Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
Share Price Volatility in Selected Pharmaceutical and Chemical
Companies listed on the CSE
Mohammad Nayeem Abdullah*, Kamruddin Parvez*, Rahat Bari Tooheen*
and Sabrina Ahsan*
This study examines the share price volatility of selected pharmaceutical
and chemical companies listed on the Chittagong Stock Exchange (CSE).
Data was compiled and analyzed from a mix of primary and secondary
sources of information. Average Share Price is the dependent variable,
while Dividend Yield, Earnings Per Share, Price Earnings Ratio and
Returns On Equity were the independent variables in the regression
analysis. The results demonstrate that share price volatility is significantly
influenced by the Price Earnings Ratio, along with Dividend Yield and
Earnings Per Share.
1. Introduction
A robust capital market is an important in modern economies. Without the existence of
sound and efficient capital market, rapid economic development could be hampered as it
provides long term funds to entrepreneurs. The capital market of Bangladesh is still highly
speculative and lacks transparency due to the absence of a defined regulatory framework.
The capital market of the country started to show vibrant behavior in the 90’s which helped
to create an interest about the stock exchange in the general public. Nowadays people in
general are more knowledgeable about the aspects and risks of the financial markets, and
hence can be stated to be more cautious in entering into a new portfolio of investments
compared to previous instances.
There are different financial variables in stock market such stock price, share index etc.
This share prices may fall in some situation and may constant or rise in another situation.
These move up or down are termed as volatility. A volatile stock would be one that sees
very large swings in its stock price. If there is a high volatility in the stock market i.e., the
market is not in consistent position and the country’s economy will be threatened. Volatility
indicates to the fluctuations in security prices that are the function of a variety of factors
such as interest rates, industrial production, commodity prices, savings, investments,
employment, political and economic developments and stability, technological changes,
corporate profits, earnings or dividend, investors’ feelings and ‘rumor’.
The primary objective of this article is to measure the stock price volatility of the selected
pharmaceutical and chemicals companies listed on the Chittagong Stock Exchange.
The specific objectives of the article are specified as follows:
 To determine the reasons of the price volatility
 To conduct a technical analysis of the factors which affect the price volatility
 *Assistant Professor, Independent Business School, Chittagong Independent University, Bangladesh.
Email: nayeem@ciu.edu.bd
 *Assistant Professor, Independent Business School, Chittagong Independent University, Bangladesh.
Email: parvez@ciu.edu.bd
 *Assistant Professor, Independent Business School, Chittagong Independent University, Bangladesh.
Email: tooheen@ciu.edu.bd
 *Research Assistant, Independent Business School, Chittagong Independent University, Bangladesh.
Email:
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
2. Literature Review
The fluctuations beliefs generated by overconfidence among Bangladesh investors led to
larger speculative component in stock prices, and the technical bubble of the U.S. market
was identified as the result of “exuberance” (Shiller, 2000; Chen, Hong, and Stein, 2002).
Given that markets in the advanced economies seemed to be more susceptible to
speculative bubbles and crashes, and many emerging markets also display similar
evidence, it seems reasonable that no one should look forward to these phenomena
disappearing from the Bangladesh markets (Ahmed et al., 2006).
Glassman and Hassett (1999) questioned the reason why the stock prices kept keep
increasing when the market was thought to be fully valued or on the verge of a crash. The
authors believed the incredible returns that stocks produced came from recognizing that
stocks had been far riskier than bonds and as a result generates more returns. Shiller
(2000) claimed that there was a bubble in the U.S. market, mostly because of
psychological factors or “ex-aberrances’” which are frequently used to illustrate a
heightened state of speculative favor. Shiller also argued that there are obvious pricing
errors due to noise in stock prices since actual stock prices are more volatile than the
present discounted value of actual dividends. Poterba and Summers (1988) conducted an
extensive study using various frequencies of data From the New York stock Exchanges
(NYSE) and 17 other equity markets. Their study consistently shows that returns are
positively correlated over longer periods. In addition, Poterba and Summers found that for
the long horizon, a mean reverting component of stock prices could explain a large portion
of variations in stock returns.
Huang (1995) used the variance ratio test to examine the random walk hypothesis for 9
Asian countries namely, Hong Kong, Indonesia, Japan, Korea, Philippines, Singapore,
Thailand, Malaysia and Taiwan. Using weekly stock price data from January 1988 to June
1992 of the nine countries, Hung applied both the heteroscedasticity unadjusted and the
heteroscedasticity adjusted variance ratio test to the data. Bekaert and Harvey (1995) test
a variety of sophisticated models of conditional volatility and find that volatility is difficult to
model in emerging markets. They find evidence, however, that the importance of world
factors in emerging market volatility may be increasing, and that volatility tends to
decrease following market liberalization. Kim and Singal (1993) suggest that there has
been no increase in volatility over time, and that volatility has tended to decrease following
market liberalization. Richards (1996) used three different methodologies and two sets of
data to estimate volatility of emerging markets.
