determinants of mauritian commercial banking...

Proceedings of the Fifth Asia-Pacific Conference on Global Business, Economics, Finance and

Social Sciences (AP16Mauritius Conference) Port Louis, Mauritius,

21-23 January, 2016 Paper ID: M628

1 www.globalbizresearch.org

Determinants of Mauritian Commercial Banking Profitability

Y. Mungly,

B. Seetanah,

K. Seetah,

R. Bhattu Babajee,

N. Pariag Maraye,

University of Mauritius, Mauritius.

N GOPY RAMDHANY,

Faculty of Law and Management

University of Mauritius.

E-mail: [email protected]

___________________________________________________________________________

Abstract Deposits, loans, inter-banks relationships, competition and prestige may be the most definite

characteristics of banks. Their performance and the way they are managed are very

important aspects to be considered in assessing the stability of an economy. In the case of

Mauritius, where banks dominate the financial scenery the previous statement holds even

greater importance. The guiding principle throughout this study will be to identify the factors

that may affect banking profitability in Mauritius and assess the degree of their impact. The

identification process is done through review of existing literature and the assessment has

been conducted on a sample of 15 banks operating in the Mauritian Banking Sector. A static

and a dynamic model were considered during the analysis. Generalized Estimating Equation

(GEE) has been used to estimate the static model and the Arellano-Bond two-step

Generalized Method of Moments (GMM) has been used for the dynamic model. Analysis of

the static models has shown that ROA is significantly and negative affected by the cost

management efficiency. The level of capital, the amount of credit risk and the diversification

all presented positive and significant impacts on ROA. ROE was also negatively and

significantly affected by cost management efficiency. Diversification and GDP growth both

showed positive and significant impacts. The analysis of the dynamic models showed no

evidence of dynamism in the determination of profits in the Mauritian Banking sector.

______________________________________________________________________

Keywords: commercial banks, profitability, return on asset, return on equity, Mauritian

Banking Sector

JEL Classification: C 19, G13, G 14

mailto:[email protected]





1. Introduction

With growing demand and globalization banks have aside from their intermediation

function, also developed new products and offer new facilities to capture more and more

customers. Earlier on products such as Automatic Teller Machines have facilitated the

retrieval of cash for mundane purposes, afterwards credit and debit cards came into use. Now

internet and mobile banking are being explored as a next way to facilitate customers’ lives.

Banks also provide facilities to importers and exporters, for example Letters of Credit. These

facilitate trading between importers and exporters who are far away by using the bank as an

intermediary. There have been many studies conducted on the banks, in particular

determinants of banking profitability. The first studies concerning determinants of banking

profitability were conducted by Haslem (1968), Short (1979), Berger, Hanweck and

Humphrey (1987) and Bourke (1989). Measures of banking profitability that have frequently

been used are Return on Assets (ROA), Return on Equity (ROE) and Net Interest Margin

(NIM). The commonly used determinants of banks’ profitability that have been used by

researchers are bank size, overhead cost management efficiency, banks’ capital, liquidity risk,

credit risk, asset diversification, foreign ownership in banks, GDP growth rate, market

concentration and inflation rate.

Studies such as Smirlock (1985), Athanasoglou et al (2006) and Yilmaz (2013),

Davydenko (2010), Obamuyi (2013), Naceur (2003) have investigated how size of banks

affects profitability. Abreu and Mendes (2001), Pasiouras et al (2006) and Rachdi (2013)

investigated the impact of overhead cost management efficiency on profitability of banks. In

addition Staikouras and Wood (2004), Goddard et al. (2004), Kosmidou et al. (2005),

Athanasoglou et al. (2005), Davydenko (2010) and Obamuyi (2013) analysed capital as

determinant of banks’ profitability. Liquidity risk as determinant of profitability in the

banking sector was investigated by Athanasoglou et al (2006), Yilmaz (2013) and Abebaw

and Deepack (2011). The impact of credit risk was considered by Athanasoglou et al (2005),

(2006), Davydenko (2010), Sufian and Chong (2008), Kosmidou et al (2005). Karkrah and

Ameyaw (2010) and Sufian and Chong (2008), Hayden et al (2006) and Gischer and Juttner

analysed the impact of asset diversification on profitability. Foreign ownership was

considered by Bonin, Hasan and Wachtel (2005). GDP growth rate as a determinant of banks’

profitability was investigated by Neely and Wheelock (1997), Demirguc-Kunt and Huizinga

(2001), Bikker and Hu (2002), Aspergis (2009), Albertazzi and Gambacorta (2009).

