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VOL 5, NO 2

THE IMPACT OF MACROECONOMIC AND BANK SPECIFIC

COMPONENTS ON THE RETURN OF EQUITY

Nurfadhilah Abu Hasan*, Noor Azizah Shaari, Yamuna Rani Palanimally &Ramesh Kumar Moona Haji

Mohamed

Nurfadhilah Abu Hasan , Faculty of Business & Finance, UTAR.

Noor Azizah Shaari, Faculty of Business & Finance, UTAR.

Yamuna Rani Palanimally, Faculty of Business & Finance, UTAR.

Ramesh Kumar Moona Haji Mohamed, Faculty of Business & Finance, UTAR.

Abstract

The aim of this study is to examine the impact of macroeconomic and bank specific components on the return

on equity (ROE) of the domestic commercial bank (AmBank). The internal determinants are operating

efficiency ratio and liquidity ratio. In addition, we also tend to examine the external factors such as financial

crisis and consumer price index. In this research, we used secondary data. Nine years quarterly time series data

(2004-2012) of the financial report of AmBank data was selected. The relationship between independent

variables and dependent variables are tested by using Ordinary Least Square method. The estimation result

showed that the operating efficiency ratio, liquidity ratio, consumer price index and financial crisis are inversely

affecting the bank profitability. The significance of this study is believed to contribute future academicians and

banker for a better understanding of the factors that will affect the bank profitability in banking industry.

Keywords: - consumer price index, operating efficiency ratio, liquidity ratio, financial crisis return on equity.

1.0 Introduction

In this research, we intend to concentrate on the impact of macroeconomic and bank specifies components on

the domestic commercial bank on the case of AmBank. This research is to determine whether consumer price

index, operating efficiency ratio, liquidity ratio, and financial crisis each might be affecting the return on equity.

They are the determinants of macroeconomic and bank specific components that might affect the return on

equity on the domestic commercial bank.

Consumer price index is the inflation rate. It is one of the macroeconomic factors that could influence the return

of equity of bank. Bank profit could directly affect by the inflation rate because it will change the interest rate of

the loan as well. According to the research of Allen and Saunders (2004), macroeconomic factors are one of the

important information that could affect the return of equity of bank. According to Frank K. Reilly (1997), there

is an inversely relationship between consumer price index and return on equity.

Operating efficiency ratio is included to find out does the ratio could affect the return of equity of the

commercial bank. This is because the operating efficiency ratio is the profit ratio of bank. It will directly

influence the return of equity of the bank. The operating efficiency ratio is the percentage of the expenses over

the profit of the bank. According to Ezra Francis (2004), the relationship between operating efficiency ratio and

return on equity are negatively related. There is a same result which is negative relationship between operating

efficiency ratio and return of equity and this statement was found by Lee and Chih (2012).

For the liquidity ratio, it is also used to find out the relationship between liquidity ratio and return of equity. It is

measured by bank’s current assets to current liabilities. High ratio may help the bank to generate more profit

from the interest income. Molyneux and Thornton (1992) found that the relationship between liquidity ratio and

return of equity are negative.

Financial crisis is another macroeconomic factor which could affect the bank’s return of equity. Financial crisis

will cause the bank experience liquidity problems because of the mismatch in their funding of loans (Lindblom,

Olsson and Willesson, 2010). There is negative relationship between financial crisis and return of equity is

stated by Ooi (2010).

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2.0 Literature Review

2.1 Return on Equity

Return on equity (ROE) is one of the proxies used to measure profitability. ROE equals the firm’s net

income divided by shareholders’ equity (Gitman at el 2012). The ROE ratio is important to investors in the

firm because it measures the return on the money that those investors have invested in the firm. Investors

usually look for firms with returns on equity that are high and growing. In general, the higher the

percentage of the ROE, it is the better, and this means that the firm is efficiently using the investors’

money. Therefore, the firm has lesser incentive to hedge when it has higher profitability. Gounopoulos et al

(2012) had investigated the relationship between the exchange rate risk and the equity performance of

financial intermediaries. A sample of the United States, United Kingston and Japanese banks and insurance

firms were identified as the sample firms for the year 2003-2011. This study used a multivariate analysis to

test the variable that drives the firms to use derivatives. The finding showed that there was a negative

correlated and insignificant relationship between derivatives and equity. The banks’ equity returns are also

negatively related to changes in foreign currency value and for those smaller banks had less incentive to

hedge or limited their currency hedging activities.

