Essay: THE IMPACT OF DIVIDEND POLICY ON SHARE PRICE VOLATILITY

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MALAYSIAN CONSTRUCTION AND MATERIAL COMPANY

CHAPTER 1
1.0 INTRODUCTION
Dividend policy remains a source of controversy despite years of theoretical and empirical research, including one aspect of dividend policy, the linkage between dividend policy and stock price risk (Allen and Rachim,1996). Paying large dividends reduces risk and thus influence stock price (Gordon, 1963) and is a proxy for the future earnings (Baskin, 1989).
This research analyzes how well the payout of dividends reflects the volatility of a company’s stock price when compared to the relationship that other related variables have on price volatility.
Volatility is the rate of change in the price of a security over a given time period and, consequently, the greater the volatility the greater the risk of substantial gain or loss. If a stock is labeled as volatile, it is more difficult to forecast what the company’s future share price will be. Likewise, many investors prefer stocks that support more predictable earnings and therefore carry less risk.
According to (Ramadan, 2013) Dividend policy of the company determines the portion to be distributed to shareholders through dividends, and the portion to be held in order to reinvest. As the main goal of financial management is to maximize the wealth of the firm’s owners, the main aspect of the paper is to inspect the association between dividend policy and the market value of the firm’s shares. Dividends are more than just an instrument to distribute net surplus revenue of costs, that any significant difference in the rate of distributions may have an impact on share prices, and here comes the important role of management, which is, reaching a dividend policy that achieve maximizes the wealth of the owners of the company.
According to MM (1961) propositions on dividend the ex-dividend price of the stock at the end of the period would go down by exactly same amount as the increase in the dividend or the value of the firm will remain independent in the ex and post dividend period. Due to such proposition, an individual dissatisfied investor can undo or alter the corporate dividend policy by reinvesting dividend (buying) or selling shares. As a result there is no particular advantage of one dividend policy that the firm might choose and the investors are indifferent of dividends and capital gain.
The current critical questions concerned with dividend policy have many similarities to those questions asked by manager in the 1950s. These questions were determined by (Lintner,1956) such as, is it better to keep dividend payments at the present amount or alter it? Do shareholders want to have fixed dividend payments, or they prefer dividend payments updated with earnings? And What kind of investor dividend policy should attract? Younger or older?
The declaration of dividends informs to the investors that the managers are working in the best interest of the shareholders. Alternatively, the presence of taxable dividends may attract more institutional shareholders, who may directly or indirectly involved in the firm’s corporate governance process and may in turn enable to run the firm well (Allen et al, 2000).
The purpose of this study was to examine the relationship between dividend policy and share price volatility. The researcher focus on company listed in Malaysian stock market. For this purpose, a sample of 35 companies which is randomly selected and the relationship between share price volatility with two main measurement of dividend policy, dividend yield and payout ratio were examined by applying multiple regression for a period five years from 2010 until 2014. The primarily regression model was expanded by adding control variable including leverage and size.
BACKGROUND OF STUDY
The year 2011 was marked with high volatility in all areas of the United States equity markets. Evidence of this volatility included fluctuations of upwards of 300 basis points on the Dow Industrial Average for a single trading day. With the stock market crash in late 2008 still looming in investors’ minds, their leniency for an underperforming market was at a bare minimum (Profilet, 2013)
Dividend payment is a major component of stock return to shareholders. Dividend payment could provide a signal to the investors that the company is complying with good corporate governance practices. Good corporate governance practices are valuable for a company as it implying that the company is able to raise funds from capital market with attractive terms. By distributing dividend, it able to attract investors and indirectly increase the company share price. This sort of company could easily raise funds through new share issuance for expansion which then would increase profits and increase share price.
According to (Baskin, 1989) several diistinct theoritical mechanisms could cause dividend yields and payout ratio to vary inversely with common stock volatility. The first two consider here,duration and rate of return, treat dividends as merely a proxy for the timing of the underlying cash flow of the business.
According to (Baskin, 1989) Firms have long-run target dividend payout ratios. Mature companies with stable earnings usually have a higher dividend pay-out ratio than growth companies. according to the (Ioana” Corina) dividend policy is very important and it influences the investor attitudes . it also impacts the financial program and capital budget of the company, company’s cash flow. A company with a poor liquidity position may be forced to restrict its dividend payments.
1.2 PROBLEM STATEMENT
There have been various researches that had been conducted in order to identify the impact of dividend policy on share price volatility in Malaysia. The impact of the dividend policy on share volatility has become very popular among academics and many studies are conducted all over the world. A different researcher uses different approaches and method which lead to inconsistent results. According to (Vahid Taghizadeh Khanqah, 2011) statement, he is explaining dividend policy has been one of the most difficult challenges facing financial economist. Despite decades of study, they have yet to completely understand the factors that influence dividend policy and the manner in which these factors interact. Their research sample consisted of 100 firms listed on the Tehran Stock Exchange in period 2006 to 2011. According to (Mohammad Hashemijoo,2012) from their research, they find that the price volatility and dividend yield are negatively correlated. The value of correlation coefficient between price volatility and dividend yield is in line with (Baskin, 1989)’s results while it is contrary to (Allen, 1996)
Since there were so many opposite and inconsistent results from the various sources, then, the impact of dividend policy on share price volatility are difficult to be obtained. Therefore in this particular study, the researcher try to investigate, Do dividend payout ratio, dividend yield, leverage and size of asset are taken in order to explain the relationship exist between the share price volatility? and Does the dividend payout ratio, dividend yield, leverage and size of asset give influence on share price volatility?
RESEARCH QUESTION
The questions are developed based on the variables of the research on theoretical framework. Theoretical framework is the foundation on which the entire research project is based.
Does a significant relationship exists between dividend payout ratio, dividend yield, leverage, size of asset and the share price volatility?
Does the dividend payout ratio, dividend yield, leverage, size of asset give influence on share price volatility?