Furthermore Demirguc-Kunt and Huizinga (1999) and Athanasaoglou et al (2006) look at

how market concentration affects profitability of banks. In addition inflation rate as a

determinant of banks’ profitability was considered by Bourke (1989), Molyneux and Thorton

(1992), Kosmidou et al. (2005), Athanasoglou et al. (2006) and Alexiou (2009).





This study was driven by a desire to further comprehend the functioning of the banking

sector. The aim of the study was to identify relevant determinants of banking profitability in

Mauritius and to investigate the significance and the degree of impact of the selected

determinants. The results of this study will be used to hopefully provide suitable advice to

banks to help them strengthen their positions. For the analysis on the profitability of banks in

the Mauritian banking sector, panel data was used. A sample of 15 banks was chosen over the

period of 9 years ranging from 2005-2013. Regression of the panel data set was conducted on

Stata 13. Generalized Estimating Equations (GEE) was used for the analysis of the static

models. To investigate whether dynamism is present in the models the Generalized Method of

Moments (GMM) was used. Mauritius provides a good case of study because the banking

industry in Mauritius has undergone appreciable changes over the last decades. The number

of banks operating in Mauritius as at January 2015 is 23. The 2008 financial crisis struck the

whole world and banks in advanced countries have failed. The picture locally, is quite

different. The Mauritian economy had suffered a slowdown but our banks showed resilience

and withstood the shock.

The paper is organized as follows: the next section presents the methodology used and

discusses the results of analysis and the last section focuses on the conclusion and the

appropriate recommendations of the study.

2. Methodology

2.1. Data and Sample

Secondary data was used for this study. The sample consists of bank level data for 15

banks covering the period 2005-2013. Most of the financial statement data was available

online from the Mauritius Bankers Association Limited website. Bank profiles for each year

since 2005 provided concise financial statements on the banks in operation in the Mauritian

banking industry. Missing data, notably loan loss provision figures for certain years were

obtained from the annual reports at the Registrar of Companies. Macroeconomic data (GDP

growth and inflation CPI) were gathered from the World Bank website. Data regarding the

market structure (competition) was collected from the Financial Stability Report issued by the

Bank of Mauritius.

2.2. Model Specification

Athanasoglou et al (2005) used the following equation to estimate a linear relationship

between the dependent and independent variables they considered. The same equation form

was used in this study.

𝝅𝒊𝒕 = 𝒄 + ∑ 𝜷𝒋 𝑿𝒊𝒕𝒋

𝑱

𝒋=𝟏

+ ∑ 𝜷𝒍 𝑿𝒕𝒍

𝑳

𝒍=𝟏

+ ∑ 𝜷𝒎 𝑿𝒕𝒎

𝑴

𝒎=𝟏

+ 𝜺𝒊𝒕





Where 𝝅𝒊𝒕 is the profitability of bank I at time t with i = 1,…., N ; t = 1,….,T, c is a constant

term and the Xs are explanatory variables grouped into bank-specific (J), industry specific (L)

and macroeconomic (M) determinants.

2.3. Variable Definition

Dependent Variable

The aim of the study was to identify determinants of banking profitability and to

determine the extent to which these determinants impact on the profitability. First and

foremost, therefore, a suitable measure of banking profits was required. From the literature, 3

main ratios have been utilized to proxy bank profits. Return on assets (ROA), Return on

Equity (ROE) and Net Interest Margin (NIM).

