Profit Performance of Financial Holding Companies

Profit Performance of Financial Holding Companies: Evidence from
Taiwan
Wan-Chun Liuand Chen-Min Hsu
Abstract
The purpose of this paper is to examine the determinants of profit performance of financial
holding companies (FHCs) by using a panel data set over the period 2001-2009. Apart from
bank-specific information, we focus on the effects of the board and ownership structure. The
empirical methods used are the conventional OLS and random effect of the panel data approach.
Our findings show that (a) business diversification, a lower financial cost, a higher liquidity ratio,
larger assets, and lower debt ratios can improve the profit performance of FHCs; (b) The director or
government ownership does not statistically increase FHCs’ profitability significantly, whereas the
relationship between foreign ownership and FHC profitability is negative and significant; and (c) A
dual-core strategy of “Banking + Insurance” has higher profit performance than the other strategies.
JEL classification: C23, G21, G34.
Keywords：financial holding companies, ownership structure, synergy, random effect.
Wan-Chun Liu([email protected]), corresponding author, is an associate professor in the Department of Logistics
Management, Takming University of Science and Technology, Chen-Min Hsu ([email protected]) is an professor in
the Department of Economics, National Taiwan University.
1
Introduction
Since the 1980s, financial liberalization and globalization have become popularized that
certain developed countries such as the United Kingdom, Japan, and Germany have undergone
financial reforms and removed many financial controls. Faced with the trend of financial
globalization, Taiwan’s government started removing entry barriers and eased restrictions on the
business scope of financial institutions. Especially after 1990, the government allowed new
applications for the establishment of financial institutions and permitted them to diversify their
businesses. The number of domestic banks increased from 24 in 1990 to 53 in 2001. The average
rate of return on assets (ROA) for banks dropped to 0.26% in 2001 from that of 0.9% in 1990. The
average rate of return on net worth (ROE) also decreased from 20.79% in 1990 to 3.61% in 2001.
The non-performing loan ratio rose from 3.71% at the end of 1997 to 7.48% at the end of 2001.
Consequently, the overbanking phenomenon emerged.
To solve the overbanking problem, the Taiwanese authorities conducted a series of financial
reforms. One example is that the Financial Institutions Merger Law and the Financial Holding
Company Act were enacted in 2000 and 2001, respectively. The Financial Holding Company Act
allows the establishment of a financial holding company (FHC) and investments in subsidiary
institutions that are engaged in different types of financial services such as banking, securities,
insurance, bills finance companies, venture capital companies, and asset management companies.
Since the Financial Holding Company Act was formally implemented in November 2001, in the 10
years that followed, 15 FHCs have been successively established in Taiwan.1 An important question
is whether the establishment of FHCs has achieved its strategy goal and operating synergies such as
cost savings, cross-selling as well as capital efficiency, and the potential benefit from economies of
scale and scope. To address this question, we analyzed the profit performance of the FHC by using
panel data for Taiwan from the period of 2001 to 2009.
The remainder of this paper is organized as follows: Section 2 presents a literature review of
relevant research on the effect of banking performance; Section 3 provides a description of the
empirical model and data; Section 4 shows our empirical results; and lastly, Section 5 offers a
conclusion.
Literature review
Numerous studies have discussed the issues of FHC performance, but most have compared the
performance of FHC banks and independent banks. For example, Mester (1996) examined different
1
Because the Taiwan Financial Holding Group was established in 2008 and was not a listed company, the financial
data were limited, and it was excluded from our sample. Thus, our data set comprised only 14 FHCs.
2
types of banks and showed that banks that join FHCs show better performance than independent
banks. Shen (2002) examined CAMEL indicators of 50 FHC banks and 44 independent banks in the
United States between 1997 and 1998 and found that, on average, FHC banks outperform
independent banks. Yamori et al. (2003) used both stochastic frontier analysis (SFA) and a market