Few studies investigate the relationship between stock market volatility and bank
performance. Albertazzi and Gambacorta (2009) use five performance indicators (Net
Interest Income, Non-Interest Income, Operating Cost, Provisions, Profit before Tax, and
Return on Equity) to investigate the influence of stock market volatility on bank
performance for main industrialized countries (Austria, Belgium, France, Germany, Italy,
Netherlands, Spain, United Kingdom and United States) during the period 1981-2003.
They report that Net Interest Income, Non-Interest Income, Provision and Return on
Equity are positively related to stock market volatility, while the stock market volatility is
negatively related to Profit before Tax. Further, no relationship between stock market
volatility and Provisions is reported. Investors’ preference is for less risk. The lesser the
amount of risk, the better the investment is (Kinder, 2002). In other words the lesser the
volatility of a given stock, the greater its desirability is. Volatility gives shrewd investors the
opportunity to take advantage of price swings to buy when prices fall well below the value
of the company and sell when they climb well above the company's intrinsic value.
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
The linkage between the dividend policy of corporations and the volatility of their stock
prices has been explored at different times by different researchers (Allen and Rachim,
1996; Baskin, 1989). Baskin (1989) examine 2344 U.S common stocks from the period of
1967 to 1986, and found a significant negative relationship between dividend yield and
stock price. While Allen and Rachim (1996) reported a positive relationship between share
price volatility and dividend yield after studying Australian listed stocks, but a negative
relationship between share price volatility and dividend payout. Irfan and Nishat (2003)
studied 160 companies listed at Karachi Stock Exchange for the period of 1981-2000.
Their results were based on cross sectional regression analysis show that dividend yield
and payout ratio is positively related to the share price volatility.
Foster (1973) examined the stock market reaction to estimates of annual earnings per
share. These estimates were made after the end of the financial year, but before the
release of audited figure. His study examined the stock price adjustment to the EPS
estimate. Rashid, Rahman and Anisur (2008) researched on relationship between
dividend policy and share price volatility and found a positive insignificant relationship
between share price volatility and dividend yield for nonfinancial firms listed in the Dhaka
Stock exchange during the period of 1999 – 2006.
3. The Methodology and Model
Experimental design examines the different links between variables by manipulating the
independent variable in order to see how the depending variable is affected. The
experimental design is often used in exploratory and explanatory approaches (Saunders
et al., 2009). The model investigated the relationship between Dividend Yield, Earning Per
Share, Price earnings Ratio, Return on Equity and Mean Share Price within the context of
Chittagong Stock Exchange. A correlation design was adopted in order to find out the
appropriate answers to the research questions and to test the hypotheses. Dividend Yield,
Earnings Per Share, Price Earnings Ratio, and Return on Equity were considered as
independent variables, whereas Average Price of Shares was considered as the
dependent variable.
The study uses secondary data from published sources to fulfil the objectives and to
develop the literature review. Data is collected from both primary (Stock Exchange, SEC)
and secondary sources like different publications of CSE, Bangladesh Bank, publications
of CSE. Secondary data was collected from Chittagong Stock Exchange Library. Individual
company data with 48 weeks of each 8 years data is collected from the annual report of
the each company’s weekly, monthly, and annual reports of eight selected pharmaceutical
and chemical companies for the years 2006 to 2013. The study used the Probability
Sampling technique to determine the final selection of companies from the sampling frame
of the pharmaceutical and chemical companies listed on the Chittagong Stock Exchange.
Earnings Per share, Dividend Yield, Price Earnings Ratio and Return On Equity were
measured by summing each year’s data from 2006 to 2013 of each company and then
average amount has been taken into consideration which was adapted from the annual
reports of each of the respective companies. Data were first analysed in a Microsoft Excel
spreadsheet. For the current study, a conversion was made through a computer package
called Statistical Package for Social Science (SPSS) software version 17 which offered a
greater feasibility in data analysis and visualization. The analysis begins with a descriptive
and inferential statistics. At first the mean of each of the sample companies was
calculated. Then comparison was made with the average yearly market price of individual
company’s stock with the industry average share, which will give the reader an insight into
trends of market price of sample companies enlisted in Chittagong Stock Exchange The
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
data was analyzed by collecting data of monthly closing market price and then average it
to get the yearly average market price of shares. The comparison between the Industry
average price of the share and market price of individual company’s stock were presented
in tabular form.
