BANKING SECTOR STABILITY AND FINANCIAL
LIBERALIZATION: SOME EVIDENCE FROM
MALAYSIA
CHOW FAH YEE* AND EU CHYE TAN **
*
Associate Professor, Faculty of Computer and Mathematical Sciences,
University Technology MARA, Shah Alam, Malaysia.
yeechowfah@yahoo.com.my
yeechowfah@salam.uitm.edu.my
**
Professor, Faculty of Economics and Administration,
University of Malaya, Kuala Lumpur, Malaysia.
tanec@um.edu.my
ABSTRACT
The stability of the banking sector is the focus of every government at this
moment. As the global financial crisis that originated from the US subprime
mortgage meltdown unfolds, each country scrambled to come up with a plan
to shore up calm and confidence. Malaysia has recovered not too long ago
from a similar banking debacle, the 1997 East Asian Financial Crisis. The
link between financial liberalization and the performance of the Malaysian
banking sector is assessed here by employing two techniques. Firstly, using
firm level data of Malaysian domestic banks to assess the allocative
efficiency of the banking sector. Specifically, it tries to determine whether
financial liberalization in Malaysia was able to promote a competitive
environment that could boost the efficiency of banks in terms of reduction
in intermediation spreads. Using cost-ratio analysis, two series of cost data
(pre liberalization and post liberalization) were calculated . Results revealed
that all save one of the banks analyzed have not become more efficient after
the banking sector is liberalized. Secondly, by mobilizing macroeconomic
data and Logistic regression technique to assess the contribution of financial
liberalization to the banking crisis of 1997. Results suggest that financial
liberalization, banks' lending rates and the ratio of M2 to foreign exchange
reserves contribute significantly to the 1997 banking sector crisis.
Keywords :
Financial Liberalization, Banking crisis, Logistic Regression,
Intermediation Spread
1
I. INTRODUCTION
The current financial meltdown that swept across the globe jolt to
memory the importance of a stable and efficient banking system. The crisis
had its roots in the “sub-prime” house mortgage sector in the United States.
It then spread to the banks which had invested in financial instruments
linked to the value of these sub-prime mortgages. In March 2008, news of a
fire-sale of one of the largest and oldest banks in USA , Bears Stearns Inc.
stunned Wall Street and pummelled global financial stocks. This was just
the beginning of the financial storm. A year later, in USA alone, there are 14
banks that are declared insolvent while the exports of numerous countries
worldwide (including Malaysia) have plummeted; thus causing factories to
shut down and workers to be laid off. The trail of destruction caused
reminisces the 1997 East Asian financial crisis (EAFC). Malaysia was one
of the countries that was severely affected then.
Besides Malaysia, Thailand, Korea, Indonesia and the Philippines were
also badly hit. The toll of the crisis was enormous as it persisted and spread
to the real sector. On average, these five economies shrank about 7.7%,
with millions of people sustaining livelihood losses (Yellen, 2007). The
East Asian financial crisis of 1997 highlighted the link between financial
liberalization and instability of the banking sector. All five countries had
deregulated their banking systems some time before the onslaught of that
banking debacle.
Malaysia went through two banking crises. The first crisis was in the mid
1980’s and the second being the 1997 EAFC. The crisis of 1980s was short
and less severe compared to the 1997 crisis. During the EAFC, the ringgit
(Malaysian currency) depreciated against the US dollar by nearly 50%,
while the stock market contracted by more than 60%. The ringgit fell from
an average of 2.42 to the US dollar in April 1997 to a low of 4.88 to the US
dollar in January 1998 ( Mohamed Ariff, 1999). Real output declined by
6.7% after 12 years of uninterrupted expansion, averaging 7.8% per annum
2
before the onslaught of the EAFC. Per capita income in nominal terms
declined by about 30% from US$4284 in 1997 to US$3018 in 1998.
This paper aims to contribute to the literature on the link between financial
liberalization and the performance of the banking sector by: a) using firm
level data of Malaysian domestic banks to assess the allocative efficiency of
the banking sector. Specifically, it tries to determine whether financial
liberalization in Malaysia was able to promote a competitive environment
that could boost the efficiency of banks in terms of reduction in
intermediation spreads; and b) mobilizing macroeconomic data to assess the
contribution of financial liberalization to the banking crisis of 1997.
II. Banking Institutions and Financial Liberalization in Malaysia
The banking system in Malaysia consists of the central bank (Bank
Negara Malaysia), banking institutions and other financial institutions as
shown in Table 1. The banking system is the largest component, accounting
for about 70% of the total assets of the Malaysian financial system (Bank
Negara Malaysia, 1999).
