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CHAPTER IV
ANALYSIS AND DISCUSSION
4.1. Process and Results of Data Analysis
In this chapter will be discussed regarding the process and results and
discussion of data processing that was done. A method of analysis that used in this
research is multiple regression analysis. The overall processing of data processed in
this research using Microsoft Office Excel 2003 and SPSS (Statistical Package of
Social Science) Program version 17.0. This program can reduce the difficulty in
processing the data.
This research covers a period from 2003 – 2008 with 18 samples in banking
companies listed in the Jakarta Stock Index. The list of the companies is presented in
the table below:
Table 4.1 List Sample of Banking Industry
No. Companies
Stock Code
1.
Bank Bumiputera Indonesia Tbk
(BABP)
2.
Bank Central Asia Tbk
(BBCA)
3.
Bank Negara Indonesia (PERSERO) Tbk (BBNI)
4.
Bank Nusantara Parahyangan Tbk
(BBNP)
5.
Bank Century Tbk
(BCIC)
6.
Bank Danamon Indonesia Tbk
(BDMN)
7.
Bank Eksekutif International Tbk
(BEKS)
8.
Bank Kesawan Tbk
(BKSW)
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9.
Bank CIMB Niaga Tbk
(BNGA)
10.
Bank International Indonesia Tbk
(BNII)
11.
Bank Permata Tbk
(BNLI)
12.
Bank Swadesi Tbk
(BSWD)
13.
Bank Victoria International Tbk
(BVIC)
14.
Bank Artha Graha International Tbk
(INPC)
15.
Bank Mayapada International Tbk
(MAYA)
16.
Bank Mega Tbk
(MEGA)
17.
Bank OCBC NISP Tbk
(NISP)
18.
Pan Indonesia Bank Tbk
(PNBN)
4.2. Correlation
Coefficient
Test
and
Regression
Macroeconomic Variables (Inflation, SBI Rate, M2,
Exchange
Rate,
GDP,
Current
Account,
Reserve
Requirement, Net Buying Asing, Dow Jones Indexes,
Fed Rate, Hang Seng Indexes, and Crude Oil Price) with
Stock Return in Banking Industry
By using the Pearson Correlation analysis with SPSS Program, the results
obtained are as follows:
Table 4.2 Correlation Coefficient and Significances of Stock Return
Variables
Inflation
SBI Rate
Pearson Coefficient
Correlation
Significances
-0.242
0.254
-0.398
0.054
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Money Supply
Exchange Rate
GDP
Current Account
Reserve Requirement
Net Buying Asing
Dow Jones Indexes
Fed Rate
Hang Seng Indexes
Crude Oil Price
-0.106
0.623
-0.419(*)
0.041
-0.218
0.306
0.294
0.164
0.104
0.629
0.663(**)
0.000
0.429(*)
0.037
0.306
0.145
0.510(*)
0.011
0.138
0.519
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
a. The correlation coefficients test between Inflation with
Stock Returns in the Banking Industry
The first coefficients test is done to explain the relationship between the level of
inflation with the stock returns in the banking industry.
The following is the null hypothesis of correlation-test that conducted by using
Pearson formula/product:
H0: ρ = 1 There is no relationship between Inflation to stock return in the banking
industry.
H1: ρ ≠ 0 There is relationship between Inflation to stock return in the banking
industry.
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Based on table 4.1, the significance value that is owned by the Inflation Rate is
0.254, because the number is above 5% then H0 accepted, it means that there is no
significant relationship between inflation and stock returns in the banking industry. In
addition, the correlation coefficient of -0.242 inflation rate shows that there is
negative correlation relationship between the inflation rate with the stock returns in
the banking industry.
b. The correlation coefficients test between SBI Rate with
Stock Returns in the Banking Industry
Further more coefficient test is conducted to explain the relationship between SBI
Rate with stock returns in the banking industry.
The following is the null hypothesis of correlation-test that conducted by using
Pearson formula/product:
H0: ρ = 1 There is no relationship between SBI Rate to stock return in the banking
industry.
H1: ρ ≠ 0 There is relationship between SBI Rate to stock return in the banking
industry.
Based on table 4.1, the significance value that is owned by the SBI Rate is 0.054,
because the number is above 5% then H0 accepted, it means that there is no
significant relationship between SBI Rate and stock returns in the banking industry.
