Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Unsystematic Risk and Bank Problems in Malaysia
Faoziah Idris
This paper examines the financial problem in commercial banks and finance
companies in Malaysia using logistic regression (LR). A comparison
between these two groups of the financial institutions was made using bank
unsystematic variables. . This study covers the period from 1988 to 1999
with a sample of 30 commercial banks and 36 financial companies. The
findings indicated that the rate of problem status using LR is 93% for
commercial banks and 95% for financial companies. On the other hand,
seven variables were found significant in finance companies, they are
DEPIB (ratio of deposit and placement of bank), LIQ (liquid asset-liquid
liabilities to shareholder funds) PSIZE( log of total asset), RREM( ratio of
total director remuneration to total assets), NIITL(ratio of net interest income
to total loan, LOANCON (ratio of loan concentration, real estate
constructions, purchase securities, credit consumption and purchase of
landed property to total loan) and LLPNPL (loan loss provision to
NPL).Among the portfolio of financial ratios used, DFLA (ratio of liquid funds
to total assets), EARNING (ratio of interest income to earning assets minus
interest expenses on interest bearing liabilities) and NPLDPTL (NPL to ratio
of deposit to total loan) were found to be significant in commercial banks.
Key words: bank problem, logit analysis, too-Big-to-fail (TBTF) doctrine
1.0 Introduction
The financial health of the banking industry plays a vital role for the economic
stability and growth. Hence, the assessment of a bank’s financial condition is a
fundamental goal for the regulators in banking supervision. This assessment
includes the prediction of bank problem and the provision of valuable information
about troubled banks with sufficient lead time for the regulators and management
to take preventive or remedial action at problem banks. In the past two decades,
many countries have experienced significant problem or problem in the financial
sector. The Malaysian banking sector was not spared from this phenomenon and
suffered from the financial crisis in 1997-98. As a result, in 2000, Bank Negara
Malaysia, the central bank of Malaysia, intervened on behalf of the government
by putting the financial sector consolidation as an important agenda for improving
the soundness of the financial system through strengthening preemptive and
prudential regulations.
This study attempts to derive an estimate for the probability of a bank with a
given set of characteristics falling into problem or non-problem. Logistic
regression (LR) was used following similar studies that used logit regressions in
distinguishing “good” and “bad” banks (Dimitras, Zanakis & Zopounidis, 1996;
Lane, 1986; and Martin, 1977).
__________________________________
Faoziah Idris, Faculty of Finance and Banking, University Utara Malaysia,
Email: faoziah@uum.edu.my
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Secondly, the study sought to understand the common fundamentals and
characteristics that govern bank problem and non-problem.
Lastly, we
investigated the causes of bank problem using selected financial ratios. We also
investigated the relationships of these ratios with the size of the bank since it was
commonly postulated that larger–sized banks would be less likely to fail. This
perception is based upon the higher possibility of a government bailout or rescue
to a bigger bank that was in problem rather than a smaller-sized counterpart.
2.0 Related Studies
Many factors contribute to the causes of bank problems. Among them were
factors such as business cycles, firm-specific or sector–specific events pertaining
to the market structure (see Berger & DeYoung, 1997; Fisher, Gueyie & Ortiz,
2000; Galloway, Lee & Roden, 1997; Neal, 1996). However, as to date, there is
no problem theory used in the previous studies that elaborated and focused
specifically on the causes of bank probelm. Apparently, most studies on the
banking problem are much related to the theory of financial intermediation and
the frameworks of public theory “Too Big to Fail” by Kane (1988). The financial
intermediation theory was seen as are much related function in the study of bank
problem, because it mobilized the domestic financial resources through a variety
of instruments, such as the facilitation of credit to productive activities, and as the
depository of the nation’s savings.