The banking institutions are traditionally the
largest mobilizers of deposits. Recent statistics show that they still are: in
2005, for example, the banking institutions mobilized around 83% of the
total deposits of the financial system and held about 67% of the financial
system’s total assets.
The 1997 financial crisis revealed the structural weakness of the
Malaysian financial system. Strong loan growth between 1994 – 1997,
which averaged about 25% per annum, had led to the high loan exposure of
the banking system.
In addition the underdeveloped bond market also
resulted in the banking system providing a significant portion of the private
sector financing, thereby increasing the concentration of risk in the banking
sector. The crisis also exposed the vulnerability of the finance companies,
whose business was mainly hire purchase financing and consumption credit.
Thus the industry became highly vulnerable amidst rising interest rates and
a slowdown in the economic activity. As a solution, a merger programme
3
for the finance companies was initiated in January 1998 to consolidate and
rationalize the industry.
In 1999, the domestic banks were given the flexibility to form their own
merger groups and to choose their own leader in each group to lead the
merger process. By 2001, the domestic banking sector was subsequently
merged into 10 banking groups as shown in Appendix A.
Structural
reforms after the EAFC have reshaped the financial landscape. Now
commercial banking, investment banking and Islamic banking institutions
form the nucleus of the financial system. The financial system has become
more diversified.
The number of players in the financial system have
changed significantly after the EAFC. The rationalization programme have
led to the formation of financial conglomerates.
Today all domestic
banking groups undertake full range of commercial banking, investment
banking and Islamic banking activities. The latest number of players in the
Malaysian financial sector is shown in Appendix B.
Malaysia began her path to financial liberalization on October 1978. In
line with the doctrine advocated by McKinnon (1973) and Shaw (1973),
interest rates were deregulated to promote a more liberal and competitive
financial system. However, on several occasions, the deregulation process
had to be put on hold or reversed when the economy faced adverse shocks.
Malaysia’s banking sector is considered fully liberalized on February 1991,
where each commercial bank can set its own base lending rate according to
its own cost of funds.
III. Efficiency of Malaysian Domestic Banks
A. Financial Intermediation
The most basic functions of a financial system are: firstly, to provide an
efficient payments mechanism for the whole economy and secondly
intermediating between lenders and borrowers. These basic functions are
the domain
of the banking institutions. Banks together with the other
financial intermediaries play a major role in facilitating the overall
functioning of an economy. In almost all developing economies like
4
Malaysia, banks are the major suppliers of credit to finance productive
investments and other debt-financed activities. The banking system of a
country in its sacrosanct role as an intermediary, performs the crucial task of
channelling resources from savings to investment. The greater the
financialization of savings, the greater the potential for the channelling of
savings to productitive activities; and the more efficient the system, the
better the mobilization of resources.
Table 1: The Malaysian Financial System
Financial Institutions
Financial Markets
Banking System
• Bank Negara Malaysia
• Banking institutions
- Commercial banks
(including Islamic Banks)
- Finance companies
- Merchant banks/Investment
banks
• Others
- Discount houses
- Representative office of
Foreign banks
- Offshore banks in Labuan
Non-Bank Financial Intermediaries
(NBFI)
• Provident and pension funds
• Insurance companies
( including Takaful)
• Reinsurance cos.
• Development finance institutions
• Saving institutions
- National savings bank
- Co-operatives
Money & Foreign Exchange Markets
• Money market
• Foreign exchange market
•
Capital market
• Equity market
• Bond market
- Public debt securities
- Private debt securities
Derivative markets
• Commodity Futures
• KLIBOR Futures
Offshore market
• Labuan International
Offshore Financial Centre
Other NBFI
- Unit trusts
- Universal brokers
-
Cagamas Bhd.
Credit guarantee corp.
Leasing companies
Factoring
Venture capital
Source: i): Bank Negara Malaysia, The Central Bank and the Financial
System in Malaysia, A Decade of Change, 1999
ii): Bank Negara Malaysia, Financial Stability and Payments
Systems Report, 2007
5
With financial liberalization, banks are expected to be more efficient in
their operations. With reference to the intermediation function, this means
narrowing the margin between what they pay for financial resources (the
deposit rate) and what they earn on them (the loan rate). The difference
between the deposit rate and the loan rate is referred to as the interest spread
(or interest margin). For the Malaysian banks, margin is the main source of
their profits (Cheah, 1994). In a fully liberalized environment, competition
should reduce spreads and enhance bank performance and efficiency.
According to Lin (1990), the ability of the banks to reduce their lending
margins without crippling the system’s financial health is an indicator of the
efficiency of the banking system. Hence interest margin can be regarded as
an indicator of the banks’ efficiency in financial intermediation.
