THE EFFECT OF CORPORATE GOVERNANCE ON RETURN OF STOCK PRICE WITH COMPANY SIZE AS CONTROL VARIABLE

Good Corporate Governance (GCG) has a close relationship with public trust in a company. When a company implements GCG, it will provide security to shareholders. The safer shareholders feel, the more likely it will be for them to invest funds into the company sustainably. This should stabilize the company’s stock price. This study aims to examine the effect of GCG on stock price returns. In this study, secondary data was used, and linear regression was used to test the effect. The results of this study indicate that GCG has no effect on stock price returns.


BACKGROUND
Corporate Governance is one of the most important economic pillars (Budiharjo, 2019). Corporate governance is the relationship between the stakeholders in determining the direction to be taken by the company. According to Pratiwi and Suryanawa (2014), Good Corporate Governance (GCG) is one of the pillars of the market economy system. GCG has a close relationship with public trust in the company. When a company implements GCG, it will provide a sense of security to shareholders. The company's goal in implementing GCG is to optimize the profitability of all stakeholders in the long run. The Indonesian Institute for Corporate Governance (IICG) is an institution formed to monitor the implementation of GCG in Indonesia. IICG ranks companies that implement GCG in Indonesia. The results of the commemoration conducted by the IICG are *Corresponding Author. e-mail: bagus.yosia@ciputra.ac.id referred to as the Corporate Governance Perception Index (CGPI). Retno and Priantinah (2012) found in their research that GCG affects stock prices.
Stocks are one of the financial instruments in the capital market that can be traded to other parties. The price of a company's stock is called the stock price. Stock prices can be influenced by several factors, such as the number of requests and offers for these shares. The better the implementation of GCG, the more shareholders will trust in investing funds in the company. This will make the company's share price more stable. This is reinforced by research conducted by Bandiyono (2019), which states that corporate governance has a positive influence on firm value.
Based on the existing background, this research will discuss "The Effect of Corporate Governance on Stock Price Returns with Company Size as Control Variables."

Good Corporate Governance (GCG)
Corporate governance is the relationship between the stakeholders in determining the direction to be taken by the company. Corporate Governance is one of the most important economic pillars (Budiharjo, 2019). According to Bandiyono (2019), the implementation of GCG is expected to increase the value of the company. GCG aims to direct companies to achieve a balance between rights and obligations.

The Indonesian Institute for Corporate Governance (IICG)
In order to regulate the application of GCG in Indonesia, the Indonesian Institute for Corporate Governance (IICG) was formed. IICG aims to popularize GCG in Indonesia. In addition to regulating the implementation of GCG in Indonesia, IICG also ranks companies that implement GCG.

Corporate Governance Perception Index (CGPI)
The results of the ranking that has been carried out by the IICG are referred to as the Corporate Governance Perception Index (CGPI). CGPI is the result of ranking GCG implementation in companies listed on the Indonesia Stock Exchange.

Stock Price Return
One financial instrument that can be traded to other parties is stock. Stock is proof of company ownership. The share price is the price of a company's stock. Stock prices can be influenced by several factors, such as demand and supply for these shares. Stock return is the percentage of profit derived from stock investment. According to Hartono (2014), returns can be divided into two, namely realized returns where returns have occurred and expected returns that are predicted to happen. The return formula is shown below (Hartono, 2014):

Return=Capital gain (loss)+Yield
Capital gain (or loss) is the difference between current market price and past period market price (Hartono, 2014).

Company Size
The size of the company (size) is formulated as follows (Retno and Priantinah, 2012): Size=Log(Total Assets)

Market Capitalization
The market capitalization of a company is the multiplication of the market price of the company's shares with the number of shares outstanding. Market capitalization is formulated below (Ross, 2015): Market Capitalization = Price per share × Shares outstanding

Regression Analysis
According to Ariefianto (2012), regression analysis revealed the relationship between variables by including the element of causality. Regression analysis is divided into 2, namely simple linear regression analysis, where there is one independent variable and one dependent variable, and multiple linear regression analysis, where there is one dependent variable with more than one independent variable.

Past Researches
Nuswandari (2009), in his research, found that GCG can affect a company's stock price within a certain period. In addition, research conducted by Alexakis (2011) stated that the stock price returns of companies that implement GCG have increased. According to Peni and Vähämaa (2011), a Bank that implements GCG should have a high stock return. This is reinforced by the results of the research conducted by Wiryakusuma (2019), which stated that stock price volatility is influenced by GCG. The better the implementation of GCG by a company, the higher the value of the company. GCG implementation is expected to increase company value (Bandiyono, 2019).

Research Framework
The relationship between the variables used in this study is shown in Figure  1 below:

Figure 1 Framework
Based on the results of previous studies, the hypotheses in this study are as follows: H1: Corporate Governance Perception Index (CGPI) affects the company's stock price return.