valuation approach to investigate whether Japanese banks affiliated with bank holding companies
are more efficient and profitable than independent banks. Their empirical results suggest that banks
affiliated with bank holding companies are not more cost-efficient than independent banks, whereas
banks affiliated with bank holding companies are more profit-efficient than are independent banks.
Using data from Taiwan, Hsu and Chang (2005) found that FHC banks can generate better
performance than independent banks both before and after the passage of the Financial Holding
Company Act. Chen et al. (2005) employed the DEA approach to evaluate the efficiency and
productivity growth of commercial banks in Taiwan before and after the establishment of FHCs.
They collected 15 member banks of FHCs and 24 non-member banks of FHCs for the 2000-2003
period, and the results indicated that member banks of FHCs are more efficient than non-member
banks. Hsu and Huang (2006) compared the performance of FHC banks and independent banks by
using the financial ratios and factor analysis method for Taiwan’s banks between 2002 and 2004,
and the results showed that FHC banks outperform independent banks. Liu et al. (2006) used a
sample of 14 banks under an FHC and 33 independent banks, and they employed DEA to evaluate
the banks’ operating efficiency. Their results showed that banks under an FHC perform better than
independent banks for technical, pure technical, and scale efficiency. Wang et al. (2007) used a
sample of 13 subsidiary banks of FHCs for the years 1999 to 2004 to investigate the impact of
banks joining FHCs on their operating efficiency. Their results showed that the establishment of
FHCs is a benefit for most subsidiary banks. The performance and productivity of the 13 sample
banks after joining FHCs are all positive and demonstrate continued growth. However, Lin and Lee
(2010) discussed the performance difference of merger and acquisition activities of 14 FHCs in
Taiwan before and after their establishment in 2002, and they found weak evidence for the
improved performance of 14 FHCs after they began operating.
In addition, the Financial Holding Company Act represents important efforts to address
corporate governance problem. The relevant literature suggests that the board and ownership
structure plays an important role in determining a key factor of corporate governance. Although
these empirical studies focused mostly on banking firms, empirical research on FHC performance is
limited. For instance, Claessens et al. (2001) used bank-level accounting data and macroeconomic
data from 80 OECD countries for the period spanning 1988–1995 to examine the extent and effect
of foreign presence in domestic banking markets. Their empirical results showed that foreign banks
have higher profit than domestic banks in developing countries, but the opposite is true for
developed countries. Chantapong (2005) examined the performance of domestic and foreign banks
in Thailand from 1995–2000 for profitability and other characteristics after the East Asian financial
crisis, and the results showed that foreign bank profitability is higher than the average profitability
of domestic banks. Yildirim and Philippatos (2007) analyzed the cost and profit efficiency of
banking sectors in 12 transitional economies of Central and Eastern Europe (CEE) over the period
3
1993-2000 and found that foreign banks are more cost-efficient but less profit-efficient than
domestically owned private banks and state-owned banks. Micco et al. (2007) indicated that private
banks are more profitable than state-owned banks by using developing and industrialized countries
data. Lensink et al. (2008) compared the efficiency of foreign and domestic banks by applying SFA
on a sample of 2,095 banks in 105 counties. Their results showed that foreign ownership negatively
affects bank efficiency. Lin and Zhang (2009) employed a panel of Chinese banks over the
1997-2004 period to evaluate the effect of bank ownership on performance. Their empirical results
showed that the “Big Four” state-owned commercial banks are less profitable and less efficient, and
have a worse asset quality than other types of banks except “policy” banks. Furthermore, banks
undergoing foreign acquisition or public listing recorded better pre-event performance. Berger et al.
(2009) examined the efficiency effects of foreign ownership of Chinese banks over 1994–2003, and
also found the Big Four state-owned banks to be less efficient, foreign banks to be most efficient,
and minority foreign ownership to be associated with a significantly improved efficiency.
Zahra and Pearce (1989) indicated that the composition of the board of directors of different
