The establishment of FHCs has become popular in recent years. However,
whether establishing FHCs creates value remains controversial. At the end of 2003/Q1,
13 FHCs were established and operated for over one year in Taiwan. This work focuses
on designing a FHC whole benchmark value measurement model to assess FHC business
value, and determine whether establishing an FHC generates financial synergy. A new
financial crisis risk model is also designed to estimate FHC risk of bankruptcy at a
particular VaR. The main contributions of research are the development of the FHC
Benchmark Value Measurement Model, the Value-risk Relation Model and the ZVaR
Model, and the analysis demonstrated that FHC benefit from clear financial synergies.
However, some FHCs do not benefit from dispersed financial risk, but instead suffer
increased financial risk. The
ZVaR Model can be used to evaluate the degree to which
each FHC faces a financial risk associated crisis, and some FHCs have ZVaR values
exceeding 2.9, while for others the values are below 1.23, indicating that none of the
FHCs became insolvent. When the relationship between the values and risks of FHCs is
considered, FHCs that combine banking, insurance and security firms (such as FuBang
FHC) enjoy greater risk dispersal and high overall operational value. Notably, FHCs that
comprise only a bank and a security firm (like YuShan FHC) face increased risk and
reduced operating value. The results presented in this study can not only help FHC
managers to understand the overall operational value of their enterprises, to help control
business performance, but also to understand the risks they face. The findings also
provide a useful reference for investors when evaluating an FHC or making decisions
regarding the selection of FHC stocks as investment targets.
17 trang |
Chia sẻ: huongthu9 | Lượt xem: 534 | Lượt tải: 0
Bạn đang xem nội dung tài liệu Evaluating total operational value and associated risks of financial holding companies in Taiwan, để tải tài liệu về máy bạn click vào nút DOWNLOAD ở trên
Yugoslav Journal of Operations Research
Volume 20 (2010), Number 2, 276-292
DOI:10.2298/YJOR1002275C
EVALUATING TOTAL OPERATIONAL VALUE AND
ASSOCIATED RISKS OF FINANCIAL HOLDING
COMPANIES IN TAIWAN
Li-Hui CHEN
Associate Professor of Departments of Business Administration, College of
Management and Director of Accounting Office in Shu-Te University. Taiwan, R.O.C.
Received: August 2004 / Accepted: May 2010
Abstract: This study comprises several different parts. The first part applies a normal
benchmark valuation model established by Penman to assess the potential whole
operational values of FHCs. The second part applies the concept of measuring financial
risk as earnings variance to establish a financial risk measurement model. This model can
be used to examine the degrees of financial risk before and after FHC’s establishment,
and to distinguish different combinations of FHC based on risk diversion efficiency. The
final part of this research constructs a new value-risk relation model that can be applied
to cross-analysis for measuring total operation value of FHCs with different degrees of
financial risk. Through completion of the above steps this study will demonstrate what
combination of FHC offers the co-benefits of risk diversion and high whole operational
value.
Keywords: Financial holding companies, whole operational value, financial risk, value at risk,
value-risk relation model.
AMS Subject Classification: 91B30, 91B28.
1. INTRODUCTION
An enterprise can use external growth strategies, including mergers, joint
ventures, and the establishment of holding companies, to achieve objectives of
expansion; increasing market share in the short-term, adjusting production scope to
optimize economies of scale and operating multiple businesses to disperse operational
risk during expansion [6]. The economic advantages of mergers are not obvious [31],
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 276
owing to risks involving site, production, personnel and executive decision-making [33].
In the U.S. financial markets, the deposit-loan ratio of commercial banks is restricted;
only investment banks can underwrite securities; insurance companies can only supply
insurance products and bank branch establishment is subject to geographical limitations
[16]. These restrictions prevent financial institutions from offering timely and extensive
services, restricting financial development [20]. Accordingly, financial institutions have
established banking holding companies (BHC), which cannot disperse systematic risk
[17]. To exploit economic synergies [4], financial institutions have devised the concept of
integrating firms that operate in various financial industries as financial holding
companies (FHC). The establishment of FHCs is a new trend in financial development
[25]. Recently, the Japanese government lifted a ban on the establishment of holding
companies. The U.S. government permitted financial institutions to found FHCs, and
thus boosting financial system development. Financial firms can gain various advantages
by transforming themselves into FHCs, including being able to supply customers with
various products, enjoying economies of scale [14]; reducing overall enterprise risk and
increasing total FHC operating value, and supporting risk management [7]. The
Taiwanese government implemented the Law governing Financial Holding Companies in
June, 2001, which allowed financial institutions to transform themselves into FHCs to
increase their competitiveness.