Population and Sample
The population is a collection of elements that have specific characteristics so that it can be used to draw conclusions. The sample is a collection of selected elements in the population (Sanusi, 2011). In this study, the population used is all companies listed on the IDX. The sample used in this study are companies listed on the IDX and have applied GCG for five consecutive years in 2010-2014.

Data Collecting Method
Study documentation is used to collect data in this study. Data in this study were obtained from Yahoo Finance, IDX, and IICG.

Double Linear regression
According to Sugiarto (1992), estimating the regression parameters for multiple regression models is essentially just an extension of the concept of simple linear regression. In multiple linear regression, there is more than one variable. The multiple linear regression model can be formulated as (Sugiarto, 1992): (3.7) Information: = 1, 2, ..., = intersection (the point of intersection between the regression line and the Y-axis) = partial regression coefficient for , with = error = the number of explanatory variables in the model = number of observations

Hypothesis test
The research hypothesis will be tested with the following regression equation: The significance level (á) is chosen at 5% or 0.05. The significance level is used for testing. The criteria supported or not supported by the hypothesis are as follows: a. If the significance level is ≤ 0.05, it can be said that the hypothesis is supported, which means that the independent variable significantly influences the dependent variable. b. If the significance level is > 0.05, it can be said that the hypothesis is not supported, which means that the independent variable does not significantly influence the dependent variable.

Descriptive Statistics Test
Descriptive statistics are used to illustrate the sample data profile. In this research, descriptive statistics are shown with the following results:

Classical assumption test
The classic assumption test aims to see whether there is data normality, multicollinearity, autocorrelation, and heteroscedasticity in the regression model. A good linear regression model must have normally distributed data and not have multicollinearity, autocorrelation, or heteroscedasticity.

Normality test
A normality test is used to test whether the data is normally distributed or not. (Priyatno, 2014). The results of the normality test are shown in the following table: In Table 2, it can be seen that the significance value (Asymp. Sig. (2-tailed)) of residual return has a value of 0.980. If the significance value is greater than 0.05, then the residual value is normally distributed.

Multicollinearity Test
Multicollinearity test is used to look for a perfect or near-perfect linear relationship in the regression model (the correlation coefficient is high or even 1) (Priyatno, 2014). The better the regression model, then there should be no perfect or near-perfect correlation between the independent variables (Priyatno, 2014). The results of the multicollinearity test are shown in the following table: In Table 3, it can be seen that the value of tolerance is 0.374, and the value of VIF is 2.677. The tolerance value is more than 0.1, and the VIF value is less than 10, so it can be stated that there is no multicollinearity.

Autocorrelation Test
The autocorrelation test aims to see whether there is a correlation between observational elements arranged according to time or place. A good regression model should not have any autocorrelation (Priyatno, 2014). The results of the autocorrelation test are shown in the following table:

Heteroscedasticity Test
A good regression model does not have heteroscedasticity (Priyatno, 2014). Heteroscedasticity is a residual variance that is different in all observations of the regression model. The test results are shown in the following table:  (2019) In Table 5, it can be seen that the significance value of the independent variables is more than 0.05. So it can be concluded that there was no heteroscedasticity.

Linear regression analysis
Linear regression analysis is used to determine the effect of a linear relationship between independent variables on the dependent variable. This study uses a simple linear regression analysis, which analyzes the linear relationship between 1 independent variable and one control variable with one dependent variable. The T-test is used to determine the effect of independent variables on the dependent variable. The results of the linear regression analysis are shown in the following table: From Table 6 it can be seen that the CGPI variable has a significance value (Sig.) of 0.18.

Discussion
From the t-test that has been done, the results show that the significance value of CGPI is greater than 0.05. Thus, CGPI has no effect on return. However, in Table 6, the significance value of CGPI can be seen. The size is less than 0.05. This shows that when the CGPI variable is given the size control variable, the size variable will strengthen the CGPI variable, causing CGPI to affect the return but with size as the control variable. From the results of this study, it was found that H1 is not supported, the Corporate Governance Perception Index (CGPI) has no effect on the company's stock price returns. The results of this study support the researches conducted by Tjondro and Wilopo (2011) and Budiharjo (2016), which stated that GCG does not affect stock price returns.

Managerial Implication
This research proves that the application of GCG has no influence on stock price returns, but the implementation of GCG must always be carried out continuously in the organization. The application of GCG is done not only to increase stock price returns but is expected to provide a competitive advantage for the company.
For investors, although the application of GCG has no effect on stock price returns, it is expected that the application of GCG can be considered in selecting stocks. Investors are expected to consider not only short-term profits but also long-term.

Conclusion
Based on research that has been done, the results show that corporate governance has no effect on stock price returns. This is reinforced by research conducted by Budiharjo (2016), which stated that stock price returns are not influenced by GCG applied to the company. Tjondro and Wilopo (2011) also stated that GCG does not affect stock price returns. This might happen due to many factors in determining stock prices, such as the law of supply and demand.

Suggestions
In this study, the variables used include CGPI, stock price returns, and company size. It is hoped that more diverse variables can be used in future studies.