structures affects a company's operating policies, thus influencing the company's operating
performance. Pi and Timme (1993) examined the relationship between cost efficiency and board
independence by using a sample of U.S. banks. Their results showed that when a CEO is also the
chairman of the board, the bank’s cost efficiency and return on assets decreases. Adam et al. (2010)
explained the importance of the board of directors in a company and stated that the board of
directors is fundamental in a company’s development.
Certain studies also found that bank size affects performance. Bauer et al. (1993), in a study of
U.S. banks in 1986, showed that large banks are less efficient than small banks. Miller and Noulas
(1996) employed DEA to analyze the relative technical efficiency of 201 large banks from 1984 to
1990, and found that larger and more profitable banks have higher levels of technical efficiency.
Simultaneously, however, larger banks are more likely to operate under decreasing returns to scale.
Berger and Mester (1997) analyzed 6,000 U.S. commercial banks for the period 1990-1995 and
found that large banks are more profitable and more efficient than small banks. Bosworth et al.
(2003), in a study that used 1998 data, employed DEA to examine the relationship among executive
compensation, asset size, profitability, and three measures of efficiency (overall technical, pure
technical, and scale) for the largest BHCs in the United States. They found that larger BHCs are
more efficient than smaller BHCs. Lo and Lu (2006, 2009) employed Seiford and Zhu (1999)
two-stage transformation process, including profitability and marketability performance, by
employing DEA to evaluate the performance of 14 FHCs in Taiwan, and found that large FHCs
perform better than small FHCs. Hu et al. (2009) used DEA to compare the performance of 14
FHCs in Taiwan and to demonstrate that FHCs can improve their performance by increasing their
size.
4
Empirical model and data description
Our sample is an unbalanced panel that includes financial and corporate governance annual
data of 14 FHCs during the period 2001-2009. Among the 14 FHCs, some FHCs were established
in 2002 and 2003, and because the required information is unavailable for all FHCs for every year,
our panel data set is an unbalanced panel. The main data source is from the Taiwan Economic
Journal (TEJ) database.
The empirical model is constructed as follows:
 it  f ( fin it , ownershipit , X i )
(1)
where it in Eq. (1) is the dependent variable (one of the two profitability performance indicators of
a bank: ROA and ROE) for financial holding company i in year t. ROA is defined as the ratio of net
income after tax to total assets; it represents the earning performance of a bank based on total assets.
ROE is measured as the ratio of net income after taxes to shareholder equity. It represents the
earning performance of a bank based on the stake of shareholders. The fin variable represents the
financial characteristics of FHCs, ownership represents the board and ownership structure indicators,
and X is the other control variables.
Regarding the financial characteristics of FHCs, we selected seven variables and classified
them into five categories: risk diversification, management efficiency, liquidity, and capital
adequacy and scale.2 Among them, two indicators are used to evaluate risk diversification: nonoperating expenses ratio and the Herfindhal Hirschman Index (HHI). The non-operating expenses
ratio is considered to capture the degree of financial holding reinvestment activities in those which
are not related with the operation of business and is defined as non-operating expenses divided by
total operating revenues. A high non-operating expenses ratio indicates that the degree of
reinvestment activities of an FHC is too high, result in worse the performance of an FHC. But
diversification investment in many industries may reflects better competitive advantage, tends to be
better profitable. The relationship between non-operating expenses ratio and FHC profit
performance is therefore mixed. The HHI captures the degree of business concentration of FHCs
and is calculated by summing the squared share of the assets of subsidiaries in the holding company.
Higher value of HHI implies that an FHC's business is more concentrated. We expect a positive
relationship between concentration and profits. The finance operational cost ratio is used to evaluate
the managerial efficiency indicator of FHCs. A higher ratio indicates inefficient bank management
and increases the probability of banking distress. Thus, the coefficient of finance operation cost
