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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) 40 41 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 42 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. 43 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 44 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 45 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: 46 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. 47 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 48 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 49 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. 51 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 52 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 53 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. 54 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. 56 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 63 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 65 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 66 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 67 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: 68 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.