-score approach is frequently
used in banking studies.

Degree of diversification (HHI)
    The early studies on non-financial industries diversitification were based on SIC
codes with segmental accounting data. But the degree measurement of SIC-code
classification for banks is not granular enough and is not consistent across countries.
Moreover, segmental reporting is not consistent across banks and across time (Elsas,
Hackethal, Holzhäuser, 2006) Instead, we use an adjusted Herfindahl-Hirschman
index to measure revenue diversification. Various authors have applied a similar
measuring method (see, e.g., Acharya et al. 2002; Stiroh & Rumble 2003; Stiroh
2004). Equation below shows how this study constructed the diversification index.

                   INT  2  COM  2  TRAD  2  OTI  2 
     HHI i  1                                        
                   TOR   TOR   TOR   TOR  
                                                   
  The variable definitions are as follows: INT denotes gross interest revenue, COM
denotes net commission revenue, TRAD denotes net trading revenue, OTI denotes all
other net revenue and TOR denotes total operating revenue. Denominator TOR is
equal to the sum of the absolute values of INT, COM, TRAD and OTI, and we use
gross interest revenue so that our diversification measure is not unduly distorted by
the profitability of the bank’s interest business. We use accounting data from the TEJ
database which consistently reports gross numbers for the other revenue categories.
     We subtract the sum of squared revenue shares from unity so that HHI increases
in the degree of revenue diversification, the higher values of HHI, mentioning the
degree of diversification is more higher. By definition HHI can take on values
between zero (the bank is fully specialized in only one business areas) and 0.75 (the
bank generates a fully balanced revenue mix from all four business areas).

Bank board network density (BDen)

     This variable related to the degree of board relationship density; we calculate it
by summing the relationship linkage values and dividing the values by the amounts of
all board members, and then timing 100%. In this study, we define three relationship
of linkage: kindred, colleague, and schoolfellow. We use values 0 and 1 to indicate
that whether the relationship existing. We build a matrix to construct these
relationship network values, for that we can use the social network analysis software
UCINET to get the value of bank board network density.

Table.1 Network density marix
                    A              B               C               D

    A               0              1               0               1

    B               1              0               1               0

    C               0              1               0               0

    D               1              0               0               0

     UCINET 6.05 (Borgatti, Everett, and Freeman, 2002) is a comprehensive
program for the analysis of social networks and other proximity data. It is the best
known and most frequently used software package for the analysis of social network
data and contains a large number of network analytic routines.

Group network density (RDen)

   The variable RDen related to the degree of board relationship density, likes
BDen, we calculate it by summing the relationship linkage values and dividing the
values by the amounts of all board members, and then timing 100%. In this study, we
define the relationship of linkage: the cross-holding linkage to other firms within
same group and the amount of linkage of same board member in different companies
in the same group. We use values 0 and 1 to indicate that whether the relationship
existing. We build a matrix to construct these relationship network values, for that we
can use the social network analysis software UCINET to get the value of bank board
network density.

Financial holding company (FHC)

   We build a dummy variable FHC, and we give value 1 if a bank belongs to a
financial holding company, and we give value 0 if it does not.

Size (size)

  The natural log of the book value of total assets.

Regression model

  We construct the regression model as bellow equation
EAR i   0   1 HHI i   2 Fi   3 RDen i   4 BDen i   5 Sizei   i

Z i   0   1 HHI i   2 Fi   3 RDen i   4 BDen i   5 Sizei   i

3. Empirical results
Degree of diversification
  We can see the degree of banks’ degree of diversification from figure.1 during
1995-2006, the trend is probably increased these years.
                                Figure.1 Degree of diversification 1995-2006

                                                       Degree of diversification
               HHI index










                        1995   1996   1997   1998   1999   2000      2001   2002   2003   2004   2005   2006

Descriptive statics
  Table. 2 shows the descriptive statics from 2002 to 2006, and Table. 3 shows the
descriptive statics from 1995to 1999
    Table.2 Descriptive statics 2002-2006
                                    N                  Min               Max            Mean                 Stdev.

        z-score                           36                 -.61         174.68         34.2725              47.00317

        EAR                               36             14.83           5727.39        467.1136             967.83332

        HHI                               36                  .13               .59              .2961            .11719

        RDen                              36                  .09               .99              .5361            .25269

        BDen                              36                  .11               .92              .4467            .24685

        Size                              36             12.00             21.00         19.1111                 1.68655

        Table.3 Descriptive statics 1995-1999
                                    N                  Min               Max            Mean                 Stdev.

        z-score                           33                 6.97        1479.24        149.3185             258.03272

        EAR                               33                 2.93        2050.18        161.8373             382.18725

        HHI                               33                  .05               .61              .2197            .11902

        RDen                              33                  .09               .99              .5248            .25076

        BDen                              33                  .12          45.00             3.4533              9.16987

        Size                              33             13.00             21.00         18.8485                 1.50252

Regression results:
Model: Z i   0   1 HHI i   2 Fi   3 RDen i   4 BDen i   5 Sizei   i
    Table.4 ANOVA 2002-2006

    Model                        Sum of Squares                df         Mean Squares                   F              Sig.

    1             Regression            36605.991                    5            7321.198               5.394           .001

                  Residual              40719.442                   30            1357.315
                  Total                 77325.433                   35

     Table.5 Coefficients 2002-2006
                                     Unstandardized                 Standardized

        Model                           Coefficients                Coefficients             t                   Sig.

                                    B             Std. Error             Beta

        1            constants      -22.910            102.615                                   -.223                  .825

                     HHI             79.194             67.857                 .197              1.167                  .252

                     RDen          -108.939             36.024              -.586            -3.024                .005*

                     BDen            27.675             36.485                 .145               .759                  .454

                     Size               3.776            4.927                 .135               .766                  .449

                     F               19.573             18.890                 .206              1.036                  .308

From Table.5, we can find the significant positive relationship between Z score and
We do not find any significant relationship from 1995-1999.

   As the result, we can see the close relationship network within the group may cause
the corporate governance problem to increase a bank’s risk. In this study, we can’t
find other significant relationship might be the sample size too small.. We may
conclude samples from other countries in Asia, and find any other relationship.

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