In addition, the correlation coefficient of -0.398 SBI Rate shows that there is negative
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correlation relationship between the SBI Rate with the stock returns in the banking
industry.
c. The correlation coefficients test between Money Supply
with Stock Returns in the Banking Industry
Further more coefficient test is conducted to explain the relationship between
Money Supply with stock returns in the banking industry.
The following is the null hypothesis of correlation-test that conducted by using
Pearson formula/product:
H0: ρ = 1 There is no relationship between Money Supply to stock return in the
banking industry.
H1: ρ ≠ 0 There is relationship between Money Supply to stock return in the
banking industry.
Based on table 4.1, the significance value that is owned by the Money Supply is
0.623, because the number is above 5% then H0 accepted, it means that there is no
significant relationship between Money Supply and stock returns in the banking
industry. In addition, the correlation coefficient of -0.106 Money Supply shows that
there is negative correlation relationship between the Money Supply with the stock
returns in the banking industry.
d. The correlation coefficients test between Exchange Rate
with Stock Returns in the Banking Industry
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Further more coefficient test is conducted to explain the relationship between
Exchange Rate with stock returns in the banking industry.
The following is the null hypothesis of correlation-test that conducted by using
Pearson formula/product:
H0: ρ = 1 There is no relationship between Exchange Rate to stock return in the
banking industry.
H1: ρ ≠ 0 There is relationship between Exchange Rate to stock return in the
banking industry.
Based on table 4.1, the significance value that is owned by the Exchange Rate is
0.041, because the number is below 5% then H0 rejected, it means that there is
significant relationship between Exchange Rate and stock returns in the banking
industry. In addition, the correlation coefficient of -0.419 Exchange Rate shows that
there is negative correlation relationship between the Exchange Rate with the stock
returns in the banking industry.
e. The correlation coefficients test between GDP with Stock
Returns in the Banking Industry
Further more coefficient test is conducted to explain the relationship between
GDP with stock returns in the banking industry.
The following is the null hypothesis of correlation-test that conducted by using
Pearson formula/product:
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H0: ρ = 1 There is no relationship between GDP to stock return in the banking
industry.
H1: ρ ≠ 0 There is relationship between GDP to stock return in the banking
industry.
Based on table 4.1, the significance value that is owned by the GDP is 0.306,
because the number is above 5% then H0 accepted, it means that there is no
significant relationship between GDP and stock returns in the banking industry. In
addition, the correlation coefficient of -0.218 GDP shows that there is negative
correlation relationship between the GDP with the stock returns in the banking
industry.
f. The correlation coefficients test between Current Account
with Stock Returns in the Banking Industry
Further more coefficient test is conducted to explain the relationship between
Current Account with stock returns in the banking industry.
The following is the null hypothesis of correlation-test that conducted by using
Pearson formula/product:
H0: ρ = 1 There is no relationship between Current Account to stock return in the
banking industry.
H1: ρ ≠ 0 There is relationship between Current Account to stock return in the
banking industry.
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Based on table 4.1, the significance value that is owned by the Current Account is
0.164, because the number is above 5% then H0 accepted, it means that there is no
significant relationship between Current Account and stock returns in the banking
industry. In addition, the correlation coefficient of 0.294 Current Account shows that
there is positive correlation relationship between the Current Account with the stock
returns in the banking industry.
g. The
correlation
coefficients
test
between
Reserve
Requirement with Stock Returns in the Banking Industry
Further more coefficient test is conducted to explain the relationship between
Reserve Requirement with stock returns in the banking industry.
The following is the null hypothesis of correlation-test that conducted by using
Pearson formula/product:
H0: ρ = 1 There is no relationship between Reserve Requirement to stock return in
the banking industry.
H1: ρ ≠ 0 There is relationship between Reserve Requirement to stock return in
the banking industry.
Based on table 4.1, the significance value that is owned by the Reserve
Requirement is 0.629, because the number is above 5% then H0 accepted, it means
that there is no significant relationship between Reserve Requirement and stock
returns in the banking industry. In addition, the correlation coefficient of 0.104
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Reserve Requirement shows that there is positive correlation relationship between the
Reserve Requirement with the stock returns in the banking industry.
h. The correlation coefficients test between Net Buying Asing
with Stock Returns in the Banking Industry
Further more coefficient test is conducted to explain the relationship between Net
Buying Asing with stock returns in the banking industry.
The following is the null hypothesis of correlation-test that conducted by using
Pearson formula/product:
H0: ρ = 1 There is no relationship between Net Buying Asing to stock return in
the banking industry.
