Determinants of stock prices in dhaka stock exchange (dse),

European Journal of Developing Country Studies, Vol.13 2012
ISSN(paper)2668-3385 ISSN(online)2668-3687
www.BellPress.org

Determinants of Stock Prices in Dhaka Stock Exchange (DSE),
Bangladesh.
Elizabeth C. Kalunda and Siti Haryati
National University of Singapore
Abstract
The sole focus of this very research was to delineate the major determinants of stock price in
case of the largest stock market in Bangladesh named as Dhaka Stock Exchange (DSE). The
researchers have used panel data pertaining to five sectors of DSE - Food and Allied, Fuel
and Power, Engineering, Pharmaceuticals and Chemicals, and Healthcare sectors for the
period 2006-2010 and used fully modified ordinary least squares method. As per the research
result variables like - dividend, price- earnings ratio and leverage were significant
determinant of share prices for all the aforementioned sectors. Moreover, profitability did
influence share prices only in the case of the Food and Allied, Engineering, and Healthcare
sectors respectively.
Key words: Dividend, P/E ratio, leverage, profitability, fully modified ordinary least square
method, panel data, cointegration, Unit root test.

INTRODUCTION
Humans by nature are always on the lookout for returns and prefer more to less than taking
high risk opportunities. Equity investment can be such an investment which will yield
considerable amount of return without taking any outrageous or wild guess. Apart from that
firms in need of capital for their establishment are also capitalizing this situation by issuing
equity securities. All these create an environment that leads to the smooth functioning of the
of the capital markets. However, the returns from equity investment are subject to vary
depending upon various factors such as the performance of the particular stock, the market
imperfections, interactions between macro and micro level variables etc. With proper
knowledge and understanding of the impact and or value of that information always opens the
door for outperforming the market and helps in making a hand full.

In the securities market, whether the primary or the secondary market, the price of equity is
significantly influenced by a number of factors which include book value of the firm,
dividend per share, earnings per share, price-earnings ratio and dividend cover (Gompers,
Ishii & Metrick, 2003). The most basic factors that influence price of equity share are demand
and supply factors. If most people start buying then prices move up and if people start selling
prices go down. Government policies, firm’s and industry’s performance and potentials have
effects on demand behavior of investors, both in the primary and secondary markets. The
factors affecting the price of an equity share can be viewed from the macro and micro
economic perspectives. Macro economic factors include politics, general economic conditions
- i.e. how the economy is performing, government regulations, etc. Then there may be other
factors like demand and supply conditions which can be influenced by the performance of the
company and, of course, the performance of the company vis-a-vis the industry and the other
players in the industry.

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Information of a particular stock would help investors make wise investment decisions and
enable firms to enhance their market value. Therefore the impact of information on the
shareholders value creation is tremendous. The factors that influence share prices could either
be internal factors, such as earnings, dividend, book value, etc. or external factors such as
interest rate, government regulations, foreign exchange rate, etc. Several empirical researches
have been carried out to identify the factors that influence stock price. The pioneering work
on share price determinants by Collins (1957) for United States identified dividend, net profit,
operating earnings and book value as the underlying factors influencing share prices.
Followed by Collins (1957), there have been other attempts to identify the determinants of
share prices for different markets. Campbell and Shiller (1988, 1989) and Campbell (1991)
attempt to break up stock price movements (returns) into the contributions of changes in
expectations about future dividends and future returns. And keeping all those view on mind
this research effort will try to shed some light on the exploration of the determinants of the
stock prices in Bangladesh.

LITERATURE REVIEW
Several scholars not only from the field of finance have tried to locate the underlying reasons
for which stock prices move. Karathanassis and Philippas (1988) pointed out dividend,
retained earnings and size as the most influential factors while studying the Greek market. In
Kuwait earnings per share and financial leverage prove to have significant impact on the
market price of stock as per Midani (1991). In line with this view AL-Omar and AL-Mutairi
(2008) showed book value per share also exerts some influence on the share price on the same
market. Dividend yield, leverage, payout ratio and size of the firm are the factors to be
assessed while making investment decisions by the investors in Pakistan [Irfan and Nishat
(2002)]. On the other hand Nepalese stock showed significant reaction only due to dividend
[Pradhan (2003)]. According to Sunde and Sanderson (2009) in Zimbabwe analyst reports,
availability of substitutes, earnings, Government policy, investor sentiments, Lawsuits,
macroeconomic fundamentals, management, market liquidity and stability, mergers and
takeovers, technical influences determines the price that investors are willing to pay for any
particular share. In Bangladesh Khan (2009) and Uddin (2009) ion their respective studies
have identified factor such as dividend, earning per share and net asset value per share as the
most influential element to cause any change share value.