There are several reasons that contribute to the problem in the prediction
of bank problem or corporate problem or “business failure”. Firstly, bank problem
could involve business failure at various levels and large bail-out or bank
nationalization (Demirguc-Kunt & Detragiache, 1977) or a large non-performing
loan problem. Consequently, the problem of one bank will lead to a downward
spiral for the whole economy of a country via the “contagion-effects” where the
cost of failure of a bank with a large network of related companies may also
cause the financial system problem. As such, early and accurate bank problem
predictions will enable preventive and corrective actions to be taken in to prevent
failure. Secondly, the stronger competition among banks has made the
government to impose regulation in many countries to overcome bankruptcy or
increasing failure rates. Finally, The New Basel Capital Accord, which replaced
the original Capital Accord of 1988, effective in most countries in 2005 allowed
banks to use their own internal systems in order to determine their adequate risk
equity coverage. Hence, the New Capital Accord creates a great incentive for
banks to develop their own internal risk assessment models including prediction
of bank problem.
The study of the unsystematic risk begins with the investigation of
management quality that had played a big role in determining the future direction
of the bank. Theoretically, the management quality of a bank is strongly related
to its asset quality. The management has an overview of a bank’s operations,
manages the quality of loans and has to ensure that the bank is profitable. In
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
doing so, the management sets the profitability objectives and the risk levels to
be undertaken by the bank’s supervision. For example, a factor that problem
banks seem to have in common is the problems that they have in their loan
portfolio. Therefore, these types of banks have inadequate control systems in
monitoring and spotting problem loans. Perhaps another reason to this is the
management may be too aggressive in expanding the bank’s loans by over
lending and does not follow their loan policies strictly and stringently. At the same
time, the management seems unable to screen and monitor borrowers through
numerous credit exception rules on loan portfolio. This would likely result banks
to have more risky loan exposure. The first study that adopted the ratio of
earning assets to total assets as a proxy for measuring management efficiency in
relation to net interest margins was by (Angbazo, 1997). The study indicated that
the high exposure of risk taking in credit risk and interest rate by the
management led to lower net interest margin that affected the growth of revenue
and profitability of the bank.
We define adequately capitalized banks as those banks with at least 5.5
percent primary (tier 1) capital to total assets ratio for the whole sample period.
This capital threshold is consistent with regulatory standards for adequate
capitalization (Jagtiani, J.,Kalori,C. Lemieux, and H.Shin, 2003). It is an
advantage to use the threshold for adequate capital, since capital deficiency
could be a core fundamental in identifying and providing an early signal of bank
problem. Furthermore, it allows for the possibility that two banks with an equally
risk would be in a different standing if one has a set aside reserve to cover for a
significant amount of the problem loans, or if it has a higher level of adequate
capital. (See Appendix 3)
3.0 Description of the Data
The bank level data used for this study comprised of selected balance sheet, and
profit and loss item of financial institutions in Malaysia. The external data
represented statistics on banking industry and selected macroeconomic
variables. The financial items were extracted from the annual reports of 30
commercial banks and 36 finance companies operating in 1988-1999. They were
then computed into relevant ratios. These amounted to twenty five ratio
variables. All were considered in the setting up of the problem prediction models.
(See Appendix 1)
4.0 Methodology
The logit is one of the most commonly employed parameter in detecting potential
failure risk. The logit model assumes that there is an underlying response
variable Zi defined by the regression relationship. This model was adopted from
Ohlson (1980) and Gujerati (1995, p.554). It formulated a multiple regression
model consisting of a combination of variables, which best distinguished problem
and the non-problem banks using the formula:
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Pi = E(Y = 1| X1i, X2i, … Xki) =
1
1  e Z i
(1)
where Zi = 0 + 1X1i + 2X2i + … + k Xki, Y = 1 representing problem banks,
Pi then represents the probability of problemed banks, Xhi = financial ratio h of
firm I, h = 1, …, k. Zi ranges from –∞ to ∞. Equation (1) is known as the
cumulative logistic distribution function.
This study estimated two logit models using maximum likelihood. Each
sample bank was assigned a dummy value, Y = 0 (non-problem) using the ratio
of primary capital to total assets greater than or equal to 5.5 percent, and Y = 1
(problem) otherwise.
Both models estimated the probabilities of problem institutions from a
sample of 30 commercial banks, and 36 finance companies, respectively. Here
we wanted to investigate and highlight which variables were common to
problems commercial banks and problem finance companies. By doing so, we
could identify the characteristics that were valuable in determining problem in
both types of institutions, regardless to their function and specialization.
