Order To Deal With Money Volatility

Published Date: 02 Nov 2017

In order to deal with money, volatility is the prior concern for the investors. Investors who invest their funds in stock or any other assets want to know how they are exposed to how much volatility or risk. Due to this reason the study is involved in finding out the impact of price volatility on stock returns. If the investors knew the volatility this will help them to find out the potential series of values in which the stock will be. Hence when the investors come to know that how much they are exposed to volatility, they then make the conversant decisions regarding the investment.

Volatility can be computed in excess of fixed time period by the series of the prices of an assets or stocks about their mean value (abken &nandi). It ensue that volatility refers to the variance in the price of stock or an asset. If the stock is regarded as highly volatile then price will fluctuate greatly over time On the other hand stocks that are less volatile will be priced in such a way which will deviate for a little time period

Volatility is used to measure how the stock price will fluctuate. Historical volatility is one method to measure the volatility. It can be calculated by investigating the changes in the past in the original stock price. Over a period of time historical volatility can be measured. For example using the prices of stocks for past thirty days the 30 day historical volatility can be measured. However the historical volatility of the 30 days can be different from the 365 day of volatility. This why because the stock have had an odd activities which made the stock as flat for most of the years. By assuming the past volatility can predict the future volatility these volatility measures can be used to find out the chances of occurrence of future price movements over the next days, months or years etc. Since volatility is linked with a risk means that the higher the stock is volatile higher will be the risk. As a result the higher the stock is risky; the difficult it is to find precisely what the price of the stock in the future will be (Criss 95-96)

Fluctuated stock prices reduce the portfolio return and tend to increase the portfolio risk. The security return is basically associated with expectation that public made about the company considering the stock future performance, if public come to know that company is underestimated, they will buy the security of this company, this will generate the demand for company stocks, its security price will rise and reaches the maximum, now public believe that stock price is at highest level and when fall, they start to sell the stock, the stock will actually experience fall in its value. That’s why stock is fluctuating in its value all the time. This fluctuation in stock has warned the investors, regulators and the government body. Some analyst suggest that derivatives e.g. futures and options are the causes of enhancing speculative activity that weaken the stock market by bring higher volatility in stocks (Ranjan, S. D. and Raman, 2002).

Furthermore stock prices that wholly reflect all the available information are considered in an efficient market. Hence fluctuation in the stock price responds to new information. The focal predicament with fluctuation in the stock prices influence the financial market efficiency is detrimental excess volatility that ends up in crisis of financial market. In such circumstances the difference between the intrinsic value of stocks and its related market value is noteworthy and has several consequences.

1.2 KARACHI STOCK EXCHANGE:

Karachi Stock Exchange (KSE) was established in 18th September 1947. The other exchanges in Pakistan are the Lahore Stock exchange (est. 1974) and the Islamabad Stock Exchange (est. 1997). KSE is the biggest of the three exchanges where, according to an estimate, more than 85% of the trade occurs, and in LSE14% and 1% in ISE. 671 companies were listed in KSE at the end of the year 2007. KSE-100 index is representative of the KSE and is normally used as the proxy of the overall market. A number of securities trade in the market, but the market is known for the trade of the ordinary shares. Trade in Futures started in 2003, but are not common in the market. The exchange also plans to introduce options in the near future and according to their estimates by 2012, 50% of the stock market trading will be in derivatives. Due to the implementation of the code of the corporate governance, the number of companies reduced immensely during 2001 - 2003. The rapid growth in capitalization and trading volume were observed after 2001. The market was first and third in 2003 and 2006, respectively, when the turnover ratio of the market observed (Global Stock Markets Fact book, 2004; 2007).

Stocks are included in the KSE 100 index on the basis of following three rules.

Largest market capitalization in each sector for 34 indexes.

For 66 index places the company’s selection are made on the basis of highest capitalization in descending order.

The trading of a default company is ceased for six months.