B. Cost Ratio Methodology
Revell (1980) conducted a comprehensive survey on the cost of
intermediation and interest-rate margin. The main objective was to
investigate an assumed increase in the cost of intermediation in the OECD
countries. Gross earning margins (GEM) was the variable used to indicate
cost of intermediation as it represents what the customers of the banking
institutions have to pay for the services offered. In this paper, the cost-ratio
analysis procedure established by Revell
is adopted to evaluate the
efficiency of the banking system in terms of intermediation costs. By
comparing two series (pre-liberalization and post-liberalization) of cost ratio
data, we would be able to ascertain whether the banking system’s cost of
intermediation has increased or decreased over time. This in turn allows us
to infer about banks’ efficiency following financial liberalization. The ratio
of GEM over bank’s total assets is used as a proxy for the bank’s efficiency.
GEM would appear in the numerator, while the total assets of the banking
institutions would be the denominator.
The items that are extracted from the income statements of banks to
calculate the gross earning margin figures are: 1. Interest received
Interest paid
2.
3. Other income (net). Items 1 and 2 include only interest
received and paid on loans and deposits. Interest and income from other
6
sources such as investment in securities, foreign exchange operation and
other fees and commission are found in item 3. Interest margin is the
difference between item 1 and 2. While the total sum of interest margin and
item 3 gives the broadly defined margin ( gross earning margin) received by
banks in their financial operations. This sum is also commonly referred to as
the “banker’s spread”. In other words, GEM can be define as follows:
GEM = (Interest received – Interest paid) + Other Income
=
Interest margin
+
Other income
C. Data for Cost Ratio Analysis
Related data of selected banks in Malaysia are compiled from 1987 to
1997. These years are chosen based on data constraints. 1987 is the earliest
year that has data in the form of aggregation described above, or in a form,
from which the required data can be extracted from the published sources.
While, 1997 is the last year in the series due to the banking merger exercise
that followed after the financial crisis of 1997. The crisis had affected a
number of banks adversely.
There is also the issue of the bank entity itself. Banks that are chosen are
those that have been established long enough (at least from 1987 to 1997)
and have not encountered significant changes in management or mergers
during the period under study. A number of banks in the Malaysian banking
system have gone through mergers during the 1990’s. Mergers give rise to
considerable consolidation in banks’ accounts making it very difficult to
compare the banks’ accounts from one period to another, especially if
previous period is before the merger. Owing to the above constraints, only
seven of the Malaysian domestic banks are selected for a cost ratio analysis.
The main sources of statistical information are the Balance Sheets and Profit
and Loss Accounts of the banking institutions. The cost ratio series from
these banks provide an indication of the banking institutions’ efficiency over
the years, especially before and after financial liberalization.
7
D. Results of Cost Ratio Analysis
The mean (or average) intermediation spread is calculated for both the pre
liberalization years (1987 – 1991) and for the post liberalization years
(1992 – 1997). The mean GEM/Total Assets ratios are given in Table 2
below, while some descriptive statistics for these ratios are given in
Appendix C. From these mean values (as seen in Table 2), it would seem
that most of the banks (five out of seven) showed an increase in the spread
in the post liberalization period. Only banks F and D had a reduced
GEM/Total Assets ratio in the post liberalization period.
In order to
investigate if there were indeed significant changes in the mean ratios
between the pre and post-liberalization periods, statistical analysis using the
non parametric method is employed here. Non parametric test is used when
the distribution of the data is not normal.
From Table 3, the test reveals no significant difference in the GEM ratio
between pre and post liberalization periods for all banks except for Bank D.
This implies that the intermediation spread of all the banks sampled did not
change materially with financial liberalization except for one, i.e. Bank D.
Hence, the results generally indicate that a more liberalized environment in
Malaysia did not seem to have enhanced bank efficiency in terms of
reducing the bankers’ spread. Out of seven, only one bank had a lower
average intermediation spread in the post- liberalization period.