5.0 Results
5.0.1 The Descriptive Statistics
Table 1.1 present the descriptive results for both financial institutions using
twenty-five independent variables from the various attributes to capture variation
in bank risk (such as liquidity risk, market risk, credit risk) and external factor of
economic variables (such as GDP and CPI). The descriptive result shows both
commercial banks and finance companies exhibit the same variables that score
the highest means. Both institutions consistently capture P-size as the highest
mean score of 22.49(CB) and respectively follow by 20.99(FC) and NPLDPTL of
20.87 (CB), 19.42(FC), INTCO 0.943(CB) and 0.964(FC). Apparently, among
the highest mean score of the independent variables are from bank specific
characteristics variables such as P-size, NPLDPTL and INTCO. Out of the
twenty-five variables only two external variable of CPI represent the
macroeconomic attributes and BTLDGP. As such, the descriptive results suggest
that bank problem could be determined by P-size, NPLDPTL, INTCO, CPI and
BTLDGP. As a whole, the result shows the bank specific characteristics
variables and external variables are important variables to develop lojit models of
bank problem.
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Table 1.1 Descriptive Statistics Overall Commercial Bank and Finance Companies
Variables
CA
LF LOANS
INTCO
LAS
DEPPUB
DEPIB
DINTDEP
DLFA
CPI
INTRS
BTLGDP
PSIZE
POPA
PIDI
PSEXP
PTOI
PMGT
IMPLICIT
INTRATE
EARNINGS
NIITL
LOANCON
NPLDPTL
LLPNPL
LLPEQUITY
Valid N (listwise)
Commercial Bank
Mean
Std. Deviation
0.053
0.016
0.058
0.080
0.943
0.227
0.450
0.088
0.491
0.118
0.130
0.098
0.056
0.052
0.107
0.056
4.367
0.113
2.007
0.210
0.656
0.137
22.495
1.522
0.018
0.011
0.392
0.088
0.0003
0.001
0.607
0.103
0.539
0.100
0.016
0.011
0.859
0.974
0.059
0.033
0.048
0.021
0.346
0.095
20.877
0.904
0.235
0.075
0.890
0.305
Finance Companies
Mean
Std. Deviation
0.047
0.052
0.046
0.118
0.964
0.117
0.506
0.059
0.564
0.111
0.117
0.133
0.062
0.015
0.114
0.083
4.388
0.113
2.010
0.201
0.285
0.078
20.992
1.747
0.015
0.014
0.456
0.061
0.0003
0.001
0.675
0.039
0.578
0.050
0.0258
0.019
1.902
0.907
0.063
0.085
0.056
0.051
0.340
0.126
19.417
1.422
0.153
0.064
0.655
0.373
5.0.2 The Descriptive Statistics of Variables Used to Estimate Logit Model
Table 1.2 and table 1.3 reports mean score value results for (FC) problem vs.
(FC) non-problem and (CB) problem vs non-problem (CB) which includes the
twenty five variables of the unsystematic risk to explain bank problem. Again, the
results explain almost the same variables as compare to Table 1.1. Similarly,
both descriptive statistics show P-size as the highest mean score of problem and
non-problem finance companies and Commercial bank. For example, P-size
problem (FC) is (20.99) and non-problem P-size (FC) (19.90). On the other hand
P-size problem (CB) is (22.495) and non-problem (CB) P-size is (21.997). The
second highest mean problem (FC) is NPLDT (19.417) and non-problem (FC) is
(17.39), non-problem (CB) is (19.388), and problem (CB) is (20.877) and follows
by problem (FC) is CPI (4.388) and (4.416), and non-problem(CB) is (4.399).and
problem (CB) is 94.367). The result again consistently shows P-size, NPLDT and
CPI as important variables that could be explained finance companies and
commercial bank problem.