2.2 Consumer Price Index (Inflation)

According to Olaniyan (2000), she stated that the consumer price index is an important indicator of

macroeconomic and it showed that there is a significant negative relationship with bank profitability in

their research. Khrawish (2011) found that there is a negative relationship between consumer price index

and return on equity. Yet, Mwenda and Mutoti (2011) also agree that there is a negative (relationship

between consumer price index and return on equity. Ogowewo and Uche (2006) showed that there is a

negative relationship between consumer price index and bank profitability. They believed that the bank is

always get affected by high inflation rate. The high inflation rate means a higher consumer price index will

cause the instability of macroeconomic and brings the risk into a bank. Hence, it decreases the bank

profitability. Giammarino (1998) said that the negative correlation between profits margin and consumer

price index may reflect the fact that banks are not able to pass on price increases through higher prices

even though costs do increase. This is then reflected in stock returns, where the reduced expected cash flow

result in lower prices. Beside, Boyd (1997) found that there is a strong negative correlation between

inflation and profitability. This correlation obtains both for bank lending activity and for equity markets.

Moreover, inflation had a negative relationship with the profit margin which will, in turn, reduce return on

equity and expected growth (Fuller and Perry, 1981). Nevertheless, it could be a positive or negative

relationship between consumer price index and return on equity depends on the prediction of inflation rate.

Banks can increase the revenue by adjusting interest rate if inflation rate is predicted (Perry, 1992).

2.3 Operating Efficiency Ratio

The return on equity is an inevitable measure of profitability of business (Dissanayake, 2012). Thus, a

positive of return on equity may be attainable by reducing the levels of service or internal operation cost.

The operating expenses are primarily driven by reductions in non-personnel expenses. According to

Dissanayake (2012), his study stated that an increase in the administrative expense ratio is hypnotized to be

associated with a decrease in financial self-sufficiency and vice versa. This meaning that the relationship

between return on equity and operating efficiency ratio is thought to be negatively proposition. Ncube

(2009) also observed that the bank profitability can be explained in the term of operating efficiency ratio.

Hence, Mohd Said and Mohd Hanafi (2011) had also suggested that a higher expense is means to a lower

profit in their research. Reduced of expenses will improve the efficiency and hence raise the profitability of

a financial institution. According to Khizer Ali, Muhammad Akhtar and Hafiz Ahmed (2011), there is a

significant negative relationship between operating efficiency ratio and bank profitability. Same result

obtained by Alkhatib (2012) stated that operating efficiency ratio have a weak negative relationship with

bank profitability. Furthermore, Bourke (1989) found that cutting the expenses cost could improves the

efficiency ratio and hence increase the return on equity of bank, this mean that there is a negative

correlated between operating efficiency ratio and return on equity in a bank. Another result found by

Berger and Mester (1997) mentioned that return on equity have no positive relationship with operating

efficiency ratio and suggesting that they maybe have negative relationship with each other. They said a

lower of operating efficiency ratio can bring a high profit to a bank.

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2.4 Liquidity Ratio

According to Yu (2000), liquidity ratio is one of the main determinants of bank capital ratio. It could be a

source that will lead to bank failures. Molyneux and Thorton (1992) showed that there is a negative

relationship between liquidity ratio and return on equity. They stated that liquid assets are important to a

bank because it is a type of asset which can be easily converted into cash. Therefore, the liquid assets could

influence the rate of return of a bank. In short, a high liquidity ratio will lead to a low return on equity for a

bank. Besides, Izhar and Asutay (2007) indicated that the liquidity ratio has a significant negative impact

on bank profitability. Raheman and Nasr (2007) also agreed that the two variables have significant

negative impact on each other. According to Blatt (2001) and Eljelly (2004), they found that the liquidity

ratio is significantly in explained to return on equity. A higher of current ratio will cause a higher liquidity

ratio in a bank. By using the formula to calculate the liquidity ratio, current assets over current liabilities

are used to conduct the liquidity ratio. Therefore, the past researcher believed that the liquidity ratio has a

significant negative impact on return on equity. Layroudi et al (1999) studied in a research of the relation

between liquidity ratio and profitability in companies of London Stock Exchange in period of 1993-1997.

The results of their research stated there is a negative relationship between liquid ratio and profitability. In

addition, Wang (2002) also told us that the relationship between liquidity ratio and return on equity is a

negative correlation.

2.5 Financial Crisis

The crisis which started in 2007 is characterized by a sharp shortage of liquidity in the financial systems

around the world. According to Ooi Shuat Mei (2010), the growths were encouraging for the first three

quarters of 2008. Like most of the major trading partners, the negative effect from the crisis had slowed

down economic activities in these countries, which caused a negative growth of export for quarters 2008

and severe contraction for the first two quarters of 2009. According to Hidayat and Muhamad Abduh

(2012), return on equity as dependent variable, there are four explanatory variables which are significantly

affecting the return equity and post crisis situation. It proved that financial crisis will directly affect return

on equity and showed a negative relationship. Besides, a global economic recession and financial crisis on

Malaysia was significantly reducing the whole banking system and hence the bank profitability, the return

on assets and return on equity had been affected and decreased significantly (Abidin and Rasiah, 2009).In

addition, a bank was found that it is hard to keep a high leveraged during the financial crisis happened.