𝑹𝑶𝑨 =𝑷𝒓𝒐𝒇𝒊𝒕 𝒃𝒆𝒇𝒐𝒓𝒆 𝒊𝒏𝒕𝒆𝒓𝒆𝒔𝒕 𝒂𝒏𝒅 𝒕𝒂𝒙

𝑻𝒐𝒕𝒂𝒍 𝒂𝒔𝒔𝒆𝒕𝒔

𝑹𝑶𝑬 =𝑷𝒓𝒐𝒇𝒊𝒕 𝒃𝒆𝒇𝒐𝒓𝒆 𝒊𝒏𝒕𝒆𝒓𝒆𝒔𝒕 𝒂𝒏𝒅 𝒕𝒂𝒙

𝑬𝒒𝒖𝒊𝒕𝒚

𝑵𝑰𝑴 =𝑰𝒏𝒕𝒆𝒓𝒆𝒔𝒕 𝑰𝒏𝒄𝒐𝒎𝒆 − 𝑰𝒏𝒕𝒆𝒓𝒆𝒔𝒕 𝑬𝒙𝒑𝒆𝒏𝒔𝒆

𝑻𝒐𝒕𝒂𝒍 𝒂𝒔𝒔𝒆𝒕𝒔

The literature argues much on the suitability of each of these ratios as profitability yard

sticks. NIM appears to be an adequate measure since the main business of banks is to give out

loans (where interest income comes from) and take in deposits (where interest expense

occurs). However, banks also derive significant income from non-interest sources, such as

fees and commissions. This tends to lessen the merits of using NIM as profitability measure.

ROA gives an indication on the earning power of a bank’s assets. It shows the amount earned

per unit of assets. The only negative aspect of ROA is that off balance sheet items are

excluded. ROE measures the return of the shareholders on a unit of their capital. The

drawbacks associated however are of concern. Firstly banks that have low capital levels will

generate high ROEs. Low level of capital also implies that the bank leverage is high

indicating high risks. Also ROE is not adequate since the level of capital is imposed by the

regulatory authority. This study considered 2 models; where model 1 considered ROA as

dependent variable and model 2 considered ROE.

Independent Variables

Size, S

The impact of size on profitability will depend on whether the larger banks are still

enjoying economies of scale due to their decreased fixed cost per unit or whether bureaucratic

costs has smothered the advantages that they were previously enjoying. (Eichengreen and

Gibson, 2001). The size of banks is measured by the logarithm of real assets.

Overhead cost management efficiency, C





OVER is calculated from the ratio of operating expenses over total assets. This ratio has

been preferred over COST to NET INCOME due to inconsistencies that arise when profits are

negative. The ability of the management to control the operating expenses is indicative of the

efficiency of the bank.

𝐎𝐕𝐄𝐑 = 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐄𝐱𝐩𝐞𝐧𝐬𝐞𝐬

𝐓𝐨𝐭𝐚𝐥 𝐚𝐬𝐬𝐞𝐭𝐬

Capital, CAP

This variable is defined in this study by the ratio of equity to total assets.

𝐂𝐀𝐏 =𝐄𝐪𝐮𝐢𝐭𝐲

𝐓𝐨𝐭𝐚𝐥 𝐀𝐬𝐬𝐞𝐭𝐬

The potential effect of CAP has been illustrated in the literature review using 3

hypotheses the signalling, the bankruptcy cost and the risk-return hypothesis.

Liquidity Risk, LOANTA

Several measures have been employed by different researchers in order to capture

liquidity risk of banking institutions. Liquidity risk is the inability of banks to honour their

maturing liabilities. The impact of liquidity risk on a bank is likely to be negative due to

higher risk of loss related to excessive loan giving. The liquidity measure used in this study

will be the ratio of loans to total assets.

𝐋𝐎𝐀𝐍𝐓𝐀 =𝐓𝐨𝐭𝐚𝐥 𝐋𝐨𝐚𝐧𝐬


Credit Risk, CRE

The loan providing function of banks is its main source of income. It is important that a

bank is able to overcome any information asymmetry that may exist and therefore insure that

the borrower will be able to pay back. This credit assessment is crucial in determining the

loan loss provision of the bank for the year. Better screening allows managers to boost

profitability by reducing non-performing loans. Credit risk is modelled by the ratio of loan

loss provisions to total loans.