Table 2: Mean GEM/Total Assets Ratio for Both Periods
Bank Pre
( x pre )
Post
( x post )
Diff erence
( x pre − x post )
Change
A
B
C
D
E
F
G
5.2742
4.6962
3.4383
2.9766
3.5260
3.0860
3.6083
-0.6087
-0.4142
-0.3423
0.4609
-0.1319
0.2194
-0.4200
Increase
Increase
Increase
Decrease
Increase
Decrease
Increase
4.6654
4.2819
3.0960
3.4375
3.3941
3.3054
3.1883
8
Table 3: Mann-Whitney U Test Results
Bank Pre
( x pre )
Post
( x post )
Diff erence
( x pre − x post )
p-values
Results
A
B
C
D
E
F
G
5.2742
4.6962
3.4383
2.9766
3.5260
3.0860
3.6083
-0.6087
-0.4142
-0.3423
0.4609
-0.1319
0.2194
-0.4200
0.361
0.144
0.068
0.028*
0.361
0.361
0.068
Not significant
Not significant
Not significant
Significant
Not significant
Not significant
Not significant
4.6654
4.2819
3.0960
3.4375
3.3941
3.3054
3.1883
* p-value < 0.05
IV. Financial Liberalization and Banking Sector Stability
A. Financial Liberalization
Proponents of financial liberalization believe that deregulation would
bring about a host of benefits which would boost economic growth; among
them, improving the efficiency and performance of the financial system,
product innovation and lower prices. However, in the last three decades, we
have witnessed the pitfalls that a liberalized regime could bring. Amongst
them, increased use of credit to purchase assets and finance consumption,
asset price inflation and volatility and financial fragility.
Alba (1999), Akyuz (1993) and a World Bank study (1990) noted that
with financial liberalization came a generalized surge in bank lending and a
greater bank exposure to the real estate sector. While Agrawal (1992)
pointed out that financial liberalization often leads to the prices of shares ,
real estate first rising sharply, inducing many people to invest or speculate
in these markets with some funds borrowed from banks at very high real
interest rates. The prices later decline, causing many people who had earlier
borrowed at high real interest rates to become insolvent and this leaves the
banks with a large portfolio of non-performing loans which eventually
causes their insolvency.
9
B. The 1997 Financial Crisis
Various views had been put forward to explain the cause of the crisis.
Among them , poor macroeconomic management. However, as noted by
Akyuz (2004) and Jomo (2001), the majority of the East Asian countries
that were affected ( including Malaysia), have track records of sustainable
development and macroeconomic discipline. Akyuz stressed that the great
susceptibility of domestic financial condition in developing countries to
currency instability is due primarily to the existence of large stocks of
public and private debt denominated in foreign currencies. In his opinion,
this is the main reason why currency crises in emerging markets spill over to
domestic financial markets, not bad macroeconomic policies.
Then, they are those that attributed the crisis to crony capitalism. The
more plausible explanation would be the inefficient process of financial
liberalization. After Malaysia had fully liberalized her financial sector on
Feb 1991, there was a tremendous loan growth in the banking sector. For the
1992-1994 period, total annual loan growth averaged 12.2%. In 1995, the
growth rate surged to 26.8%. Together with liberalization of international
capital flows, the supply of money and credit in the economy was boosted to
unhealthy levels.
According to the 1997 Bank Negara report, large amount of foreign funds
entered in 1992 and 1993 mostly in the Kuala Lumpur stock exchange,
pushing capitalization to 375% of GDP at the end of 1993. With liquidity
abound and a bullish stock market, Malaysian consumers amassed huge
amounts of debts (loans for purchase of stocks and shares, and installment
credit for cars and properties). With the onslaught of the contagion in mid
1997, there was a large and rapid withdrawal of funds by the foreign
portfolio investors, thus contributing to the dramatic fall of the Kuala
Lumpur stock exchange index ( by 50%). The property bubble subsequently
burst. As a result, non-performing loans in the banking system rose. Before
long, the impact of the crisis was transmitted quickly to the real sector .
10
C. Empirical Studies on Banking Crises
Demirguc-Kunt and Detragiache (1998), Kamisky and Reinhart (1999)
and also Cole and Slade (1998) stressed that financial liberalization is a
contributing factor to the banking crises that had occurred. Demirguc-Kunt
and Detragiache explored empirically the relationship between banking
crises and financial liberalization in 53 countries, including Malaysia (for
1980 – 1995), using a multivariate logit framework. They found that
banking crises are more likely to occur in liberalized financial systems. The
results
derived
showed
a
number
of
factors
including
adverse
macroeconomic developments, bad economic policies as being the other
potential explanatory variables. While Kaminsky and Reinhart reported that
in 18 out of the 25 banking crises surveyed by them, the financial sector had
been liberalized.
Zhuang (2002) also tested empirically the link between financial
liberalization and bank instability. The factors considered in these studies
included both bank specific and macroeconomic variables. The ratio of M2
to foreign exchange reserves, total bank loans divided by the country’s GDP
and the current account balance have been found to affect bank stability.
V. Multivariate Logistic Regression Analysis
A. The Model
The logistic regression model can be expressed as follows:
P(Yi = 1) =
1
1 + e −Z
,
i = 1,…, N
------------ (1)
Where Z = b0 + b1X1 + b2X2 + … + bMXM
P = the probability that the observed value Y takes the value 1
N = the number of observations
X = the explanatory variables
M = the number of explanatory variables
Y = the dependent variable ;Y =1 for bank crisis period and Y= 0
for non crisis period.
