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5.2 Limitations of Study 
After carry out empirical test for our study, we noticed that there are some 
limitations that prevent us for further improvement. 
First of all, it would be the matter of data set. The data set that we used to 
study on the research is on monthly basis. This was proposed by most of the previous 

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researchers whom did on the same study as us. Nevertheless, after testing on all the 
variables, we would observe that data which extracted on monthly basis was not 
enough to generate the accurate and reliable result. Therefore, this inaccurate monthly 
data set would cause the result to be inefficient. And the range of the data is from 
2007 to 2012, this range is quite small to show the long term impact of these variables 
to the banking stock return. 
This study, selects 4 macroeconomic factors to test the impact of 
macroeconomic factors to banking industry stock return. But there are a lot of other 
macroeconomic factors can be use, like GDP, IP and so on. And also can choose other 
plates in stock market to do the test. 
While, another limitation is matter with the econometric model that
employed in the test namely Generalized Least Square (GLS) model. When get the 
empirical result, may be it will have some problem open. And the model is 
simplification, if by employing other advance model, for example Generalized Auto 
Regressive Conditional Heteroscedasticity (GARCH) model, the result would be act 
in different way.
Though what recommended by most of previous researchers were same as 
what we are used in our study. There may be still a problem that the macroeconomic 
indicators used in this study may not be sufficient to generate for better result. 
5.3 Recommendations and Future Research 
In order to make a more precise and exact research, it is a need to improve and 
overcome those constraints. Since there are three major limitations stated on the 
above sections, hence, we would suggest the solutions for each of them. 
To overcome the data constraint, we may be get a try on using the data series 
extracted on daily basis. As some of the researchers found that, the result has shown 
more exact by using daily data on carry out the relevant empirical studies. 
For the factors constraint, future research may be done by adding more 
macroeconomic variables, such as Gross Domestic Productions (GDP) or Foreign 

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Direct Investment (FDI), in order to test on the impact of each of them of banking 
stock return. The additional variables that expected to use should be more relevant to 
the study and be supported by related supporting materials. 
To improving the empirical result, it is better to apply Generalized Auto 
Regressive Conditional Heteroscedasticity (GARCH) model rather than Genelized 
Least Square (GLS), as this economic model is more advance in addressing and 
solving for econometric problems, such as heteroscedasticity. Previous research 
(Bollerslev, 1986, 1990; Muneer et. al, 2011) were found that the Generalized Auto 
Regressive Conditional Heteroscedasticity (GARCH) model is sustainable in 
capturing assets returns and volatility by allowing the means of assets return to be 
depends on their time-varying variance together with other contributory factors. 
Other than these, future researchers may try to extend the study on other 
industry sectors in the Chinese stock market.