𝐂𝐑𝐄 =𝐋𝐨𝐚𝐧 𝐥𝐨𝐬𝐬 𝐩𝐫𝐨𝐯𝐢𝐬𝐢𝐨𝐧𝐬

𝐓𝐨𝐭𝐚𝐥 𝐥𝐨𝐚𝐧𝐬

Asset Diversification, MIX

According to portfolio theory an ideal bank would diversify away any risks that are not

systematic. Diversification includes geographical diversification-moving operations to

regions where business cycle is uncorrelated to local economic environment- or to offer

services which are non-interest bearing but which involve costs such as commission and fees

to the customers. Thus the profit of banks is no longer solely dependent on the repayment

capacity of customers. MIX is measured by the ratio of non-interest income to total assets.





𝐌𝐈𝐗 =𝐍𝐨𝐧 𝐈𝐧𝐭𝐞𝐫𝐞𝐬𝐭 𝐈𝐧𝐜𝐨𝐦𝐞


Foreign Ownership, FORO

The presence of foreign banks in a country is very beneficial. Apart from increasing the

level of competition, foreign banks also bring in new risk management techniques and

technologies which local firms will be encouraged to adopt in order to stay competitive. The

impact of foreign ownership is investigated by using a binary dummy variable. It takes the

value of 1 when 30% or more of the bank’s capital is foreign owned.

Business cycle, GDP

The business cycle that a country is going through highly impacts the amount of loans

given out. More prosperous times will encourage investment while gloomy years will make

investors more cautious. In years of prosperity, entrepreneurs will be more likely to be

demanding loans to invest in real assets and in setting up new businesses, more loans from

banks will imply more interest revenue and hence more profits for the institutions. The proxy

used for business cycle will be the GDP growth rate.

Concentration, CONC

Market concentration is an important factor to be considered. The SCP hypothesis argues

that in highly concentrated markets, the top banks through collusion or other non-competitive

behaviour are able to impose monopolistic rents on their customers. The situation nears that

of a monopoly. In this study the Herfindahl-Hirschman Index HHI has been used as a

concentration measure.

Inflation, INF

In general, inflation impacts on the profitability of banks by increasing expenses. Perry

(1992) posited that the capacity of banks to predict inflation will determine the impact on

profitability. If banks correctly predict inflation, they are able to adjust their rates of interest

so that their income exceeds their increased expenses, therefore having no or a positive

impact on profitability. The Consumer Price Index (CPI) data collected from the World Bank

website is used to proxy current inflation.

Based on the above explanations, the following equation is constructed;

MODEL 1

ROA = c + ε + β1S + β2C + β3CAP + β4LOANTA + β5CRE + β6MIX + β7FORO

+ β8CONC + β9GDP + β10INF

MODEL 2

ROE = c + ε + β1S + β2C + β3CAP + β4LOANTA + β5CRE + β6MIX + β7FORO

+ β8CONC + β9GDP + β10INF





Static Model

In order to analyse the panel data set Stata statistical package version 13 was used. In

order to analyse the data using ordinary least squares regression the assumptions underlying

the model must not be violated. To ensure proper estimates, tests were conducted on the data

set to ensure stationarity, non-autocorrelation, homoscedasticity and cross-sectional

independence.

Dynamic Model

Several studies have considered the possibility that the determination of profit might

depend on the previous period results. Flamini (2009) due to this suspected persistence has

adopted a dynamic specification to the model and has added a lagged dependent variable

among the regressors. The equation evolves to:

πit = c + γπi,t−1 ∑ βj Xitj

J

j=1

+ ∑ βl Xtl

L

l=1

+ ∑ βm Xtm

M

m=1

+ εit

Where πi,t−1 is the one period lagged profitability and γ measures the speed of mean

reversion. A value of delta close to 0 will imply that profits are persistent but they will

eventually return to their equilibrium value. On the other hand value closer to 1 will imply a

slower mean reversion. A value close to 0 will indicate that there is a high level of

competitiveness in the market.

Models 1 and 2 are modified with the inclusion of a lagged profitability variable among the

regressor, and the following 2 equations are obtained.