RESEARCH OBJECTIVE
To identify the impact of dividend policy on share price volatility
1.5 SCOPE OF STUDY
The purpose of this study to examine the impact of dividend policy on stock price volatility in Malaysia. For this research it will focus on 35 company listed in Bursa Malaysia and will be examined by applying multiple regression model for a period 2010 until 2014. Data selection will take into consideration the availability of data and their consistency.
1.6 SIGNIFICANCE OF STUDY
1.6.1 Stockholders and investors:
Information plays a great role in stockholders and investors investment decision-making. These research finding could provide the basis for a deeper and more accurate and understand of the dividend policy. It also will lead to a better and more accurate decision-making for the stockholders. For the stockholder, they will understand the impact of the dividend policy on share price volatility and will lead more accurate in decision making in investment field.
1.6.2 Research universities and institutes
They can apply the others’ research findings, take up new research and thereby engage in the production of new science.
1.6.3 Managers
The managers can use the research results in order to make the right decision about the improvement in the firm’s performance.
1.6.4 Students
By having this research, the students especially come from field of financing, accounting and investment may use this kind of information to extend their knowledge in their study. It also may use as a general information.
1.6.5 Researcher
New researcher may use this research as a guideline for them to conduct new research regarding to this topic. The researcher may compare their research results to make it for more understanding.
1.7 LIMITATIONS OF STUDY
In conducting this research,there were a few limit existed:
1.7.1 The data should be in period 2010-2014
All the data such as the highest and lowest the price of stock price, long term debt, total asset, must be find in period within 2010-2014.
1.7.2 The data must be obtained by referring to Bursa Malaysia and Datastream.
All the data should be obtained from bursa Malaysia and data stream because the data are more accurate compare to the others website.
1.7.3 Data Reliability
The researcher uses secondary data. There will be possibly that some information were not accurate and cannot be used for this study. Most of the data , come from Data stream and Bursa Malaysia website. Thus, its reliability and accuracy is depended entirely on the published material.
1.8 DEFINITION OF TERMS
1.8.1 Price Volatility
Price volatility is the statistical measure of the dispersion in stock returns. The dependent variable in the estimation model of the study. Price volatility indicates the volume of uncertainty of changes in the stock value. According to the (Ramadan, 2013) High price volatility indicates that a stock value can theoretically span to cover a large range of values, meaning that the stock price can change significantly within short time horizon in either direction. Following Baskin, (1989) price volatility can be computed annually by using the following formula:
Where: P is the price volatility for ith cross-sectional firm during the tth period,
HP the highest stock price for ith cross-sectional firm during the tth period,
tock price for ith cross-sectional firm during the tth period
1.8.2 Dividend Yield
Dividend yield is a way to measure how much cash flow you are getting for each dollar invested in an equity position. In other words, it measures how much “bang for your buck” we are getting from dividends. In the absence of any capital gains, the dividend yield is effectively the return on investment for a stock. The dividend yield as one of the proxies of the dividend policy is an indicator of the percentage return on a stock from its dividend price volatility or the uncertainty. The Dividend yield can be computed annually by dividing cash dividend per share for common stocks by the per share market value as follows:
Where DY is the dividend yield
CDS is the cash dividend per common stock
MV is the market value per common share
However, the researcher do not use this kind of the formula, the researcher got the data directly from the Datastream.
1.8.3 Dividend Payout Ratio
The dividend payout ratio provides an indication of how much money a company is returning to shareholders, versus how much money it is keeping on hand to reinvest in growth, pay off debt or add to cash reserves. This latter portion is known as retained earnings. The Dividend Payout Ratio can be computed by sum of the cash dividend paid to common shareholders is divided by the net income after tax for each year. It is calculated based on the following formula.
Payout ratio=DPS/EPS
Payout ratio= dividend payout ratio for firm i in year t
EPS= earning per share for firm i in year t
DPS = dividend per share for firm i in year t.
1.8.4 leverage
For calculating this variable the ratio of total long term debt to total asset is computed for each year . Figures for long-term debt and total asset were obtained directly from Datastream. This represents all interest bearing financial obligations excluding amounts due within one year for example debentures, mortgages and loans with maturity greater than one year.
Leverage = LD / ASSET
Leverage = financial leverage for i at the end of year t
LD = Long term debt for firm i at the end of the year
ASSET= total assset for firm i atthe end of the year t
1.8.5 Size
Size is one of the control variable measured by using the natural logarithm of total asset.
SIZE=Ln(ASSET)
Size=company size for firm i at the end of year i
Asset= total asset for firm i at the end of year i
1.9 SUMMARY
Basically, in this chapter, the problems, the independent and dependent variables have been determined to help to identify the objectives of this research. The independent variables that have been chosen are dividend yield, dividend payout ratio,leverage and size of asset. Hence, in the next chapter, this paper will discuss about the previous researchers reviews to identify the impact of dividend policy on share price volatility.

CHAPTER 2
LITERATURE REVIEW
2.1 INTRODUCTION
The topic that will be discussed in this chapter is the impact of dividend policy on share price volatility. Besides that, this research also covering the review of the previous research about the impact of dividend policy on share price volatility. This chapter will end up with summary that summarize the literature review.
2.2 PREVIOUS STUDY
Based on previous research, there are a lots of results after they did a research on variables towards the price of the stock market. Their relationship towards stock market can be positive or negative relationship. For instance, there will be positive and negative relationship between share price volatility and dividend payout ratio, dividend yield leverage and size of asset.
