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Table 6 the correlation between INF, EX, MS, INF in the model (1)
R INF EX MS INT R 1.000000 INF 0.036732 1.000000 EX 0.181177 -0.274795 1.000000 MS 0.154864 -0.042590 0.218778 1.000000 INT -0.201709 -0.088381 0.084616 -0.308027 1.000000 Note: R is banking industry stock return, INF is inflation rate, EX is exchange rate, MS is money supply, and INT is interest rate. 42 Table 7 is a correlation matrix of selected macroeconomic factors and the Shanghai exchange stock return (MRSH). From the table, we can see the same result with table 6. From the table, we can see that INF and EX has negative relationship, and the coefficient is -0.274795, it means that there is weak correlation between them. And INF and MS have a negative relationship, and the coefficient is -0.042590, also is a weak correlation between them. And INF and INT also have a negative relationship, and the coefficient is -0.088381, also is a weak correlation. And EX and MS has a positive relationship, and the coefficient is 0.218778, also is a weak correlation. And EX and INT also has a positive relationship, and the coefficient is 0.084616, also is a weak correlation. And MS and INT have a negative relationship, and the coefficient is -0.308027, also is a weak correlation. The result shows that these four factors can be together in the same model. Table 7 the correlation between INF, EX, MS, INF in the model (2) MRSH INF EX MS INT MRSH 1.000000 INF 0.062185 1.000000 EX 0.183810 -0.274795 1.000000 MS 0.115516 -0.042590 0.218778 1.000000 INT -0.196444 -0.088381 0.084616 -0.308027 1.000000 Note: MRSH is Shanghai exchange stock return. INF is inflation rate, EX is exchange rate, MS is money supply, and INT is interest rate. 43 Table 8 is a correlation matrix of selected macroeconomic factors and the Shenzhen exchange stock return (MRSZ). From the table, we can see the same result that like table 7, from the table, we can see that INF and EX has negative relationship, and the coefficient is -0.274795, it means that there is weak correlation between them. And INF and MS have a negative relationship, and the coefficient is -0.042590, also is a weak correlation between them. And INF and INT also have a negative relationship, and the coefficient is -0.088381, also is a weak correlation. And EX and MS has a positive relationship, and the coefficient is 0.218778, also is a weak correlation. And EX and INT also has a positive relationship, and the coefficient is 0.084616, also is a weak correlation. And MS and INT have a negative relationship, and the coefficient is -0.308027, also is a weak correlation. The result shows that these four factors can be together in the same model. Table 8 the correlation between INF, EX, MS, INF in the model (3) MRSZ INF EX MS INT MRSZ 1.000000 INF 0.055263 1.000000 EX 0.193566 -0.274795 1.000000 MS 0.091355 -0.042590 0.218778 1.000000 INT -0.230250 -0.088381 0.084616 -0.308027 1.000000 Note: MRSZ is the Shenzhen exchange stock return. INF is inflation rate, EX is exchange rate, MS is money supply, and INT is interest rate. 44 Table 9 is a correlation matrix of selected macroeconomic factors, the Shanghai exchange stock return (MRSH), Shenzhen exchange stock return (MRSZ). From the table, we can see that MRSH and MRSZ have a positive relationship, and the coefficient is 0.967839, it means that there is a strong correlation between them. It means that these two factors can’t in the same model when use the regression. And MRSH with INF, EX, MS, there is a positive and weak correlation between them. But MRSH and INT is a negative and weak correlation. And MRSZ with INF, EX, MS, there is a positive and weak correlation between them. But MRSH and INT is a negative and weak correlation. Table 9 the correlation between MRSH, MRSZ, INF, EX, MS, INF MRSH MRSZ INF EX MS INT MRSH 1.000000 MRSZ 0.967839 1.000000 INF 0.062185 0.055263 1.000000 EX 0.183810 0.193566 -0.274795 1.000000 MS 0.115516 0.091355 -0.042590 0.218778 1.000000 INT -0.196444 -0.230250 -0.088381 0.084616 -0.308027 1.000000 Note: MRSH is Shanghai exchange stock return; MRSZ is the Shenzhen exchange stock return. INF is inflation rate, EX is exchange rate, MS is money supply, and INT is interest rate. 45 4.2.2 Regression Analysis In this study, we employ the GLS method to determine the impact of the macroeconomic variables on the banking industry stock return, Shanghai exchange stock return and Shenzhen exchange stock return. 4.2.2.1 Impact of Macroeconomic Variables on Banking Industry Stock Returns The result of the GLS estimation about the impact of macroeconomic factor on R is presented in Table 10. The R is banking industry stock return which is a dependent variable. According to the result, the inflation rate has a positive and insignificant association with banking industry stock return. As reported in Table 10 below, the coefficient estimate of a 1 is 0.995560, indicating that an increase in the inflation rate by 1 unit will cause banking industry stock to respond by an increase of 0.99556 units. If a decrease in the inflation rate by 1 unit will cause banking industry stock to respond by a decrease of 0.99556 unit, but there is not significantly affects to the stock return which according to the result, the P-value is 0.5979. This result is supported by earlier study such as Tan and Floros (2012). For the exchange rate, the regression result indicate that exchange rate has a positive and significant association with banking industry stock return, the coefficient estimate of a 2 is 0.402124, it means when EX change 1 unit, the return will change positive 0.402124 unit, and exchange rate is significantly affects the banking stock return at 10% significant level depend on the result that P-value is 0.0907. This result is supported by the study of Choi, Elyasiani and Kopecky (1992). Banking industry stock return has a positive relationship with money supply(MS), from the table, we can see that when MS change 1 unit, the return will change positive 0.843772 unit, but is not significantly affects the stock return because of the P-value is 0.4978. The result is same with Zatul and Mohamed (2007). Here, banking industry stock return has a negative relationship with interest rate (INT), the result shows that interest rate change 1 unit, the banking industry stock return will change negative 2.187996 unit, it means an increase in the interest rate by 1 unit will cause banking industry stock to respond by an increase of 2.187996 unit. If a decrease in the interest rate by 1 unit will cause banking industry 46 stock to respond by a decrease of 2.187996 units. And there is a significant affect to the return at the 10% significant level which according to the P-value is 0.085. The result also supported by the earlier studies, such as Mohammad and Orouba (2006), Elyasiani and Mansur (2004), Saadet, Gülin and Gökçe (2011). Yüklə 264,94 Kb. Dostları ilə paylaş:
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