ratio is expected to be negative.
We used the liquidity ratio as a proxy for FHC liquidity. The liquidity ratio is defined as
2
7 indicators, as listed in Appendix A, Table A1. Because these financial ratio variables are highly correlated,
multicollinearity might be serious. To solve the problem, we applied principal components analysis (PCA) to reduce the
number of variables. We deleted the explanatory power of lower variables, only retained the explanatory power of higher
variables. We finally selected seven variables.
5
current assets divided by current liabilities. A liquidity ratio measures a company's ability to pay
short-term obligations. A higher liquidity ratio is indicative of a greater capability of the company to
pay its obligations. The coefficient of liquidity ratio is therefore expected to positive. We selected
the debt ratio and double leverage ratio as proxies for capital adequacy indicators. The debt ratio
represents the percentage of total debt by its total assets. It shows the proportion of a company's
assets that are financed through debt. A higher debt ratio indicates a lower borrowing capacity of an
FHC. The expected sign of debt ratio is negative. The double leverage ratio is the ratio of the
amount of an FHC’s long-term investment to its net worth, and is used to judge whether the FHC’s
debt ratio from reinvestment is too high. A double leverage ratio that is too high implies that a large
portion of long-term investment funding of an FHC is derived not from its own capital but from
borrowing, leading to the possibility of excessive risk. To avoid excessive debt investment, an
FHC's double leverage ratio cannot exceed 125%. We expect a negative relationship between
double leverage ratio and profits. We used the logarithm of total assets as the measure for FHC
scale.
We measured the board and ownership structure using three indicators: director ownership,
government ownership, and foreign ownership. 3 Director ownership is defined as the percentage
of common shares held by directors and supervisors of the FHC. Government ownership is defined
as the percentage of shares owned by the government. Foreign ownership is calculated as the
percentage of shares owned by foreign people and institutions.
In addition, because FHCs combine at least two different financial activities subsidiaries, such
as banking, insurance (life insurance or property insurance), securities and bills finance companies,
it is expected to generate cross-selling opportunities among various financial activities, providing an
effective way to create synergy. In our sample, all 14 FHCs have security companies, 2 of 14 FHCs
do not engage in commercial banking activities, one FHC was engaged in investment banking, and
another FHC was a bills finance company. Most FHCs had banking as its core business, and some
had securities, insurance, or bills financing as their core business. Some FHCs were already
conducting two core businesses in operations (see Appendix A, Table A2).
We also wish to investigate whether an FHC with so-called twin-engine or dual-core strategies;
that is, FHCs engaged in a two-core business strategy can improve its overall performance. To
capture the effect of dual-core strategies, we first consider three subsidiary dummy variables: Bank,
Insurance, and Bills. The relative dummy variables Bank, Insurance, and Bills take the value 1 if an
FHC owns a bank, insurance, and bills subsidiaries, respectively, and 0 otherwise. We then add
three interaction term variables (Bank × Insurance、Bank × Bills、and Insurance × Bills) in our
regression model to measure the relation between the dual-core strategy and FHC performance.
In the empirical method, we apply the ordinary least squares (OLS) method and the maximum
likelihood estimation (MLE) method for panel data modeling with random effects to estimate
coefficients. Because our model has a time-invariant variable, we cannot estimate Eq. (1) by using a
3
We also conducted analyses including the largest shareholding ratio, which measures the percentage of common
shares owned by the largest shareholder, and the result is not statistically significant. Therefore, we abandoned this
variable in this study.
6
fixed-effects estimator. Therefore, we assume that there is no correlation between the regressors and
the individual effect and use a random-effects estimator. The Hausman test can be used to select
between fixed effects and random effects. The null hypothesis of the Hausman test is a random
effect, and an alternative hypothesis is a fixed effect. The 2 result of the Hausman test is 3.267. The