2.2.1 Relationship Between Dividend Yield And Share Price Volatility
According to (Mohammad Hashemijoo, 2012) from their research,it find that the price volatility and dividend yield are negatively correlated. The value of correlation coefficient between price volatility and dividend yield is in line with (Baskin,1989)’s results while it is contrary to (Allen, 1996). Other than that, (Irfan, 2003) the correlation between the variables utilized for the overall period is negatively significant between price volatility and dividend yield. Besides that, according to (Afzalur Rashid) the regression coefficients confirm the similar results,the dividend yield,debt,growth are is positive,but none of this are significant. This is due to high multicolinearity the statistical software removed the textile Dumme analysis. Moreover, by looking at the finding of (Ramadan, 2013) the experiental results showed significant negative effect between dividend yield and share price volatility. It indicates that as the jordanian industrial firms increase their dividend yield, the stock price tend to stability,as the price volatility fall, thus the share price risk fall while the research which were conducted by (Vahid Taghizadeh Khanqah, 2011) ,the correlation between dividend yield and share price volatility show a negative relationship and this is in line with that of (Baskin, 1989), but it is contrast with Allen and Rachim (1996). In addition, according to (Ioana” Corina, Vol. 4, No. 2) the correlation between share price volatility and dividend yield is negatively relationship and significant. By looking the results from (Yasir Habib, 2012) it shows that The regression results showed that relationship of share volatility with dividend yield is positive, From the regression results, the relationship between dividend and share price volatility is non-significantly positive. This study results is matched with earlier study conducted by (Allen, 1996) and contradicts with Baskin (1989) study while from correlation analysis there is a positive relationship between dividend policy and stock price volatility but this relationship is not significant. Other than that, according to (Al-Shawawreh, 2014 ) he find that there is Very Weak positive Relationship Between Dividend Yield And Share Price Volatility.
2.2.2 Relationship Between Dividend Payout Ratio And Share Price Volatility
According to (Vahid Taghizadeh Khanqah, 2011) the correlation between price volatility and dividend payout ratio is negative as expected and in line with the correlation in both (Baskin, 1989) and (Allen and Rachim,1996). However, for the regression results,it shows that, the positive relationship between the dividend payout ratio and share price volatility. But that of dividend payout is contrary to expectation. Furthermore, by looking at (Ioana” Corina, Vol. 4, No. 2) finding, both the dividend policy measures (dividend yield and payout ratio) have significant impact on the share price volatility. The relationship is not reduced much even after controlling for the above mentioned factors. Whereas payout ratio measure is having significant impact only at lower level of significance. Other than that, according to (Ramadan, 2013) price volatility negatively correlated and significant with dividend payout ratio. (Afzalur Rashid) the coefficients dividend payout ratio are negative and significant. (Mohammad Hashemijoo, 2012) Price volatility and dividend payout are negatively correlated and it is significant at level of 1%. it is consistent with both (Baskin, 1989)’s findings and (Allen & Rachim, 1996) results. (Yasir Habib, 2012) The regression results showed that negative relationship between payout ratio share volatility.
2.2.3 Relationship Between Leverage And Share Price Volatility.
According to (Dr.zahra Lashgari, june 2014) the results of the fixed effects regression model shows that payout ratio, leverage, and firm size effects adversely affect stock price volatility . (Allen, 1996) found that there is positive relationship between share price volatility and earnings volatility and leverage in the Australian listed companies during 1972 to 1985. (Christie, 1982) found a positive correlation between the degree of leverage on a firm’s balance sheet and the volatility of its stock. (Asma Rafique Chungtai, 2013) Leverage and price earnings ratio were also found to have significant role in explaining the determinants of share prices. Results suggested that there exists a significant positive
relationship between stock prices and dividends payouts.The same positive relationship exists between stock prices and financial performance. However, leverage affects the stock prices significantly but in a negative direction meaning by increased leverage is considered as a signal of financial burden by investors and so affects the market prices adversely. Debt, dividend and ownership structure significantly affects firm value (Alonso, 2005). This research finding documented based on 101 non-financial Spanish companies publicly traded during 1991-1995. Firms with positive growth opportunities indicated that debt has negative influence on firm value. Debt plays active role to discipline managers in firms that do not have growth opportunities.
2.2.4 Relationship Between Size And Share Price Volatility.
According to (Al-Shawawreh, 2014 ) there is positive Significant relationship Between Share Price Volatility And Size. Asset growth rate has a significantly positive effect on stock price volatility. Other than that, there is a potential relationship between size and volatility. Size of a firm could significantly provide explanation on the share prices (Karathanassis and Philiappas, 1988). Higher average return could be seen in smaller stocks. As the size of the firm increase, the company share price would likely to decline (Atiase, 1985). According to (Allen, 1996) small firms are less involved in diversification activities, thus it will be less subjected to (Rahman) investor’s scrutiny compared to large company. result, stocks of small firm traded in a market, would be less informed, more illiquid and would face higher price volatility. Besides that, according to (Mohammad Hashemijoo, 2012) results it shows that there is negative significant Relationship Between Share Price Volatility And Size .
2.3 SUMMARY
In this chapter, all available literature from previous researchers finding on the topic as well as the dependent and independent variables chosen for this research. This review is used as a basis and support for the study.
CHAPTER 3
METHODOLOGY
3.1 INTRODUCTION
This research are using multiple regression analysis to explore the association between share price changes and both dividend yield and dividend payout ratio. All selected variables were regressed by using e-views software. In this chapter will explain how the data will be regressed.
3.2 SAMPLE / DATA
3.2.1 Population / Sample
The cost of the research can be reduced by using sampling in order to complete the research. To have an acceptable result over the data, the sampling frame will cover within 5 years which is from 2010 until 2014. Data were obtained and are collected are mostly retrieving from the finance sector from the website, journals, and collected in yearly basis. The sample of this study comprises of 35 companies were listed in Kuala Lumpur Stock Market Exchange (KLSE). The time series data used are from 2010 until 2014 for all the independent variable (Dividend yield, Dividend payout ratio, leverage and size) and dependent variable (stock price volatility).
3.3 DATA COLLECTION
Data collection involves the process of collecting and gathering all the information needed either from primary or secondary data. In this study, researcher only limits its data collected from secondary resources. Secondary data refers to the statistical material which is obtained from others record. There were a few types of secondary data that have been used by the researcher like Datastream, Bursa Malaysia Website, Journal and article.
3.3.1 Internet
3.3.1.1 Bursa Malaysia
Bursa Malaysia had been updated all the information of the selected company. Therefore, this research collects some of the data for the variables which are Dividend yield, dividend payout ratio, earning per share and dividend per share from the Bursa Malaysia portal. The data were collected consist of 35 random company from year 2010 until 2014.