FHCs have the following characteristics. FHCs comprise a group of financial
firms that combine into a single firm and offer distinct financial services. FHCs satisfy
customers by offering one-stop shopping services, and thus resemble a financial
supermarket [8]. FHCs have sufficient capital to support financial product research and
organizational expansion, and integrate financial institutions to increase market share;
promote sales of financial products, and boost earnings. FHCs also offer the advantages
of a BHC [14, 25]. An insurance company that has been reorganized into an FHC can
reduce its risk, and increase the operation value of its constituent parts [7]. Additionally,
financial companies that restructure themselves into FHCs exploit preferential tax rules
or offset profits against losses to reduce payable tax for the FHC to a value lower than the
total sub payable by the constituent firms. Enterprises in an FHC provide mutual support
with financing, and prepare consolidated financial statements to raise their capital
adequacy ratios [11], thus increasing operation value [27]. Restated, financial institutions
that reorganize themselves into FHCs have increased operational value [2], and
consequently should enjoy increased stock value. However, the reality is that stock
values generally reduce following FHC formation. Furthermore, the financial risk faced
by an FHC increases with changes in operating procedures [28]. The persistence of
improved business performance for FHCs is uncertain. This study thus examines
operational instability and the increased financial risk of FHCs, and measures their
overall operational value and financial risks.
This work examines Taiwan based FHCs, examines overall enterprise
operational value and financial risk in the year following establishment. Analysis is
conducted to determine the structure of the relationships between the overall operating
value and financial risk. This study has five main research objectives: first, to modify the
business whole benchmarking value measurement model developed by Penman into a
new FHC whole value measurement model adapted to evaluate the overall objective
value of an FHC; second, to obtain the earnings, book value and market value of every
FHC in Taiwan and then apply the whole value measurement model for FHCs estimate
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 277
the overall operating value for different combinations, and then compare it with that for
the corresponding FHCs. The effect of FHC establishment on overall benchmarking
value is also determined, to clarify whether FHC establishment creates financial
synergies; third, to assess Z-scores for FHCs and their individual constituent enterprises
to determine the efficiency of financial risk dispersal; fourth, to integrate the VaR (value
at risk) and Z-score models into a single financial crisis measurement model, called a
VaRZ model. The model is used to measure VaR for bankrupt FHCs; the final objective is
to perform cross analysis to determine what combination of firms in an FHC minimizes
financial risk while increasing overall operational value. The results of this study can
help FHC managers not only to understand overall operating value but also perceive
financial risk and thus adopt hedging strategies to mitigate extreme financial crises.
Additionally, this study provides a good reference for investors in FHCs making risk-
hedging decisions.
2. BIBLIOGRAPHY
An FHC is generally formed when firms merge in order to reduce operational
financial risk. Numerical methods are available for evaluating firm value. One such
method is the Asset-based Valuation Model, which applies the Net Realization Value
Method, Modified Book Value Method, Fair Market Value Method, Replacement Cost
Method and Strategy Value Method [6]. These methods focus on evaluating assets, but
ignore the influence of liabilities and net value on firm value, profitability and future cash
flows, leading to the underestimation of firm value. The second method is the Cash Flow
Discounted Model, which considers enterprise value to be the total net value of future
expected cash flows. This method uses weighted-average cost of capital (WACC) as the
discount rate [24]. The advantages of this method include its consideration of the time
value of currency, the effect of accounting method on earnings, business and financial
risk, perpetual operation value and demand for working capital. However, this method
also suffers limitations associated with the difficulty of estimating prospective cash flows
and the lack of objective methods for determining the discount period. The third method
is the Adjusted Cash Flow Discounted Method, which is based on the Cash Flow
Discounted Model, Adjusted Present Value Method and Substance Option Method. The
first method simultaneously considers all present values of equity funds and the way in
which the finance strategy influences the firm’s present value [17]. The latter method
focuses on the values of the firm’s accounts [6]. However, this method suffers the same
weaknesses as the Cash Flow Discounted Method.
Stockholder equity has an objective market value. Virtually all suppliers and
buyers in financial markets thus consider stock values in their investment decision-
making [32]; Thomson [30] considered return on equity (ROE), return on assets (ROA)
and ratio of capital to assets to measure FHC performance. As ROE or ROA increase, or
the capital to assets ratio of an FHC decreases, operational performance increases, as
does shareholder wealth. Shareholder benefits can be increased if FHCs effectively use
derivatives for hedging. Hedging strategy quality thus influences the wealth of FHC
owners [7]. Most studies on this area use a single numerical measurement of business
value, for example market value, book value or earnings. These studies assume that these
numerical values are unrelated [5], but some researchers see this assumption as false
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 278
([10]; [21]). Penman [21] generated a whole business benchmark value measurement
model that not only combined three methods for measuring firm value - discounted
dividends, value of growth opportunities and multi-step development - and also modified
the traditional appraisal method that simply selected the unit variable used to determine
the entity value. Penman claimed that book value and earnings jointly determined the
business value. Earnings are obtained from the Income Statement and book value is
obtained from the Balance Sheet, each of which reflect firm overall working situation.
The book value and earnings, reasonably weighted, correspond to the overall entity
value. When business earnings increase or book value decreases, expected ROE
increases, and future earnings can be more heavily weighted in the measurement of firm
value.
Several factors influence business, most notably financial risk [4]. High returns
motivate managers to accept high risk [26]. A business that cannot disperse financial risk
and avoid bankruptcy has a value of zero [1]. Risk measurement methods include
stationary and dynamic measurement methods; the former characterize risk using
statistical values such as probability, expected value, variation, bias and peak, while the
latter represent expected risks and variations thereof using time series. Such measurement
methods focus on business operations variation or uncertainty [22]. Value at risk (VaR) is
already an important index for assessing market risk. VaR is the greatest loss that can be
borne, within confidence limits, during a particular period in a normal market situation.