In the second portion of the statistical analysis, the standard deviation was calculated for
each of the selected companies. Afterwards, a Correlation Analysis was carried out to
establish the strength of the relationship between the measured variables. The scale
model suggested by David (1971) was used to describe the relationship between the
variables as follows:
a) 0.70 and above
b) 0.50 to 0.69
c) 0.30 to 0.49
d) 0.10 to 0.29
e) 0.01 to 0.09
– Very Strong Relationship
– Strong Relationship
– Moderate Relationship
– Low Relationship
– Very Low Relationship
Furthermore, a linear regression analysis was also carried out to determine the extent to
which independent variables influence the dependent variable.
4. The Findings
4.1 Measurement Stock Price Volatility by using Percentage Change compared to
Industry Average
YEAR
INDUST
ACI
%
AMBE
%
BXPH
%
ORION
%
RY
chang EPHA chang ARMA chang
INFU
chang
MEAN
e
RMA
e
e
e
2006
478.89
67.87
-0.86
45.37
-0.91
52.59
-0.89
146.5
-0.69
2007
625.67
112.9
-0.82
50.545
-0.92
54.98
-0.91
104.78
-0.83
2008
909.85
369.5
-0.59
-0.87
99.941
-0.89
154.65
-0.83
2009
642.78
453.7
-0.29
114.18
6
209.86
-0.67
162.15
-0.75
451.13
-0.30
2010
1259.2
431.7
-0.66
343.75
-0.73
157.87
-0.87
748.94
-0.41
2011
1125.45
261.2
-0.77
424.84
-0.62
102.36
-0.91
638.16
-0.43
2012
225.87
162.6
-0.28
269.36
0.19
66.42
-0.71
44.47
-0.80
2013
236.13
150.1
-0.36
238.62
0.01
52.31
-0.78
43.6
-0.82
YEAR
4.2 Percentage Change for the Last 4 Selected Companies
INDUST LIBRAI
%
SQURP
%
RECKIT
%
RY
NFU
chang HARMA chang
TBEN
chang
MEAN
e
e
e
IBNSI
NA
%
chang
e
2006
478.89
537.22
0.12
2274.3
3.75
164.19
-0.66
543.01
0.13
2007
625.67
584.998
-0.07
3221.4
4.15
263.198
-0.58
612.46
-0.02
2008
909.85
1268.86
0.39
3808.4
3.19
422.11
-0.54
1041.1
0.14
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
2009
642.78
1667.75
1.59
294.89
-0.54
639.53
-0.01
1263.3
0.97
2010
1259.2
2440.21
0.94
3645.7
1.9
1491.64
0.18
1540.5
0. 22
2011
1125.45
3834.7
2.41
3057.4
1.72
1015.35
-0.1
1387.6
0. 23
2012
225.87
270.36
0.20
216.14
-0.04
759.64
2.36
93.98
-0.58
2013
236.13
288.02
0.22
186.89
-0. 21
749.2
2.17
90.131
-0.62
In analysis of the price of share of the company, we have taken mean share price per year
of each company. In this case the percentage change of the share price has been
observed through one to another. Some of the companies Mean prices were above the
Industry Average price. LIBRA INFUSION had shown a sharp price increase from the
Industry average up to 2011 then drastically the price fallen in 2012 and were in line with
the Industry average. SQUARE PHARMACEUTICALS had shown the most price
fluctuation with the Industry Average. From 2006 it was well above the Industry Average
but in 2009 it dropped sharply below the Industry average and it again showed instability
till the end of 2014. RECKITT BENCKISER shown most prominent performance
compared to other companies as it has experienced a stable outlook of the price of the
shares. It increased gradually from 2006 and continued it in the other years as well. In
2014 it was well above the Industry Average price.