3.3.1.2 Data Stream
Most of the data were collected from the database like total asset, total long term debt, dividend yield for the year 2010 until 2014 from 35 companies which is choose randomly.
3.3.1.3 Journals and Articles
Journals and articles are used in this research for support material and all relevant information about dependent and independent variables.
3.4 VARIABLES
3.4.1 Dependent variable
In this research, the researcher used share price volatility as a dependent variable. It indicates the outcome of the change brought about by changes in the independent variables.
3.4.2 Independent variables
In this research, Dividend payout ratio, dividend yield were selected as an independent variable. Those independent variables will act how share price volatility will be affected when their price or index increase or decrease. In addition, there is two of expanded control variable.
3.5 RESEARCH DESIGN
Research design involve a series of rational decision making choices with issues relating to decision regarding the purpose of the study, the types of investigation, the extent of researcher interference, the study setting, unit of analysis and the time horizon of the study. This study engages in the hypothesis testing whether the dividend payout ratio, dividend yield, leverage and size has significant relationship or not.
3.5.1 Purpose of the study
The most common and useful purpose are exploration, description and causal testing. Many studies can and often more than one of these purposed but each has different implications for other aspects of research design. The purpose of this study is to identify the impact of the dividend policy on share price volatility.
3.5.2 Study setting
As the research study is based on secondary data obtained from Bursa Malaysia and Datastream, the study setting in my study is a field experiment where it is a non-contrived setting with moderate interference.
3.5.3 Unit of analysis
The units of analysis used in this study are the units component variables such as the total of cash dividend paid to shareholder, dividend payout ratio, leverage and size.
3.5.4 Time horizon
This study will use data from datastream and data from Bursa Malaysia website from 2010 until 2014. The researcher were interested in identify the impact of dividend policy on share price volatility. It is under the cross sectional studies or one short studies where the data gathered in short period based on the previous year of the data to meet a research objectives.
3.6 THEORETICAL FRAMEWORK
size

3.7 STATISTICAL / ECONOMICAL METHOD
This research is using multiple least regressions (MLR) with panel data. Therefore, to fulfill the objectives of MLR, then tests will start with stationary.
3.7.1 Stationary Analysis
There are varieties of test for unit root test such Philips and Peron, Dickey ‘ Fuller GLS (ERS), Kwiatkowski ‘ Philips ‘ Schmidt ‘ Shin and others test. However, common root-Levin, Lin, Chu and individual root fisher PP had been chosen for unit root test. This is because based on my observation, many researchers were used common root-Levin, Lin, Chu for unit root test. Denote that significance at 5%. In order to get the stationary data, the p-value of each variable must below 5%, and then null hypothesis can be rejected. All data must be stationary to continue with other test. If all the data not stationary on the same level, then, it will be the spurious regressions. If the data stationary at first difference, then, all variables need to transform using percentage change. Percentage change can transform all variables stationary at level. It can be calculated by ZY = @pc(Y) in the eviews software. Then, replace Y with other variables to transform the data. Below is the null and alternate hypothesis for stationary data.
H_0: Data is non-stationary
H_1: Data is stationary
3.7.2 Descriptive Analysis
Descriptive statistics are used throughout data analysis in a number of different ways. From the histogram of statistics, it will show the results of mean, median, maximum, minimum, standard deviation, skewness, kurtosis, Jarque Bera and p-value of Jarque Bera. From all the results, mean, median, standard deviation, and p-value of Jarque Bera were focused on this research.
3.7.3 Correlation Analysis
The objective to do this test is to observe if there exists any linear relationship or correlation of the dependent variable with any of the independent variable using the stationary data. In this test, there will be three values for each pair of variables. The top most value shows the correlation coefficient, the middle value shows the t-test statistic and the bottom value shows the p-value for the t-test statistic. From the result we can reject null hypothesis if p-value less than 5% significance level. It is to show that the independent variables related to the dependent variable. Below is the null and alternate hypothesis for correlation.
H_0: There is no correlation
H_1: There is correlation
3.7.4 Regression Analysis
Regression was done to identify whether the coefficient is significant or not. There are varieties of method to test the coefficient of data such least square method, cointegrating regressions, ordered choice, stepwise least squares and many more. However, least squares method was use because it is the easier way to regressed the data. When the p-value of F-statistic less than 5%, it shows that coefficient is significant. The theoretical multiple linear regression can be written as
Y_t= ”_0+ ”_1 X_1t+ ”_2 X_2t+ ”_3 X_3t+”_4 X_4t+”_t
Y = share price volatility
X_1 = dividend payout ratio
X_2 = dividend yield
X_3 = leverage
X4 = size
”_i = Regression coefficient
”_t = Error term
Based on the model above, the overall test was done to identify the coefficient is significant or not by the probability of F-statistic. If the result of probability (F-statistic) in regressions less than 5%, it shows that the coefficient is significant. Therefore, null hypothesis can be rejected. Besides that, partially F-test means by testing the coefficient of variables one by one. Same goes to partially F-test, when the probability of t-statistic less than 5%, it shows that the coefficient is significant. Hence, null hypothesis can be rejected. Below is the null and alternate hypothesis for overall test and partially test.
Overall test = H_0 : ”_1= ”_2= ”_3=”_4=0
H_1: ”_1= ”_2= ”_3=”_4’0
Partially test = ”_i=0
”_i ‘0
3.7.5 Test On Assumption Of Multiple Linear Regressions
3.7.5.1 Normality test
Jarque Bera test is use as the normality test. For normality test, we focused on the p-value of the Jarque Bera statistic. The p-value need to be more than 5% to not reject the null hypothesis and the residuals are normally distributed. Below is the hypothesis for normality test.
H_0: Error term is normally distributed
H_1: Error term is not normally distributed
3.7.5.2 Autocorrelation test
When the’. P-value (presented by Prob(F-statistic) less than 5%, then alternate hypothesis is accepted which mean that the data used are serially dependent for the error term. (Have autocorrelation problem).