VaR can estimate risk more immediately and explicitly than can traditional methods.
Furthermore, VaR can simply express the maximum loss and associated probability.
Methods commonly used to calculate VaR include the variable-covariable method,
historical simulation method and Monte-Carlo simulation model [29]. Jackson et al. [13]
claimed that the historical simulation method was the best among these because it
accurately predicts fluctuations in the data and has an easily understandable numerical
model.
3. NUMERICAL MODEL
3.1 Variables and assumptions
The notation used for the numerical model in this paper is detailed below.
T : the end of the period in this case a season considered in the models.
τ :τ th season in the period considered by the models with τ =0,1,2,,T .
k : cost of equity capital, plus 1,such that 1+= kρ .
T
tV : equity value discounted based on perpetual dividends during period t .
τ+td
~ : expected reinvestment dividend.
τ+tX
~ : expected net earnings less dividends.
c
tX τ+
~ : expected earnings, including expected reinvestment dividends.
cd
tX τ+
~ : expected earnings combined with expected and reinvested dividend during
imP : stock market price of the i th FHC.
*iB : per book value of the i th FHC in the whole benchmark value measurement
*iX : earnings per share of the i th FHC in the whole benchmark value measurement
τjW : actual weight of the j th firm during the model period.
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 279
∗iwˆ : assessed weight of earnings after the establishment of FHC in the whole
τφ : discount factor of earnings; that is )1( −ττ ρρ .
*ˆ
iP : estimated whole benchmark value of the i th FHC in the whole benchmark
*ˆ ijp : estimated whole benchmark value of the j th subsidiaries in the i th FHC in
ijC : contribution of each of the jth subsidiaries in the i th FHC to whole benchmark
iG : accuracy of the whole benchmark value measurement model of the i th FHC.
ije : total net value of assets of the j th subsidiaries during the last season before
∗
ipV : whole benchmark value of i th FHC before its establishment in the whole
)(tPi : price of the i th firm at time t .
)(tPiΔ : vector daily loss or gain in stock prices (change in prices).
−HS
)(P
: q th probable simulated price on the first day in the future period ( )1(iP ).
tVaR1 : Value at Risk during period t .
0PV : mixed price of each subsidiary within an FHC.
)(qPV : stock price under the q th loss state of FHC.
PVΔ : mixed loss or income value of each subsidiary of the FHC.
ntVaR : VaR of a FHC with n subsidiaries at period t .
nVaR : mean VaR during the change wicket of FHC before its establishment.
1VaR : average VaR after FHC establishment.
*
VaRP : stock price of FHC, accounting for VaR and
*ˆ
iP .
lr : coefficient of each factor that explains the degree of risk, l =1,2,3,4.
EBIT : earnings before tax and interest.
TA : total assets.
NWC : net working capital, namely current assets minus current liabilities.
MVE : market value of equity, namely the product of the common stock market price
BVE : book value of equity.
TD : total liabilities.
ARE : accumulated retained earnings.
FON : outstanding quantity of common stock.
The following assumptions are made to establish the numerical model and
support effective research.
1. The price of equity is the discounted value of the dividend over an infinite
horizon, and capital cost and current earnings determine future expected
earnings.
2. The earnings and book value reflect all information regarding future
earnings and price.
3. k denotes the total interest rate of the three-month Treasury bond and the
historical risk premium of 6%.
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 280
4. The parameter used in the historical simulation method was %5=α ; the
moving wicket was 250 days and the evaluation period was the following
day.
3.2 Establishment of the whole benchmark measurement model
The business benchmark measurement model developed by Penman [21] is
applied initially to establish an FHC whole benchmark value measurement model. This
model considers FHC book value and earnings. Based on the business benchmark
measurement model, stockholder equity is the present value of future perpetual
dividends. Therefore, stockholders equity has price:
)~()1(
1
1 ∑−≡
= +
− T c
t
T
t
T
t XEV τ τ
ρ (1)
and
∑ ∑ ∑ −+≡
= = = +
−
++
T T T
t
T
ttt
c
tt dXEXE
1 1 1
]
~
)1(~[)~(
τ τ τ τ
τττ ρ (2)
Equation (1) specifies the equity value during period t as the sum of firm
discounted expected earnings, based on the equity capital cost during period τ+t .