11
Equation (1) above is a cumulative logistic distribution function with P
representing the probability of a bank crisis which can be estimated.
Logistic regression is appropriate when the dependent variable is grouped
into discrete states. The explanatory variables include the financial
liberalization variable and other control variables. Like most studies on
financial liberaliztion, the removal of interest rate controls is considered the
centerpiece of financial liberalization. For Malaysia, it is only on February
1, 1991 that the BLR (base lending rate) was freed from the administrative
control of the central bank.
B. Variables In The Model
In this study the control variables that capture the characteristics of the
banking system namely, ratio of M2 (currency in circulation plus demand
and time deposits) to foreign exchange reserves (an indicator of
vulnerability to sudden capital outflows) and lending rate are included. The
ratio of M2 to foreign exchange reserves is a measure of the country’s
ability to withstand the pressure of substituting foreign currency for
domestic currency by investors. A rise in the M2/ foreign exchange reserves
ratio implies a decline in the foreign currency backing of the short-term
domestic currency liabilities of the banking system. Hence this would make
the banking system vulnerable to sudden capital outflow.
Malaysia opened its capital account as part of its ongoing financial
liberalization program. With this development, there was a massive inflow
of foreign capital, in particular portfolio investment. When the crisis
precipitated in July 1997, these funds took flight easily. As noted by ChanLau (1998), the private capital flows to the five economies most affected by
the EAFC, reversed from a net inflow of US$93 billion in 1996 to a net
outflow of US$12 billion in 1997.
Lending rate is included in the model as after financial liberalization, with
the elimination of interest rates controls, banks are free to set the rates
charged on borrowing. Hence, banks could be motivated to profit from the
new found freedom of setting interest rates, as long as interest gains are
larger than the loss from the increased risk. In essence, this means that
12
unregulated financial markets can lead to higher interest rates and greater
risk- taking (Akyuz, 1993). Apart from these variables, the rate of real GDP
growth (Rgdpgrow) is also included, given that adverse shifts in the
macroeconomic condition of a country could weaken its financial sector.
Hence, for this study the Z variable in equation (1) is as follows:
Z = b0 + b1fin.lib + b2lend + b3M2tofor + b4Rgdpgrow
Where :
Z
= the logged odds of the banking crisis
finlib
= liberalized or non-liberalized period
lend
= banks’ lending rates
M2tofor = ratio of M2 to foreign exchange reserves
Rgdpgrow = rate of real GDP growth
C. Data for Logistic Regression
Logistic regression literature suggests that the sample should contain a
reasonable representation of both the alternative outcomes for the dependent
variable (which in this case is a banking crisis period or non banking crisis
period). In order to get a reasonable number of the banking crisis period,
quarterly data is used. Furthermore, 1990 is the earliest year in which
quarterly data for GDP is available. Hence, quarterly data for the period of
1990 until 2005 is used in this study. All the data needed are sourced from
the International Monetary Fund’s International Financial Statistics and
Bank Negara Malaysia’s
Quarterly Economic and Monthly Statistical
Bulletins.
D. Logistic Regression Results
Based on the model building strategies given by Hosmer and Lemeshow
(2000) and model evaluation criteria by Menard (2001), the following
model as shown in Table 4 is considered the most appropriate to address the
banking crisis hypothesis of this study. Three of the predictor variables;
13
financial liberalization (Fin Lib) , ratio of M2 to foreign exchange reserves
(M2tofor) and bank lending rates (Lend) are statistically significant. The
following two tables furnish the results of the model used while the rest are
shown in Appendix D.
The odds ratio for the financial liberalization
variable is 16.31. This implies that when all the other variables are held
constant, with financial liberalization the banking sector is 16 times more
likely to encounter a banking crisis.
Table 4: Estimation Results
Predictors
Fin. lib.
Lend
M2tofor
Constant
B
2.79
1.19
0.90
-21.13
S.E
1.37
0.54
0.33
6.76
Wald
4.18
4.85
7.46
9.78
df
1
1
1
1
Sig.
0.041**
0.028**
0.006**
0.002**
Exp (B)
16.31
3.30
2.45
0.000
Notes:
i)
ii)
iii)
iv)
v)
vi)
vii)
B
= the logistic regression coefficient
S.E.
= standard error of the coefficient
Wald = Wald statistic to test the significance of the individual coefficient
df
= degree of freedom
Sig.