H1: ρ ≠ 0 There is relationship between Net Buying Asing to stock return in the
banking industry.
Based on table 4.1, the significance value that is owned by the Net Buying Asing
is 0.000, because the number is below 5% then H0 rejected, it means that there is
significant relationship between Net Buying Asing and stock returns in the banking
industry. In addition, the correlation coefficient of 0.663 Net Buying Asing shows
that there is positive correlation relationship between the Net Buying Asing with the
stock returns in the banking industry.
i. The correlation coefficients test between Dow Jones
Indexes with Stock Returns in the Banking Industry
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Further more coefficient test is conducted to explain the relationship between
Dow Jones Indexes with stock returns in the banking industry.
The following is the null hypothesis of correlation-test that conducted by using
Pearson formula/product:
H0: ρ = 1 There is no relationship between Dow Jones Indexes to stock return in
the banking industry.
H1: ρ ≠ 0 There is relationship between Dow Jones Indexes to stock return in the
banking industry.
Based on table 4.1, the significance value that is owned by the Dow Jones Indexes
is 0.037, because the number is below 5% then H0 rejected, it means that there is
significant relationship between Dow Jones Indexes and stock returns in the banking
industry. In addition, the correlation coefficient of 0.429 Dow Jones Indexes shows
that there is positive correlation relationship between the Dow Jones Indexes with the
stock returns in the banking industry.
j. The correlation coefficients test between Fed Rate with
Stock Returns in the Banking Industry
Further more coefficient test is conducted to explain the relationship between Fed
Rate with stock returns in the banking industry.
The following is the null hypothesis of correlation-test that conducted by using
Pearson formula/product:
50
H0: ρ = 1 There is no relationship between Fed Rate to stock return in the banking
industry.
H1: ρ ≠ 0 There is relationship between Fed Rate to stock return in the banking
industry.
Based on table 4.1, the significance value that is owned by the Fed Rate is 0.145,
because the number is above 5% then H0 accepted, it means that there is no
significant relationship between Fed Rate and stock returns in the banking industry.
In addition, the correlation coefficient of 0.306 Fed Rate shows that there is positive
correlation relationship between the Fed Rate with the stock returns in the banking
industry.
k. The correlation coefficients test between Hang Seng
Indexes with Stock Returns in the Banking Industry
Further more coefficient test is conducted to explain the relationship between
Hang Seng Indexes with stock returns in the banking industry.
The following is the null hypothesis of correlation-test that conducted by using
Pearson formula/product:
H0: ρ = 1 There is no relationship between Hang Seng Indexes to stock return in
the banking industry.
H1: ρ ≠ 0 There is relationship between Hang Seng Indexes to stock return in the
banking industry.
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Based on table 4.1, the significance value that is owned by the Hang Seng Indexes
is 0.011, because the number is below 5% then H0 rejected, it means that there is
significant relationship between Hang Seng Indexes and stock returns in the banking
industry. In addition, the correlation coefficient of 0.510 Hang Seng Indexes shows
that there is positive correlation relationship between Hang Seng Indexes with the
stock returns in the banking industry.
l. The correlation coefficients test between Crude Oil Price
with Stock Returns in the Banking Industry
Further more coefficient test is conducted to explain the relationship between
Crude Oil Price with stock returns in the banking industry.
The following is the null hypothesis of correlation-test that conducted by using
Pearson formula/product:
H0: ρ = 1 There is no relationship between Crude Oil Price to stock return in the
banking industry.
H1: ρ ≠ 0 There is relationship between Crude Oil Price to stock return in the
banking industry.
Based on table 4.1, the significance value that is owned by Crude Oil Price is
0.519, because the number is above 5% then H0 accepted, it means that there is no
significant relationship between Crude Oil Price and stock returns in the banking
industry. In addition, the correlation coefficient of 0.138 Crude Oil Price shows that
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there is positive correlation relationship between Crude Oil Price with the stock
returns in the banking industry
4.3. Test of Classical Irregularities Assumption in Stock
Return
Test of classical irregularities assumption in this research by using SPSS
program includes:
4.3.1. Multicollinearity Test
Multicollinearity test was conducted to test whether the regression model
found the correlation between the independent variable. If there is correlation, then it
is said there are symptoms multicollinearity. A good regression model is does not
have multicollinearity symptom.