Equation (2) demonstrates that part of the expected value results from the reinvestment of
the current dividend, and the expected earnings are the combination of growth of current
earnings and returns on reinvested dividends. As T tends to infinity, the equity value
approaches the benchmark value, *P , for which the expected earnings equals the sum of
expected net earnings and expected returns on the reinvested dividends. Using the book
value and earnings to measure the business value is more reasonable than using a single
variable, such as book value or earnings. Accordingly, Penman [21] changed Eq. (1) to:
)()1( τττττττ φ jjjjjj dXWBWP −+−= (3)
Equation (3) uses estimated weights to merge the earnings and book value into
an inner value. Based on the hypothesis of the irrelevance of MM dividends (Ross et al,
2002), Eq. (1) is substituted into Eq. (3) to yield the expected whole benchmark value of
one subsidiary company of an FHC :
)(ˆ)ˆ1(ˆ *****
*
iiiii XwBwP φ+−= (4)
The weight in Eq. (4) enables the consolidation of earnings and book value to
create a benchmark value measurement model for a firm. The weight τjW can be
obtained from Eq. (3):
)/()( ττττττ φ jjjjj BXBPW −−= (5)
According to the theorem of Penman, the weight of FHC earnings can be
assessed based on the median weight calculated during every sample period. The weight
can be expressed as:
][ˆ * τji WMedianw = (6)
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 281
The model accuracy is measured by comparing the expected whole benchmark
value from Eq. (4) with the actual market value, namely:
%100)/ˆ1( * ×−−= imiimi PPPG (7)
Accuracy exceeding 80% indicates acceptable evaluation efficiency [9]. If an
FHC has n subsidiaries, then the contribution of the jth subsidiary to the overall
operational value of the ith FHC is [24]:
∑=
=
n
j
ijijij eeC
1
/ (8)
Furthermore, the expected whole benchmark value of the FHC is:
∑ ×=
=
n
j
ijijip pCV
1
** ˆ (9)
Equation (9) describes the constructed FHC whole benchmark value
measurement model, which is based on the concept of whole benchmark value developed
by Penman.
3.3 Constructing a model for measuring financial risk
3.3.1 VaR model
The historical simulation method for determining VaR considers historical
series of stock value data and assumes that stock prices return completely during future
evaluation periods. The interval during which the prices are altered is known as the
"changing wicket", during which the daily return is the natural logarithm of the closing
price for the current day divided by that closing price. The distribution of historical net
incomes is also considered for simulating and predicting future returns. Initially, the
distribution of stock-price returns for an individual firm is simulated. )(tPi denotes the
price of the i th asset in period t , and the stock prices for the last N+1 days are simulated
. After deducting the prices on the previous two days, the )(tPiΔ values of asset N are
derived as a row of income data. This row represents the N types of income on the
following day in the future. During a changing wicket 250 days [12], the stock price
)0(iP plus historical income from )1(−Δ iP to )( NPi −Δ describe the N likely income
situations on the next day:
HS - )()0()( qPPqP iii Δ+= , q =1,2, .,N (10)
Equation (10) describes the q th situation where the simulated price is the price
on the following day in the future (Historical Simulation of S, HS-S). The distribution of
stock returns is determined for every stock price, for various %αp to evaluate the loss of
liquidation risk. If α =5%, enabling the VaR to be expressed as [12]:
)]([%51 qPpVaR it Δ= (11)
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 282
Equation (11) can determine financial VaR of an FHC. When various assets and
combinations of firms are considered, the historical income of each subsidiary must be
simulated separately. The previous simulated income distribution of an FHC is then
determined by multiplying the weighted sum of the historical simulated income of each
subsidiary by the weights determined based on the net values of the assets of each
subsidiary [3].
Consider n subsidiaries of an FHC, where 1C to nC denote the net weightings
of the assets of each subsidiary. The price of the FHC, as a combination of its
subsidiaries in the qth situation, is then:
)0()0()0( 22110 nn PCPCPCPV +++= K (12)
At time t=1(situation q ) :
))(()( 11 qPHSCqPV −= + ...))(( 22 +− qPHSC )),(( qPHSC nn −+ Nq ,...,2,1= (13)
The stock income vector of FHC is:
0)()( PVqPVqPV −=Δ , Nq ,...,2,1= (14)
PV denotes the evaluated stock price and ΔPV represents the income value of
the FHC. If ΔPV values are sorted in ascending order, the income distribution of the
portfolio on one day can be described. Given α =5%, VaR is as follows:
)]([%5 qPVpVaRnt Δ= (15)
To determine the whole risk of a subsidiary during a specific period, the
following equation is used:
TVaRVaR
T
t
ntn /
1
∑=
=
(16)
while the whole VaR of an FHC is:
TVaRVaR
T
t
t /
1
11 ∑= =
(17)
Equations (16) and (17) can evaluate the VaR of any FHC. Finally, the stock
price of the FHC can be determined using VaR:
)1( 10
* VARPVPVaR −×= (18)
Firm value varies inversely with risk [15], even when the risk and value are
simultaneously high [23]. The concept of marginal risk probability developed by Luciano
et al. [18] is applied in this study to derive the relationship between the business value
and risk of an FHC [19]. Expanding Eq. (18) into a Value-risk Relation Model yields:
α<≤= )ˆPr()( ** VaRiq PPVF (19)
Equation (19) presents the relationship between the value and risk of
establishing an FHC.
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 283
3.3.2 VaRZ model
The VaR model of FHC presented above can measure only the market risk
value, but does not indicate whether the FHC can operate perpetually. This study thus
combines Eq. (18) with the Z-score model [1] due to establish a new VaRZ model. The
model can be used to measure the marginal value of a financial risk crisis, and thus to
assess whether an FHC experiencing a financial crisis is an insolvency risk given a
certain VaR. The Z-score financial risk crisis model is:
TA
AREr
TD
MVEr
TA
NWCr
TA
EBITrZ 4321 +++= (20)
Z-score<1.23 indicates that the firm has a high probability of insolvency;
1.232.9 indicates the absence of insolvency risk.