MODEL 3

ROA = c + ε + γROA t−1 + β1S + β2C + β3CAP + β4LOANTA + β5CRE + β6MIX

+ β7FORO + β8CONC + β9GDP + β10INF

MODEL 4

ROE = c + ε + γROE t−1 + β1S + β2C + β3CAP + β4LOANTA + β5CRE + β6MIX

+ β7FORO + β8CONC + β9GDP + β10INF

Arellano and Bond (1991) propose a two-step GMM estimator. Assumptions of

independence and homoscedasticity of error terms are made in the first step, and in the second

step those assumptions are relaxed. This is because in the second step of the estimation the

residuals of the first step are used to construct a consistent estimate of the variance-covariance

matrix. Therefore the outcome of the regression with the two-step GMM estimator will be

heteroscedastically consistent.





3. Results and Discussion

3.1 Static Model

The Fisher type test based on Augmented Dickey-Fuller was used to test for unit roots. It

is noted that all the variables with the exception of CRE and INF are stationary(see appendix

3(a)). In order to convert them to stationary data series, the first difference of the data series is

generated. The regression is then run with the first differenced data series. The Hausman

specification promotes the adoption of the fixed model in the first place for model 1 and 2 but

the results also reports ‘V_b-V_B is not positive definite’ in both cases. The robust Hausman

specification test confirmed that fixed effects model need to be used in both models.

Another assumption in linear regression is that of no autocorrelation. The Lagram-

Multiplier test for autocorrelation in panel data confirms the presence of autocorrelation in the

two models. The result of the Modified Wald test for group-wise heteroscedasticity indicates

presence of heteroscedasticity in both models. In order to determine whether there is presence

of contemporaneous correlation in the data set, the Pesaran test was employed. Results lead to

the non-rejection of the null hypothesis, meaning that there is no contemporaneous correlation

problem.

Table 1: Summary of Test Results

MODEL 1 MODEL 2

ROA as Dependent ROE as Dependent

Hausman Test 0.0448 0.0140

Robust Hausman Test 0.0006 0.0001

Lagram-Multiplier test 0.0037 0.0000

Modified Wald test 0.0000 0.0000

Pesaran test 0.9962 0.0667

Source: Own Computation

The panel data set considered in this study violates the assumptions of heteroscedasticity

and autocorrelation. The regression was conducted using Generalized Estimating Equations

with the robust option. The robust option yield results which are heteroscedastic and

autocorrelation consistent. The results are presented below.

Table 2: Static Model Regression Results

MODEL 1 ROA MODEL 2 ROE

Coefficient P>z Coefficient P>z

S 0.0005282 0.695 0.011447 0.580

C -0.5754013*** 0.000 -8.15725 0.000***

CAP 0.0326674*** 0.001 0.078702 0.679

LOANTA 0.0065938 0.273 0.086997 0.259





DCRE 0.0951492** 0.033 1.087797 0.164

MIX 0.7148345*** 0.000 4.025008 0.001***

FORO -0.0048306 0.159 -0.08029 0.073*

GDP 0.1231357 0.153 2.579264 0.035**

Logconc -0.0049878 0.730 -0.14731 0.487

DINF 0.0121542 0.509 0.212919 0.140

_cons 0.0319676 0.756 0.955697 0.568

*Significant at 10% level, **Significant at 5% level, ***Significant at 1%level


The Size (S) is measured by the log of total assets and has been included in the

profitability equation to account for economies of scale. In both models the impact of S is

insignificant in the determination of the profitability of banks. This is in line with the findings

of Athanasoglou et al (2005), Elsiefy (2013) and Davydenko (2010). Athanasoglou et al

(2005) explain the non- significance of the size variable on profits. They state that smaller

banks tend to target rapid increase of assets while disregarding profits. This means that when

smaller banks increase their asset base to grow, they are now comparable in size to medium

sized banks which are profitable. Therefore, part of the medium sized banks would be

profitable while the rest (small banks which have grown) are not profitable since they have

just started business; hence showing that size is not important in determining profits.