The following is a table of multicollinearity test results of all macroeconomic
variables:
Coefficients(a)
Model
Collinearity Statistics
Tolerance
1
(Constant)
Inflation
SBIRate
MoneySupply
ExchangeRate
GDP
ReserveRequirement
DowJones
.289
.273
.454
.197
.287
.369
.184
VIF
3.463
3.661
2.202
5.083
3.487
2.712
5.440
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FedRate
HangSeng
CrudeOil
CurrentAccount
NetBuyingAsing
.356
.356
.511
.391
.760
2.806
2.806
1.956
2.556
1.316
a Dependent Variable: StockReturn
Table 4.3 Tolerance Value and VIF for 12 independent variables
Based on table 4.2 above, VIF value is < 10 and the tolerance value is < 1,
thus the regression model is free from multicollinearity problem.
4.3.2.Autocorrelation Test
Autocorrelation testing conducted on this research by considering the number
Durbin-Watson in the following table:
Model Summary(b)
Adjusted R
Std. Error of
Model
R
R Square
Square
the Estimate
Durbin-Watson
1
.906(a)
.820
.624
.0827395
1.512
a Predictors: (Constant), Net BuyingAsing, Reserve Requirement, Crude Oil, SBI
Rate, Money Supply, Dow Jones, Current Account, GDP, Fed Rate, Hang Seng,
Inflation, Exchange Rate
b Dependent Variable: Stock Return
Table 4.4 Autocorrelation Test Dependent Variable Stock Return in Banking
Industry
The value of DL and DU with N=24 and k=12 is 0.362 and 1.092. The DurbinWatson (D-W) score for this research is 1.512, since the numbers existed between DU
and 4-DU it means there is no autocorrelation.
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DL & DU 2.908
Figure 4.1 Autocorrelation Test using Durbin Watson with Dependent Variable
Stock Return in Banking Industry
4.3.3. Heteroscedasticity Test
A good regression model is does not have heteroscedasticity symptom.
Heteroscedasticity test in this research is looking at the scatterplot has been done on
the regression test has been done before.
If there is no particular pattern in the dependent variable scatterplot, means it
is free from heteroscedasticity. And conversely if there is a certain pattern on the
scatterplot, then heteroscedasticity occurred in this research.
Scatterplot graph obtained from the output of regression test through the
increment plots with SRESID as Y and ZPRED as X.
55
Scatterplot
Dependent Variable: StockReturn
Regression Studentized Residual
3
2
1
0
-1
-2
-2
-1
0
1
2
3
Regression Standardized Predicted Value
Figure 4.2 Scatterplot Dependent Variable Stock Return in Banking Industry
Based on the figure above, it can be seen that there is no clear or certain
pattern formed by the dots. The dots are spread a long the area. Thus, it can be
concluded that there is no heteroscedasticity occurred in the regression model. So that
the regression models that used is reliable to predict dependent variable based on the
input of independent variables.
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4.3.4.Normality Test
Histogram
Dependent Variable: StockReturn
10
Frequency
8
6
4
2
0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Regression Standardized Residual
Normal P-P Plot of Regression Standardized Residual
Dependent Variable: StockReturn
Expected Cum Prob
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Observed Cum Prob
Figure 4.3 Normality Test Dependent Variable Stock Return in Banking Industry
57
Having completed normality test based on two previous pictures, the result is
residual value of data distribution shows a normal distribution (bell curve). In normal
probability plot, it shows that the spread of dotted are around the line. Based on these
two reasons, the regression is met the normality, or the residual of the model can be
considered as a normal distributed.
4.4. Regression
Equation Results
Dependent Variables
Stock Return in Banking Industry
Regression equation from this study can be formulated as follows:
Stock Return in banking industry =
Y1 = β0 + β1.Inflation + β2.SBI Rate + β3.Money Supply + β4.Exchange Rate
+ β5.GDP + β6.Current Account + β7.Reserve Requirement + β8.Net Buying
Asing + β9.Dow Jones Indexes + β10.Fed Rate + β11.Hang Seng Indexes +
β12.Crude Oli Price + е
Regression equation that has been formulated then after processed by using
SPSS 15 regression coefficients obtained data-processing results as follows:
58
Coefficientsa
Model
1
(Constant)
Inflation
SBIRate
MoneySupply
ExchangeRate
GDP
CurrentAccount
ReserveRequirement
NetBuyingAsing
DowJones
FedRate
HangSeng
CrudeOil
Unstandardized
Standardized
Coefficients
Coefficients
B
Std. Error
Beta
.149
.061
.656
.316
.115
-.991
.315
-.768
.745
.775
.182
-1.821
.781
-.673
-1.761
.906
-.464
.005
.022
.050
-.913
.459
-.419
.013
.004
.475
-.784
.586
-.399
.235
.113
.448
.164
.241
.146
-.080
.119
-.120
t
2.443
2.756
-3.140
.962
-2.333
-1.944
.242
-1.991
3.234
-1.339
2.089
.680
-.670
Sig.