This reason leads to the return on equity have a significant decreased (Propst, 2012). Femandez and Arana

(2010), Jasmine (2008), Suflan (2011) and Sufian and Habibullah (2010), all of them also agreed that there

is a negative relationship between the financial crisis and return on equity.

3.0 Methodology

3.1 Research Design

The purpose of our research is to examine the relationship between dependent variable, the return on equity

and independent variables which are consumer price index, liquidity ratio, operating efficiency ratio and

financial crisis. Moreover, some specific and relevant tests will be conduct in order to acquire the accurate

results.

3.2 Data Collection Method

Secondary data was collected for this research study. Secondary data is the data that have been previously

collected for some purpose other than the completion of a research project (Zikmund, 2003). It also knows

as a historical data. It means that it is a data that already exist in public.

3.3 Target population

In this research, we used the secondary time series data. Sample size is determined by the data availability

which from 2004 to 2012 in quarterly, consists of nine years. So this data consists of 36 observations. All

of these data get from the quarterly financial reports of AmBank. By using the quarterly financial reports,

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we can obtain the data of the dependent variable which is return on equity. The operating efficiency ratio

and liquidity ratio are the two independent variables that obtained from the quarterly financial reports also.

Whereas, the rest of the two independent variables financial crisis and consumer price index (inflation) are

obtained from the various databases; there are Trading Economics, World Bank Development Indicator

and Global Development Finance, Penn World Table 7.0 and World Economic Outlook Database.

3.4 Data Analysis

The collected data of our research were analyzed by using EViews program with Least Squares regression.

EViews can be used for general statistical analysis and econometric analyses, such as cross-section and

panel data analysis and time series estimation and forecasting. It will provide us the results on the

relationship between dependent and independent variables. In our research, we are using time series data to

perform and run the test in EViews. By doing this, we are able to get a more accuracy results and data. The

hypothesis will be tested based on the result in Least Squares regression. The significant level is set at 5%.

According to Fisher (1971), it is because5% is the maximum acceptable probability for determining the

statistical significance and it is better to understand in a model.

3.4.1 Model Estimation

ROE = 0 + 1 CPI t + 2 EFF t + 3 LIQUIDITY t + 4DCRISIS + t

Where,

Dependent variable:

ROE = Return on equity, %

Independent variables:

CPI = Consumer Price Index (inflation)

EFF = Operating Efficiency Ratio

LIQUIDITY = Liquidity Ratio

CRISIS = Financial Crisis

The dependent variable is return on equity (ROE) which is measured in net profit after taxes over

shareholder equity. There are four independent variables in the model. Consumer price index (CPI) is

measured in index. Operating efficiency ratio (EFF) is measured in non-interest expenses over operating

income. Liquidity ratio (LIQUIDITY) is measured in current assets over current liabilities. Financial crisis

(CRISIS) is measured in one if the crisis happened during year2007 and year 2008, otherwise zero.

3.5 Research Instruments

3.5.1Multicollinearity

According to Ethington(2005), multicollinearity is an econometric problem which occurs due to the

existence of a nearly exact linear relationship between two or more independent variables in the same

equation. To test the seriousness of multicollinearity problem, we are using the variance inflation factor

(VIF). VIF is the most accurate method to detect the seriousness of multicollinearity problem (Ethington,

(2005.).The formula to detect the VIF is VIF= (1/1-R2x1x2). If the result of VIF is less than 10, it meansthat

there is no multicollinearity problem exists. Otherwise there is a serious multicollinearity problem exists in

the model.

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3.5.2 Autocorrelation

Mizon (2004) found that an autocorrelation refers to an error term at t-period correlated with error term at

t-1 period (past error term).In this report, Breush-Godfrey LM test will be applied to detect the

autocorrelation problem. In the Breush-Godfrey LM test, null hypothesis tends to be accepted to prove that

there is no autocorrelation problem.

3.5.3Heteroscedasticity

Bollerslev(1986) said that heteroscedasticityisis the econometric problem where there is omission of

reasonable independent variable that originally should be included into the model. In this research, ARCH

(Autoregressive Conditional Heteroscedasticity) test will be use among the heteroscedasticity hypothesis

testing to detect the heteroscedasticity problem because ARCH test is only apply in time series data (Engle,

1982). In the ARCH test, null hypothesis is tends to be accepted to prove that there is no heteroscedasticity

problem.

3.5.4 Model Specification Error

Ramsey (1967) said that in order to check the model specification error, we used the hypothesis testing and

decision rule to decide whether there is a model specification error or not. So, we have to run the Ramsey

Reset test in order to examine the model specification error. In this test, we use p-value of F-statistics to

test hypothesis testing. If the value is larger than 0.05 (the significant level), null hypothesis will be made

as there is no model specification error in the model.