= the p-value for the Wald statistic
Exp(B) = the odds ratio of the predictor variable
**
Indicates significance level at 0.05 and less
Table 5 : Classification Table for Prediction of Banking Crisis
Observed
0
Crisis
0
1
53
2
Overall percentage
Predicted
Percentage
Crisis
Correct
1
0
7
100.0
77.8
96.8
The other two variables, namely ratio of M2 to foreign exchange reserves
(m2tofor) and lending rates of the commercial banks (lend) could also raise
albeit to a smaller extent the odds of a banking crisis happening, as they
14
grow. The classification table below suggest that this model predicted the
outcome (banking crisis) very well. The percentage of the non-occurrence of
the crisis correctly predicted is 100 per cent; while the percentage of
occurrence of the crisis correctly predicted is 77.85 per cent, giving an
overall success rate of 96.8 per cent.
VI. Conclusion
The impact of financial liberalization on the performance and stability of
Malaysian banks has been assessed based on cost-ratio-analysis and logistic
regression analysis. The cost ratio analysis has revealed that all save one of
the banks analyzed, have not become more efficient (in terms of having a
lower intermediation spread) after the banking sector is fully liberalized.
This indicates that a liberalized environment was not sufficient to promote
efficiency among the banks, conversely, the banks seem to be able to keep a
larger interest spread and hence, higher profits
The logistic regression analysis showed that financial liberalization could
independently exert a negative effect on the stability of the banking sector
when other factors are controlled. Besides financial liberalization, the factors
that could contribute significantly to a banking sector crisis were the M2 to
foreign exchange reserves ratio and the banks’ lending rates. Results of the
logistic regression also suggest that capital account liberalization attracted
mobile capital that caused damaging effects to the Malaysian economy for
the period studied.
One of the views put forward to explain the EAFC was poor
macroeconomic management. Hence this study provides empirical evidence
that apart from financial liberalization, the banking crisis also has a lot to do
with the banking sector’s performance and not much with the country’s
macroeconomic condition. This is plausibly due to the adoption of financial
liberalization before putting in place an effective prudential regulatory
system. In a liberalized environment, banks may be tempted to take
excessive risks in their lending activities; overlending to the broad property
15
sector and for speculative purposes rather than for sound investment
purposes, in the absence of adequate monitoring.
This study reinforces the recommendation made by central bankers after
the EAFC; that the process of financial deregulation should be accompanied
by stronger prudential supervision and regulation. Supervision of banking
institutions is just as important if not more for non- financial public listed
firms.
Regulatory agencies should ensure that financial institutions are
properly managed, transparent in their operations and have strong risk
management. Indeed, a deregulated banking system ought to have more
not less supervision. This could not resonate more truth than now, as half
the world is in recession, in large part due to unfettered financial
liberalization.
16
References
Alba, P. et. al. (1999), ‘Volatility and Contagion in a Financailly Integrated
World’,in Asia Pacific Financial Dergulation, (eds), Brouwer, routledge,
London.
Agrawal, P. (1992), ‘Financial Liberalization and Financial Crisis in
Developing Countries’ in National Conference on Economic
Globalization; Issues, Challenges and Responses by Developing
Countries. Langkawi, Malaysia.
Akyuz, Y. ( 2004), Managing Financial Instability and Shocks in a
Globalizing World, Public Lecture, Tun Ismail Ali Chair, University of
Malaya, Kuala Lumpur, Malaysia.
Akyuz, Y. (1993), Financial Liberalization: the Key Issues, UNCTAD,
no.56.
Bank Negara Malaysia. (1999). The Central Bank and the Financial System
in Malaysia, A Decade of Change. Kuala Lumpur, Malaysia.
Bank Negara Malaysia. (2007). Financial Stability and Payments System
Report. Kuala Lumpur, Malaysia.
Chan-Lau, J. and Chen Z.H. (1998)., ‘Financial Crisis and Credit Crunch as
a Result of Inefficient Financial Intermediation-With Reference to the
Asian Financial Crisis’, IMF Working Paper, International Monetary
Fund.
Cheah, K.G. (1994)., Financial Institutions in Malaysia. Institute of Bankers
Malaysia, Kuala Lumpur, Malaysia.
Cole, D.C. and Slade, B.F.( 1998)., ‘The Crisis and Financial Sector
Reforms’, Asean Economic Bulletin , 15, 338-346.
Demergic-Kunt, A.,Detragiache, E. (1998)., Financial Liberalization and
Financial Fragility. IMF Working Paper, International Monetary Fund .
Hosmer, D.W. and Lemeshow S. (2000)., Applied Logistic Regression.
John Wiley and Sons Inc.
Jomo, K.S. (ed) (2001), Malaysian Eclipse: Economic Crisis and
Recovery. Zed Books, London
Kaminsky, G.L. and Reinhart, C.M. (1999). “ The Twin Crises: The Causes
of Banking and Balance-of-Payments Problems”, American Economic
Review, 89, 473-500.