3.7.5.3 Heteroscedasticity test
Variety of test can be used test heteroscedasticity such as Breusch-Pagan-Godfrey, Harvey, White and others. However, the test that was used here is White Test. If p-value of F-probability is more than the 5% significance level, we fail to reject the null hypothesis and the residual are homoscedastic. Below is the hypothesis for heteroscedasticity.
H_0: Error term is homoscedastic
H_1: Error term is heteroscedastic
3.7.5.5 Multicolinearity
To check for multicollinearity, Centered value correlation is employed. The results of be below 0.8 to show no serious multicollinearity.
3.8 HYPOTHESIS STATEMENT
3.8.1 Main Hypothesis Statement
H_0= There is no significant influence between stock price volatility and dividend payout ratio, dividend yield, earning volatility.
H_1= There is significant influence between stock price volatility and dividend payout ratio, dividend yield, leverage and size.
3.9 SUMMARY
This chapter explains the research design that will be applied in the study. The purpose of this study is to determine the relationship between the dependent variable stock price volatility and the independent variables (Dividend yield, dividend payout ratio, leverage and size). A set of annually observations for each of the variables beginning from 2010 to 2014 are used. All the data will be regressed using E-views software. The result of the tests will be highlighted and discussed in the next chapter. The empirical result from the test is expected to provide insights for answering the hypothesis statement.
CHAPTER 4
DATA ANALYSIS
4.1 INTRODUCTION
In this chapter, we will discuss more detail about the relationship between dividend yield,dividend payout ratio,leverage and size of asset with share price volatility which is focusing on construction and material company. From the result of all tests, it can prove that all independent variables have relationship or not towards share price volatility.
There are several test that have been develop where it is include Descriptive Statistic Test, Pearson Correlation Test, Multiple Linear Regression, Jarque Bera Test, Correlation Test, Heteroskedascity Test (White), and Multicollinearity test. Other that, pooled model, fixed effect model, random effect model and hauseman test also be run to get the result. Via this test, the result will give more accurate information and indirectly it also can determine the significant relationship between the dependent variable and independent variable.
4.2 PANEL UNIT ROOT TEST
Before analyzing research data, are examine the stationary of variables. To evaluate the stationaryof the variables we use unit root tests. For this purpose, the Levin, Lin, and Chu test for the common unit root and Philips ‘ Prawn test for cross- section were used. From table (1), the results showed that the significance level (p- value) PP’ Fisher (Phillips, P.C.B and P. Perron) and Levin, Lin & Chu , for all variables Less than 5 percent.
Variable method statistic probability
PRICE VOLATILITY Levin,Lin,Chu
PP-Fisher -15.6723
137.671 0.0000
0.0000
DIVIDEND YIELD Levin,Lin,Chu
PP-Fisher -41.6518
159.610 0.0000
0.0000
DIVIDEND PAYOUT RATIO Levin,Lin,Chu
PP-Fisher -23.7568
122.251 0.0000
0.0001
LEVERAGE Levin,Lin,Chu
PP-Fisher -35.5250
136.719 0.0000
0.0000
SIZE Levin,Lin,Chu
PP-Fisher -55.0480
116.365 0.0000
0.0004
Therefore, all variables are stationary during the period studied.
4.3 DESCRIPTIVE
Y X1 X2 X3 X4
Mean 0.998739 3.759543 0.345580 0.076896 5.892711
Median 0.932505 3.230000 0.289855 0.071102 5.834511
Maximum 2.211329 12.41000 1.428571 0.202773 7.261183
Minimum 0.255405 0.000000 -0.791367 -0.072402 4.833523
Std. Dev. 0.381482 2.333813 0.288545 0.041676 0.521238
observation 175 175 175 175 175
Based on the table above, the results show the results of descriptive analysis share price volatility based on the maximum value, minimum value, standard deviation and mean. The results shows the maximum value for share price volatility as dependent variable is 2.211329 , while the maximum value for dividend yield, dividend payout ratio, leverage and size of asset as independent variable are 12.41, 1.428571, 0.202773 and 7.261183. By refering at the result above, the minimum value for share price volatility is 0.255405 while dividend yield, dividend payout ratio, leverage and size of asset are 0.000000, -0.791367, -0.072402,
4.833523. The value of mean is derived from an average of n numbers computed by adding some functions of the numbers and dividing by some function of n. The data recorded above shows that the mean value for share price volatility is 0.998739 . In addition the mean value for dividend yield, dividend payout ratio,leverage and size of asset are 3.759543, 0.345580, 0.345580, 0.076896 and 5.892711. Besides that, results for standard deviation are derived from the square root of variance.
4.4 CORRELATION ANALYSIS
Correlation analysis need to be done because to identify the relationship between dependent variable (stock price volatility) and independent variables (dividend yield,dividend payout ratio,leverage,size of asset). The result of the correlation matrix of the stationary data (return rate of Y, X1, X2, X3 and X4 respectively) is shown as table below.
variable Correlation xy probability
X1 (dividend yield) 0.265968 0.0004
X2 (dividend payout ratio) -0.132248 0.0811
X3 (leverage) -0.092765 0.2221
X4 (size of asset) -0.631392 0.0000
From the tabele above,, the P-Value for dividend yield and size of asset are 0.0004 and 0.000 which are below 5% significance level. Thus, null hypothesis is rejected which means there is a positive correlation between dividend yield and size of asset with share price volatility.
On the other hand, the P-Value dividend payout ratio and leverage are 0.0811 and 0.2221 respectively. Both of the value are higher than 5% which means it fail to reject null hypothesis as there are no correlation between dividend payout ratio and leverage with share price volatility.
4.5 REGRESSION ANALYSIS
Dependent Variable: Y
Method: Panel Least Squares
Date: 12/08/15 Time: 22:53
Sample: 2010 2014
Periods included: 5
Cross-sections included: 35
Total panel (balanced) observations: 175
Variable Coefficient Std. Error t-Statistic Prob.