In the past few years it has been observed that the opening of the Pakistani equity market to foreign investors has rendered itself to recurrent crashes which signified that KSE, LSE, ISE are highly volatile markets. The variations in the stock prices are normal phenomena and are observed daily in more or less in all stock markets of the world.

The risk averse investors who are the average investors have a objective to maximize their return with a least possible risk. Hence given the rate of return, stock markets that are comparatively less volatile likely to attract more investors. However perception rather than the realization matters in the individual investment decisions. Earlier period experience matters to the degree that it effects future expectations about return and volatility. In other words investors consider expected return and expected volatility whilst making their investment decisions, conditional upon the available information. Therefore the stock market activities are typically governed by the information. The information that comes up as an uncertain event, the so called good or bad news causes shocks to the stock prices and later results in the volatility.

1.3 Significance of Study:

The study will consider and elaborate the influence of stock price volatility on returns. The study is important in a sense the detailed results of the relationship between price volatility and return. Moreover the study will be helpful to identify that how the volatility affects the return. The volatility will be calculated by using the method of standard deviation.

1.4 PROBLEM STATEMENT:

One problem arises in the investment decision stays in the line of the financial theory. That is if the wide range of safe assets exists in market still why investors takes a risk of volatility? This research studies the nature of volatility on return and how the former affects the later one. Moreover finding the results will be beneficial for the individual investor to know how the price volatility affects the return.

1.5 HYPOTHESIS:

The study will take the null hypothesis Ho and its alternative H1. Our null and alternative hypothesis will be as follows:

H0: stock returns are not affected by the volatility in the stock prices.

H1: stock returns are affected by the volatility in the stock prices.

1.6 OBJECTIVE OF STUDY:

The study has been carried out to deal with the following issues relevant to activities at KSE.

To prove how risk affects the selected stocks.

To prove the relationship between risk and return of the selected stocks and

To prove the nature of volatility in the selected stocks and how volatility affects the selected stock return

1.7 SCOPE OF STUDY:

The study has undertaken to cover the following aspects:

The study is based on the secondary data

Quantitative data has been carried out on the basis of three companies

The monthly data is used in the study for analysis of five years

However there are three stock exchanges in the Pakistan but the study is limited to the Karachi stock exchange.

Time limitation will somehow impede in the study.

1.8 VARIABLES:

Following variables will be used for analysis

A). Dependent variable

Stock return

B). Independent Variables

Price volatility

Size of the firm

CHAPTER 2 LITERATURE REVIEW

It is important for all the countries in the world to understand the behavior of the stock market risk and return. However, it is still important for the developing countries to understand such behavior especially when the market consists of the risk averse investors. The higher the presence of the volatility in the stock market will ultimately lead the investor to demand the higher risk premium, which ultimately results in creating the higher capital cost which in turn hiders to slow the economic development as well as in the investments.

According to (rajni mala, Mahendra reddy) the firms who are volatile are the ones which are responsive to the government regulations, in such a situation, over the years the liquidity has been low and the IPOs have been quite underpriced. Moreover interest rate in the study has been used in the regression for the stock return volatility showed that changes in the interest rates has a significant impact on the stock market volatility.

French, Schwert aand stambaught(1986) investigated the relationship between the stock return and the stock market volatility. They found that there is positive relationship between the level of volatility which is predictable and the expected risk premium. Furthermore their evidence also found that if positive relationship exists between the predictable volatility in stock returns and expected risk premium then low current stock prices and increased future expected risk premium exists only when there is a positive unexpected change in volatility.

David and schiller(1990) analyzed that rather than the movements in the expost values for which ordinary investors has a very little knowledge, the actions of the individual investors are more likely accounted for capital gain and loses. According to them volatility is function of both the psychological factors and the economic variables.

William (1990) explained in his study that the level of stock market volatility have an expressed concerned for the regulators, investors, dealers and press. However a lot of movements in the prices are merely due to the historical high levels of well-liked indexes. In October 1939, the decline in the stock prices has led the NYSE the worst 25 days in the history in terms of percentages. However, volatility was not a major concern in the 1980 for which the upward trend in the stock volatility has not been associated with the sock index future and options trading. But the evidence showed that the increased volatility is due to the use of computerized trading.

