An Attempt to Model Some Risks in Banking Sector Standard...
Transcript of An Attempt to Model Some Risks in Banking Sector Standard...
Proceedings of the First Middle East Conference on Global Business, Economics, Finance and Banking
(ME14 DUBAI Conference) Dubai, 10-12 October 2014
ISBN: 978-1-941505-16-8 Paper ID_D432
1 www.globalbizresearch.org
An Attempt to Model Some Risks in Banking Sector –
Standard Bank Pty
Kwabena A. Kyei,
Department of Statistics, University of Venda,
Thohoyandou, South Africa.
Albert Antwi,
Department of Statistics, University of Venda,
Thohoyandou, South Africa.
Netshikweta, M. L.,
Department of Advanced Nursing,
University of Venda
Thohoyandou, South Africa.
_____________________________________________________________________
Abstract
Standard bank Pty, like any other profitable organizations, aims mostly to maximize profit
and minimize cost. Under perfect market conditions, the lower the cost of making profit, the
higher the profit and vice versa, but due to market imperfections this is not always true.
Sometimes the amount spent on advertisement does not generate the expected income or
revenue and hence affects net profit. The expected revenue is sometimes low as compared to
the cost of generating such revenue implying that it would have been better to spend little on
advertisement. Standard bank spends quite a substantial amount of money on advertisement
in a bid to market its products, especially loans. To minimize risks due to market
imperfections, there is needs for a model to estimate the risk involved in the individual factors
that affect it so that the bank does not over- spend on some cost of operation. This paper aims
at examining the estimates of the independent variables, i.e. size, deposit and loans, gross
domestic product, inflation and market capitalization and construct individual models for
each of the two dependent variables Net Interest Margin (NIM) and Return on Capital
Employed (ROCE). Secondary data on the internal factors, from 1997 to 2010 was obtain
from Standard bank Pty and those on the external factors were taken from World Bank’s
website and other internet sources. The empirical evidence from the data permits us to make
the following conclusions:
Size, Loan and Deposits significantly affects the estimation of NIM, as shown in the
model below;
2 80.056 0.001 1.319 10 * NIM log Size Loan Deposit and
o Size, Market Capitalization and Deposits significantly affects the estimation of ROCE
as shown below ;
ROCE = 0.032 – (2.51 x 10-9
) Size – (5.667 x 10-5
) Mc*Deposit
____________________________________________________________________
Key Words: Minimize cost, Net Interest Margin, Market capitalization, Market
imperfection.
Proceedings of the First Middle East Conference on Global Business, Economics, Finance and Banking
(ME14 DUBAI Conference) Dubai, 10-12 October 2014
ISBN: 978-1-941505-16-8 Paper ID_D432
2 www.globalbizresearch.org
1. Introduction
Any attempt which limits the negative effects of risk-taking behavior is an incentive to
risk taking rather than risk reduction (Bessis, 2001). The best way to go about risk taking is
the deposit insurance which protects depositors against bank failures. The strategy of the
Standard Bank is to build a leading African financial services organization using all its
competitive advantages to the full (Standard Bank, 2009). It focuses on delivering superior
sustainable shareholder value by serving the needs of their customers through first-class on-
the-ground operations in chosen countries in Africa. It also connects other selected emerging
markets to Africa and to each other, applying their sector expertise, particularly in natural
resources, globally. Its key differentiator is people who are passionate about their strategy
wherever in the world they are based (Standard Bank, 2007 – 2010;
http://www.standardbank.com/overview.aspx).
The major contributing component of a bank’s risk is Credit risk which can be attributed
to the losses in the event of default of the borrower, or in the event of a deterioration of the
borrower’s credit quality. The risk in any bank is dictated by default risk, exposure risk and
recovery risk, where default risk, for example, is measured by the probability that default
occurs during a given period of time, and the risk depends upon the credit standing of the
borrower (Bessis, 2001). Exposure risk is generated by the uncertainty prevailing with future
amounts at risk and recovery risk depends upon the type of default and numerous factors,
including guarantees received from the borrower. Banks’ profits however, are dependent on
return on asset (ROA), return on equity (ROE), return on capital employed (ROCE) and net
interest margin (NIM), which controlled by several variables. These factors are categorized
into internal and external. Among the internal factors are size, capital, deposit and loans
among others. The external factors include Gross domestic product, inflation and market
capitalization.