4.3 Measurement of Volatility through Standard Deviation
YEAR
ACI
AMBEE
BX
ORION
LIBRA
PHARM PHARM
INFU
INFU
A
A
2.24
3.99
4.56
5.73
41.59
2006
SQUR
PHARM
A
92.81
RECKI
TTBE
N
31.69
IBNS
INA
54.36
2007
40.56
4.34
6.15
13.23
93.62
822.01
66.1
105.9
2008
147.73
28.63
33.35
42.74
366.24
904.81
53.62
193.3
2009
33.15
36.03
8.32
126.84
242.07
329.52
315.53
190.2
2010
52.24
169.63
16.7
379.43
1062.8
235.86
261.41
120.9
2011
34.32
59.17
16.52
193.04
1137.2
416.26
78
140.4
2012
55.16
40.99
13.69
4.09
34.37
37.32
116.79
21.23
2013
15.91
31.14
6.22
4.04
109.59
16.01
113.39
11.87
It is clearly noticeable that LIBRA and SQUR had experienced the most ups and downs of
the market. Due to these fluctuations, these two companies are most sensitive to the price
volatility. Both stocks show volatility as they have a high standard deviation and large
dispersion tells us how much the return on the fund is deviating from the expected normal
returns. Amongst the two, LIBRA’s standard deviation drastically increases from 93.62 to
366.24 in the year 2008. After that, it again significantly increased to 1137.2 in 2011 and
later dropped sharply in 2013 which might be due to huge demand of shares in the
market. So from the above analysis LIBRA’s most volatile situation was in 2011. Market
price of the shares of ACI, AMBEE, BX, ORION, RECKITT, and IBNSINA showed less
volatility in the years 2006 to 2013. The fluctuation was little and the trend curve shows the
almost all the companies have experienced a stable situation where the variability of the
price was explained little small change and a low standard deviation indicates less risk as
less chances of price volatility.
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
A Pearson correlation analysis was conducted on all variables to explore the relationship
between the variables. The analysis of Bivariate correlation was subject to two tailed tests
at two different levels of significance 0.01% and 0.05%.
4.4 Correlational Matrix
EPS DIV_YIELD P/E_RATIO
EPS
DIV_YIEL
D
P/E_RATI
O
ROE
AVERAG
E SHARE
PRICE
Pearson
Correlation
Sig. (2tailed)
N
Pearson
Correlation
Sig. (2tailed)
N
Pearson
Correlation
Sig. (2tailed)
N
Pearson
Correlation
Sig. (2tailed)
N
Pearson
Correlation
Sig. (2tailed)
N
1
_
ROE
_
_
AVERAGE SHARE
PRICE
.311
.0473
8
_
1
_
_
8
.686*
.045
8
_
_
1
_
8
.843**
.009
8
_
_
_
1
8
-.025
.952
.311
.686*
.843**
8
-.025
.473
.045
.009
.952
8
8
8
8
8
1
8
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
It can be seen that the variable EPS shows it has a positive correlation coefficient and
there is a somewhat moderate correlation with the dependent variable Average Share
Price (r =0.311, p<0.05). Dividend Yield is also found to have a positively insignificant
impact on Average Share Price (r =0.686, p<0 .05). The variable, P/E Ratio is strongly
and positively correlated with Average Share Price (r = 0.843, p<0 .01). Finally, ROE is
seen to have negative correlation and absolutely no statistical relationship between ROE
and Average Share Price (r = -0.025, p>0.01) which means that there is no statistical
relationship between ROE and Average Share Price (p =0.952).
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
4.5 Regression Analysis of Average Share Price and Earnings Per Share
Model
Unstandardized
Standardized
Coefficients
Coefficients
B
Std. Error
Beta
(Constant) 474.045
401.942
EPS
16.976
21.144
a. Dependent Variable: Average Price
t
Sig.
R
Square
1.179
.311 .803
1
.283
.0473
0.57
Earnings Per Share was found to be related to the Average Share Price of the 8 selected
companies since the coefficient of determination is 0.57 or 57% which is quite statistically
significant. Here, B refers to the slope of the regression line. The intercept is where the
regression line strikes the Y axis when the independent variable has a value of 0.
However, in this table the intercept is 474.045 (Constant). Putting all these together, the
regression equation would take the form:
Y = 16.976X + 474.045, where Y=Average Share Price and X = Earnings per share.
4.6 Regression Analysis of Average Share Price and Dividend Yield Dividend Yield
Model
Unstandardized
Standardized
Coefficients
Coefficients
B
Std. Error Beta
(Constant) 108.710
329.237
Dividend
48.900
21.174
Yield
a. Dependent Variable: Average Price
t
Sig.
R
Square
.330
.686
.752
2.309 .045
0.471
Dividend Yield was found to be positively and insignificantly related to the Average share
price of the 8 selected companies since the coefficient of determination is 0.471 or 47.1%
Here, B refers to the slope of the regression line. The intercept is where the regression
line strikes the Y axis when the independent variable has a value of 0. However, in this
table the intercept is 108.71 (Constant). Putting all these together, the regression equation
would take the form:
Y = 48.9X + 108.71, where Y=Average Share Price and X = Dividend Yield.