The relationship between cost efficiency and profitability is a negative one as it was

expected in models 1 and 2. C impacts ROE to a greater extent that it impacts ROA. This is

shown by the coefficient of -8.05 as compared to -0.575 for model 1 as shown in table 5. This

shows that cost management efficiency is a very important determinant of banking

profitability. Most of the literature dealing with the profitability of banks and their

determinants has found a negative and significant relationship between overheads

management and profitability. Guru et al (1999), Abreu and Mendes (2001), Kosmidou et al

(2005), Athanasoglou et al (2006) and Pasiouras et al (2006) have noted this negative

correlation in their respective studies. Davydenko (2010), Rachdi (2013) and Obamuyi (2013)

explain this observation by employing the conclusions of Athanasoglou et al (2006) in their

study on South Eastern European banks. This may also be applied in the Mauritian context.

The poor expenses management capacity of banks, result in part of those expenses being

passed onto clients and the other part are netted against profits, which reduces the profitability

of banks.

The expectations regarding the sign of capital were rightly formulated for model 1. The

impact on banking profits is significant. The bulk of literature also expressed a positive

correlation between the capital of a bank and the profits it made. The studies covered various





time periods and also concerned different countries and banks. The most notable researchers

having found a positive relationship are Staikouras & Wood (2004), Goddard et al (2004),

Athanasoglou et al. (2005), Kosmidou et al. (2005), Obamuyi (2013), Davydenko (2010),

Sufian and Chong, Rachdi (2013) and Yilmaz (2013). Obamuyi (2013) in his study

elaborated 3 hypotheses which could explain the effect of capital on profits; the signalling

theory, the bankruptcy cost hypothesis and the risk return hypothesis. The positive

relationship that has been observed following regression analysis gives support to the first

two.

The liquidity risk measure (LOANTA) shows a positive but insignificant relationship

with ROA while the relationship with ROE is a positive and significant one. This finding

follows the line of thought of Athanasoglou et al (2006), Abebaw and Deepack (2011) and

Yilmaz (2013) who also found a positive relationship between liquidity risk and profitability.

At first glance one would most likely to expect a negative relationship between the dependent

variable and this particular regressor but the case of the correlation being positive can be

supported by the risk return trade-off between liquid and less liquid assets and the ability of

banks to impose adequate screening. While it is true that in period of crises the bank would

need liquidity to honour short term obligations, it may have taken adequate steps to prevent

these risks from cropping up, by, for example, imposing stricter screening before the

allocation of loans-less default would impose less pressure on the bank’s liquidity position.

Having done so, the banks find themselves with excess liquidity which they are more than

pleased to invest in higher yielding but also illiquid assets. This may be an explanation for the

positive relationship observed.

Credit risk is another important determinant of profitability according to the results

obtained. The impact of credit risk on profits is positive in both models but and significant at

the 5% level only in model 1. This is contrary to what was expected and what most of the

literature reports. For instance, Athanasoglou et al (2006), Sufian and Chong (2008),

Davydenko (2010) and Kundid et al (2011) have observed a negative relationship between

credit risk and bank performance. From all the literature examined, only Kosmidou et al

(2005) in their study of the UK banking sector have found a similar positive correlation. They

supported their observation by employing the risk-return hypothesis; where the higher risk

you take the more rewards you get. This explanation can be extrapolated to the Mauritian

Banking industry. Credit risk was measured by the loan loss provision which reflects the level

of credit risk. The results show that higher risks implied higher return; we can therefore

assume that when the loan loss provisions were increased, there had been careful scrutiny of

loan takers to screen out the ones most unlikely to pay. The provisions concerned the bulk of

persons which were deemed as reliable. Therefore, the banks, although taking risks, were

taking controlled risks, through their screening procedure.





The asset mix (MIX) of banks can also be understood as the level of diversification of

activities. Its impact on ROA and ROE is observed to be highly significant and positive, the

impact on ROE being stronger on ROE than on ROA as shown by the coefficients. In model

1, a one percent change in MIX causes a 0.71% change in ROA while in model 2, a one

percent change in MIX causes a 4.59% change in ROE. The research of Sufian and Chong

(2008) yielded a positive coefficient for their measure of diversification. Karkrah and

Ameyaw (2010) have noted a similar relationship in their study of the Ghanaian banks. The

portfolio management theory provides a suitable explanation for this. According to theory to

reduce to a minimum the risks faced by an organisation it should aim to diversify its business

operations. In this case, the main business of banks is giving out loan and the main source of

income is the interest collected from loan takers. Diversification would therefore be non-

interest sources of income. It can be said that banks in Mauritius have successfully diversified

their income sources.