The VaRZ model below was developed to examine whether an FHC facing financial risk
is likely to face a bankruptcy crisis:
TA
AREr
TD
FONP
r
TA
NWCr
TA
EBITrZ VaRVaR 4
*
321 +×++= (21)
The VaRZ model can be used to evaluate the liquidation risk.
4. POSITIVE RESEARCH
The models developed in Section 3 are used to demonstrate the hypotheses. Six
hypotheses are posited. Hypothesis 1 is that the new FHC whole benchmark value
measurement model can be used to objectively measure the whole operational value of an
FHC; hypothesis 2 is that the establishment of an FHC generates financial synergy;
hypothesis 3 is that the establishment of an FHC disperses financial risk; hypothesis 4 is
that the new VaRZ model can measure the degree to which an FHC risks insolvency in a
financial crisis, given a certain VaR, and hypothesis 5 is that FHCs tend to have high
value and low financial risk. Financial data were obtained for 13 FHCs for the period
2002/Q1 to 2003/Q1 were obtained to test these five hypotheses. The FHCs considered
all had an operating history exceeding) one year; included HuaNan FHC, FuBang FHC,
GuoTai FHC, KaiFa FHC, YuShan FHC, FuHua FHC, JauFeng FHC, TaiShin FHC,
ShinGuang FHC, GuoPiau FHC, JianHua FHC, JungShin FHC and JihSheng FHC.
4.1 Positive analysis
This section described the content of analysis related to the hypotheses.
Table 1: *ˆ iw of each FHC after it is established
(1) Measurement of the whole benchmark value and accuracy
Equations (5) and (6) evaluated the weight *ˆ iw of earnings of individual FHCs
listed in Table 1, to measure the benchmark value of each FHC. Table 2 lists the whole
benchmark values *iˆP of the FHCs, calculated from Eq. (4). Comparing all *iˆP values with
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 284
the market value imP gives accuracy of 87.38%. Accuracy exceeding 80% indicates that
the model has good evaluation efficiency and can be accepted (Deng and Guo, 1996).
Table 2: Accuracy degree examination for whole operational values of FHC
FHC *iˆP
imP %100)/ˆ1( * ×−−= imiimi PPPG
HuaNan 20.39 23.61 86.36%
KaiFa 18.72 13.89 65.23%
GuoPiau 8.29 7.4 87.97%
JungShin 27.91 28.97 96.34%
FuHua 11.08 10.61 95.57%
GuoTai 28.2 39.7 71.03%
YuShan 11.81 16.73 70.59%
JauFeng 15.91 17.45 91.17%
JianHua 14.94 13.83 91.97%
JihSheng 7.94 7.87 99.11%
FuBang 32.72 27.86 82.56%
ShinGuang 10.39 10.19 98.04%
TaiShin 18.22 18.22 100.00%
Mean of G -- -- 87.38%
Unit:Dollar/ per stock)
(2) Financial synergy
Equation (6) yields the median of the weights, *wˆ =0.18, of each FHC subsidiary
over a decade. This median can be considered to assess the whole benchmark value of an
FHC before its establishment. The whole benchmark value of the ith FHC before its
FHC *ˆ iw
HuaNan -0.999
FuBang -1.129
GuoTai -2.717
KaiFa -0.443
YuShan -0.237
FuHua 0.156
JauFeng -0.047
TaiShin -0.364
ShinGuang -0.101
GuoPiau 0.351
JianHua -0.183
JungShin -1.234
JihSheng 0.440
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 285
establishment is given by Eqs. (4) and (8), and is *ipV . The
*
ipV value of other FHCs are
obtained similarly. After the FHC established,
*
iˆP and
*
ipV are compared to determine
whether financial synergy exists (Table 3). Therefore, the hypothesis :oH 0ˆ ** ≤− ipi VP vs.
:1H 0ˆ ** >− ipi VP is rejected because, when 05.0=α , the P-value was smaller, being 0.037.
FHC establishment results in favorable increase in whole benchmark value.
Table 3: Whole operational values of FHCs before and after their establishment
FHC *ipV *iˆP
* * *ˆ( ) / 100%i ip ipP V V− ×
HuaNan 19.13 20.39 6.59%
KaiFa 15.82 18.72 18.33%
GuoPiau 10.77 8.29 -23.03%
JungShin 19.19 27.91 45.44%
FuHua 8.93 11.08 24.08%
GuoTai 16.59 28.2 69.98%
YuShan 13.03 11.81 -9.36%
JauFeng 15.29 15.91 4.05%
JianHua 12.72 14.94 17.45%
JihSheng 12.1 7.94 -34.38%
FuBang 17.64 32.72 85.49%
ShinGuang 12.69 10.39 -18.12%
TaiShin 13.88 18.22 31.27%
Mean -- -- 16.75%**
(t-value) -- -- (1.707982)
** 5% of Significance level (Unit: Dollar/per stock)
(3) Evaluating VaR and Z-score
This work applied the Historical Simulation method to estimate VaR, with the
estimation period being the following day in future, α of 5% and moving wicket of 250
days. Equations (16) and (17) were used to estimate the VaR, and to estimate the VaR of
every FHC. Table 4 listed the detailed outputs. Subsequently, the significant statistical
examination method verifies whether the establishment of FHC is associated with risk
reduction. According to Table 4, as well as knowledge of the VaR of each FHC before
and after establishment, the hypotheses :oH VaRafter - VaRbefore 0≤ v.s :1H VaRafter -
VaRbefore >0 are examined and statistical significance tests are performed. The P-value was
fond to be 0.0683, less than the level of significance of 10%, and thus hypothesis oH
that represented the VaR after FHC established was exceeding before it established, was
rejected.