2. Literature review
As banks enter new markets and introduce new products, they do encounter new risks and
market structures that were not well understood by supervisors. At the 2012 Bank Directors
Summit held in New York on 15 February, 2012, bank directors discussed the changes of
serving customers and clients profitably {Bank Governance Leadership Network; ( i.e.
Moody, 2012)}. Aremu et al., (2013) studied the determinants of banks’ profitability in a
developing economy: evidence from Nigerian banking industry using data from 1980 to 2010.
Results from the study revealed that contrary to views of some authors, Bank Size (Natural
Logarithm of Total Asset and Number of Branches) and Cost Efficiency did not significantly
determine bank profitability in Nigeria. Banks need to earn an attractive risk-adjusted return
for their shareholders a case which is currently difficult because of new regulations and the
Proceedings of the First Middle East Conference on Global Business, Economics, Finance and Banking
(ME14 DUBAI Conference) Dubai, 10-12 October 2014
ISBN: 978-1-941505-16-8 Paper ID_D432
3 www.globalbizresearch.org
state of the global economy. Risk management provides banks with a better view of the future
and the ability to define the business policy accordingly. Risks can appear “theoretical”
compared to the more practical realities such as business volume, margins and fees (Bessis,
2001). For a period of five years, (i.e. between 1985 and 1989) about 400 Texan banks failed
over Linked to real estate lending, dependent on the energy business. Banks were
concentrated in limited fields, so when oil prices fell, it resulted in prompting regional
recession (www.ephilipdavis.com/5007). In Nigeria, Credit Risk (Loan Loss Provision-Total
Assets) and Capital Adequacy (Equity-Total Assets) were found to be significant drivers
which affected bank profitability both in the long run and short run respectively (Aremu et al.,
2013).
Systematic risks have been reduced drastically in many countries through changes to
capital and liquidity standards, banks’ capital and liquidity positions. At the Bank Directors
Summit in 2012 mentioned earlier, the participants noted three risks, namely funding
shortages when new capital is required; the continuity change; and collateral management as a
result of ratings triggers (Moody, 2012). Most participants accepted that a boost in the
regulation and supervision was necessary to offset financial crisis in order to bring profit
(Moody, 2012).
Javaid et al. (2011) analyzed the determinants of top 10 banks’ profitability in Pakistan
over the period 2004 to 2008. They focused on the internal factors only. They used the pooled
ordinary least square (POLS) method to investigate the impact of assets, loans, equity, and
deposits on one of the major profitability indicator of banks which is return on asset (ROA).
3. Terminologies
Risk is the possibility that something bad, unpleasant or dangerous may happen; and in
business, it is a danger that a certain unpredictable contingency can occur, which will
generate randomness in cashflow.
Return on asset is a ratio calculated by dividing the net income over total assets.
ROA measures the profit earned per dollar of assets and reflects how well bank management
uses the real investment resources of the bank to generate profit.
Return on Equity is the ratio of net income to total equity. It measures the rate of return on
the ownership interest (shareholders’ equity) of the common stock owners. ROE shows how
well a company uses investment funds to generate earning growth.
Return on capital employed is the ratio of non- markup income to capital employed. It
compares earnings with capital invested in the company. Unlike ROA, it takes into account
sources of financing.
Proceedings of the First Middle East Conference on Global Business, Economics, Finance and Banking
(ME14 DUBAI Conference) Dubai, 10-12 October 2014
ISBN: 978-1-941505-16-8 Paper ID_D432
4 www.globalbizresearch.org
Net interest Margin is defined as the net interest divided by total assets. It measures the
difference between the interest income generated by banks or other financial institutions and
the amount of their lenders, relative to the amount of their assets.