Surprisingly the ownership structure goes against what was expected in both models. While

Bonin, Hasan and Watchel (2005) concluded that for developing countries foreign ownership

is positively linked to profitability, the study reports a negative and insignificant relationship

(at 5% level). A possible explanation might be that foreign branches of large multinationals

have techniques and perceived fame to which most of the local firms have not yet caught up

to. From the results displayed in table 4 it can be seen that macroeconomic and industry

specific factors are not significant in determining the profitability of banks, whether measured

by ROA or by ROE. DINF and logconc present p-values which are insignificant at the 5%

level in both cases. GDP is insignificant in model 1 but becomes statistically significant in at

the 5% level in model 2.

The results of this study for models 1 and 2 provide no evidence for the Structure-Conduct-

Hypothesis since the effect of logconc is an insignificant one in both cases. This observation

is also reported in the studies of Demirguc-Kunt and Huizinga (1999), Kosmidou et al.

(2005), Pasiouras and Kosmidou (2007), Flamini et al (2009), Davydenko (2010) and

Trujillo-Ponce (2013). However the sign observed differs from all the above mentioned

studies; all of them had noted positive but insignificant relationships. Only Ben Naceur

(2003) and Roman and Dănuleţiu (2013) who had used concentration ratios found a negative

relationship. The impact of business cycle on profitability of banks is very much documented.

Many researchers have used various measures of business cycle in their studies. Neely and

Wheelock (1997) used per capita income, Obamuyi (2013) used a dummy to proxy business

cycle and the larger proportion of the research has employed GDP growth as measure. This

study has followed the last trend and has found that as expected the relationship between GDP

and ROA is a positive but insignificant one. In model two however, the impact of GDP

remains positive but becomes significant. The degree of the impact is also very interesting. A





1% change in GDP causes more that 2.5% change in ROE. This positive relationship can be

explained by the fact that during periods of economic prosperity investors are more likely to

be taking up loans for real investments. More loans taken would imply more interest income

for the banks and therefore increased profitability.

Finally the impact of inflation (DINF) on the profitability of banks is positive but remains

insignificant in both models. Studies that have included inflation as a determinant of

profitability have generally noted a positive relationship (Bourke (1989), Molyneux and

Thorton (1992), Kosmidou et al. (2005), Athanasoglou et al. (2006), Davydenko (2013) and

Yilmaz (2013). Inflation will, in a general manner impact on the expenses of the bank. In

times of inflation expenses will increase. However Perry (1992) argued that the impact of

inflation also depends on the predictive capacity of banks that is in times of expected inflation

banks will adjust interest rates so that their income increases more than their expenses. Since

the sign of DINF if a positive one, it can be assumed that here in Mauritius, banks have

reliable predictive skills.

3.2. Dynamic Model

The two-step Arellano Bond GMM estimator is used to test the presence of dynamism in

the models studied. The Sargan test result provides evidence for the acceptance of the null

hypothesis of validity of over-identifying restrictions in both models 3 and 4.

Table 3: Summary of Sargan test

MODEL 1 MODEL 2

ROA as Dependent ROE as Dependent

Sargan Test 1.0000 1.0000


The regression results of model 3 and 4 are presented below.