null hypothesis of the Hausman test means that random effect is better than the fixed effect.
Therefore, using the random effect yields efficient results.
Empirical results
Table 1 presents means, standard deviations (SDs), maximums, and minimums of all the
regression variables. Table 2 shows the results of the correlation matrix and variance inflation
factors (VIF) of the variables. The variance inflation factor values are less than 10 for all
independent variables, indicating no significant collinearity among the independent variables. The
Pearson correlation coefficient was lower than 0.5, and the maximum was no higher than 0.65. This
indicates that these variables are not a significant collinearity problem.
[Place Table 1 about here]
[Place Table 2 about here]
Tables 3 and 4 present the OLS and panel data model with random-effects regression results,
respectively. In each table, Columns (1) to (4) show the estimation for ROA, and Columns (5) to (8)
show the results for ROE. Models (1) and (5) consider only the financial characteristics of FHCs.
Models (2) and (6) are to add the board and ownership structure indicators. The interaction terms of
different subsidiary attribution dummies are added in Models (3) and (7). Models (4) and (8) are
used to estimate the results of all independent variables.
[Place Table 3 about here]
[Place Table 4 about here]
We first discuss the results of non-operating expenses ratio on FHC performance measures.
The estimated coefficient is only significant in Model (1). The rest of the models in Tables 3 and 4
are non-significant, irrespective of whether the dependent variable is ROA or ROE. Thus, this
indicates that the impact of non-operating expenses ratio on FHC profit performance is relatively
small. Table 5 presents the non-operating expenses ratio of FHCs according to company in various
years. Most of the non-operating expenses ratio of the FHCs’ contribution to the overall revenue is
not large. The first FHC is only the best for non-operating expenses ratio. Its values continue to be
7
positive over the sample period, with an average ratio of 6.62%.
[Place Table 5 about here]
Regarding business concentration, only the coefficients of the HHI on ROA are significantly
negative. This indicates that an FHC's business is more concentrated, and tends to be less profitable.
In other words, FHCs are more profitable when engaged in financial activity that is more diverse
and widespread. Table 6, which displays the HHI of FHCs, shows that the business concentration of
Fubon, Cathay, Yuanta, and Shin Kong FHCs are lower; however, those with commercial banks as
the core business, such as E. SUN, First, Hua Nan, Chinatrust, and Taishin have a higher business
concentration.
[Place Table 6 about here]
Regarding the management efficiency indicators, the coefficient of the finance operational cost
ratio in Table 3 or Table 4 are negative and significant, indicating that if an FHC has a relatively
strong profit performance, it enjoys a lower finance operational cost ratio. When the HHI of FHCs
is small, their finance operational cost ratio is greater. For example, Fubon and Cathay and Shin
Kong Financial Holding Companies started a wider range of business activities, but their finance
operational cost ratio is relatively higher (Table 7). Although FHCs hope that by continuing the
integration through mergers and acquisitions and developing a diverse business model will achieve
cross-selling effectiveness, the cultural differences among different subsidiaries make the
integration more difficult, indicating a significantly positive correlation between business
diversification and the finance operational cost rate. To make business diversification achievable for
the synergy of cost savings (i.e., the relationship between diversification of operations and the
financial cost rate is negative), cost control is essential to FHCs.
[Place Table 7 about here]
The liquidity ratio appears only significantly positive in the ROA model. This indicates that an
increase in the FHC liquidity ratio tends to raise FHC earning performance based on total assets.
The coefficients of the logarithm of total assets are significantly positive in all the regressions,
indicating that larger FHCs tend to yield more profit.4 In other words, large FHCs experience
economies of scale. Therefore, we suggested that the integration of FHCs promotes economies of
scale and creates a greater synergetic effect. Regarding the debt ratio, the results also show that the
4
The empirical result is consistent with those obtained by Lo and Lu (2009) and Hu et al. (2009).
8
coefficient of the debt ratio only has a significantly negative effect on ROA.