Size is used to capture the fact that larger bank are better placed than smaller ones in
harnessing economies of scale in transactions to the plain effect that they will tend to enjoy a
higher level of profits. The total asset of a bank is used as a proxy for the bank size.
Systematic risk is what makes financial system to undergo contagious failure following other
forms of shock/risk.
4. DATA TYPE & SOURCE
Secondary data on the internal factors, from 1997 to 2010 was obtain from Standard bank
Pty and those on the external factors were taking from World Bank’s website and other
internet sources. All financial data were nominated in Rand (million).
4.1 Software Employed
Technology advancement has led to the development of several statistical software
packages which help in the analysis of data. This has replaced the traditional manual
computation method which included knowing several formulae and performing a lot of
tedious calculations which was not only time consuming, but also a waste of materials and
other resources.
Some of these software packages includes Statistical package for Social Scientist (SPSS),
SAS, Minitab, Genstat, Excel etc. Other programming languages such as Matlab can also be
used to generate statistical outputs. For the purpose of this research IBM SPSS Version 21
and MINITAB 15 were used in the analysis of the data.
Model building
Empirical evidence from preliminary analysis of the data and a look at the Scatter plots below
suggested that the following models were appropriate fits onto the data:
NIM model
2
0 0 1 2y n logx kx x …………………………………………….............…… 1
Where;
0y
Represent NIM,
0 1 2, ,x x x
represent size, loans and deposit respectively;
α, n and k are the parameters to be estimated, and
γ is the random error
Equation (1) can be transformed into linear form as
Proceedings of the First Middle East Conference on Global Business, Economics, Finance and Banking
(ME14 DUBAI Conference) Dubai, 10-12 October 2014
ISBN: 978-1-941505-16-8 Paper ID_D432
5 www.globalbizresearch.org
2
0 1 2
0 3 4 5 1
[ ( ) ]
2
logy log n logx kx x
Y nx mx px
-………………………………….2
Where
0Y represent NIM,
3 4,x x and 5x transformed representations of size, loans and deposit respectively;
,mn
and p are the transformed parameters to be estimated, and
1 is the transformed random error
This model was chosen because empirical evidence from preliminary analysis and a look at
the scatter plot in Figure 1b of NIM against each of loans and an interaction between market
capitalization and deposit shows an association which is not linear but kind of a curve and the
best model that can fit on these points is a logarithmic relationship.
ROCE model
The models for this component are:
1 0 1 0y x and 1 6 2 2y xx ……………………………………3
These equations can be combined to give the single equation below:
1 0 1 0 2 6 2x xY x ................................................................................................ 4
Where:
Y1 represent ROCE;
2 6,x x are deposit and market capitalization respectively;
0 1 2,
are the parameters to be estimated and
represent the random error
This model was chosen because a look at Figure 1a indicates a linear relationship between
ROCE and size and empirical evidence suggested an additional non-linear relationship
between ROCE and the interaction of loans and deposit.
For the purpose of this paper only ROA, ROCE and NIM have been considered against
all the independent variables except capital. In this case multiple linear regression method has
been used to fit models onto the data through the use of appropriate software in the estimation
of the estimates in the models. SPSS outputs generated from the data are shown in Tables 1 –
6.
Results of the Analysis estimates and their relevance
There is a negative quadratic logarithmic relationship between size; and NIM, and a
positive linear relationship between NIM and the interaction of loan and deposit which is
quite similar to the results obtained by (Rahman et al., 2013) in closely related studies in
Proceedings of the First Middle East Conference on Global Business, Economics, Finance and Banking
(ME14 DUBAI Conference) Dubai, 10-12 October 2014
ISBN: 978-1-941505-16-8 Paper ID_D432
6 www.globalbizresearch.org
terms of the direction of association between variables. For example, a 2 rand change (R2
change) in size will decrease the NIM component by 4R 3.01 10
whilst R1 change in each
of loan and deposit will result in an increase of NIM by R81.319 10 which is very
significant in monetary value.