.033
.019
.009
.357
.040
.078
.813
.072
.008
.208
.061
.510
.517
Collinearity Statistics
Tolerance
VIF
.289
.273
.454
.197
.287
.391
.369
.760
.184
.356
.356
.511
3.463
3.661
2.202
5.083
3.487
2.556
2.712
1.316
5.440
2.806
2.806
1.956
a. Dependent Variable: StockReturn
Table 4.5 Regression Coefficient Dependent Variable Stock Return in Banking Industry
Based on regression coefficient table above,
Y = 0.149 + 0.316 X1 – 0.991X2 + 0.745X3 -1.821X4 – 1.761X5 + 0.005X6 – 0.913X7
+ 0.013X8 – 0.784X9 + 0.235X10 + 0.164X11 – 0.080X12
ANOVAb
Model
1
Regression
Residual
Total
Sum of
Squares
.343
.075
.419
df
12
11
23
Mean Square
.029
.007
F
4.179
Sig.
.012a
a. Predictors: (Constant), NetBuyingAsing, ReserveRequirement, CrudeOil, SBIRate,
MoneySupply, DowJones, CurrentAccount, GDP, FedRate, HangSeng, Inflation,
ExchangeRate
b. Dependent Variable: StockReturn
Table 4.6 ANOVA Dependent Variable Stock Return in Banking Industry
59
Model Summaryb
Model
1
R
.906a
R Square
.820
Adjusted
R Square
.624
Std. Error of
the Estimate
.0827395
DurbinWatson
1.512
a. Predictors: (Constant), CrudeOil, SBIRate, ReserveRequirement,
NetBuyingAsing, MoneySupply, DowJones, CurrentAccount, GDP,
FedRate, HangSeng, Inflation, ExchangeRate
b. Dependent Variable: StockReturn
Where: Fcalculated = 4.179 and R2 = 0.820
The result from the statistic calculation, it shows that there are six coefficient
parameter of the regression is positive, which is Inflation (X1), Money Supply (X3),
Current Account (X6), Net Buying Asing (X8), Fed Rate (X10), and Hang Seng
Indexes (X11), which means that changes in one of the independent variable will
result in changes in the dependent which is the same way if the independent variable
is constant
While the other variables are SBI Rate (X2), Exchange Rate (X4), GDP (X5),
Reserve Requirement (X7), Dow Jones Indexes (X9), and Crude Oil Price (X12) have
coefficient parameter of the regression is negative, which means that the changes in
one of the independent variable will resulted in the changes for the dependent
variable in reverse if the other independent variable is considered constant.
4.4.1.Final Equation Analysis Dependent Variable Stock
Return in Banking Industry
60
• The R value is 0.906 represent that the correlation between stock returns in
banking industry with twelve independent variables is having a high correlation
and a very close relationship because of greater than 0.5.
• The R2 value is 0.820, it means 82% stock return in banking industry can be
explained by the variation of the twelve independent variables. While the rest is
explained by other variable that is not included in this research.
• From the ANOVA Test or F-Test on the table above, obtained Fcalculated is 4.179
with level of significance 0.012 smaller than 0.05. Then the regression model can
be used to predict stock returns in banking industry. Or it can be said that there
are independent variables that affected stock returns in banking industry.
• A constant value at the end of
equations is 0.149 can be interpreted if the
independent variables in the model is assumed to be equal to zero, the average
variable outside the model will increase the stock return in banking industry by
0149.
• The value of Inflation regression coefficient is 0.316 means that the Inflation
variable has a positive effect to the stock return in the banking industry. This
shows that when Inflation increases 1 point, then the stock return in banking
industry will increase to 0.316 points and the other way when Inflation decreased
1 point, then the stock return in banking industry will experience a decrease of
0.316 points.