Regarding the board and ownership structure variables, the coefficients on director ownership
or government ownership are not statistically significant, implying that an increase in the shares of
directors, supervisors, or government do not affect the profitability of FHCs. However, foreign
ownership has a negative significant value in Models (2), (4), and (6), indicating that a greater
number of shares of foreign capital tend to yield less profit and worsen the performance of FHCs.
This finding is inconsistent with our expectations, and is thus noteworthy. Specifically, if foreign
investment pursues only short-term profit rather than long-term investment, government
authorities should strengthen the supervision of short-term cross-border capital flow and
circumvent potential economic risks.
Finally, we estimated the effect of a dual-core strategy. The results show that the coefficients of
these estimators for the interaction terms Bank × insurance are only positive and statistically
significant. This indicates that a combination of banks and insurance may be more profitable. The
combination of a bills finance company and banks or insurance companies is weaker. In other words,
our evidence supports the notion that synergies between commercial banking and insurance
companies are stronger. This implies that an FHC with a dual-core strategy, including both
commercial banks and insurance companies, are relatively more profitable. According to the
analysis of “2010 financial holding operating performance Rankings” provided by the Taiwanese
Financial Management Association in September 15, 2010, their results also show that an FHC with
commercial banking and insurance companies is more profitable, such as Fubon and Cathay
Holding Companies, which employ the dual-core strategy of“insurance + banking.”
In summary, a well-performing FHC tends to have a lower business concentration (HHI), a
lower finance operational cost ratio, a higher liquidity ratio, larger assets, and lower debt ratios.
However, an FHC with a rising share in foreign capital leads to less profit. Moreover, the
“Insurance + Banking” dual-core strategy has a higher profit performance than the other strategies.
Conclusion
Using an unbalanced panel data set of 14 FHCs with annual observations from 2001 to 2009,
we investigated the determinants of profit performance of financial holding companies,
specifically, the effects of the board and ownership structure. The conclusions of this study are
summarized as follows: First, FHCs are more profitable when they are associated with more
diversified businesses, a lower finance operational cost ratio, a higher liquidity ratio, larger assets,
and lower debt ratios. Second, the director or government ownership does not statistically
significantly increase FHC profitability, whereas the relationship between foreign ownership and
FHC profitability is significantly negative. This shows that raising the share of foreign capital
worsens FHC performance. Finally, the dual-core strategy of “Banking + Insurance” has a higher
profit performance than other strategies. This indicates that an FHC with so-called dual-core
9
strategies, including both commercial banks and insurance companies, generate more profit.
Therefore, we suggest that integration of resources between banks and insurance companies
generates cross-selling opportunities to create synergy and enhance competitive advantages and
improve FHC performance.
10
Notes
1.
Because the Taiwan Financial Holding Group was established in 2008 and was not a listed company, the
financial data were limited, and it was excluded from our sample. Thus, our data set comprised only 14 FHCs.
2.
We also conducted analyses including the largest shareholding ratio, which measures the percentage of
common shares owned by the largest shareholder, and the result is not statistically significant. Therefore, we abandoned
this variable in this study.
3. The empirical result is consistent with those obtained by Lo and Lu (2009) and Hu et al. (2009).
11
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14
Tables
Table 1 Summary Statistics
Variable
Risk diversification
Non-operating income and
expenses ratio(%)
HHI (Hirschman-Herfindahl
index)
Managerial efficiency
Finance operational cost
ratio(%)
Liquidity
Liquidity ratio(%)
Capital adequacy
Debt ratio(%)
Double leverage ratio(%)
Scale
The logarithm of total assets
Board and ownership structure
Director ownership(%)
Government ownership(%)
Foreign ownership(%)
Mean
Standard
Deviations
Maximum
Minimum
-0.29
5.19
11.63
-42.02
0.74
0.16
0.32
1.00
63.76
26.53
155.31
8.07
270.54
194.29
1046.07
63.91
87.38
109.26