There is also a negative linear relationship between ROCE and size and also between
ROCE and the interaction of market capitalization and deposit. This result is inconsistent with
closely related studies done by (Rahman et al., 2013) in the sense that they found a positive
linear relationship between ROCE and size. However the latter relationship of ROCE is
consistent with the same study. If size changes by R2 and both market capitalization and
deposit change by R1 each, there will be a reduction in ROCE by 910R and R
55.667 10 accordingly. These changes in ROCE cannot be overlooked, hence the relevance
of size, deposit and market capitalization in the estimation of risk or profit of standard bank.
The relationship between ROA and deposit was found to be positive log-linear which is
similar to the positive linear association obtained by Molyneux and Thornton (1992) and
Bikker and Hu (2002). An R2 increment in deposit will result in quite substantial increase of
R2.0477 in ROA and hence the contribution of deposit in estimation of profit of standard
bank cannot be under-estimated.
NIM
2 80.056 0.001 1.319 10 * NIM log Size Loan Deposit ……...…....….…….. 5
Where 1size and 0 1size
ROCE
9 5[ [ 0.032 2.51 10 ] 5.667 10 * ]ROCE Size Mc Deposit ……………...….……… 6
5. Residual analysis
5.1 Independence assumption
The residual-time order plot in Figures 2 & 3 indicates no pattern. This implies that the
time-ordered errors neither show positive nor negative autocorrelation, suggesting that the
error terms occur in a random pattern over time. Hence the independence assumption is
approximately assumed to be satisfied (Bowerman et al., 2001).
5.2 Normality assumption
A look at the normal probability plots in Figures 2 & 3 portraits a fairly straight-line graph.
Furthermore, the histogram plots exhibit a bell-shaped nature around the mean of Zero.
Therefore the normality assumption is approximately satisfied.
5.3 Constant variance
Proceedings of the First Middle East Conference on Global Business, Economics, Finance and Banking
(ME14 DUBAI Conference) Dubai, 10-12 October 2014
ISBN: 978-1-941505-16-8 Paper ID_D432
7 www.globalbizresearch.org
A look at the residual against ROA, NIM and ROCE and the residual against predicted values
plots (as shown in Figures 2 & 3) indicates no pattern (i.e. funneling in or out). This means
that the constant variance assumption is approximately satisfied. The satisfaction of this
assumption is further affirmed by the non-pattern nature of residual against observation order
plot.
5.4 Model validation
The values of ROA, NIM and ROCE in Table 1 are calculated using the estimated regression
models as shown below:
2 8 0.056 0.001* 1337521 1.319 10 *713025*0.592691 NIM log
0,024042576
9 5 0.032 2.51 10 1337521 5.667 10 ]278,4 0.592691ROCE
0,019291987
( ) 1.108 1.034 (0.592691)log ROA log
1,342895877
1,34289587710ROA
0,045405046ROA
5.5 Error analysis
From the error analysis in the table above, it can be noted that the error between the actual
values for the year 2010 and the estimated values for that same year using the estimated
regression models are very negligible hence the models are valid in the estimation of NIM,
ROA and ROCE (Medenhall & Scheaffer, 1973).
When the estimates were substituted into the assumed models as seen above, new
estimates for NIM, ROA and ROCE for the 2010 financial year were seen to be appropriate
for the model. Residual analysis on these estimates was found out to be in close proximity to
the assumptions underlying the methodology used in the study.
6. Limitations of the study
The main challenge of this study was the access to data information. All efforts to get all
the required data from Standard Bank Pty proved futile as they never replied all the
correspondence that was sent to them. The researchers had to source some of this information
from the annual statements from the Bank’s website of which some of the years were missing.
This prolonged the time frame-work in which the research was initially to be conducted.