3.5.5 Normality Test

According to Ul-Islam (2011), normality test has been conduct to test whether the error term is normality

distributed or not.In our research, we use Jarque-Bera test to conduct this normality test (Bowman and

Shenton, 1975; Shenton and Bowman, 1977; Bera and Jarque, 1982 and Jarque and Bera, 1987).

According to Bowman and Shenton (1975), the formula of JB test is:

]

We are using EViews to conduct this Jarque-Bera test. By using EViews, we will look for the p-value for

JB-statistic. In null hypothesis, the assumption will be the error term is normally distributed. So, if the p-

value of JB-statistic is greater than α=0.05, we should not reject the null hypothesis.

3.5.6 T-test

Kruschke (2013) said that sample t-test is to determine the differences betweentwo sets of variables in

significant.

3.5.7 R-squared

R-Squared, also known as coefficient of determination which is a statistical term saying how good one

term is at predicting another. The higher the value of R-Square, the better is the prediction of one term

from another said by Cameron, & Windmeijer (1996).

3.5.8 Expected Sign

Expected sign is a statistical technique which shows the relationship between two variables. The positive

expected sign means that one variable increase, the other variable will also increase while negative

expected sign means that when one variable increase, the other variable will be decrease.

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4.0. Data Analysis

4.1 Regression Analysis

Table 4.1: Least Squares Regression

Dependent Variable: ROE

Method: Least Squares

Date: 03/20/13 Time: 18:32

Sample: 2004Q1 2012Q4

Included observations: 36

Variable Coefficient Std. Error t-Statistic Prob.

CPI -0.001244 0.001943 -0.640528 0.5265

EFF -0.277153 0.112698 -2.459250 0.0197

LIQUIDITY 1.001787 0.927444 1.080159 0.2884

CRISIS -0.092841 0.040074 -2.316718 0.0273

C -0.733722 1.011046 -0.725706 0.4735

R-squared 0.364744 Mean dependent var 0.061639

Adjusted R-squared 0.282775 S.D. dependent var 0.073857

S.E. of regression 0.062549 Akaike info criterion -2.577494

Sum squared resid 0.121283 Schwarz criterion -2.357560

Log likelihood 51.39488 Hannan-Quinn criter. -2.500731

F-statistic 4.449800 Durbin-Watson stat 2.018960

Prob(F-statistic) 0.005872

Source: Output from EViews

4.2 Measurement Scale

4.2.1 Multicollinearity

Table 4.2 shows the result of the strength of correlation within independent variables. The range of

multicollinearity test is between 1 to -1. The closer to number of one, the stronger the correlation is.

Table 4.2

ROE CPI EFF LIQUIDITY CRISIS

ROE 1 0.22311 -0.34102 0.43251 -0.40439

CPI 0.22311 1 -0.44942 0.43312 -0.15362

EFF -0.34102 -0.44942 1 -0.22169 -0.17518

LIQUIDITY 0.43251 0.43312 -0.22169 1 -0.47741

CRISIS -0.40439 -0.15362 -0.17518 -0.47741 1

Source: Output from EViews

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Table 4.3 shows the two independents which have highly pair correlation among all of the other

independent variables. In the result, we found that the CPI and LIQUIDITY have highly pair correlation, r

(0.433124).

Table 4.3

CPI LIQUIDITY

CPI 1 0.433124

LIQUIDITY 0.433124 1

Hence, we have to run a least square regression with CPI as dependent variable and LIQUIDITY as

independent variable to obtain the R-square and calculate the variance inflation factor (VIF).

Table 4.4

Dependent variable Independent Variable R-square

Consumer Price Index Liquidity ratio 0.187597

Variance Inflation Factor (VIF)

We found that the R-square is 0.187597. So, we use the VIF to calculate the seriousness multicollinearity

problem.

Variation Indicator Factor, VIF =

=

= 1.23092

Since VIF < 10, which is 1.23092, we may conclude that there is no serious multicollinearity problem in

this model.

4.2.2 Autocorrelation

To test the autocorrelation problem, we used the hypothesis testing and decision rule to decide whether

autocorrelation problem exists or not:

Autocorrelation hypothesis testing

Decision Rule: reject if p-value of F-statistic <α = 0.05. Otherwise do not reject .

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We are using Breusch-Godfrey Serial Correlation LM Test to get the p-value of F-statistic in following

Table 4.5:

Table 4.5

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.361089 Prob. F(2,29) 0.7000

Obs*R-squared 0.874713 Prob. Chi-Square(2) 0.6457

Source: Output from EViews

Decision: Since the p-value of F statistic (0.7000) is greater than α=0.05, do not reject H₀.