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Table 1.3 Descriptive Statistics FC Problem and Non Problem
Variables
CA
LF LOANS
INTCO
LAS
DEPPUB
DEPIB
DINTDEP
DLFA
CPI
INTRS
BTLGDP
PSIZE
POPA
PIDI
RREM
PTOI
PMGT
IMPLICIT
INTRATE
EARNING
NIITL
LOANCON
NPLDPTL
LLPNPL
LLPEQUITY
Valid N (listwise)
problem
Mean
0.047
0.046
0.964
0.506
0.564
0.117
0.062
0.114
4.388
2.010
0.285
20.992
0.015
0.456
0.0003
0.675
0.578
0.026
1.902
0.063
0.056
0.340
19.417
0.153
0.655
Std. Deviation
0.052
0.118
0.117
0.059
0.111
0.133
0.015
0.083
0.113
0.201
0.078
1.747
0.014
0.061
0.001
0.039
0.050
0.019
0.907
0.085
0.051
0.125
1.422
0.064
0.373
Non problem
Mean
Std. Deviation
0.0763
0.028
0.092
0.2694
0.959
0.109
0.500
1.083
0.552
0.102
0.123
0.122
0.062
0.017
0.120
0.089
4.416
0.120
2.022
0.218
0.304
0.083
19.908
0.994
0.0123
0.015
0.475
0.075
0.0004
0.0005
0.682
0.013
0.574
0.056
0.030
0.030
1.667
0.716
0.051
0.024
0.053
0.039
0.333
0.164
17.390
1.095
0.139
0.115
0.828
0.466
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Table 1.3 Descriptive Statistics CB Problem and Non-Problems
problem
Variables
CA
Non problem
Mean
0.053
Std. Deviation
0.016
Mean
0.081
Std. Deviation
0.039
LF LOANS
0.058
0.080
0.065
0.114
INTCO
0.943
0.227
1.002
0.163
LAS
0.450
0.088
0.466
0.064
DEPPUB
0.491
0.118
0.532
0.073
DEPIB
0.130
0.098
0.099
0.086
DINTDEP
0.056
0.052
0.050
0.014
DLFA
0107
0.056
0.119
0.080
CPI
4.367
0.113
4.399
0.331
INTRS
2.007
0.210
2.010
0.219
BTLGDP
0.557
0.137
0.716
0.154
PSIZE
22.495
1.522
21.997
1.353
POPA
0.018
0.011
0.018
0.013
PIDI
0.392
0.0878
0.412
0.116
PSEXP
.0003
0.001
0.0003
0.0004
PTOI
0.607
.103
0.640
0.042
PMGT
0.539
0.100
0.555
0.077
IMPLICIT
0.016
0.011
0.016
0.014
INTRATE
0.859
0.974
0.680
0.999
EARNINGS
0.059
0.033
0.051
0.050
NIITL
0.048
0.021
0.487
3.091
LOANCON
0.346
0.095
0.390
0.117
NPLDPTL
20.877
0.904
19.388
1.402
LLPNPL
0.235
0.075
0.221
0.230
LLPEQUITY
0.890
0.305
0.850
0.647
Valid N (listwise)
Table 1.4: The Significant Variables of Commercial Bank
Financial ratio
DFLA
EARNING
NPLDPTL
problem CB
Mean
0.083
0.038
19.570
Non-problem CB
SD
.0.051
0.027
1.435
Mean
0.089
0.034
19.390
SD
.0.051
0.020
1.402
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Table 1.5: The Significant Variables of Finance Companies
Financial ratio
DEPIB
PSIZE
RREM
LIQ
NIITL
LOANCON
LLPNPL
problemFC
Non-problem FC
Mean
0.117
20.992
0.000
1.902
SD
0.133
1.746
0.000
0.906
Mean
0.157
21.447
0.000
1.699
SD
0.097
1.536
0.000
0.698
0.056
0.334
0.655
0.500
0.125
0.378
0.045
0.334
0.148
0.020
0.144
0.091
Table 1.4 shows the results of the significant variables used in the study. Most of
the financial variables used are related to the liquidity risk and credit risk. The
results indicate three variables are found significant in the commercial bank.
They are DFLA, EARNING and NPLDTL. DFLA represent of (Ratio of liquid
funds (cash and short term to total assets) is the overall measure of liquidity risk
that would give a signal of the healthy bank. This implies that liquid funds are
significant important in order to reduce bank exposure to liquidity risk. One
possible reason identified in the theoretical literature is that the bank is motivated
to hold liquid assets (particularly cash and securities) to protect against
unexpected withdrawal by depositors or draw downs by
the borrowers
(Saidenberg and Strahnan, 1999), which could to bank run. Commonly, bank
problem is often characterized by runs on banks, where depositors withdraw a
large of amount of funds from a large number of intermediaries (Beng and Ying,
2001). On the other hand, EARNING has a significant and negative impact on
the non-problem bank. It represent the ratio of interest income to earning asset
minus interest expense on interest bearing liabilities indicates on average about
0.038 and 0.034 of the total assets in the commercial bank is earning interest.
