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Figure 4 the trend of R, MRSH, MRSZ and interest rate 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
‐10
‐8
‐6
‐4
‐2
0
2
4
6
8
2006
2007
2008
2009
2010
2011
2012
2013
R
MRSH
MRSZ
INT

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4.2 Empirical Results 
4.2.1 Correlation Analysis 
It is often necessary to examine the relationship between two or more financial 
variables. There are many ways to examine how sets of data are related. So this study 
uses the correlation analysis to test the relationship between the variables in each 
model. 
The correlation coefficient is a measure of how two data series are closely 
related. In particular, the correlation coefficient measures the direction and extent of 
linear association between two variables. A correlation coefficient can have a 
maximum value of 1 and a minimum value of -1. The correlation cannot exceed 1 in 
absolute value. A correlation coefficient greater than 0 indicates a positive linear 
association between the two variables: When one variable increases (decreases), the 
other also tends to increase (decrease). A correlation coefficient less than 0 indicate a 
negative linear association between the two variables: When one variable increases 
(decreases), the other also tends to decrease (increase). A correlation coefficient of 0 
indicates no linear relation between the two variables. The closer the coefficient is to 
either -1 or 1, the stronger the correlation between the two variables (Emrah, 2009). 
Table 6 is a correlation matrix of selected macroeconomic factors and the 
banking industry stock return (R). 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. 

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