Conclusion: There is enough evidence to conclude that there is no autocorrelation problem exists in our

model at the significant level of 0.05.

From the result above, we can conclude that autocorrelation problem does not exist in our model.

4.2.3 Heteroscedasticity

To test the heteroscedasticity problem, we used the hypothesis testing and decision rule to decide whether

heteroscedasticity problem exists or not:

Heteroscedasticity hypothesis testing

Decision Rule: reject if p-value of F-statistic <α= 0.05. Otherwise do not reject .

We are using ARCH Test to get the p-value of F-statistic in following Table 4.6:

Table 4.6

Heteroskedasticity Test: ARCH

F-statistic 0.715507 Prob. F(1,33) 0.4037

Obs*R-squared 0.742767 Prob. Chi-Square(1) 0.3888

Source: Output from EViews

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Decision: Since the p-value of F statistic (0.4037) is greater than α=0.05, do not reject H₀.

Conclusion: There is enough evidence to conclude that there is no heteroscedasticity problem exists in our

model at the significant level of 0.05.

From the result above, we can conclude that heteroscedastity problem does not exist in our model.

4.2.4 Model Specification Error

To check the model specification error, we used the hypothesis testing and decision rule to decide whether

model specification error exists or not:

Model Specification Error hypothesis testing

Decision Rule: reject if p-value of F-statistic <α= 0.05. Otherwise do not reject .

We are using Ramsey Reset Test to get the p-value of F-statistic in following Table 4.7:

Table 4.7

Ramsey RESET Test:

F-statistic 0.883303 Prob. F(1,30) 0.3548

Log likelihood ratio 1.044659 Prob. Chi-Square(1) 0.3067

Source: Output from EViews

Decision: Since the p-value of F statistic (0.3548) is greater than α=0.05, do not reject H₀. Conclusion:

There is enough evidence to conclude that there is no model specification error exists in our model at the

significant level of 0.05.

From the result above, we can conclude that model specification error does not exist in our model.

4.2.5 Normality test

To check the normality test, we used the hypothesis testing and decision rule to decide whether model is

normally distributed or not normally distributed.

Normality hypothesis testing

Decision Rule: Reject if p-value of Jarque-Bera <α= 0.05. Otherwise do not reject .

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We are using histogram of normality test to get the p-value of Jarque-Bera in following Table 4.8:

Table 4.8

0

2

4

6

8

10

12

-0.10 -0.05 -0.00 0.05 0.10 0.15 0.20

Series: Residuals

Sample 2004Q1 2012Q4

Observations 36

Mean -9.64e-18

Median -0.014093

Maximum 0.175848

Minimum -0.109525

Std. Dev. 0.058866

Skewness 1.019686

Kurtosis 4.038757

Jarque-Bera 7.857088

Probability 0.019672

Source: Output from EViews

Decision: Reject since p-value of Jarque-Bera (0.019672) < 0.05.

Conclusion: There is not enough evidence to conclude that the error term is normally distributed.

From the result above, we can conclude that the error term in our model is not normally distributed. The

reason is we have insufficient data which only consists of 36 observations. Normally, most of the

researchers will use at least 100 observations to conduct their research project (Ahad et al, (2011).

According to Mantalos (2010), the test will always get not normally distributed result unless the sample

size is large enough.

4.3 Inferential Analysis

4.3.1 F-statistic test

In order to determine the significance of the model, the probability of F-statistic is taken into account.

Decision Rule: Reject if p-value of F-statistic <α = 0.05. Otherwise do not reject .

Decision: Reject since the p-value of F-statistic (0.005872) is smaller than α = 0.05.

Conclusion: There is not enough evidence to conclude that at the 0.05

significant levels.

From the result above, we can conclude that there is at least one independent variable is significant to the

dependent variable.

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4.3.2 T-test

Hypothesis testing between consumer price index and return on equity

Decision rule: Reject H0 if p-value of t-test <α= 0.05. Otherwise do not reject H0.

Decision: Do not reject H0 since p-value of t-test (0.5265) is large than α= 0.05.

Conclusion: We have sufficient evidence to conclude that consumer price index is statistically insignificant

in explaining return on equity.

From the result, consumer price index has an insignificant relationship with return on equity. According to

Garza-Garcia (2010) and Mwenda and Mutoti (2011), there is an insignificant relationship between

consumer price index and return on equity. Ben Naceur et al (2006) also agreed the consumer price index

has no significant impact on banks’ profitability. Furthermore, Chortareas, Garza-Garcia, Claudia

Girardone (2010) found that there is no significant sign between consumer price index and banks

profitability. Nwakanma and Ajibola (2013) also stated that there is no significant effect between consumer

price index and banks profitability.

Hypothesis testing between operating efficiency ratio and return on equity

Decision rule: Reject H0 if p-value of t-test <α= 0.05. Otherwise do not reject H0.