• The value of SBI Rate regression coefficient is -0.991 means that the SBI Rate
variable has a negative effect to the stock return in the banking industry. This
61
shows that when SBI Rate increases 1 point, then the stock return in banking
industry will decrease to -0.991 points and the other way when SBI Rate
decreased 1 point, then the stock return in banking industry will experience an
increase of -0.991 points.
• The value of Money Supply (M2) regression coefficient is 0.745 means that the
Money Supply (M2) variable has a positive effect to the stock return in the
banking industry. This shows that when Money Supply (M2) increases 1 point,
then the stock return in banking industry will increase to 0.745 points and the
other way when Money Supply (M2) decreased 1 point, then the stock return in
banking industry will experience a decrease of 0.745 points.
• The value of Exchange Rate regression coefficient is -1.821 means that the
Exchange Rate variable has a negative effect to the stock return in the banking
industry. This shows that when Exchange Rate increases 1 point, then the stock
return in banking industry will decrease to -1.821 points and the other way when
Exchange Rate decreased 1 point, then the stock return in banking industry will
experience an increase of -1.821 points.
• The value of GDP regression coefficient is -1.761 means that the GDP variable
has a negative effect to the stock return in the banking industry. This shows that
when GDP increases 1 point, then the stock return in banking industry will
decrease to -1.761 points and the other way when GDP decreased 1 point, then
the stock return in banking industry will experience an increase of -1.761 points.
62
• The value of Current Account regression coefficient is 0.005 means that the
Current Account variable has a positive effect to the stock return in the banking
industry. This shows that when Current Account increases 1 point, then the stock
return in banking industry will increase to 0.005 points and the other way when
Current Account decreased 1 point, then the stock return in banking industry will
experience a decrease of 0.005 points.
• The value of Reserve Requirement regression coefficient is -0.913 means that the
Reserve Requirement variable has a negative effect to the stock return in the
banking industry. This shows that when Reserve Requirement increases 1 point,
then the stock return in banking industry will decrease to -0.913 points and the
other way when Reserve Requirement decreased 1 point, then the stock return in
banking industry will experience an increase of -0.913 points.
• The value of Net Buying Asing regression coefficient is 0.013 means that the Net
Buying Asing variable has a positive effect to the stock return in the banking
industry. This shows that when Net Buying Asing increases 1 point, then the
stock return in banking industry will increase to 0.013 points and the other way
when Net Buying Asing decreased 1 point, then the stock return in banking
industry will experience a decrease of 0.013 points.
• The value of Dow Jones Indexes regression coefficient is -0.784 means that the
Dow Jones Indexes variable has a negative effect to the stock return in the
banking industry. This shows that when Dow Jones Indexes increases 1 point,
then the stock return in banking industry will decrease to -0.784 points and the
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other way when Dow Jones Indexes decreased 1 point, then the stock return in
banking industry will experience an increase of -0.784 points.
• The value of Fed Rate regression coefficient is 0.235 means that the Fed Rate
variable has a positive effect to the stock return in the banking industry. This
shows that when Fed Rate increases 1 point, then the stock return in banking
industry will increase to 0.235 points and the other way when Fed Rate decreased
1 point, then the stock return in banking industry will experience a decrease of
0.235 points.
• The value of Hang Seng Indexes regression coefficient is 0.164 means that the
Hang Seng Indexes variable has a positive effect to the stock return in the banking
industry. This shows that when Hang Seng Indexes increases 1 point, then the
stock return in banking industry will increase to 0.164 points and the other way
when Hang Seng Indexes decreased 1 point, then the stock return in banking
industry will experience a decrease of 0.164 points.
• The value of Crude Oil Price regression coefficient is -0.080 means that the Crude
Oil Price variable has a negative effect to the stock return in the banking industry.
This shows that when Crude Oil Price increases 1 point, then the stock return in
banking industry will decrease to -0.080 points and the other way when Crude Oil
Price decreased 1 point, then the stock return in banking industry will experience
an increase of -0.080 points.
4.4
Stepwise Method Analysis
64
Stepwise methods were conducted to get the best model of a regression
model. By definition stepwise method is a combination of forward and backward
methods, the first variable that entered is the variable with the highest significant and
correlation with the dependent variables, the second variables that entered is variable
with the highest partial correlation and still significant, after a certain variable get into
the model so then other variables in the model are evaluated, if there are no
significant variables then these variables excluded.
The following table is the output result of the Stepwise Method performed by
SPSS program:
Variables Entered/Removed
Model
1
Variables
Entered
Variables
Removed
Net
Buying
Asing
.