Hussain and uppal (1998) analyzed the effects of market opening to investors who are international as well as the characteristics of distribution of Pakistani equity market. They used the daily data of 36 companies, and indices of 8 sections for which they examined the behavior of stock returns. Their study showed that when market was opened both average return and the volatility increased significantly after one year dropped back.

Hussain (1997) in his another study, using the daily data investigated how returns are related to the stock market volatility in the Pakistani equity market. The study found that there is persistency of variance in the returns which implies that for a long period of time shocks to volatility continued. It was also found that dependence in the stock returns was reduced when the volatility has been controlled, but not completely eliminated which eventually showed that return can be predictable but partially.

According to khilji (1993) for eleven groups of stocks used monthly stock returns to investigate the monthly stock return time series behavior. The study suggested that returns of various series were usually had positive mean, leptokurtic and positively skewed since the returns of various series were not normal. In the study the historical beta were not different form one rather they were found statistically different from zero by assuming that each group in the industry efficient and diversified portfolio. It means that the investor who by having the diversified portfolio of stocks in the industries will bear the same amount of risk as investors who are having one industry portfolio.

According to Ahmed and Rosser erratic suggested that tendency to expect best in the future economic development is based on the rising but volatile market prices. Despite of that they also suggested that Pakistani economy might not be subject to stable and be indecisive.

Specifically at the firm level Farid and Ashraf (1995) analyzed the influence of trading volume on the stock price volatility. They selected the ten companies for the first six months and calculate the daily average turnover. They found that stock price volatility were quite high ranging from twenty six percent to fifty one percent per annum. During the first half of 1994 they examined that there is a strong positive correlation the expected rate of return, stock price volatility and the trading volume. This showed the trend to the investors to invest only in those stocks that give short term gains. It has been observed in the study that when the market is in uptrend majority of investors enters in the market and vice versa.

Panu chaopricha, peng chen and denise pollard (2007) proposed that apart from the stock price volatility stock returns are also affected by the certain firms characteristics. These characteristics are used for the fundamental analysis. The most popular ones are the book to market ratio, size of the firm and price to earnings ratio. They found that these characteristics have a strong negative relationship with stock returns.

Bekaert and Harvey (1995) analyzed that in order to determine the overall capital cost, volatility is an important input. They noted that increase effect of international factors on the volatility at the national level market is mainly due to and increased amalgamation of the national markets. Their study also showed that market volatility can also be influenced by the liberalized capital market. They also revealed that correlation between the liberalized market and volatility has been found positive in some countries which create greater importance to the policy makers.

Fama & French (1980) found and concluded that the trade itself causes the volatility. This means that if the level of the trade volume increases then price movement will also increase. Besseminder & seguin (1993) found that reaction between volume and prices causes the asymmetrical volatility.

French & Roll (1986) examined the volatility and found that at time of trading hours volatility is higher. It has been argued that due to arrival of new information it is the trading volume that triggers the volatility in stock prices. Their study suggested that factors like inflation rate, interest, credit policy, corporate earnings, financial leverage as well as the social and political variables causes the volatility in the stock prices.

Madhavan (1992) explains that price deviation is a form of volatility. His study proposed that investors who demand low volatility often lessen the unnecessary risk which is tolerated by those investors. Ultimately this helps the traders in the market to liquidate their assets easily without dealing with the hostile huge price movements

Kainer j (2002) examine that growth in the economy can be well predicted by the analysts by studying the degree of volatility in the stock market. In addition to this that volatility when properly structured can help the investors to attain diversification by holding more stock in their portfolio.

Amir & kashif (2011) compared the structure of variance of diverse frequencies of stock return volatility. Using the daily weekly and monthly data they found that daily data are more volatile than the weekly and monthly data and the properties of their data series area also different from one another. They also found that previous studies failed to measure the volatility because of loss of information in the weekly and monthly data.