Decision: Reject H0 since p-value of t-test (0.0197) is smaller than α= 0.05.

Conclusion: We have insufficient evidence to conclude that operating efficiency ratio is statistically

insignificant in explaining return on equity.

From the result, operating efficiency ratio has a significant relationship with return on equity. According to

Ezra Francis (2004.), the operating efficiency has a significant negative correlation with bank profitability.

Ezra Francis (2004) also agreed that there is significant negative correlation within the two variables.

Furthermore, Sufian and Habibullah (2010) also found that the significant relationship due to increase of

operating efficiency will decreases a bank profitability. Guru et al (2002) also observed that operating

efficiency expenses is significant to explain bank profitability.

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Hypothesis testing between liquidity ratio and return on equity

Decision rule: Reject H0 if p-value of t-test <α= 0.05. Otherwise do not reject H0.

Decision: Do not reject H0 since p-value of t-test (0.2884) is large than α= 0.05.

Conclusion: We have sufficient evidence to conclude that liquidity ratio is statistically insignificant in

explaining return on equity.

From the result, liquidity ratio has an insignificant relationship with return on equity. According to Kok et

al (2012) and Alpera and Anber (2011), they examined that there is no relationship between liquidity ratio

and return on equity. Liquidity ratio is a determinant which has insignificant impact on return on equity

(Salem & Rahman 2011). Niresh (2012) also stated that the liquidity is insignificant explain to return on

equity. According to Owolabi, Obiakor, & Okwe, (2011), they said although the liquidity ratio has

negative impact on return on equity, but they are not significant on each other.

Hypothesis testing between financial crisis and return on equity

Decision rule: Reject H0 if p-value of t-test <α= 0.05. Otherwise do not reject H0.

Decision: Reject H0 since p-value of t-test (0.0273) is smaller than α= 0.05.

Conclusion: We have insufficient evidence to conclude that financial crisis is statistically insignificant in

explaining return on equity.

From the result, financial crisis has a significant relationship with return on equity. According to Bikker

and Hu (2002), Albertazzi et al (2009), they found that the financial crisis has the significant impact on

return on equity. Berger and Bouwman (2009) also proved that the financial crisis bring a significant

influences on bank profitability. Besides, during the period of financial crisis, banks found that there is

difficult to remain the high leverage and lead to a significant impact on return on equity ( Propst, 2012).

4.3.3 Econometric model

Regression result:

ROE = - 0.733722 – 0.001244 CPI – 0.277153 EFF + 1.001787 LIQUIDITY – 0.092841 CRISIS

T-stats= (-0.640528)**(-2.459250)** (1.080159)** (-2.316718)**

R2 = 0.364744 Ṝ

2 = 0.282775

F-stat = 4.449800 Probability (F-stat) = 0.005872

*significant at 0.01 level **significant at 0.05 level ***significant at 0.1 level

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4.3.4 R-squared

The coefficient of determination, R2

is 0.364744. It means 36.47% of the variation in dependent variable

can be explained by the variation in independent variables.

4.3.5 Interpret the coefficient

The coefficient of the intercept is -0.733722, the result shows that when the entire independent equal to

zero, the return to equity will decrease 0.733722%.

The coefficient of the consumer price index is -0.001244; it shows that when the consumer price index

increased in 1%, the return on equity will decrease 0.001244%, on average, holding other factors constant.

The coefficient of the operating efficiency ratio is -0.277153; it shows that when the operating efficiency

ratio increased in 1%, the return on equity will decrease 0.277153%, on average, holding other factors

constant.

The coefficient of the liquidity ratio is 1.001787; it shows that when the liquidity ratio increased in 1%, the

return on equity will increase 1.001787%, on average, holding other factors constant.

The coefficient of the financial crisis is -0.092841; it shows that when the financial crisis increased in 1%,

the return on equity will decrease 0.092841%, on average, holding other factors constant.

4.3.6 Expected sign

Based on table 4.1, the expected sign for CPI, EFF and CRISIS is negative whereas the LIQUIDITY is

positive sign.

-ve -ve +ve -ve

ROE = f (CPI, EFF, LIQUIDITY, CRISIS)

1. Expected sign < 0, EViews result showed that there is a negative relationship between consumer

price index and return on equity. This implies that an increase in consumer price index will lead to the

decrease of return on equity. According to Ogowewo and Uche (2006), a negative relationship

between consumer price index and return on equity was found in their research. The research showed

that there is a negative impact exists between consumer price index and bank profitability. Therefore, a

consistent result (negative sign) has gained.

2. Expected sign < 0, EViews result showed that there is a negative relationship between operating

efficiency ratio and return on equity. This implies that an increase in operating efficiency ratio will

lead to the decrease of return on equity. According to Alkhatib (2012), they stated that there is a weak

negative relationship between operating efficiency ratio and return on equity. Another result also

showed that the bank profitability has no positive relationship with operating efficiency ratio (Berger

and Mester, 1997). Therefore, a consistent result (negative sign) has gained.