HangSeng
.
2
a
Method
Stepwise
(Criteria:
Probabilit
y-ofF-to-enter
<= .050,
Probabilit
y-ofF-to-remo
ve >= .
100).
Stepwise
(Criteria:
Probabilit
y-ofF-to-enter
<= .050,
Probabilit
y-ofF-to-remo
ve >= .
100).
a. Dependent Variable: StockReturn
Table 4.7 Stepwise Moethod for Net Buying Asing and SBI variables
Based on the table above, from the twelve (12) independent variables that
existed but there are only two variables included in the regression models. The first
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model put only Net Buying Asing variables and the second model adds Hang Seng
Indexes variables in regression models.
Coefficientas
Unstandardized
Coefficients
Model
1
(Constant)
NetBuyingAsing
2
(Constant)
NetBuyingAsing
HangSeng
B
.066
.018
.054
.015
.381
Standardized
Coefficients
Std. Error
Beta
.021
.004
.663
.020
.004
.560
.174
.339
t
3.122
4.158
2.677
3.621
2.195
Collinearity Statistics
Sig.
Tolerance
VIF
.005
.000
1.000
1.000
.014
.002
.907
1.102
.040
.907
1.102
a. Dependent Variable: StockReturn
Table 4.8 Regression Coefficients with Dependent Variables Net Buying Asing and
Hang Seng Indexes
From the table above, it can be seen that these two variables Net Buying Asing and
Hang Seng Indexes are significant, as shown from VIF and tolerance values that the
numbers are < 10. Final Regression Equation Model is as follows:
Y = 0.054 + 0.015 X8 + 0.381 X11
Model Summary c
Model
1
2
R
.663a
.738b
R Square
.440
.545
Adjusted
R Square
.415
.501
Std. Error of
the Estimate
.1032191
.0952789
DurbinWatson
1.640
a. Predictors: (Constant), NetBuyingAsing
b. Predictors: (Constant), NetBuyingAsing, HangSeng
c. Dependent Variable: StockReturn
Table 4.9 Coefficient of Determination by the Independent Variables Net Buying
Asing and Hang Seng Indexes
For regression with more than two independent variables, Adjusted R2 is used
as the coefficient of determination. The adjusted R2 in the first model is 0.415 and the
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second model with the addition of variables Hang Seng Indexes, Adjusted R2 is
increased to 0.501. The higher value of Adjusted R2 is the better for regression model,
because the independent variables can be explained more the dependent variable.
This means 50% stock return in banking industry can be explained by the Net Buying
Asing and Hang Seng Indexes variables, and the remaining 50% can be explained by
other causes.
4.5
Hypothesis Testing Dependent Variable Stock Return in
Banking Industry
4.5.1 t-Test
Hypothesis testing in this research was conducted with due regard tcalculated
values and the regression results to determine the significance of independent
variables separately for the dependent variable with 95% confidence level or at the
level of alpha = 5%. With condition that if an independent variable is significant for
the dependent variable it means that there is an influence of independent variables on
the dependent variable. In this research t-Test is used to test whether the hypothesis
proposed in this research are accepted or rejected by knowing whether the
independent variables individually affect the dependent variable.
Method in the determination of t-table using the determinate of significance
level 5% where df = n-k (in this research df = 24-12 = 12).
Table 4.10 Regression Coefficient Values, tcalculated and Decision Dependent Variable
Stock Return in Banking Industry
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Regression
Variable
tcalculated
t-table
Significance
Decision
Coefficient
Inflation
0.316
2.756
2.1788
0.019
Reject H0
SBI Rate
-0.991
-3.140
2.1788
0.009
Reject H0
M2
0.745
0.962
2.1788
0.357
Accept H0
Exchange Rate
-1.821
-2.333
2.1788
0.040
Reject H0
GDP
-1.761
-1.944
2.1788
0.078
Accept H0
Current
0.005
0.242
2.1788
0.813
Accept H0
-0.913
-1.991
2.1788
0.072
Accept H0
0.013
3.234
2.1788
0.008
Reject H0
-0.784
-1.339
2.1788
0.208
Accept H0
Fed Rate
0.235
2.089
2.1788
0.061
Accept H0
Hang Seng
0.164
0.680
2.1788
0.510
Accept H0
-0.080
-0.670
2.1788
0.517
Accept H0
Account
Reserve
Requirement
Net Buying
Asing
Dow Jones
Indexes
Indexes
Crude Oil
Price
Hypothesis Test 1:
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Independent variable Inflation has a regression coefficient of 0.316 and
tcalculated = 2.756 where tcalculated > t-table. This value showed that independent
variables have a significant influence on the dependent variable. Therefore, the
decision taken was Reject H0.