Boris, in his paper analyzed the volatility in the international stock market. He suggested that volatility is mainly due to the asymmetry behavior of stock prices in the market. He added that when there is a decline in the market, there will be an increase in the volatility. As a result the high volatility decreases the returns and the returns increases when the volatility is low.

2.1. Theoretical Framework:

IV DV

Stock Return

Stock Price volatility

Chapter 3 Research Design & Methodology

Target Population:

The target population for this study is the KSE 100 index

Sample size:

Since the target population is KSE 100 index, five companies have been selected for the study as a sample size. Monthly data of five years i.e. from 2008 to 2012 have been used in the study. The data is collected from the Karachi stock exchange and business recorder websites and is secondary and quantitative in nature.

Variables:

In order to find the impact of price volatility on the stock returns of five companies, dependent and independent variables have been determined in this study which is explained below.

Dependent Variable:

Stock Return:

When investing in the stocks investors always wants find that how much they had gained or loss from the investment. Investors need to be compensated for bearing the risk due to fluctuation in the stock prices. Many investors think that increase in the price variation of stocks will increase the stock returns and vice versa. In order to construct this variable, the study undertook the natural log of current price of stock divided by the previous price of stock

Independent Variable:

Following are the independent variables:

Price Volatility:

Price volatility means variation in the prices of stocks. Fluctuation in the stock prices may lead to the rate of return on stocks. This means that price volatility can affect the return on stocks. In order to construct this variable the study uses the standard deviation method.

Size of the firm:

Size of the firm can be determined by the market capitalization, total assets or sales revenue of the firm. The study uses the market capitalization to determine the size of the firm. This variable can be constructed by multiplying the price of stock with number of outstanding shares.

Model Specification:

Multiple Regression:

In order to find the impact of price volatility on the stock return of the five companies within the sample every company’s stock return will be regressed with its price volatility this study uses the regression model. Since the study has more than one independent variable multiple regression model will be used. Statistical tools such as SPSS and Ms excel has been used in this study

The model has been constructed as follows:

Y= α+β1X1+β2X2+ε

Where

Y represents the dependent variable

α represents the constant

β represents the coefficient

X 1 and X2 are the independent variables (price volatility and size of the firm)

ε represents the standard error.

In order to develop further understanding the equation is further modified as follows

SR= α+β1PV+β2SZ+ε

Where

SR = stock return ln(P1/P0)

P0 is the previous price of stock

P1 is the current price of stock

α represents the constant which means that what will be the stock return if price volatility and size of the firm is zero.

β represents the coefficient/slope which means that how much stock return will change with respect to price volatility and size of the firm.

Hypothesis

The hypotheses for the study are as follows

Null hypothesis (1)

H0(1) Price volatility has no effect on stock return of Indus motor Co. Ltd

Alternative Hypothesis (1)

H1(1) Price volatility has effect on stock return of Indus motor Co. Ltd

Null Hypothesis (2)

H0(2) Price Volatility has no effect on the stock returns of the Nestle Pakistan

Alternative Hypothesis (2)

H1(2) Price Volatility has effect on the stock returns of the Nestle Pakistan

Null Hypothesis (3)

H0(3) Price Volatility has no effect on the stock returns of the Engro Corporation Ltd

Alternative Hypothesis (3)

H1(3) Price Volatility has effect on the stock returns of the Engro Corporation Ltd

Null Hypothesis (4)

H0(4) Price Volatility has no effect on the stock returns of the Fauji Cement Co Ltd

Alternative Hypothesis (4)

H0(4) Price Volatility has effect on the stock returns of the Fauji Cement Co Ltd

Null Hypothesis (5)

H0(5) Price Volatility has no effect on the stock returns of the Siemens Pak Eng Co Ltd

Alternative Hypothesis (5)

H0(5) Price Volatility has no effect on the stock returns of the Siemens Pak Eng Co Ltd

Chapter 4 DATA ANALYSIS

In this section monthly data for five years that is from 2008 to 2012 of five companies has been used for the analysis. Since the study has more than one independent variable multiple regression method is used in the study. For the application of multiple regression Statistical tools such as SPSS and the Ms EXCEL has been used.