NPLDTL represent of Non- performing loans to ratio of deposits to total loans and
the average mean are 19.570 to 19.390. the results indicates that the higher
ratios of non-performing loans are associated with a higher probability of bank
problem.
Table 1.5 shows that the significant variables of finance company are different
form the significant variables found in the commercial bank. They are seven
variables found to be significant in the sample they are P-SIZE, LIQ, LOANCON,
DEPIB, RREM, and NIITL AND LLPNPL.
Among the highest score mean are,
P-SIZE (20.00) and (21.447), LIQ (1.902) and (1.70), LOANCON (0.334) and
(0.334). The results supports and consider bank sizes are important variable.
Generally, banks are divided into different sizes base on assets size and average
capital ratio and bank would significantly affect to the growth rate of total loans.
As larger bank are better and able to diversity their loan portfolios, thus reducing
their assets risk (Calomiris and Mason, 2000). A study of comparative analysis
bank failures and fundamental by (Arena, 2005) also used bank size as a
measure of total assets as a bank-level fundamental and found that foreign
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
banks are perceived as more stables and safer than domestic banks, because
they may be able to resort to upstream financing from the mother institutions,
which could contribute to stabilize the supply of credit, in particular during bad
times, and they behave a much more stable deposits base. She found that no
big bank in term of size failed in the banking crises in East Asia and Latin
America during the nineties. As such, banks with smallest assets and lowest
capital leverage ratio are most affected by financial problem. Hence, it indicate
that PSIZE as important dimensions of bank activity and as a proxy for too big to
fail where a large banks may be less likely to fail, given the expected relative
advantage of large banks of diversifying risk taking .The log of bank assets,
which represents the size of bank, is almost similar on average, for problem bank
and for non problem banks throughout the sample period. Therefore, we expect
that large banks may be less likely to fail, given the expected relative advantage
of large banks, such as, in raising new capital, alleviating illiquidity, and
diversifying risk. For example, commercial banks with higher levels of assets
would be in a more secure financial position, even if they have the same high
level of non-performance loan.
The second highest score mean and significant variable is LIQ (liquid asset
minus liquid liabilities to total shareholder’s fund) which indicates that finance
companies are not exposed into high fractions of liquid assets and exposure to
liquidity risk is high and will increase the probability of problem. The finance
companies LLPNPL (Loan loss provision to Non-performing loans) represent of
ratio loan loss provision and mostly been allocated to non-performing loans.
Therefore a higher ratio of LLPNPL in particular is associated with a higher
probability of problem. The LOANCON indicates loan concentration, real estate,
construction, purchase of securities, credit consumption and purchase of landed
property) to total loans. The LOANCON results are associated with a higher
probability of problem, that real estate loans were quite risky. The study suggests
that credit risk was a problem in the finance companies.
The LLP/NPL (loan loss provision to Non-performing loans) considered a
traditional proxy to measure of assets quality. LLP/NPL is expected to be
positively related to risk of bank problem as such a higher means of ratio nonperforming loan to total asset also have a positive impact on bank problem. The
study, estimate that loans were classified as a bad loans only if they had been in
arrears for six month or more, and banks would frequently restructure such loans
to reduce the size of reported portfolio problems (Lindgren et al.1999) and
(Arena, 2005), (Ahmad, 2003).
The result in (Table 1.6) indicates that the logit models are highly
significant and the model shows a good predictive power and able to correctly
predict 98.9%of the problem commercial bank and 44.4% of the non-problem
commercial bank in the sample. Overall the model could predict 94.9 of the
sample.