3. Expected sign > 0, EViews result showed that there is a positive relationship between liquidity ratio

and return on equity. This implies that an increase in liquidity ratio will lead to the increase of return

on equity. According to the Izhar and Asutay (2007), Blatt (2001) and Eljelly (2004), they said that the

liquidity ratio has the significant negative impact on return on equity. Therefore, it is not consistent

with the expected sign which is positive. To prove the liquidity ratio is positively affecting the return

on equity, we have found some evidence from past researchers. According to Bourke (1989) and

Sufian and Habibullah (2010), they found a significant positive relationship between liquidity ratio

and bank profitability. It indicates that the higher the liquidity ratio of a bank, the higher profitability

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of a bank is. Furthermore, Haron and Azmi (2004) and Wasiuzzaman and Tarmizi (2010) found that

the liquidity ratio has a significant positive impact in explaining of return on equity.

4. Expected sign < 0, EViews result showed that there is a negative relationship between financial

crisis and return on equity. This implies that an increase in financial crisis will lead to the decrease of

return on equity. According to Hidayat and Abduh (2012), they showed that there is a negative impact

exists between financial crisis and return on equity. Moreover, an inverse relationship also exists

between financial crisis and bank profitability in a past research (Ooi 2010). Therefore, a consistent

result (negative sign) has gained.

4.4 Summary of Statistical Analyses

This research is to examine the impact of macroeconomic and bank specific components on domestic

commercial bank (AmBank). The macroeconomic factors include financial crisis (CRISIS) and consumer price

index (CPI) while bank specific components refer to the operating efficiency ratio (EFF) and liquidity ratio

(LIQUIDITY). The quarterly data is collected over the period of 2004 to 2012 and Least Square Regression is

used to examine between return on equity (dependent variable) and each of the independent variables

respectively with 5 percent significant level.

Table 5 Summary of Econometric Problems

In this research, we can know that the econometric model has no suffer in any econometric problems such as

multicollinearity, autocorrelation, heteroscedasticity and model specification error except the normality test. We

get a not normally distributed result in this research. This is because we have insufficient data which only

consists of 36 observations. According to Ahad, et al (2011), most of the researchers will use more than 100

observations to conduct their research project in order to get a more precise result.

Table 6: Summarize of Major Findings

Dependent variable Independent Variable Least Square Regression T-Test

Return on Equity Consumer Price Index Negative relationship Insignificant

Return on Equity Operating Efficiency Ratio Negative relationship Significant

Return on Equity Liquidity Ratio Positive relationship Insignificant

Return on Equity Financial Crisis Negative relationship Significant

The table above shows the relationship between dependent and independent variables respectively. There is an

insignificant negative relationship between consumer price index and the return on equity of AmBank while the

operating efficiency ratio carry a significant negative relationship with return on equity in this research. Next,

insignificant positive relationship between liquidity ratio and return on equity has been show in the table above.

Lastly, there is a significant negative relationship between financial crisis and the return on equity of a bank.

Econometric problems Description on results

Multicollinearity Not serious multicollinearity problem

Autocorrelation No autocorrelation problem

Heteroscedasticity No heteroscedasticity problem

Model specification error No model specification error problem

Normality test Not normally distributed

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5.0 Discussions of Major Findings

Consumer price index (inflation) refers to the overall general upward price movement of goods and services in

an economy. According to Ogowewo and Uche (2006) there is a negative impact exists between consumer price

index and return on equity. This implies that an increase in consumer price index will lead to the decrease of

return on equity which is consistent with our result of negative relationship.

Operating efficiency ratio is used to analyze how well a company is using its assets as well as its liabilities.

According to Alkhatib (2012), they stated that there is a weak negative relationship between operating

efficiency ratio and return on equity.This implies that an increase in operating efficiency ratio will lead to the

decrease of return on equitywhich is consistent with our result of negative relationship.

Liquidity ratios are designed to test a company's ability to meet its short-term financial obligations. According

to the Izhar and Asutay (2007), Blatt (2001) and Eljelly (2004) stated that the liquidity ratio has the significant

negative impact on return on equity which is inconsistent with our results of positive relationship.

Financial crisis is an economic recession or depression caused by a lack of necessary liquidity in financial

institutions. According to Hidayat and Abduh (2012), they showed that there is a negative impact exists between

financial crisis and return on equity. This implies that an increase in financial crisis will lead to the decrease of

return on equity which is consistent with our result of negative relationship.