X1 0.036010 0.011016 3.268976 0.0013
X2 -0.267163 0.083859 -3.185860 0.0017
X3 -1.959411 0.521227 -3.759227 0.0002
X4 -0.434986 0.043580 -9.981406 0.0000
C 3.669603 0.274566 13.36511 0.0000
R-squared 0.480439 Mean dependent var 0.998739
Adjusted R-squared 0.468214 S.D. dependent var 0.381482
S.E. of regression 0.278191 Akaike info criterion 0.307137
Sum squared resid 13.15633 Schwarz criterion 0.397560
Log likelihood -21.87449 Hannan-Quinn criter. 0.343815
F-statistic 39.29975 Durbin-Watson stat 0.586986
Prob(F-statistic) 0.000000
The Multiple Linear Regression Model used is specified as:
Y1 = ”0 + ”1 X1 + ”2 X2 + ”3 X3 + ”4 X4 + ”
Y= 3.669603 + 0.036010×1 + (-0.267163)x2 + (-1.959411)xi3 +(-0.434986)x4
Where, Y are the dependent variable, ”i is the coefficient measuring the share price volatility for a change in independent variable , X are the independent variables, and ” is error term. In the study, the following factors are used:
Y1: Price Volatility X1: Dividend Yield (DY)
X2: Dividend payout ratio (DPR) X3 : Leverage
X4: Size Of Asset (SA)
from the results above, we can conclude that 46% of Independent variable is being explained by dependent variable. Besides that, the dubin watson stat shows that the less than 2,thus,it means that the data positively auto correlated. Table above shows that the dividend yield (X1) dividend payout ratio (x2) long term debt(X3) and size of asset (X4) has an impact toward share price volatility on material and construction companies in Malaysia. The p-value of dividend yield,dividend payout ratio,leverage and size of asset are 0.0013,0.0017,0.0002 and 0.0000 that are significance at 5% level. The is no probability shows that some variables are not able to influence the share price volatility material and construction companies in Malaysia, as all the independent variable are significant at significant level. The coefficient of dividend payout ratio is in negative value. The coefficient is (-0.267163) which mean that for one percent increase in DPR, the share price volatility will decrease by (-0.267163%. Other than that, leverage and size of asset also have negative value (-1.959411,-0.434986) .
4.5.1 Coefficient
If the p-value t-test statistic is lower than the 5% significance level the variable should be retained in the regression model. The results obtained from the regression table can be explained by inputting the result into the econometric equation.
Y= 3.669603 + 0.036010xi1 + (-0.267163)xi2 + (-1.959411)xi3 +(-0.434986)xi4
Based on the equation, the dividend yield shows a positive relationship with price volatility. Other variables such as dividend payout ratio, leverage,size of asset are significant since their p-value are less than 5% significant level.
4.5.2 F-Statistic
The P-value of the F-test is 0.0000. Therefore, at 5% level of significance we can reject the null hypothesis and can conclude that at least one of independent variable is useful in predicting price volatility.
4. 5.3 Coefficient of Determination (R2)
Coefficient of determination of the model is 0.480439. It means that only 48% of the variation in price volatility can be explained by the variation in the independent variables (dividend yield,dividend payout ratio,leverage,size of asset). The balance of 52%% is determining by other factors.
4.5.4 Dividend Yield
H0 = There is no significant relationship between dividend yield and share price volatility
Ha = There is a significant relationship between dividend yield and share price volatility
The p-value for t-statistic is 0.0013. This value is less than 5% significant level. Therefore the null hypothesis can be rejected. Hence, there is significant relationship between dividend yield and share price volatility
4.5.6 Dividend payout ratio
H0 = There is no significant relationship between dividend payout ratio and share price volatility
Ha = There is significant relationship between dividend payout ratio and share price volatility
The p-value for t-statistic is 0.0017. This value is less than 5% significant level. Therefore the null hypothesis can be rejected. Hence, there is significant relationship dividend payout ratio and share price volatility
4.5.7 leverage
H0 = There is no significant relationship between leverage and share price volatility
Ha = There is significant relationship between leverage and share price volatility
The p-value for t-statistic is 0.0002. This value is less than 5% significant level. Therefore the null hypothesis can be rejected. Hence, there is significant relationship leverage and share price volatility
4.5.8 size
H0 = There is no significant relationship between size and share price volatility
Ha = There is significant relationship between size and share price volatility
The p-value for t-statistic is 0.0002. This value is less than 5% significant level. Therefore the null hypothesis can be rejected. Hence, there is significant relationship size and share price volatility
4.6 NORMALITY TEST
Jarque-Bera test is use to identify whether the error term are normally distributed or not. If the p-value of Jarque-Bera is greater than 5% significance level, thus it fails to reject null hypothesis. By looking at the result above it shows that Jacque-Bera is at 8.932575 and the corresponding p-value is 0.011490. Since p-value is less than 5% level of significance the null hypothesis is not accepted which mean the data is abnormally distributed which fulfils the assumption of a poor regression.
4.6 AUTOCORELATION
fixed
Dependent Variable: Y
Method: Panel Least Squares
Date: 12/12/15 Time: 21:20
Sample (adjusted): 2011 2014
Periods included: 4
Cross-sections included: 35
Total panel (balanced) observations: 140
Convergence achieved after 14 iterations
Variable Coefficient Std. Error t-Statistic Prob.
X1 0.017078 0.006659 2.564579 0.0118
X2 -0.079828 0.046243 -1.726289 0.0874
X3 -0.862216 0.363071 -2.374788 0.0195
X4 -0.421596 0.166644 -2.529915 0.0130
C 3.474738 1.003589 3.462312 0.0008
AR(1) 0.428368 0.118375 3.618727 0.0005
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.936090 Mean dependent var 0.976158
Adjusted R-squared 0.911165 S.D. dependent var 0.368042
S.E. of regression 0.109696 Akaike info criterion -1.347260
Sum squared resid 1.203311 Schwarz criterion -0.506791
Log likelihood 134.3082 Hannan-Quinn criter. -1.005718
F-statistic 37.55641 Durbin-Watson stat 1.825673
Prob(F-statistic) 0.000000
Inverted AR Roots .43
As can be observed, the probability value to be used here is that of the’ Adjusted R-squared’. P-value (presented by Prob(F-statistic)) is 0 which is less than 5% level of significance. Therefore the alternate hypothesis is accepted which mean that the data used are serially dependent for the error term. (Have autocorrelation problem).