Table 4: Dynamic Model Regression Results

MODEL 3 ROA MODEL 4 ROE

Coefficient P>z Coefficient P>z

L1. 0.537651 0.175 0.065992 0.720

S -0.00364 0.126 -0.13662*** 0.002

C -1.15226*** 0.000 -19.0385*** 0.001

CAP 0.019184 0.407 -1.4933 0.104

LOANTA 0.010459 0.161 0.125846 0.327

DCRE 0.084592*** 0.001 -1.66766 0.113

MIX 0.970638*** 0.000 7.097434*** 0.001

GDP 0.149837 0.287 2.46535 0.295





logconc -0.01161 0.329 0.204523 0.186

DINF -0.04339 0.153 -0.13578 0.765

_cons 0.143845 0.084 1.239668 0.150

*Significant at 10% level, **Significant at 5% level, ***Significant at 1%level


In both models 3 and 4 the insignificance of the lagged profitability variable was

observed. This indicates that the profitability of the previous period has no significant effect

on the profitability of the current period. This is contrary to the findings that Athanasoglou et

al (2006) and Flamini et al (2009) have noted. In Model 3, the coefficient of the lagged

variable shows a moderate persistence of profits, that is the profits tend to persist for some

time before eventually returning to their equilibrium value. In model 3, under the GMM

model, capital which was previously significant in model 3 becomes insignificant, implying

that in the short run it does not affect profitability. The C, DCRE and MIX variable however

remain significant at the 5% level. The coefficients of C and MIX have strengthened showing

that these variables affect the profitability on the short run. On the other hand, the coefficient

of DCRE has decreased in value, meaning that the short-run effects are weaker. In model 4,

some interesting things are noted. Firstly, GDP which was previously significant in the

determination of profitability is no longer significant, indicating that there is no short run

effect of GDP on profitability. Secondly, C and MIX show significance, indicating that their

effects are present in the short run as well. The coefficient of both variables show increased

impact on profitability in the short run. Thirdly and most surprisingly, the effect of size which

was previously observed as insignificant becomes significant and negative in the short run.

4. Conclusions and Recommendations

The problem stated at the beginning of this study was ‘What are the determinants of

Mauritian Commercial Banking Profitability’. For this purpose a sample of 15 banks was

chosen from the population of 23 banks available. To be able to identify suitable

determinants, the work of previous researchers has been carefully read. To proceed with the

analysis, the financial statements of the sampled banks were scrutinized to gather the

information relevant to the computation of ratios. The calculated ratios have then been put

under analysis. As per the procedure of previous authors having treated the same topic, tests

pertaining to the stationarity, autocorrelation, contemporaneous autocorrelation and

heteroscedasticity were conducted. It was noted that the proxies for credit risk and inflation

were not stationary and adequate measures were taken. Further tests revealed presence of

heteroscedasticity and autocorrelation. The regression was thus run using Generalized

Estimating Equations.





It has been observed that ROA and ROE are both affected significantly by cost

management efficiency. While ROA also presents significant dependence on the equity

position and the amount of credit risk taken, ROE on the other hand, is significantly affected

by the GDP growth. ROA demonstrated that the ownership structure is not significant in

determining bank performance while ROE at the 10% significance level showed evidence in

its favour. In an attempt to investigate whether previous period profits have the tendency to

persist over time, a dynamic model was employed. Evidence from both ROA and ROE

showed that previous period profits do not affect the present period profits.

4.1 Industry Recommendation

The Bank of Mauritius should regularly check on the capital situation of the banks in the

industry. The results of the study have demonstrated that adequate levels of capital have a

positive impact on profitability. This monitoring would also be in line with the preservation of

a stable financial environment.

It has been noted that concentration does not affect profits significantly. However a trend

towards a more concentrated market is not desirable. The Bank of Mauritius should therefore

implement adequate measures to maintain the present trend of decreasing concentration.

The results have also shown that cost management efficiency have an important part to play

in determining the performance of the banks. The measure employed in this study is cost to

total assets. It is therefore important that banks pay utmost attention to the way operating

expenses are managed.

Credit risk has also been identified as a major determinant of profits. The study has

isolated the coefficient of credit risk to be positive indicating higher credit risk yields higher

profits which is consistent with risk return theory. However, it is important that the credit risk

taken are provided for and also that the screening at inception is done adequately. This study

recommends that the screening before giving out loans be done strictly enough to put aside

bad borrowers.

The diversification proxy has also shown significant influence on profitability. The

recommendation concerning asset diversification is to diversify the risks faced by the firm by

finding other sources of income. This diversification however should not come at the expense

of screening and monitoring of loans.

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