Lin S. Y. (1990)., ‘Productivity in the Banking System’,
Journal of Economic Studies. XXVII, No.1 , 65-87.
17
Malaysian
McKinnon, R. (1973)., Money and Capital in Economic Development . The
Brookings Institution, Washington D.C.
Menard, S. (2001). Applied Logistic Regression Analysis. Sage Publications.
Mohamed, Ariff .(1999). “The Malaysian Financial Crisis: Economic
Impact and Recovery Prospects”, The Developing Economies, XXXVII 4 , 417-438.
Revell, JRS. (1980). Costs and Margins in Banking: An International
Survey. Organisation for Economic Cooperation and Development.
Shaw. E. (1973)., Financial Deepening in Economic Development. New
York: Oxford University Press.
World Bank. (1990). Financial Systems and Development, Policy and
Research Series, The World Bank.
Yee C. F. Yap B.W. and Mazni M. (2004). Financial Liberalization and
Allocative Efficiency of The Banking System. IRDC Paper, Universiti
Teknologi MARA, Kuala Lumpur , Malaysia.
Yellen, J.L.( 2007). The Asian Financial Crisis Ten Years Later: Assessing
the Past and Looking to the Future, Article downloaded from the net;
http://www.frbsf.org/news/speeches/2007/0206.html
Zhuang, J. (2002). Asian Financial Crisis: What Can an Early Warning
System Model Tell Us? ERD Working Paper No. 26, Asian Development
Bank
18
Appendix A: Merger Programme for Malaysian Domestic Banks
Original
Anchor
Banking
Group
1. Affin Bank Berhad Group
Perwira Affin Bank Bhd
Asia Commercial Finance Bhd
Perwira Affin Merchant Bank
Bhd.
2. Alliance Bank Berhad Group
Multi-Purpose Bank Berhad
3. Arab-Malaysian Bank Bhd.
Group
Arab-Malaysian Bank Berhad
Arab-Malaysian Finance Bhd
Arab-Malaysian Merchant Bhd
4. Bumiputra Commerce Bank
Berhad Group
Bumiputra Commerce Bank
Berhad
Bumiputra Commerce Finance
Berhad
Commerce International
Merchant Bankers Berhad
5. EON Bank Berhad Group
EON Bank Berhad
EON Finance Berhad
6. Hong Leong Bank Berhad
Group
Hong Leong Bank Berhad
Hong Leong Finance Berhad
7. Malayan Banking Berhad
Group
Malayan Banking Berhad
Mayban Finance Berhad
Aseambankers Malaysia Bhd
8. Public Bank Berhad Group
Public Bank Berhad
Public Finance Berhad
9. RHB Bank Berhad Group
RHB Bank Berhad
RHB Sakura Merchant
Bankers Bhd
10.Southern Bank Berhad
Group
Southern Bank Berhad
Merged with
Resultant
Merger
Entity
after
BSN Commercial Bank (M) Bhd
BSN Finance Bhd
BSN Merchant Bankers Berhad
Affin Bank Berhad
Affin ACF Finance Berhad
Affin Merchant Bank Berhad
International Bank Malaysia Bhd
Sabah Bank Berhad
Sabah Finance Berhad
Bolton Finance Berhad
Amanah Merchant Bank Berhad
Bumiputra Merchant Bankers Bhd
Alliance Bank Berhad
Alliance Finance Berhad
Alliance
Merchant
Bank
Berhad
MBf Finance Berhad
Arab-Malaysian Bank Berhad
Arab-Malaysian
Finance
Berhad
Arab-Malaysian
Merchant
Bank Berhad
Bumiputra Commerce Bank
Berhad
Bumiputra Commerce Finance
Berhad
Commerce
International
Merchant Bankers Berhad
Oriental Bank Berhad
City Finance Berhad
Perkasa Finance Berhad
Malaysian International Merchant
Bankers Berhad
EON Bank Berhad
EON Finance Berhad
Malaysian
International
Merchant Bankers Berhad
Wah Tat Bank Berhad
Credit
Corporation
Berhad
Hong Leong Bank Berhad
Hong Leong Finance Berhad
(Malaysia)
The Pacific Bank Berhad
PhileoAllied Bank (M) Berhad
Sime Finance Berhad
Kewangan Bersatu Berhad
Malayan Banking Berhad
Mayban Finance Berhad
Aseambankers
Malaysia
Berhad
Hock Hua Bank Berhad
Advance Finance Berhad
Sime Merchant Bankers Berhad
Public Bank Berhad
Public Finance Berhad
Public Merchant Bank Bhd
Delta Finance Berhad
Interfinance Berhad
RHB Bank Berhad
RHB Delta Finance Berhad
RHB
Sakura
Merchant
Bankers Berhad
Bah Hin Lee Bank Berhad
United Merchant Finance Berhad
Perdana Finance Berhad
Cempaka Finance Berhad
Perdana Merchant Bankers Bhd
Source: Bank Negara Malaysia 2001
19
Southern Bank Berhad
Southern Finance Berhad
Southern Investment Bank
Berhad
Appendix B: Malaysian Financial Sector: Number of Players
Financial Institutions
Commercial Banks
Finance Companies
Investment Banks/Merchant Banks
Universal Brokers
Discount Houses
Islamic Banks
Insurance Companies
Reinsurance Companies
Takaful Operators
Retakaful Operators
Development Financial institutions
1
1999
34
32
12
5
7
2
56
11
2
0
14
2007
22
01
14
1
0
11
41
7
8
2
13
Rationalization of finance companies and commercial banks within a banking group.
