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

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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,


Netshikweta, M. L.,

Department of Advanced Nursing,

University of Venda


_____________________________________________________________________

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.



ISBN: 978-1-941505-16-8 Paper ID_D432


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

http://www.standardbank.com/overview.aspx



ISBN: 978-1-941505-16-8 Paper ID_D432


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.

http://www.ephilipdavis.com/5007



ISBN: 978-1-941505-16-8 Paper ID_D432


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



ISBN: 978-1-941505-16-8 Paper ID_D432


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



ISBN: 978-1-941505-16-8 Paper ID_D432


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



ISBN: 978-1-941505-16-8 Paper ID_D432


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



ISBN: 978-1-941505-16-8 Paper ID_D432


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.

http://www.tapestrynetwork.com/



ISBN: 978-1-941505-16-8 Paper ID_D432


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.

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http://www.standardbank.com/overview.aspx



ISBN: 978-1-941505-16-8 Paper ID_D432


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



ISBN: 978-1-941505-16-8 Paper ID_D432


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



ISBN: 978-1-941505-16-8 Paper ID_D432


Residual Plots

Figure 2

Figure 3

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Normal Probability Plot Versus Fits

Histogram Versus Order

Residual Plots for NIM

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An Attempt to Model Some Risks in Banking Sector Standard...

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