13.97
9.35
97.00
138.26
21.18
86.95
8.93
0.43
9.63
7.47
17.36
10.34
56.60
3.54
5.38
20.59
7.95
16.60
32.84
62.93
0.00
0.03
15
Table 2 Correlation Matrix and VIF value
Non-operating
HHI
expenses ratio
Finance
Liquidity Debt
operational
ratio
ratio
cost ratio
The
Double
logarithm Director Government Foreign Bank × Bank × Insurance
leverage
VIF
of total ownership ownership ownership Insurance Bills
×Bills
ratio
assets
Non-operating
expenses ratio
1.00
HHI
0.18
1.00
Finance
operational cost
ratio
0.13
-0.20
1.00
Liquidity ratio
0.07
-0.36
0.40
1.00
Debt ratio
Double leverage
ratio
The logarithm of
total assets
Director
ownership
Government
ownership
Foreign
ownership
Bank×Insurance
Bank×Bills
Insurance×Bills
0.38
0.26
0.20
0.26
1.00
-0.25
-0.08
0.12
0.02
-0.06
1.00
0.17
-0.06
0.21
0.31
0.65
-0.07
1.00
0.28
0.14
0.22
0.00
0.20
-0.09
0.12
1.00
0.19
0.21
0.06
-0.18
0.18
-0.20
0.42
0.44
1.00
-0.03
-0.14
-0.12
-0.09
0.23
0.00
0.35
-0.17
-0.09
1.00
0.17
-0.02
0.03
-0.26
0.33
0.22
0.31
-0.24
-0.10
0.28
-0.23
-0.20
0.31
0.23
0.15
-0.12
0.06
-0.03
0.63
0.29
0.30
0.41
-0.03
0.39
0.56
0.16
0.50
-0.07
0.22
-0.10
1.36
2.24
1.44
1.92
2.69
1.16
4.28
1.73
2.43
1.74
1.00
0.04
0.48
1.00
0.57
4.12
2.35
3.11
1.00
16
Table 3 Estimation Results--OLS
Dependent
variable
Non-operating
expenses ratio
HHI
Finance
operational
cost ratio
Liquidity ratio
Debt ratio
Double
leverage ratio
The logarithm
of total assets
Director
ownership
Government
ownership
ROA
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.026
(2.14)
-1.381*
(-1.81)
-0.047***
(-6.01)
0.017
(1.38)
-2.083**
(-2.84)
-0.049***
(-6.89)
0.019
(0.95)
-0.663
(-0.83)
-0.050***
(-6.32)
0.013
(0.78)
-1.376
(-1.65)
-0.050***
(-7.14)
0.188
(1.30)
-19.190
(-1.60)
-0.306***
(-4.08)
0.170
(1.36)
-22.280
(-1.72)
-0.319***
(-3.77)
0.134
(1.03)
-14.550
(-1.36)
-0.321***
(-4.05)
0.152
(1.27)
-15.680
(-1.29)
-0.323***
(-3.78)
0.002***
0.001***
0.001***
0.001***
-0.002
-0.005
-0.001
-0.005
(4.03)
-0.033***
(-3.34)
(3.41)
-0.016*
(-2.15)
(4.44)
-0.032**
(-2.91)
(3.95)
-0.018**
(-2.52)
(-0.18)
-0.077
(-0.86)
(-0.41)
-0.062
(-0.54)
(-0.14)
-0.065
(-0.82)
(-0.43)
-0.077
(-0.79)
-0.011
(-1.42)
1.123***
(3.51)
-0.008
(-1.39)
1.073**
(2.98)
0.001
(0.19)
0.008
(0.99)
-0.007
(-0.90)
0.665*
(2.09)
-0.006
(-1.01)
0.822*
(2.16)
0.0017
(0.17)
0.002
(0.21)
-0.151
(-1.22)
12.690**
(2.80)
-0.152
(-1.32)
14.710**
(2.80)
0.004
(0.05)
-0.024
(-0.25)
-0.132
(-1.19)
5.510
(1.35)
-0.147
(-1.44)
8.602
(1.59)
-0.022
(-0.20)
-0.126
(-1.38)
*
-0.014***
(-3.09)
Foreign
ownership
Bank×Insuranc
e
Bank×Bills
Insurance×Bill
s
R-squared
ROE
0.6341
0.6487
0.873**
(2.18)
0.053
(0.24)
-0.607
(-1.63)
0.6579
-0.013***
(-3.10)
0.700
(1.68)
0.165
(1.00)
-0.761**
(-2.24)
0.6643
-0.119*
(-2.14)
0.4780
0.4934
9.674**
(2.45)
5.203***
(3.62)
-6.843
(-1.68)
0.5226
-0.092
(-1.37)
9.585**
(2.46)
5.372**
(2.93)
-7.042
(-1.65)
0.5321
Note: Absolute values of t statistics (based on White heteroskedastic-consistent standard errors) are
presented in brackets.***、**and* statistically significant at the 1%、5% and 10% level, respectively.
17
Table 4 Estimation Results—random effect
ROA
Non-operating
expenses ratio
HHI
Finance
operational
cost ratio
Liquidity ratio
Debt ratio
Double
leverage ratio
The logarithm
of total assets
Director
ownership
Government
ownership
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.026
(1.14)
-1.370**
(2.23)
-0.047***
0.019
(0.93)
-2.029***
(3.14)
-0.050***
0.019
(0.70)
-0.663
(1.06)
-0.050***
0.013
(0.48)
-1.376**
(2.19)
-0.050***
0.195
(1.04)
-20.824
(1.38)
-0.310***
0.180
(0.92)
-27.052
(1.56)
-0.325***
0.134
(0.78)
-14.549
(1.22)
-0.321***
0.152
(0.82)
-15.680
(1.27)
-0.323***
(7.03)
(7.77)
(6.89)
(7.39)
(5.74)
(5.50)
(5.74)
(5.44)
0.002**
0.001
0.001***
0.001
-0.003
-0.008
-0.002
-0.005
(2.27)
-0.032
(1.20)
(1.26)
-0.015
(0.37)
(2.13)
-0.032
(1.26)
(1.31)
-0.018
(0.58)
(0.17)
-0.05
(0.54)
(0.38)
-0.015
(0.13)
(0.08)
-0.065
(0.72)
(0.25)
-0.077
(0.72)
-0.01
(1.09)
1.118***
(3.38)
-0.004
(0.48)
1.100***
(2.68)
0.005
(0.47)
0.007
(0.66)
-0.007
(0.64)
0.665
(1.23)
-0.006
(0.54)
0.822
(1.43)
0.002
(0.16)
0.002
(0.23)
-0.115
(0.89)
12.017***
(3.36)
-0.07
(0.47)
13.380**
(2.55)
0.066
(0.57)
-0.005
(0.04)
-0.132
(1.00)
5.510*
(1.72)
-0.147
(1.10)
8.602*
(1.75)
-0.021
(0.19)
-0.126
(0.78)
-0.014***
(3.06)
Foreign
ownership
Bank×Insuranc
e
Bank×Bills
Insurance×Bill
s
Wald χ2
p-value
ROE
77.92
0.00
103.99
0.00
0.873**
(2.50)
0.053
(0.15)
-0.607*
(1.92)
89.74
0.00
-0.013**
(2.15)
0.700*
(1.88)
0.165
(0.50)
-0.761**
(2.23)
133.73
0.00
-0.127**
(1.97)
41.40
0.00
63.60
0.00
9.674**
(2.31)
5.203***
(2.61)
-6.843
(1.42)
60.62
0.00
-0.092
(1.56)
9.585**
(1.96)
5.372**
(2.57)
-7.042
(1.36)
127.74