SR= α+β1PV+β2SZ+ε

Table 1: Regression analysis for Indus motor Co Ltd

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.180493

R Square

0.032578

Adjusted R Square

0.00137

Standard Error

0.840861

Observations

60

ANOVA

 

df

SS

MS

F

Significance F

Regression

2

1.357157

0.678579

0.959736

0.389096

Residual

57

40.3017

0.707047

Total

59

41.65886

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

0.230996

0.412971

0.559352

0.578113

-0.59596

1.057955

PV

-0.01384

0.010248

-1.35082

0.182092

-0.03436

0.006678

SIZE

-5.9E-12

2.32E-11

-0.25627

0.798662

-5.2E-11

4.05E-11

Interpretation:

Table 1 in the regression output consist of three sections i.e. regression statistics, ANOVA and the coefficients. In the first section the multiple R represents he coefficient of correlation. It tells us the direction and magnitude of the variables. The result show of the coefficient of correlation is 18.0493% which means that both the dependent and independent variables move positively in the same direction

Rsquare represents the coefficient of determination. It can be calculated to the power of R. it describes that how much proportion of variance is explained in the dependent variable by the independent variable. The result shows that approximately 3.285% of variance in the stock return is due to price volatility. Since this is very low figure it is not a good sign. However the standard error figure is very high which is 84.08% means that there are 84.08% unexplained variations.

ANOVA stands for analysis of variance. It is included in the regression analysis to see whether our model is fit or not. For its interpretation we will look at the F ratio. It can be calculated by dividing the MS regression by MS residual. The F ratio in the table is statistical insignificant because the p-value is greater than the 0.05 which means that the model is not fit for analysis

The third section shows the coefficient of variables. In that intercept means the constant value. In the table the constant is equal to 0.230996 which is statistically insignificant. It means that stock return will be 0.230996 if the price volatility does not change. Furthermore the p-value of price volatility is greater than 0.05 which is statistically insignificant. This shows that null hypothesis will be accepted i.e. stock returns of Indus motor Co Ltd are not affected by the price volatility and the alternative hypothesis will be rejected. The regression equation will be as follows.

SR= 0.230996+ (-0.01384) PV+ (-5.9E-12) SZ+ε

Table 2: Regression analysis for Nestle Pakistan

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.417814

R Square

0.174568

Adjusted R Square

0.145606

Standard Error

0.617477

Observations

60

ANOVA

 

df

SS

MS

F

Significance F

Regression

2

4.596215

2.298107

6.027384

0.004221

Residual

57

21.73283

0.381278

Total

59

26.32905

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

0.072053

0.167542

0.430061

0.668773

-0.26344

0.40755

PV

0.002608

0.000752

3.468921

0.001002

0.001103

0.004114

SIZE

-2.3E-12

1.56E-12

-1.47659

0.145289

-5.4E-12

8.23E-13

Table 2 in the regression output also consist of three sections i.e. regression statistics, ANOVA and the coefficients. In the first section the multiple R represents he coefficient of correlation. It tells us the direction and magnitude of the variables. The result show of the coefficient of correlation is 41.7814% which means that both the dependent and independent variables move positively in the same direction

Rsquare represents the coefficient of determination. It can be calculated to the power of R. it describes that how much proportion of variance is explained in the dependent variable by the independent variable. The result shows that approximately 17.4568% of variance in the stock return is due to price volatility. However the standard error figure is very high which is 61.7477% means that there are 61.7477% unexplained variations.

ANOVA stands for analysis of variance. It is included in the regression analysis to see whether our model is fit or not. For its interpretation we will look at the F ratio. It can be calculated by dividing the MS regression by MS residual. The F ratio in the table is statistical significant because the p-value is less than the 0.05 which means that the model is fit for analysis.