Source: Bank Negara Malaysia, Financial Stability and Payments Systems
Report 2007.
20
Appendix C: Descriptive Statistics for GEM Ratios
GEM ratios in the pre-liberalization period (1987 – 1991)
Bank Minimum
Maximum Mean
Median
A
B
C
D
E
F
G
5.4950
4.7921
3.3688
3.9005
4.1935
3.7205
3.5003
4.7476
4.2508
3.0572
3.5260
3.2432
3.2419
3.2780
3.8359
3.7920
2.8192
2.9303
2.9230
2.9157
2.7646
4.6654
4.2819
3.0960
3.4375
3.3941
3.3054
3.1883
Std.
Dev
0.6172
0.3612
0.2604
0.3592
0.5201
0.3246
0.2807
N
5
5
5
5
5
5
5
GEM ratios in the post-liberalization period (1992 – 1997)
Bank Minimum
Maximum Mean
Median
A
B
C
D
E
F
G
6.7638
5.2605
3.8811
3.2597
3.7666
3.5637
4.2003
4.9977
4.6038
3.4572
2.9857
3.5227
3.0705
3.5027
4.2320
4.1848
2.9943
2.6607
3.2290
2.6649
3.2769
5.2742
4.6962
3.4383
2.9766
3.5260
3.0860
3.6083
21
Std.
Dev
0.9128
0.3887
0.3041
0.2886
0.2231
0.4322
0.3289
N
6
6
6
6
6
6
6
Appendix D : Logistic Regression Output Using SPSS
Block 1: Method = Forward Stepwise (Likelihood Ratio)
Omnibus Tests of Model Coefficients
Chi -square
16.094
df
1
Significance
0.000
Block
16.094
1
0.000
Model
Step 2 Step
16.094
8.571
1
1
0.000
0.003
Block
24.665
2
0.000
Model
Step 3 Step
24.665
4.347
2
1
0.000
0.037
Block
29.012
3
0.000
Model
29.012
3
0.000
Step 1 Step
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1
35.270
.229
.406
2
26.669
.328
.583
3
22.352
.374
.663
Classification Table
Observed
Predicted
Crisis
Step 1
0
1
0
51
2
96.2
1
5
4
44.4
53
0
88.7
100.0
1
4
5
55.6
Overall Percentage
crisis 0
53
0
93.5
100.0
1
2
7
77.8
crisis
Overall Percentage
Step 2 crisis 0
Step 3
Percentage
correct
Overall Percentage
96.8
22
Variables in the Equation
Step
m2tofor
B
0.75
S.E.
0.24
Wald
10.17
df
1
Sig.
0.001
Exp(B)
2.113
1
constant
-9.61
2.69
12.76
1
0.000
0.000
Step
lend
1.05
0.48
4.76
1
0.029
2.85
2
m2tofor
0.79
0.30
7.03
1
0.008
2.19
constant
-18.23 6.05
9.07
1
0.003
0.000
Step
fin. lib
2.79
1.37
4.18
1
0.041
16.31
3
lend
1.19
0.54
4.85
1
0.028
3.30
m2tofor
0.90
0.33
7.46
1
0.006
2.45
constant
-21.13 6.76
9.78
1
0.002
0.000
Variables not in the equation
Step 1
variables
fin.lib
score
5.537
df
1
sig
0.019
lend
7.097
1
0.008
rgdpgrow
0.251
1
0.617
12.420
5.829
3
1
0.006
0.016
0.442
1
0.506
6.895
2
0.032
1.182
1
0.277
1.182
1
0.277
Overall statistics
Step 2 variables
fin.lib
rgdpgrow
Overall statistics
Step 3
variables
rgdpgrow
Overall statistics
23