4.7 Regression Analysis of Average Share Price and Price Earnings Ratio
Model
Unstandardized
Coefficients
B
Standardiz t
ed
Coefficient
s
Std. Error Beta
(Consta
-245.259
289.721
nt)
1
PE Ratio 29.503
7.672
a. Dependent Variable: Average Price
.843
Sig.
R
Square
-.847
.430
3.846
.009
0.711
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
Price Earnings Ratio was found to be strongly related to the Average Share Price of the
selected companies since the coefficient of determination is 0.711 or 71.1% which is
highly significant. Here, B refers to the slope of the regression line. The intercept is where
the regression line strikes the Y axis when the independent variable has a value of 0.
However, in this table the intercept is negative 245.259 (Constant). Putting all these
together, the regression equation would take the form:
Y = 29.503X - 245.259, where Y=Average Share Price and X = Price Earnings Ratio.
4.8 Regression Analysis of Average Share Price and Return on Equity
Model
Unstandardized
Standardiz t
Sig.
Coefficients
ed
Coefficient
s
B
Std. Error Beta
R
Square
(Constan
749.502
458.792
1.634
.153
t)
1
ROE
-1.443
23.160
-.025
-.062
.952
0.001
a. Dependent Variable: Average Price
Table 4.8 depicts that among the eight selected companies in the CSE, there is no
supported relationship between Return On Equity and Average Share Price. ROE was
found to be statistically not related to the Average share price since the coefficient of
determination is 0.001 or 0.1% which is least significant but as the p>0.05 which means
the variables have no relationship between themselves. (p=0.952).
4.9 Multiple Regression Analysis
Model
Unstandardized
Coefficients
B
-359.969
19.153
Standardiz t
ed
Coefficient
s
Std. Error Beta
501.769
-.717
24.574
.351
.779
(Constant)
EPS
Dividend
11.594
32.437
Yield
PE Ratio
23.690
13.383
ROE
-7.695
28.827
a. Dependent Variable: Average Price
Model Summary
Model R
R
Square
Sig.
.525
.0493
.163
.357
.0445
.677
-.136
1.770
-.267
.0175
.807
Adjusted R Std. Error
Square
of the
Estimate
a
1
.892
.796
.523
47.509
a. Predictors: (Constant), ROE, PE Ratio, EPS,
Dividend Yield
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
Furthermore, in order to strengthen the result even more, multiple regression analysis was
undertaken to measure the overall influences that all the independent variables (Earnings
Per Share, Dividend Yield, Price Earnings Ratio and Return on Equity) have collectively on
the dependent variable (Average Share Price). The results showed that Earnings Per
Share, Dividend Yield, and Price Earnings Ratio if well managed can influence Average
Share Price of the selected pharmaceutical and chemical companies by 79.6% (R Square
= 0.796) but only one variable i.e. Return On Equity has no influence over Average Share
Price. Among the four variables, it was noted that addressing PE Ratio (Beta=23.690)
should take priority over EPS (Beta=19.153), Dividend Yield (Beta=11.594) and ROE
(Beta = -7.695). So from the model, it can be stated clearly that the Average Share Price’s
volatility depends more on PE Ratio because it has the highest beta and the higher the
beta, the steeper the slope of the regression line and hence the more impact the
independent variable has on dependent variable. However, the regression model was
significant at 0.05 levels.
The equation of the Regression Analysis is as follows:
PV = 19.153EPS + 11.594DY + 23.690PER – 7.695ROE - 359.969
From the above equation, it was found that for every one unit increase in Earnings Per
Share, share price volatility increases by 19.153 units and other factors remain
unchanged. Similarly, for every one unit increase in dividend yield, share price volatility
increases by 11.594 units, other factors kept constant. Also for every one unit increase in
Price Earnings ratio, share price volatility increases by 23.690 units, other factors kept
constant. Moreover, for every one unit increase in Return on Equity, share price volatility
decreases by 7.695 units vice versa. Finally negative 359.969 in the model indicates that
share price volatility stands at -359.969 when all predictor variables are equal to zero.
5. Conclusions
The empirical results of this study demonstrates a statistically significant relationship
between share price volatility and certain factors which influence it, the Price Earnings
Ratio being the most significant. The study has also implied that share price volatility has
significant inverse association with ROE. Based on the overall findings of this study,
Dividend Yield, P/E Ratio and EPS exercise the greatest influence on share price volatility
amongst the predictor variables. The results of this study also showed that after dividend
yield, earnings volatility has the most impact on share price volatility and induces
significant positive influence on the volatility of share prices. The results find little or no
relationship between return on common stockholders’ equity and share market price. The
results support the conclusion that managers of companies may be able to change the
volatility of share prices by altering their dividend policy.
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