Equation (20) assesses the Z-score, considering the weights of the subsidiaries
of each FHC, as a measure of the influence of FHCs establishment. Table 5 presents the
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 286
calculated difference between the Z-scores of established FHCs and those of the
constituent firms prior to FHC establishment. Statistical testing is conducted to determine
whether FHC establishment disperses risk. The hypotheses
: 0o after beforeH Zscore Zscore− = vs. 1 : 0after beforeH Zscore Zscore− ≠ are tested. From the table,
given a significance level of 05.0=α , the P-value was 0.0217 and thus oH was rejected,
meaning that establishing FHCs increases financial risk.
Table 4: Eestimated VaR of every FHC and their component companies
FHC Before established VaR(1) After established VaR(2)
After minus Before ((2)-
(1))
HuaNan -0.039400 -0.065400 -0.026000
FuBang -0.039500 -0.033400 0.006100
GuoTai -0.033400 -0.036800 -0.003400
KaiFa -0.039100 -0.041200 -0.002100
YuShan -0.029600 -0.043100 -0.013500
FuHua -0.035300 -0.044900 -0.009600
JauFeng -0.041200 -0.049300 -0.008100
TaiShin -0.048800 -0.048900 -0.000100
ShinGuang -0.039100 -0.059100 -0.020000
GuoPiau -0.038700 -0.051300 -0.012600
JianHua -0.045300 -0.037800 0.007500
JungShin -0.038400 -0.033500 0.004900
JihSheng -0.047600 -0.045400 0.002200
Mean -0.039646 -0.045392 -0.00575**
(t-value) -- -- (-2.003021)
** 5% of Significance level
Table 5: Evaluated Z-scores of FHCs
FHC scoreZ − [1] (before established)
scoreZ − [2]
(after established) [1]-[2]
HuaNan 2.3413 1.8987 0.4426
FuBang 9.5931 4.1643 5.4288
GuoTai 6.6144 3.2385 3.3759
KaiFa 27.8838 10.2334 17.6504
YuShan 3.6436 2.5672 1.0764
FuHua 2.1880 2.0892 0.0988
JauFeng 13.5483 2.0448 11.5035
TaiShin 1.7742 1.4397 0.3345
ShinGuang 4.9317 1.8816 3.0501
GuoPiau 32.5322 6.0346 26.4976
JianHua 4.9206 2.2694 2.6512
JungShin 39.5642 2.6133 36.9509
JihSheng 3.4290 1.6869 1.7421
Mean 11.7665 3.2432 8.5233**
(t-value) (2.6379)
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 287
** 5% of Significance level
(4) Measuring VaRZ
This work applied historical simulation to estimate VaR, over an evaluation
period of the following day in the future; α was 5% and the change wicket was 250
days. Equation (17) was used to estimate the VaR of each FHC, and Eq. (20) was used to
yield the VaRZ numerical model (as shown in Eq. (21)) for measuring financial risk for
specific VaR values. Table 6 lists the analytical results, and reveals that FuBang FHC,
GuoTai FHC, KaiFa FHC and JungShin FHC did not hit a critical point of bankruptcy
during the Asian financial crisis. Meanwhile, the other firms sampled came close to
bankruptcy. TaiShin had the highest probability of insolvency, with a VaRZ value of
close to 1.23.
Table 6:
VaRZ values of FHCs
*** VaRZ <1.23 FHC that probably was exposed to risk of bankruptcy during financial crisis.
** 1.23< VaRZ <2.9 express uncertainty
* VaRZ >2.9 express without bankruptcy crisis
(5) Value-risk relationship and cross analysis
The whole benchmark and VaR values of FHCs were determined, and Eq. (19),
the Value-risk Measurement Model, was applied to determine the FHCs operational
values given various risks, as listed in Table 7. Among the FHCs, after FHC
establishment increased the value-risk, including FuBang, KaiFa, FuHua, TaiShin,
FHC VaRZ
NanHua 1.8655**
FuBang 4.1082
GuoTai 3.1922
KaiFa 9.8339
YuShan 2.5297**
FuHua 2.0482 **
JauFeng 2.0007 **
TaiShin 1.3998 **
ShinGuang 1.8603 **
GuoPiau 5.7622
JianHua 2.2339 **
JungShin 2.5792 **
JihSheng 1.6605 **
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 288
ShinGuang, GuoPiau, JianHua and JihSheng. However, when the whole industry was
investigated, there was no clear increase in value-risk following FHC establishment,
which meant that FHC establishment could not enhance whole operational value of
financial institutions and bring benefits of financial risk dispersion. A schematic curve
was then plotted using the two dimensions of VaR, and the whole operational was
expressed in Fig. 1. FHC combinations that disperse high risk and boost operational
value were thus determined.