Zhang (2004) designed a multi-index model to determine the effect of industry, country and
international factors on asset pricing. Byers and Groth (2000) defined the asset pricing
process as a function utility (economic factors) and non-economic (psychic) factors. Clerc and
Pfister (2001) posit that monetary policy is capable of influencing asset prices in the long run.
Any change in interest rates especially unanticipated change affects growth expectations and
the rates for discounting investment future cash flows. Ross’ (1976) APT model which could
be taken as a protest of one factor model of CAPM which assumes that asset price depends

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only on market factor believe that the asset price is influenced by both the market and non-
market factors such as foreign exchange, inflation and unemployment rates. One of the
defects of APT in spite of its advancement of asset pricing model is that the factors to be
included in asset pricing are unspecified. Hartone (2004) argues that a significantly positive
impact is made on equity prices if positive earnings information occurs after negative
dividend information. Also, a significantly negative impact occurs in equity pricing if positive
dividend information is followed by negative earning information. Al – Tamimi (2007)
identified company fundamental factors (performance of the company, a change in board of
directors, appointment of new management, and the creation of new assets, dividends,
earnings), and external factors ( government rules and regulations, inflation, and other
economic conditions, investor behavior, market conditions, money supply, competition,
uncontrolled natural or environmental circumstances) as influencers of asset prices.

Therefore it is easily grasped that various factors have emerged as determinants of share
prices for different markets namely dividend, retained earnings, size, earnings per share,
dividend yield, leverage, payout ratio, book value per share, foreign exchange rate, gross
domestic product, lending interest rate, analyst reports, availability of substitutes, Government
policy, investor sentiments, lawsuits, macroeconomic fundamentals, management, market
liquidity and stability, mergers and takeovers, and technical influences. For the discovery of
various factors that can have some impact on the market has attracted the interest of many
scholars in this part of the world but not many from Bangladesh. From Bangladesh context,
only a limited a number of studies have attempted to identify the share price determinants.
The empirical evidences, however, differ from study to study depending upon the choice of
the firms, sample period and econometric methodology chosen for empirical investigation.

Most of the studies undertaken have used either time-series or cross-section data. There have
also been attempts to identify the share price determinants using panel data. However, such
studies have applied the conventional regression analysis and examined whether the data fits
into fixed effect or random effect model. These exercises ignore the time series properties of
the data and hence, it is likely that the results generated might be suffering from spurious
relationship. The present study differs from the earlier empirical works in the sense that it
employs the panel unit root tests to understand the time series properties of the data and
applies the panel cointegration test to examine the long run equilibrium relationship between
share price and the chosen explanatory variables. Subsequently, fully modified ordinary least
squares (FMOLS) method is employed to estimate the impact of the chosen variables on share
prices, if cointegration is established among the variables. We also attempt to identify the
share price determinants across different sectors, as they are likely to vary from one sector to
the other. The rest of the paper is organized as follows: section 3 deals with the research
methodology followed by discussion of empirical results presented in section 4 and section 5
presents the concluding remarks.

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ECONOMETRIC METHODOLOGY
Panel Cointegration Test
In this study, we use the econometric methodology proposed by Pedroni (1999) which is
meant for testing cointegration among a set of variables. This test is an extension of the Engle
and Granger (1987) two step residual based procedure for testing the null hypothesis of no
cointegration in the case of heterogeneous panels. The major advantage of this test is that it
allows for individual member specific fixed effects, deterministic trends and slope
coefficients. The methodology involved in testing for cointegration among a set of variables is
discussed below with respect to the model used in this study. To identify the factors that
influence share prices, panel regression of share prices (SP) on dividend (DPS), profitability
(ROA), price earning ratio (PE) and leverage (DE) as in equation (1) is estimated.