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Proceedings of 23rd International Business Research Conference
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. Table 1.6 Classification Table (The Cut –Off Is 0.05) Commercial Bank
Predicted
Observed
Step 3
Non problem
problem
Overall Percentage
Non problem
70
5
problem
1
4
Percentage Correct
(%)
98.6
44.4
92.5
Table 1.7: Classification Table (The Cut –off is 0.05) Finance Companies
Predicted
Observed
Step 7
Non problem
problem
Overall Percentage
Non problem
33
2
problem
2
42
Percentage Correct (%)
94.3
95.5
94.9
Table 1.7 indicates the model is able to correctly predict 95.5% of problem
finance companies and 94.3% of the non-problem companies in the sample.
Overall the model could correctly predict 94.9 of the sample. Note that table 1.6
and Table 1.7 are based on the observations of commercial bank and finance
companies for the period (1988 to 1999). The total number of institutions in the
model did not match with the number mentioned earlier. For example, we studied
35 finance companies and 30 commercial bank, but modeled 81 and 85
commercial bank.
Both finance companies and commercial bank were
consolidated and merged and eventually prior to merging there were 81 of (FC)
and 80 (CB)
6.0 Conclusions
This study provides a test of problem prediction models in Malaysia. The result
indicates that finance companies which are smaller size in assets are more
problem than commercial bank that is considered larger-sized. The results are
contrary to the finding studied by (Bongini, 2000) on the sample of financial
intermediaries in Korea where, the percentage of problemed institution was
smaller among smaller-sized than the larger-sized institutions.
The results support the hypothesis that the “Too –Big-to- Fail” Doctrine
applied by the central bank in Malaysia when they believe that some event will
result in severe economic problem. As such, the adopted of a doctrine too-big-tofail was successfully safeguards the big banks from problem. Specifically,
estimating a logit model proves that the probability of problem is systematically
smaller for the institutions that have larger-sizes in assets. As a result, those
institutions that have larger-sized in assets stands a better chance to survive in
the financial intermediaries. It is proved that smaller institutions find it harder to
compensate a deposit drain by promptly collecting funds in the wholesaler
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Proceedings of 23rd International Business Research Conference
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financial market. In the event of systemic crisis, smaller-sized financial
institutions are more likely to experience liquidity problem and to be unable to
solve problem either in the market or with the help of the authorities. These
findings are consistent with (Kasyap and Stein, 1997) which provide that
monetary restriction hurt smaller banks more than larger one.
All in all, the finding indicate that the internal factors contributing the most
factors to bank problem and finds that the macroeconomic variables does not
explain the likehood of problem. Changing in bank unsystematic variable factor
is found to add to bank problem of both commercial banks and finance
companies. Thus, the bank-level fundamental is significantly affected the
likehood of bank problem. The study supports the view that the problem banks in
the unsystematic banking crisis fundamental also contributes to weakness in
their assets quality, liquidity and capital structures prior to the on set of the crises.
References
Angbazo, L. (1997). “Commercial bank net interest margins, default risks,
interest rate risks and off-balance sheet banking”, Journal of Banking
and Finance, 21, pg 55 – 87.
Arena, M. (2005). “Bank failures and bank fundamentals: a comparative
analysis of Latin America and East Asia during the nineties using bank
level data”, Bank of Canada Working Paper 2005-19.
Berger, A. N. & DeYoung, R. (1997). “Problem loans and cost efficiency in
commercial banks”, Journal of Banking and Finance,21, pg 849 – 870.
Bongini, P., Ferri, G. & Kang, T. S. (2000). “Financial intermediary in the
Republic of Korea: small is beautiful?”, World Bank working Paper.
Demirguc-Kunt, A. & Detragiache, E. (1998). “The determinants of banking
crises in developing and developed countries”, IMF Staff Papers,
45(1), pg 81 – 109.
Dimitras, A. I., Zanakis, S., & Zopounidis, C. (1996). “A survey of business
failures with an emphasis on prediction methods and industrial
applications” European Journal of Operational Research, 90, pg 487513.
Espahbodi, P. (1991). “Identification of problem banks and binary choice
models”, Journal of Banking and Finance, 15, pg 53 – 71.
Fisher, K. P., Gueyie, J. P. & Ortiz, E. (2000). “Risk taking and charter value
of commercial banks’ from the NAFTA countries”, Paper presented at
the 1st International Banking and Finance Conference, Nikko Hotel,
Kuala Lumpur, Malaysia.