7. Conclusion
Proceedings of the First Middle East Conference on Global Business, Economics, Finance and Banking
(ME14 DUBAI Conference) Dubai, 10-12 October 2014
ISBN: 978-1-941505-16-8 Paper ID_D432
8 www.globalbizresearch.org
From the results of the data analysis above, these conclusions could be made:
Size, Loan and Deposits significantly affects the estimation of Net Interest Margin of
Standard Bank as shown in the model below;
2 80.056 0.001 1.319 10 * NIM log Size Loan Deposit
………………… 7
Size, Market Capitalization and Deposits significantly affects the estimation of
Return On Capital Employed as shown below ; and
9 5 0.032 2.51 10 5.667 10 ] *ROCE Size Mc Deposit ………………. 8
Recommendation
Due to quite negligible error margins of the estimates obtained by the models, it is highly
recommended that for any future estimation of the risks or profit targets, the models must be
taken into account to help the bank to save money on some forms of cost of making profit and
in turn increase its net profit margins
References
Aremu, M.A., Imoh, C., & Mustapha, A.M. (2013). Determinants of banks’
profitability in a developing economy: evidence from Nigerian banking industry.
Journal of Contemporary Research in Business. January 2013, Vol. 4, No 9.
Bank Governance Leadership Network Viewpoints, (March 2012). Tapestry
Networks Inc. www.tapestrynetwork.com.
Bessis, J. (2001). Risk Management in Banking. John Wiley & Sons: New York.
Bikker, J. A. and Hu, H. (2002). Cyclical Patterns in Profits, Provisioning and
Lending of Banks and Procyclicality of the New Basle Capital Requirements, BNL
Quarterly Review, 221, pp. 143-175.
Bowerman, B.L., O’Connel, R.T., & Hand, M.L. (2001). Business Statistics in
Practice. Second Edition. McGraw-Hill Irwin: New York.
Javaid, S., Anwar, J., Zaman, K. & Gaffor, A. (2011). Determinants banks’
profitability in Pakistan: Internal Factor Analysis. Mediterrarean Journal of Social
Sciences Vol 2, No.1, January 2011.
Medenhall, W. & Scheaffer, R. L. (1973). Mathematical Statistics with Applications.
Duxbury press: California.
Molyneux, P., & Thorton, J. (1992). Determinants of European Bank Profitability; a
Note. Journal of Banking and Finance, 16, pp. 1173- 78.
Proceedings of the First Middle East Conference on Global Business, Economics, Finance and Banking
(ME14 DUBAI Conference) Dubai, 10-12 October 2014
ISBN: 978-1-941505-16-8 Paper ID_D432
9 www.globalbizresearch.org
Moody’s Investors Services. (2012). Rating Action Moody’s Reviews Ratings for
European Banks. February 15, 2012. (www.tapestrynetworks.com).
Rahman, M., Bhattacharya, D., Khatun, F., Moazzem, G. K., & Khan, T. I. (2013).
Analytical Review of Bangladesh’s Macroeconomic Performance in Fiscal Year,
2013. Report prepared by the Independent Review of Bangladesh’s Development; 3
June 2013.
Standard Bank annual reports, 2007-2010.
Standard Bank comprehensive history, 2009.
http://www.ephilipdavis.com/5007.
http://www.standardbank.com/overview.aspx.