The third section shows the coefficient of variables. In that intercept means the constant value. In the table the constant is equal to 0.072053 which is statistically insignificant. It means that stock return will be 0.072053 if the price volatility does not change. Furthermore the p-value of price volatility is less than 0.05 which is statistically significant. This shows that null hypothesis will be rejected and the alternative hypothesis will be rejected i.e. stock returns of Nestle Pakistan are affected by the price volatility. The regression equation will be as follows.

SR= 0.072053+ (0.002608) PV+ (-2.3E-12) SZ+ε

Table 3: Regression analysis for Engro Corporation Ltd

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.515509

R Square

0.26575

Adjusted R Square

0.239987

Standard Error

0.708629

Observations

60

ANOVA

 

df

SS

MS

F

Significance F

Regression

2

10.35957

5.179787

10.31511

0.00015

Residual

57

28.62286

0.502155

Total

59

38.98244

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

0.513163

0.283019

1.813176

0.075071

-0.05357

1.079898

PV

-0.0685

0.015082

-4.54177

2.94E-05

-0.0987

-0.0383

SIZE

-1.5E-12

4.74E-12

-0.30793

0.759255

-1.1E-11

8.04E-12

Table 3 in the regression output also consist of three sections i.e. regression statistics, ANOVA and the coefficients. In the first section the multiple R represents the coefficient of correlation. It tells us the direction and magnitude of the variables. The result show of the coefficient of correlation is 51.5509% which means that both the dependent and independent variables move positively in the same direction

Rsquare represents the coefficient of determination. It can be calculated to the power of R. it describes that how much proportion of variance is explained in the dependent variable by the independent variable. The result shows that approximately 26.575% of variance in the stock return is due to price volatility. However the standard error figure is very high which is 70.8629% means that there are 70.8629% unexplained variations.

ANOVA stands for analysis of variance. It is included in the regression analysis to see whether our model is fit or not. For its interpretation we will look at the F ratio. It can be calculated by dividing the MS regression by MS residual. The F ratio in the table is statistical significant because the p-value is less than the 0.05 which means that the model is fit for analysis.

The third section shows the coefficient of variables. In that intercept means the constant value. In the table the constant is equal to 0.513163. It means that stock return will be 0.513163 if the price volatility does not change. Furthermore the p-value of price volatility is less than 0.05 which is statistically significant. This shows that null hypothesis will be rejected and the alternative hypothesis will be accepted i.e. stock returns of Engro Corporation Ltd are affected by the price volatility. The regression equation will be as follows.

SR= 0.513163+ (-0.0685) PV+ (-1.5E-12) SZ+ε

Table 4: Regression analysis for Fauji Cement Co Ltd

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.451238

R Square

0.203616

Adjusted R Square

0.175673

Standard Error

0.559484

Observations

60

ANOVA

 

df

SS

MS

F

Significance F

Regression

2

4.561835

2.280918

7.28675

0.001521

Residual

57

17.84229

0.313023

Total

59

22.40413

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

0.09162

0.216257

0.42366

0.673408

-0.34143

0.524667

PV

-0.10524

0.028322

-3.71573

0.000463

-0.16195

-0.04852

SIZE

-1.7E-11

3.9E-11

-0.4237

0.673382

-9.5E-11

6.16E-11

Table 4 in the regression output also consist of three sections i.e. regression statistics, ANOVA and the coefficients. In the first section the multiple R represents the coefficient of correlation. It tells us the direction and magnitude of the variables. The result show of the coefficient of correlation is 45.1238% which means that both the dependent and independent variables move positively in the same direction.

Rsquare represents the coefficient of determination. It can be calculated to the power of R. it describes that how much proportion of variance is explained in the dependent variable by the independent variable. The result shows that approximately 20.3616% of variance in the stock return of the Fauji cement Co Ltd is due to price volatility.