4.7 HETEROSKEDESTICITY
Dependent Variable: Y
Method: Panel Least Squares
Date: 12/10/15 Time: 21:42
Sample: 2010 2014
Periods included: 5
Cross-sections included: 35
Total panel (balanced) observations: 175
White cross-section standard errors & covariance (d.f. corrected)
WARNING: estimated coefficient covariance matrix is of reduced rank
Variable Coefficient Std. Error t-Statistic Prob.
X1 0.036010 0.008495 4.239031 0.0000
X2 -0.267163 0.057966 -4.608987 0.0000
X3 -1.959411 0.396207 -4.945420 0.0000
X4 -0.434986 0.016696 -26.05307 0.0000
C 3.669603 0.117961 31.10865 0.0000
R-squared 0.480439 Mean dependent var 0.998739
Adjusted R-squared 0.468214 S.D. dependent var 0.381482
S.E. of regression 0.278191 Akaike info criterion 0.307137
Sum squared resid 13.15633 Schwarz criterion 0.397560
Log likelihood -21.87449 Hannan-Quinn criter. 0.343815
F-statistic 39.29975 Durbin-Watson stat 0.586986
Prob(F-statistic) 0.000000
There are several statistical tests that can be choose to know whether error term have a constant variance or not. Besides White Test, there are other tests which are Harvey test, ARCH Test, Glejser Test and others. The reason run this test is to determine whether the error term have constant variance or not constant variance. The hypotheses are:
H0: Error Term is Homoscedastic (Have constant variance)
H1: Error Term is Heteroscedastic (Does not have constant variance)
The p-value that is represented by the prob(F-statistic) shows the value of 0.0000. Therefore it is less than 5% significant level. Therefore the null hypothesis is rejected, which means that the sample is heteroskedastic. In other word, the error term do not have constant variance.
4.8 MULTICOLLINEARITY TEST
X1 X2 X3 X4
X1 1.000000 0.460639 0.216716 -0.311506
X2 0.460639 1.000000 0.108863 0.014045
X3 0.216716 0.108863 1.000000 -0.160769
X4 -0.311506 0.014045 -0.160769 1.000000
Multicollinearity problem exist if there is correlation coefficient higher than 0.80. Therefore, by looking at the correlation result above, it shows that, there are no correlation coeficient more than 0.8. Thus, the result were not bias and should not be cautiously interpreted. Multicollinearity occurs when the model includes multiple factors that are correlated not just to our response variable, but also to each other. In other words, it results when we have factors that are a bit redundant. Multicollinearity makes some variables statistically insignificant when they should be significant.
Redundant Fixed Effects Tests
Equation: Untitled
Test cross-section and period fixed effects
Effects Test Statistic d.f. Prob.
Cross-section F 27.475736 (34,132) 0.0000
Cross-section Chi-square 365.580397 34 0.0000
Period F 8.801976 (4,132) 0.0000
Period Chi-square 41.376311 4 0.0000
Cross-Section/Period F 25.183255 (38,132) 0.0000
Cross-Section/Period Chi-square 369.281476 38 0.0000
Cross-section fixed effects test equation:
Dependent Variable: Y
Method: Panel Least Squares
Date: 12/12/15 Time: 23:12
Sample: 2010 2014
Hypothesis statement of Redundant Effects-Likelihood Ratio Test:
H0 : Panel regression fixed effect can not be employed to the data
H1 : Panel regression fixed effect can be employed to the data
(p-value <0.1) Significant Redundant Effects-Likelihood Ratio Test, reject null means the Panel regression fixed effect should be used as an empirical model. Test Summary Statistic Cross-section F 27.475736 (0.0000)*** Cross-section Chi-square 365.580397 (0.0000)*** Period F 8.801976 (0.1794) Period Chi-square 41.376311 (0.0522) Cross-Section/Period F 25.183255 (0.0000)*** Cross-Section/Period Chi-square 369.281476 The significant results from Redundant Effects-Likelihood Ratio Test for Cross section and Cross section/period provide evidence to reject null hypothesis that Panel Regression Fixed Effect Model cannot be employed to the data. Therefore the suitable empirical model to be estimated in this study is Panel Regression Fixed Effect Model. 4.9 HAUSEMAN TEST AND FIXED EFFECT MODEL Correlated Random Effects - Hausman Test Equation: Untitled Test cross-section random effects Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob. Cross-section random 5.767575 4 0.2172 Cross-section random effects test comparisons: Variable Fixed Random Var(Diff.) Prob. X1 0.024870 0.025511 0.000003 0.7074 X2 -0.078299 -0.089238 0.000089 0.2471 X3 -0.678961 -0.810866 0.009490 0.1757 X4 -0.518690 -0.471049 0.006629 0.5585 Cross-section random effects test equation: Dependent Variable: Y Method: Panel Least Squares Date: 12/07/15 Time: 02:24 Sample: 2010 2014 Periods included: 5 Cross-sections included: 35 Total panel (balanced) observations: 175 Variable Coefficient Std. Error t-Statistic Prob. C 4.040995 0.634809 6.365685 0.0000 X1 0.024870 0.007011 3.547407 0.0005 X2 -0.078299 0.048534 -1.613290 0.1090 X3 -0.678961 0.367098 -1.849538 0.0666 X4 -0.518690 0.105943 -4.895944 0.0000 Effects Specification Cross-section fixed (dummy variables) R-squared 0.920223 Mean dependent var 0.998739 Adjusted R-squared 0.897932 S.D. dependent var 0.381482 S.E. of regression 0.121876 Akaike info criterion -1.178035 Sum squared resid 2.020125 Schwarz criterion -0.472740 Log likelihood 142.0781 Hannan-Quinn criter. -0.891948 F-statistic 41.28267 Durbin-Watson stat 1.706403 Prob(F-statistic) 0.000000 According to the text book 'panel data econometrics' it states that if the value of statistic is large, then the difference between the estimates is significant. So reject the null hyphothesis that the random effects model is consistent and we use the fixed effect estimators. In contrast, a small value of the hauseman test implies that the random effects estimator is more appropriate. FIXED EFFECT MODEL Dependent Variable: Y Method: Panel Least Squares Date: 12/13/15 Time: 06:15 Sample: 2010 2014 Periods included: 5 Cross-sections included: 35 Total panel (balanced) observations: 175 Variable Coefficient Std. Error t-Statistic Prob. X1 0.024870 0.007011 3.547407 0.0005 X2 -0.078299 0.048534 -1.613290 0.1090 X3 -0.678961 0.367098 -1.849538 0.0666 X4 -0.518690 0.105943 -4.895944 0.0000 C 4.040995 0.634809 6.365685 0.0000 Effects Specification Cross-section fixed (dummy variables) R-squared 0.920223 Mean dependent var 0.998739 Adjusted