0.00
Note: Absolute values of t statistics (based on White heteroskedastic-consistent standard errors) are
presented in brackets.***、**and* statistically significant at the 1%、5% and 10% level, respectively.
18
Table 5 the non-operating income and expenses ratio of FHC by company in various years.
Company
2001
2002
2003
2004
2005
2006
2007
2008
2009
-1.15
-0.09
-3.51
4.61
-3.19
-1.71
0.55
0.50
2.33
0.67
1.32
3.55
0.29
1.45
-4.54
6.20
-0.03
-9.04
4.24
-1.50
-15.65
-0.43
-0.95
-42.02
9.91
-0.71
-13.66
0.02
-0.05
-0.37
1.14
-3.05
-1.25
0.38
-0.41
0.09
0.84
1.62
0.00
-6.19
1.77
1.03
0.36
-0.18
-0.43
-2.70
0.59
-3.22
0.09
-0.70
-1.31
-2.01
2.55
-0.06
-0.01
-2.93
-2.05
2.26
-0.41
-0.37
-2.64
-4.51
3.51
-0.08
0.09
-0.15
-1.10
5.25
-0.30
0.18
0.00
0.29
0.30
0.20
0.11
0.00
-0.05
-5.12
1.77
0.10
1.15
0.00
2.35
0.70
11.63
0.80
0.61
-2.00
0.90
0.39
8.26
-0.55
-0.66
3.95
0.75
-3.13
7.41
0.68
-1.69
2.35
-2.15
-1.04
5.48
-1.61
-1.58
-0.99
-4.50
-0.82
2.40
0.38
-0.16
2.18
3.43
0.46
6.87
2008
2009
Name
Hua Nan
Fubon
China
Development
Cathay
E. Sun
Yuanta
Mega
JihSun
Taishin
SkinKong
Waterland
SinoPac
Chinatrust
First
Table 6 The HHH of FHC by company in various years
Company
Name
Hua Nan
Fubon
China
Development
Cathay
E. Sun
Yuanta
Mega
JihSun
Taishin
SkinKong
Waterland
SinoPac
Chinatrust
First
2001
2002
2003
2004
2005
2006
2007
0.9564 0.9449 0.9451 0.8996 0.9002 0.9026 0.9268 0.9629 0.9564
0.3567 0.3415 0.3186 0.4406 0.3877 0.6216 0.6329 0.6499 0.3893
0.6612 0.6983 0.6323 0.6420 0.5537 0.5923 0.7031 0.7477 0.7275
0.4881 0.5043
0.9201
0.8026
0.8954
0.7254
0.8074
0.5573
0.8675
0.8040
0.8773
0.7327
0.8319
0.5525
0.7703
0.7621
0.7319
0.7210
0.7492
0.5166
0.7961
0.8446
0.6971
0.7270
0.7422
0.5403
0.9697
0.8234
0.6758
0.6856
0.7193
0.5301
0.9737
0.4815
0.7206
0.6900
0.7618
0.5267
0.9836
0.5072
0.7364
0.7551
0.8078
0.5314
0.9841
0.5005
0.7812
0.7021
0.8588
0.9679
0.5937
0.6023
0.8225
0.8054
0.5731
0.6168
0.8111
0.9450
0.5578
0.8761
0.7512
0.7827
0.9471
0.5931
0.9005
0.7608
0.7870
0.9558
0.6162
0.8675
0.7671
0.7509
0.9668
0.5825
0.8505
0.7594
0.7774
0.9669
0.6125
0.8227
0.7724
0.8431
0.9620
0.6268
0.7640
0.7848
0.8709
0.9605
19
Table 7 The Finance Operational Cost Ratio of FHC by company in various years
Company
2001
2002
2003
2004
2005
2006
2007
2008
2009
Name
Hua Nan
Fubon
China
Development
Cathay
E. Sun
Yuanta
Mega
JihSun
Taishin
SkinKong
Waterland
SinoPac
Chinatrust
First
72.94
74.10
31.22
141.73 55.99
73.20 76.82
37.47 155.31
52.21
76.28
96.15
59.40
85.31
18.91
59.23
85.49
11.49
59.30
83.50
19.53
57.37
86.63
77.55
64.79
89.03
8.07
89.33
94.44
100.08
75.30
64.29
63.37
91.68
34.99
38.20
54.09
58.26
90.24
26.85
34.95
51.26
50.20
90.66
40.36
55.24
53.98
74.89
91.10
65.26
76.96
60.76
131.49
88.78
55.45
33.79
59.67
56.72
93.89
62.92
37.93
70.37
81.02
92.61
47.10
12.27
44.21
90.72
53.34
98.57
96.70
42.93
44.07
42.87
91.56
24.73
37.97
40.63
104.94
40.21
89.26
29.15
40.11
34.85
51.18
73.89
88.05
42.10
51.07
40.76
49.69
77.96
90.73
44.02
63.88
80.30
51.61
53.19
91.25
39.44
65.79
46.11
52.35
64.96
99.10
123.30
71.32
46.44
64.52
35.94
92.99
13.48
54.40
49.00
63.55
20
Appendix A
Table A1 List of Financial Ratios
Variables
Variables
Operating expense / Total Assets
Equity turnover ratio= Operating revenue /
Average equity
Operating expense / Operating revenue
The logarithm of total assets
Income-expense ratio = Operating Revenue / Burden
ratio=(Non
interest
operating
Operating expense
expenditure - Non-interest income)/ Average
assets
Non-operating expenses / Operating revenue
Fixed assets / Equity
The finance operational cost ratio
Deposits / Equity ratio
Interest expense to total debt
Financial leverage ratio (debt to equity ratio)
Liquidity ratio= Current assets / Current Debt Ratio= Liabilities / Total assets
liabilities.
Gross profit margin= Gross profit / Revenue
Total equity / Total assets
Herfindhal Hirschman Index(HHI)
The double leverage ratio
21
Table A2 The Domestic Business Territory of Taiwanese FHC in 2010
Company
Name
Established
date
Hua Nan
Fubon
China
2001/12/19
2001/12/19
2001/12/28
Development
Cathay
E. Sun
Yuanta
Mega
JihSun
Taishin
2001/12/31
2002/1/28
2002/2/4
2002/2/4
2002/2/5
2002/2/18
SkinKong
Waterland
SinoPac
Chinatrust
First
2002/2/19
2002/3/26
2002/5/9
2002/5/17
2003/1/2
Investment Commercial
Life
Property Security
Banking
Banking
Insurance Insurance
*
*

*


*

*

*
*
*
*
*
*


Bills
Securities
Venture
Finance Investment Capital
Company
Trust
Company














*


Futures
Capital
Management


*


*

*

*



*

*

*
*











*
Note: An “” represents the FHC own this division and “ ” represents the core business of an FHC.
22

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