Table 7: the VaR difference before and after FHCs establishment
Company after before difference
NanHua 20.39 22.07 -1.68
FuBang 32.72 26.93 5.79
GuoTai 28.20 38.24 -10.04
KaiFa 15.82 13.32 2.50
YuShan 11.81 16.01 -4.20
FuHua 11.08 10.13 0.95
JauFeng 15.91 16.59 -0.68
TaiShin 18.22 17.33 0.89
ShinGuang 10.39 9.59 0.80
GuoPiau 10.77 7.10 3.67
JianHua 14.94 13.31 1.63
JungShin 27.91 28.00 -0.09
JihSheng 7.94 7.51 0.43
Mean -0.001581
t-value -0.001471
** 5% of Significance level
-30
-20
-10
0
10
20
NanHua FuBang GuoTai KaiFa YuShan FuHau JauFeng TaiShin ShinGuang GuoPiau JianHua JungShin JihSheng
Value vs. Risk
Risk Value
Fig. 1: Cross-analysis of values and risks
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 289
The values in Fig.1 represent the financial synergy associated with FHC
establishment. The figure also shows risk dispersion. A value that exceeds the datum line
(equal to zero) reveals financial synergy, and risk exceeding the datum line (equal to
zero) is dispersed. Hence, Fig. 1 shows that FuBang FHC, JianHua FHC and JungShin
FHC displayed high-risk dispersal and whole operational value. Table 7 also indicates
that only FuBang FHC had high risk- dispersal efficiency and significantly increased
whole operational value. In contrast, only YuShan FHC exhibited increased risk and
reduced whole operational value.
4.2 Positive results
The main study results are as follows.
(1) This investigation developed a business whole benchmark evaluation
model, which can be used to objectively measure the value of a FHC.
Hypothesis 1 was supported.
(2) The whole benchmark value of the business after FHC establishment was
significantly increased compared to previously, implying that FHC
establishment brings financial synergy, and hypothesis 2 thus was accepted.
(3) FHC establishment significantly increases financial risk. The risks of the
JungShin FHC, GuoPiau FHC, KaiFa FHC and JauFeng FHC were
increased by more than 10 bases. FHC establishment increased firm
financial risk, consistent with theory, and thus FHCs do not enjoy any
advantages in terms of dispersed risk during the first year following their
foundation. The related hypothesis was thus rejected.
(4) The new VaRZ model was applied to objectively measure FHC insolvency
risk. Besides FuBang FHC, GuoTai FHC, KaiFa FHC and JauFeng FHC,
which have VaRZ values exceeding 2.9, the remaining FHCs faced possible
bankruptcy crisis. The FHCs were ranked in order of descending probability
of bankruptcy as follows: TaiShin FHC, JihSheng FHC, ShinGuang FHC,
HuaNan FHC, JauFeng FHC, FuHua FHC, JianHua FHC, YuShan FHC
and JungShin FHC. The VaRZ values can be compared to actual stock
prices or earnings, enabling the VaRZ model to be used to measure FHC
financial risk. Hypothesis 4 was accepted.
(5) The Value-risk Relation Model was applied to perform cross analysis and
determine that YSFHC faces high financial risk, with reduced operating
value. Hypothesis 5 was firmly rejected.
5. CONCLUSION
The establishment of FHCs has become popular in recent years. However,
whether establishing FHCs creates value remains controversial. At the end of 2003/Q1,
13 FHCs were established and operated for over one year in Taiwan. This work focuses
on designing a FHC whole benchmark value measurement model to assess FHC business
value, and determine whether establishing an FHC generates financial synergy. A new
financial crisis risk model is also designed to estimate FHC risk of bankruptcy at a
particular VaR. The main contributions of research are the development of the FHC
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 290
Benchmark Value Measurement Model, the Value-risk Relation Model and the VaRZ
Model, and the analysis demonstrated that FHC benefit from clear financial synergies.
However, some FHCs do not benefit from dispersed financial risk, but instead suffer
increased financial risk. The VaRZ Model can be used to evaluate the degree to which
each FHC faces a financial risk associated crisis, and some FHCs have VaRZ values
exceeding 2.9, while for others the values are below 1.23, indicating that none of the
FHCs became insolvent. When the relationship between the values and risks of FHCs is
considered, FHCs that combine banking, insurance and security firms (such as FuBang
FHC) enjoy greater risk dispersal and high overall operational value. Notably, FHCs that
comprise only a bank and a security firm (like YuShan FHC) face increased risk and
reduced operating value. The results presented in this study can not only help FHC
managers to understand the overall operational value of their enterprises, to help control
business performance, but also to understand the risks they face. The findings also
provide a useful reference for investors when evaluating an FHC or making decisions
regarding the selection of FHC stocks as investment targets.
REFERENCES
[1] Altman, I.E., Corporate Financial Distress and Bankruptcy, Wiley, New York, 1993.
[2] Anne, B., and David, G.H., “Intra-group, interstate strategic income management for tax,
financial reporting, and regulatory purpose”, The Accounting Review, 76 (2001) 515-536.