6.0. Implication

6.1 Implications of the Study

Fiscal policy is the interventional of government in influencing the economy by using taxation and

expenditure. There is a financial crisis happen in year 2008 and Malaysia is affected, ultimately economy

of Malaysia is in recession. Therefore, government should implement expansionary fiscal policy to

stimulate the economy. In the expansionary fiscal policy, government should decrease the tax and increase

the government spending. By reducing the tax, income of individual will increase and have surplus on

income. Individual could go for either to saving or investment when there is a surplus on income. Bank’s

fund will increase when individual choose to save the money, in other words, bank have more funds to

increase the profitability. On the other hand, profitability of bank may increase when individual invests the

money in financial market. Bank could earn the intermediary fee by managing the investment on behalf

individual.

According to Ambank financial report, they have lots of strengths. One of the strengths is that Ambank

have a good progress in strategic business transformation. This strength refers to Ambank transformed

their business model from product focused model to customer-oriented model. Ambank is more concerning

on and delivering the need of customer by shifting to customer-oriented model. This transformation could

increase the market share of Ambank. Strength of Ambank is excellence in their products and services

offered. Ambank has come out their excellence product Am Investment Bank was named Best Domestic

Bond House. This product has been rewarded in the Asset Triple A Country Awards 2011 for fourth time.

While for the service offered by Ambank, they are recognized by Corporate Governance Asia as the Best

Investor Relations and Best of Asia Corporate Asia Recognition.

6.2 Managerial Implication

6.2.1 Consumer price index

In this research, we found there is an insignificant negative relationship between consumer price

index and the return on equity of AmBank. Although consumer price index is not significant to affect

the return on equity of a bank in this research, however, consumer price index is still playing an

important role in analyze and research on various issues such as understanding regional disparities in

price movements. Government should take an action in the sector of monetary policy by maintaining

the lending rate in Bank Negara Malaysia. By doing this, government can control the inflation rate so

that the price of item and the interest rate will not get affected too much. Therefore, banks should

always pay attention to the consumer price index in order to perform better in future.

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6.2.2 Operating Efficiency Ratio

Operating efficiency ratio has significant negative relationship with return on equity. Bank have to

either increase the operating profit or decrease the operating expenses in order to generate a better

operating efficiency ratio. A lower operating expense brings a higher profit to bank. Therefore, bank

should control the operating expenses so that able to get a higher profit.

6.2.3 Liquidity Ratio

Liquidity ratio has an insignificant positive relationship with the return on equity of a bank. Although

liquidity ratio is insignificant in our research, however, it is still important to a bank. The banks need

liquidity ratio to make its daily operations. In order to maximize profitability, a bank should lower the

liquidity ratio in order to increase the income from loan. In other words, a bank could reduce the cost

of loan to increase the lending to the public. Therefore, the bank could increase its profitability.

6.2.4 Financial Crisis

Financial crisis has a significant negative relationship with return on equity. Therefore, bank should

always concern on the economy condition. Malaysia’s bank was suffered serious financial crisis

during the year 1997. In that incident, many banks, discounting houses, and finance companies were

forced to liquidate or merger with other financial institution. This case tells that impact of financial

crisis could be very serious when it comes. So, bank should up-to-date with current economy

condition.

7.0 Limitations of the Study

There are some limitations were discovered during the process of this research. First of the limitations

encountered in our research is that since we are using secondary data to analyze the return on equity of bank

(AmBank) we are only able to get the data by reviewing secondary data such as journal from past researchers

and financial reports from the bank in order to determine the factors that will influence the return on equity of it.

Besides, when reviewing journals from past researchers, we found that each researcher have own perspective

which carry different opinion on the variables that used to determine the return on equity of bank.

In addition, this research is intended to use 35 years annually data in order to get a more reliable result. But due

to the refurnishing of the bank’s main official website, we could not found back some of the data that we are

needed.

Furthermore, we also discovered that two of the independent variables in our model are insignificantly affects

the return on equity and R2 obtained is low. Last but not least, the model of our research is not normally

distributed, it is also consider as one of the limitations.

8.0 Recommendations for Future Research

After reviewing those limitations above, we trust that there are still a lots of improvement needed to do in order

to make a more precise research in future. Since the sample size is limit, we suggest that future researchers

might need to increase highly frequency of sample size such as monthly or daily. By this doing this, we strongly

believe that a more accurate result will then be obtained.

Moreover, due to the insignificant independent variable and low R2, we suggest that future researchers may

focus on more different type of variables which will directly influence the dependent variable. This is the easiest

way to enable us get a significant variables and a higher R2 can be obtained in the model too. Thus, a model will

then can be said that is best fitted. In addition, to solve the not normally distributed problem in our model, we

suggest that future researchers can apply nonparametric tests to the data. Nonparametric tests do not rely on the

underlying data to have any specific distribution. Next, the other solution to solve this not normally distributed

problem is we can apply trial and error method until the error term is normally distributed.

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