ANOVA stands for analysis of variance. It is included in the regression analysis to see whether our model is fit or not. For its interpretation we will look at the F ratio. It can be calculated by dividing the MS regression by MS residual. The F ratio in the table is statistical significant because the p-value is less than the 0.05 which means that the model is fit for analysis.

The third section shows the coefficient of variables. In that intercept means the constant value. In the table the constant is equal to 0.09162 which is statistically insignificant. It means that stock return will be 0.09162 if the price volatility does not change. Furthermore the p-value of price volatility is less than 0.05 which is statistically significant. This shows that null hypothesis will be rejected and the alternative hypothesis will be accepted i.e. stock returns of Fauji Cement Co Ltd are affected by the price volatility. The regression equation will be as follows.

SR= 0.09162 + (-0.10524) PV+ (-1.7E-11) SZ+ε

Table 5: Regression analysis for Siemens Pak Eng Co Ltd

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.800476

R Square

0.640762

Adjusted R Square

0.628157

Standard Error

0.144367

Observations

60

ANOVA

 

df

SS

MS

F

Significance F

Regression

2

2.118968

1.059484

50.83464

2.13E-13

Residual

57

1.187981

0.020842

Total

59

3.306949

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

0.421107

0.084573

4.979225

6.25E-06

0.251753

0.590461

PV

-0.02029

0.002809

-7.22205

1.36E-09

-0.02591

-0.01466

SIZE

-2.4E-11

1.05E-11

-2.32003

0.023943

-4.5E-11

-3.3E-12

Table 4 in the regression output also consist of three sections i.e. regression statistics, ANOVA and the coefficients. In the first section the multiple R represents the coefficient of correlation. It tells us the direction and magnitude of the variables. The result show of the coefficient of correlation is 80.047% which means that both the dependent and independent variables are strongly related to each other and move positively in the same direction.

Rsquare represents the coefficient of determination. It can be calculated to the power of R. it describes that how much proportion of variance is explained in the dependent variable by the independent variable. The result shows that approximately 64.0762% of variance in the stock return of the Siemens Pak Eng Co Ltd is due to price volatility.

ANOVA stands for analysis of variance. It is included in the regression analysis to see whether our model is fit or not. For its interpretation we will look at the F ratio. It can be calculated by dividing the MS regression by MS residual. The F ratio in the table is statistical significant because the p-value is less than the 0.05 which means that the model is fit for analysis.

The third section shows the coefficient of variables. In that intercept means the constant value. In the table the constant is equal to 0.421107. It means that stock return will be 0.421107 if the price volatility does not change. Furthermore the p-value of price volatility is less than 0.05 which is statistically significant. This shows that null hypothesis will be rejected and the alternative hypothesis will be accepted i.e. stock returns of Siemens Pak Eng Co Ltd are affected by the price volatility. The regression equation will be as follows.

SR= 0.421107 + (-0.02029) PV+ (-2.4E-11) SZ+ε

CHAPTER 5 CONCLUSION

Data used in this study is quantitative and secondary in nature. The study is based on the cause and effect relationship between the stock return and the price volatility in the stocks. The stock return is the dependent variable and the price volatility is the independent variable. Their cause and effect relationship is checked and noted. Five companies have been selected for the study; initially stock returns and price volatility are calculated.

The regression analysis shows that there are few companies whose stock returns are affected by the price volatility. The lower p value indicates that companies stock returns are affected by the price volatility. Mainly the stock returns of the companies in the year of 2008-09 the prices are stable. They are not volatile. This is why most investor has low concern on the investment in stocks in order to gain return or bear the loss. When the volatility is high the investors have major concern towards the profit. It means that investor will tolerate the risk for higher returns. Moreover stock returns are not only affected by the fundamental factors like price volatility and size of the firm has been used, macroeconomic variable should be utmost consideration for the performance of stock returns. These variables can be exchange rate, inflation rate, GDP rate, interest rate etc.