[3] Beder, T. S., “VaR: seductive but dangerous”, Financial Analysts Journal, 51 (1995) 12-24.
[4] Boyd, J.H., “Bank holding company merger with non-bank financial firm: effect on the risk of
failure”, Journal of Banking and Finance, 17 (1993) 43-63.
[5] Burgstahler, D., and Dichev, I., “Earnings, adaption and equity value”, Accounting Review, 72
(1997) 187-215.
[6] Copeland, T., Koller, T., and Murrin, J., Valuation: Measuring and Managing the Value of
Companies, Wiley, New York, 1994.
[7] Cummins, J.D., Phillips, R.D., and Smith, S.D., “Derivatives and corporate risk management:
participation and volume decisions in the insurance industry”, Journal of Risk and Insurance,
68 (2001) 51-91.
[8] Demsetz, R.S., and Strahan, P.E., “Historical patterns and recent changes in the relationship
between bank holding company size and risk”, Economic Policy Review, 1 (1995) 13-26.
[9] Deng, J.L., and Guo, H., The Principle and Application of Grey Forecast Theory, Chuan Wa,
Taipei, 1996.
[10] Feltham, G.A., and Ohlson, J.A., “Valuation and clean surplus accounting for operating and
financial activities”, Contemporary Accounting Research, 11 (1995) 689-731.
[11] Gaughan, P., Mergers, Acquisitions and Corporate Restructurings, Wiley, New York, 1999.
[12] Hendricks, D., “Evaluation of value-at-risk model using historical data”, Economic Policy
Review, Federal Reserve Bank of New York, 2 (1996) 39-69.
[13] Jackson, P., Maude, D.J., and Perraudin, W., “Bank capital and value at risk”, The Journal of
Derivatives, 4 (1997) 73-89.
[14] Jerry, L.J., “Effective supervision and the evolving financial services industry”, Federal
Reserve Bank of Cleveland. Economic Commentary, June (2001) 1-6.
[15] Jianmin, J., and James, S.D., “A standard measure of risk and risk-value models”,
Management Science, 42 (1996) 1691-1705.
L.,H., Chen / Evaluating Total Operational Value and Associated Risks 291
[16] Kroszner, R.S., and Rajan, R.G., “Is the Glass-steagall Act justified? A study of the U.S.
experience with universal banking before 1933”, The American Economic Review, 84 (1994)
810-832.
[17] Laderman, E.S., The Potential Diversification and Failure Reduction Benefits of Bank
Expansion into Non-Banking Activities, FRB of San Francisco Working Papers, 2000.
[18] Luciano, E., Peccati, L., and Cifarelli, D. M., “VaR as a risk measure for multiperiod static
inventory models”, International Journal of Production Economics, 81-82 (2003) 375-384.
[19] Mark, L.B., David, M.L., and Matthew, G., Intermediate statistical methods and applications
a computer package approach, Prentice-Hall, Englewood Cliffs, 1983.
[20] Mester, L.J., “Repealing Glass-steagall: the past points the way to the future, business
review”, Business Review-Federal Reserve Bank of Philadelphia, (1996) 3-18.
[21] Penman, S.H., “Combining earnings and book value in equity valuation”, Contemporary
Accounting Research, 15 (1998) 291-324.
[22] Philippe, J., Value at risk-the new benchmark for managing financial risk, McGraw-Hill,
Singapore, 2000.
[23] Rakesh, K.S., and Martin, W., “Risk-value models”, European Journal of Operational
Research, 70 (1993) 135-149.
[24] Ross, A.S., Randolph, W.W., and Jeffrey, F.J., Corporate Finance, 6th, McGraw-Hill, New
York, 2002.
[25] Santomero, A.M., “The causes and effects of financial modernization”, Business Review-
Federal Reserve Bank of Philadelphia, (2001) 1-4.
[26] Stephen, A.R., Randolph, W.W., and Jeffrey, F.J., Corporate finance, McGraw-Hill,
Singapore, 1999.
[27] Steven, A.H., “Toiling in king Solomon’s mine: a study in business valuation for transfer tax
purposes (part II)”, Valuation of Business Interests, Taxes/ November (1998) 13-29.
[28] Sudip, D., Mai, I.D., and Kartik, R., “Executive compensation and corporate acquisition
decisions”, The Journal of Finance, 6 (2002) 2299-2336.
[29] Thomas J.L., and Neil, D.P., “Value at risk”, Financial Analysts Journal, 56 (2000) 47-67.
[30] Thomson, J.B., “Unitary thrifts: a performance analysis”, Economic Review- Federal Reserve
Bank of Cleveland, 37 (2002) 2-14.
[31] Walsh, J.P., and Ellwood, J.W., “Mergers, acquisitions, and the pruning of managerial
deadwood”, Strategic Management Journal, 12 (1991) 201-217.
[32] West, H., “Hostile bidders, long-term performance, and restructuring methods: evidence from
the UK”, American Business Review, 20 (2002) 71-81.
[33] Westergren, B., “Risk management: a neglected aspect of M&A planning”, M&A Europe,
Mar/Apr (1990) 47-52.
Các file đính kèm theo tài liệu này:
- evaluating_total_operational_value_and_associated_risks_of_f.pdf