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Table 6 the correlation between INF, EX, MS, INF in the model (1) 

INF 
EX 
MS 
INT 

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

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

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). 

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