R-squared 0.897932 S.D. dependent var 0.381482 S.E. of regression 0.121876 Akaike info criterion -1.178035 Sum squared resid 2.020125 Schwarz criterion -0.472740 Log likelihood 142.0781 Hannan-Quinn criter. -0.891948 F-statistic 41.28267 Durbin-Watson stat 1.706403 Prob(F-statistic) 0.000000 By looking at the table above, it shows the result for fixed effect model. For the dividend yield (x1). There is positive relationship with price volatility (Y) which is the value 0.024870 and significant at 0.0005. Other than that, for dividend payout ratio,leverage and size of asset, there are negative relationship with price volatility but then, only size of asset is significant. The rest are not significant at 5% significant level. Besides that,the R squared value is 90.02%, it means that, 90.02% of dependent variable were explained by independent variable. In addition, the durbin watson shows the value is 1.706. CHAPTER 5 5.1 INTRODUCTION In this chapter, the summary of result and discussion will be making from the previous chapter of analysis data. Then, this chapter also will focus to conclude the analysis based on the data analysis that obtain from the research study and make some recommendations on the outcomes of results as a guideline to improve more reliable results and comprehensive study for the futures research study. 5.2 DISCUSSION The purpose of the study was to identify the impacts of selected factors towards share price volatility. The independent variables that being selected in this research are dividend yield, dividend payout ratio, leverage and size. There are 35 material and construction companies selected as the sample for this research. The time frame of this study is between years 201-2014. The findings for this study show that there is significant relationship between dividend yield and share price volatility. Since the p-value for t-statistic is 0.0013 which is less than 5% significant level. By looking at the regression analysis, dividend yield has positive relationship with share price volatility. The finding was similar with (Al-Shawawreh, 2014 ) and (Hussainey, 2010) results which is positive relationship between dividend yield and share price volatility. However it still have the result which is not in line with like research from (Mohammad Hashemijoo, 2012) which is the results shows that there is negative significant Relationship Between Share Price Volatility And Dividend Yield . Besides that, for the dividend payout ratio, leverage and size it indicates negatively significant, since their coefficient are negative and their probability are less than 5% significance level. Based on the past previous research (Dr.zahra Lashgari, june 2014) and (Mohammad Hashemijoo, 2012) the researcher also got negative significant Between Share Price Volatility And Dividend Payout. Other than that, (Mohammad Hashemijoo, 2012) also find that therere is negative significant relationship between share price volatility and size. According to (Dr.zahra Lashgari, june 2014) she find that there is negative relationship between leverage and price volatility in line with this new study result. 5.3 RECOMMENDATION Performing this research study has created a realization on how future researcher could further expand the knowledge horizon to obtain more reliable results and comprehensive study 5.3.1 Add more independent variable As can be observed from the study, the are only four selected independent variables explained the dependent variable.Therefore,more variables need to be considered such as growth in asset, earning volatility in order to improve the result to be much better than before. Inclusion of these variables would provide the researcher a broader base of understanding 5.3.2 Involve other industrial factor This study focused only on one industrial sector of the listed companies in Bursa Malaysia which is material and construction companies. For more breadth, the other sectors on the listed companies should be studied so that the relationship between independent variable and dependent variables toward different industrial sectors of listed company could be gauged or learned. 5.3.4 Use different data structure Future researcher are encouraged to used other types of data structure such as different frequency of the data set such as daily, weekly, quarterly and monthly could be used instead of yearly data set to test the consistency of the findings as well as increase the reliability of the study 5.3.5 Have more comprehensive tests Future researcher are advice to have more comprehensive test for consistency and reliability. Some of the test that can be considered are Johansen Cointegration test, Granger Causality test, Variance Decomposition and Impulse Response Function 5.3.6 Research on other country The unit of analysis in term of countries also can be conduct. Prospective researcher should also conduct similar research on other develop and emerging countries or markets. Results from such studies would be very beneficial because not only will it show the trend in different countries but it could be segregated into regional, developed and emerging markets. The information generated from the research study would be beneficial and valuable not only to investors but to all concerned parties. 5.4 SUMMARY The purpose of this chapter is to explain the conclusion that research can make based on the overall result of finding. Then, it will help the researcher to know what the recommendations that research can suggest based on the data analysis and conclusion from it that they already done. The conclusion in this chapter is the results shows that there are significant positive and negative relationship between independent variable and dependent variable. 6.0 APPENDIX RAW DATA YEAR FIRM y x1 x2 x3 X4 2010 MITRAJAYA HOLDINGs 2.21132891 8.55 0.227272727 0.155773777 5.703022118 2011 MITRAJAYA HOLDINGs 1.662056238 8.19 0.285714286 0.120443414 5.691487293 2012 MITRAJAYA HOLDINGs 1.678362717 11.76 1.0000 0.057981968 5.733318465 2013 MITRAJAYA HOLDINGs 1.754116039 4.17 0.2000 0.078377563 5.745981489 2014 MITRAJAYA HOLDINGs 1.354570923 1.77 0.10989011 0.119267867 5.804764176 2010 PINTARAS JAYA BERHAD

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