Galloway, T. M., Lee, W. B. and Roden, D. M. (1997). “Banks’ changing
incentives and opportunities for risk taking”, Journal of Banking and
Finance, 21, pg 509 – 527.
Gonzalez-Hermosillo, B. (1999). “Developing indicators to provide early
warnings of banking crises”, Finance and Development, pg 36 – 39.
11
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Gonzalez-Hermosillo, B. (1999). “Determinants of ex-ante banking system
problem: a macro-micro empirical exploration of some recent
episodes”, IMF Working Paper 99/33.
Gujerati, D. N. (1995). Basic Econometrics, McGraw-Hill International
Editions, 3rd Edition, Singapore.
Honohan, P. (1997).” Banking System Failures in Developing and Transition
Countries: Diagnosis and Prediction” Economic and social Research
Institute, Dublin.
Jagtiani, J.,Kalori,C. Lemieux, and H.Shin.(2003) “The Determinants and
Early Detection of inadequate Capitalization of U.S Commercial
Banks”, Federal Reserve Bank of Chicago.
Kane, E. J (1988). “Changing incentives facing financial services regulators”,
Paper presented ate the Federal Reserve Bank of Cleveland
Conference on Bank Structure.
Kashyap, A. N. & Stein, J. C. (1994). “Monetary Policy and Bank Lending”, in
N.G. Mankiw (ed)., Monetary Policy, University of Chicago Press,
Chicago.
Lane, W.R. and S.W. Looney and J.W. Wansley, 1986”An Application of the
Cox Proportional Hazard Model to Bank Failure.” Journal of Banking
and Finance, Vol 10, No. 4:511-531.
Martin, D (1977).”Early Warning of Bank Failure”, Journal of Banking and
Finance 1 (November): 249-76
Neal, R. S. (1996). “Credit derivatives: new financial instruments for
controlling credit risk”, Federal Reserve Bank of Kansas City,
Economic Review, 81(2), pg 15 – 57.
Ohlson, J. A. (1980). “Financial ratio and the probabilities prediction of
banckruptcy”, Journal of Accouting Research, 18, pg 109-131.
Rahman, S., Tan, L. H., Hew, O. L., and Tan, Y. S. (2004). “Identifying
financial problem indicators of selected banks in Asia”, Asian
Economic Journal, 18(1), pg 45 – 57.
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Appendix 1
Variables and definitions
Variables
Market Risk:
CA
LFLOANS
Credit risk/ default risk:
LAS
INTCO
EARNINGS
Liquidity risk:
DEPPUB
DEPIB
DINTDEP
DLFA
Definition
Ratio of total shareholder’s fund to total assets
Ratio of loans deposit and placement with financial institutions to total loans
Ratio of gross loans to total assets
Ratio of interest income to interest expense
Ratio of interest income to earning asset minus interest expense on interest
bearing liabilities
Ratio of deposits from the customer (public) to total assets
Ratio of deposits and placement of bank and other financial institutions to total
assets (inter-bank deposits)
Ratio of interest expenses to total deposits
Ratio of liquid funds (cash and short term) to total assets
Macroeconomic variables:
CPI
Yearly quarter percentage change in the consumer price index
INTRS
Short-term real interest rate
Other bank variables
PSIZE
POPA
PIDI
PTOI
PMGT
RREM
Logarithm of total assets
Ratio of total operating profit to total assets
Ratio of interest expense on deposits to total operating income
Ratio of total interest income to total operating income
Ratio of earning assets to total assets
Ratio of total director remuneration to total assets
Structural variables:
(Banking sector)
BTLGDP
NIITL
IMPLICIT
LIQ
LOANCON
NPLDPTL
LLP/NPL
LLP+EQUITY/NPL
Ratio of total banking system loans to total GDP
Ratio of net interest income to total loan
Non interest expense minus non interest revenue to earning assets
liquid asset minus liquid liabilities to total shareholder’s fund
Ratio of loan concentration (real estate, construction, purchase of securities,
credit consumption and purchase of landed property) to total loans
Non- performing loans to ratio of deposits to total loans
Loan loss provision to Non-performing loans
Loan loss provision and Equity to Non-performing loans
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