Tables
Table 1: Results from the year 2010 data
ROA NIM ROCE, Size Loans Deposit Market
capitalization
0.041680093 0.020208 0.021473 1337521 713025 0.592691 278,4
Table 2: Results – Error analysis
NIM ROCE ROA
Actual Value 0.020208 0.021473 0.041680093
Estimate 0,024042576 0,019291987 0,045405046
Absolute Error 0,003834576 0,002181013 0,003724953
% Absolute Error 0,3834576 0,2181013 0,3724953
Table 3: Model Summary - NIM analysis
Model R R Square Adjusted R
Square
Std. Error of the Estimate
1 .998a .996 .988 .000751312192330
2 .000b .000 .000 .006874770307005
Table 4: Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
Proceedings of the First Middle East Conference on Global Business, Economics, Finance and Banking
(ME14 DUBAI Conference) Dubai, 10-12 October 2014
ISBN: 978-1-941505-16-8 Paper ID_D432
10 www.globalbizresearch.org
B Std. Error Beta
1
(Constant) .205 .027 7.455 .002
DEPOSIT -.058 .010 -1.588 -5.640 .005
SIZE 1.740E-008 .000 7.875 5.696 .005
logSize -.029 .004 -2.145 -7.126 .002
LOANS -3.640E-007 .000 -13.763 -5.856 .004
GDP .001 .000 .412 2.999 .040
CPI .000 .000 .058 1.069 .345
MARKETC 2.041E-005 .000 .232 2.616 .059
Loan_depo 6.280E-007 .000 12.840 5.950 .004
2 (Constant) .021 .002 11.052 .000
ROCE
Table 5: Model Summary ROCE analysis
Model R R Square Adjusted R
Square
Std. Error of the Estimate
1 .990a .980 .953 .001615521486143
2 .000b .000 .000 .007422611657327
Table 6: Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) .011 .008 1.369 .229
DEPOSIT .023 .010 .583 2.259 .073
SIZE -3.014E-009 .000 -1.263 -1.780 .135
LOANS -1.203E-008 .000 -.421 -3.218 .024
GDP -.001 .000 -.236 -1.914 .114
CPI .001 .000 .265 2.582 .049
MARKETC .000 .000 1.302 1.469 .202
Mc_depo .000 .000 -.929 -1.233 .272
2 (Constant) .023 .002 11.296 .000
Proceedings of the First Middle East Conference on Global Business, Economics, Finance and Banking
(ME14 DUBAI Conference) Dubai, 10-12 October 2014
ISBN: 978-1-941505-16-8 Paper ID_D432
11 www.globalbizresearch.org
8000006000004000002000000
0.035
0.030
0.025
0.020
0.015
0.010
0.005
0.000
16012080400
LOANS
RO
CE
MARKET C_DEPOSIT
Scatterplot of ROCE vs LOANS, MARKET C_DEPOSIT
Figure 1a
1000
0000
7500
000
5000
000
2500
0000
0.030
0.025
0.020
0.015
0.010
0.005
0.000
4000
00
3000
00
2000
00
1000
00
0
SIZE
NIM
LOAN_DEPOSIT
Scatterplot of NIM vs SIZE, LOAN_DEPOSIT
Figure 1b
0.90.80.70.60.50.40.30.20.10.0
0.06
0.05
0.04
0.03
0.02
0.01
0.00
DEPOSIT
RO
A
Scatterplot of ROA vs DEPOSIT
Proceedings of the First Middle East Conference on Global Business, Economics, Finance and Banking
(ME14 DUBAI Conference) Dubai, 10-12 October 2014
ISBN: 978-1-941505-16-8 Paper ID_D432
12 www.globalbizresearch.org
Residual Plots
Figure 2
Figure 3
0.00500.00250.0000-0.0025-0.0050
99
90
50
10
1
Residual
Pe
rce
nt
0.030.020.010.00
0.004
0.002
0.000
-0.002
-0.004
Fitted Value
Re
sid
ua
l0.
004
0.00
30.
002
0.00
10.
000
-0.00
1
-0.00
2
-0.00
3
4
3
2
1
0
Residual
Fre
qu
en
cy
13121110987654321
0.004
0.002
0.000
-0.002
-0.004
Observation OrderR
esi
du
al
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for NIM
0.00500.00250.0000-0.0025-0.0050
99
90
50
10
1
Residual
Per
cent
0.030.020.010.00
0.0050
0.0025
0.0000
-0.0025
-0.0050
Fitted Value
Res
idua
l
0.0040.0020.000-0.002-0.004
3
2
1
0
Residual
Freq
uenc
y
13121110987654321
0.0050
0.0025
0.0000
-0.0025
-0.0050
Observation Order
Res
idua
l
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for ROCE