Hypothesis Test 2:
Independent variable SBI Rate has a regression coefficient of -0.991and
tcalculated = -3.140 where tcalculated > t-table. This value showed that independent
variables have a significant influence on the dependent variable. Therefore, the
decision taken was Reject H0.
Hypothesis Test 3:
Independent variable Money Supply (M2) has a regression coefficient of
0.745 and tcalculated = 0.962 where tcalculated < t-table. This value showed that
independent variable has not a significant influence on the dependent variable.
Therefore, the decision taken was Accept H0.
Hypothesis Test 4:
Independent variable Exchange Rate has a regression coefficient of -1.821and
tcalculated = -2.333 where tcalculated > t-table. This value showed that independent
variables have a significant influence on the dependent variable. Therefore, the
decision taken was Reject H0.
Hypothesis Test 5:
Independent variable GDP has a regression coefficient of -1.761 and tcalculated
= -1.944 where tcalculated < t-table. This value showed that independent variables has
69
not a significant influence on the dependent variable. Therefore, the decision taken
was Accept H0.
Hypothesis Test 6:
Independent variable Current Account has a regression coefficient of 0.005
and tcalculated = 0.242 where tcalculated < t-table. This value showed that independent
variable has not a significant influence on the dependent variable. Therefore, the
decision taken was Accept H0.
Hypothesis Test 7:
Independent variable Reserve Requirement has a regression coefficient of 0.913 and tcalculated = -1.991 where tcalculated < t-table. This value showed that
independent variable has not a significant influence on the dependent variable.
Therefore, the decision taken was Accept H0.
Hypothesis Test 8:
Independent variable Net Buying Asing has a regression coefficient of 0.013
and tcalculated = 3.234 where tcalculated > t-table. This value showed that independent
variables have a significant influence on the dependent variable. Therefore, the
decision taken was Reject H0.
Hypothesis Test 9:
Independent variable Dow Jones Indexes has a regression coefficient of 0.784 and tcalculated = -1.339 where tcalculated < t-table. This value showed that
independent variable has not a significant influence on the dependent variable.
Therefore, the decision taken was Accept H0.
70
Hypothesis Test 10:
Independent variable Fed Rate has a regression coefficient of 0.235 and
tcalculated = 2.089 where tcalculated < t-table. This value showed that independent variable
has not a significant influence on the dependent variable. Therefore, the decision
taken was Accept H0.
Hypothesis Test 11:
Independent variable Hang Seng Indexes has a regression coefficient of 0.164
and tcalculated = 0.680 where tcalculated < t-table. This value showed that independent
variable has not a significant influence on the dependent variable. Therefore, the
decision taken was Accept H0.
Hypothesis Test 12:
Independent variable Crude Oil Price has a regression coefficient of -0.080
and tcalculated = -0.0670 where tcalculated < t-table. This value showed that independent
variable has not a significant influence on the dependent variable. Therefore, the
decision taken was Accept H0.
The result from the research there are two categories, the fisrt is
“significance” and the other one is “not significance”. There are four variables that
categorized as significance, i.e.: Inflation, SBI Rate, Exchange Rate, and Net Buying
Asing. This means that the four variables can greatly influence the macroeconomic
condition in every sector of a country. So we need to be careful with these four
variables.
71
The other result is categorized as not significance, i.e.: Money Supply (M2),
GDP, Current Account, Reserve Requirement, Dow Jones Indexes, the Fed rate,
Hang Seng Indexes, and Crude Oil Price. This means that this variable is not greatly
influence the economic condition in every sector of a country. It is not mean that
these variables are not important but mainly we have to pay more attention to the
result that is significance.
4.5.2 F-Test
Influence of independent variables simultaneously on the dependent variables
were analyzed using the F test, that is by considering the significance of F values on
the calculated with α = 5%. If the F-test significance value < 0.05 then there is the
influence of all independent variables on the dependent variable.
In the regression test results known that Fcalculated is 4.179 with a significance
level of 0.012. Since 0.012 the probability is less than 0.05, and then the regression
model can be used to predict stock returns in banking industry. In other words, the
independent variables together have an influence on stock returns in banking
industry.