, , , , , , … … … … … … … … … .. 1

where, 1,2,3, … … … … , ; N= is the number of cross-sectional units;
1,2,3, … … … … , ; T=is the time period; ’s are the slope coefficients; is the member
specific intercept. The variables in equation (1) are integrated of the same order and said to be
cointegrated if , , is a stationary process; hence, testing for cointegration between SP, DPS,
ROA, PE and DE involves testing for stationarity of , . The stationarity of the residuals
from equation (1) can be tested by estimating the following auxiliary regression:

, , ! " , …………………………. 2

The null hypothesis 1 implies that , has unit root. In order to test the null hypothesis,
Pedroni (1999) proposes two different sets of statistics, namely, the ‘within-dimension’
statistics and the ‘between-dimension’ statistics. Within-dimension statistics are also known
as panel cointegration statistics and between-dimension statistics as group mean panel
cointegration statistics. There are seven test statistics of which, Panel Variance, Panel Rho,
Panel PP and Panel ADF statistic are within dimension statistics, while Group Rho, Group PP
and Group ADF statistics are between dimension statistics. Although the null hypothesis is
the same, the alternative hypothesis is different for the two sets of statistics. The null
hypothesis relating to within dimension statistics is defined as 1 for all i against the
alternative of # 1 for all i. The alternative hypothesis implies that there is
cointegration among the variables of all the members of the panel. The null hypothesis
pertaining to between dimension statistics is defined as 1 for all i against the alternative
of # 1 for all i. In this case, unlike within dimension statistics, a common value for
is not assumed. Thus, the alternative hypothesis implies that cointegration exists for at least
one individual member of the panel. The between dimension statistics, therefore, allows to
model an additional source of potential heterogeneity across individual members of the panel.

Fully Modified Ordinary Least Squares Method (FMOLS)
The application of OLS method to obtain the cointegrating vector from a panel leads to biased
estimates due to endogeneity problem. However, the fully modified ordinary least squares
(FMOLS) method of Pedroni (2000) accounts for heterogeneity across individual members of
the panel, corrects for serially correlated errors and resolves the endogeneity problem; hence,
the estimates are unbiased. The FMOLS produces two types of estimators, viz., pooled panel

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estimator and group mean panel estimator. The former is based on ‘within dimension’ of the
panel whereas the latter is based on ‘between dimension’ of the panel. In the case of pooled
panel estimator, the null hypothesis is defined as $% : % for all i against the alternative of
$: ' ( % for all i, where % is the hypothesized common value for under the null
and ' is some alternative value for which is also common to all members of the panel. In
the case
of group mean panel estimator, the null hypothesis is defined as $% : % for all i against
the alternative of $ : ( % for all i, where are not necessarily constrained to be
homogeneous across different members of the panel. Thus the group mean panel FMOLS
estimator provides greater flexibility by allowing heterogeneity of the cointegrating
parameters.

RESULTS AND DISCUSSIONS
The study uses panel data consisting of annual time series data over the period 2006-2010 and
cross section data pertaining to three sectors. The initial sample consisted of the various
Dhaka Stock Exchange (DSE) sectoral indices. The final data sample has been constructed
such that there are a minimum of 9 firms in each sector with continuous data on the selected
variables over the sample period. The details of the final sample1 are given in Table 1.
Secondary data on all the selected variables is obtained from Dhaka Stock Exchange, Dhaka-
1000, Bangladesh.
Table 1: Details of final sample
Serial Number Name of Sectors Number of Firms
1 Food and Allied 10
2 Fuel and Power 9
3 Engineering 14
4 Pharmaceuticals and Chemicals 12
5 Healthcare 9

As a measure of share price (dependent variable), average of yearly high and low share prices
is used. It is deflated by the wholesale price index. Earlier studies have identified various
factors as share price determinants. In this study, four factors viz., dividend, profitability,
price-earning ratio and leverage, are considered as possible determinants of share prices.

Dividend, the return that shareholders receive on their shareholdings, is a source of regular
income to them. Dividend seeking investors wish to earn current income in the form of
dividend rather than capital appreciation, and prefer firms that pay higher dividends. This
preference creates greater demand for higher dividend paying stocks, which triggers the
market price of such stocks. This way, dividend is expected to be positively related to share
prices. As a surrogate for dividend, dividend per share i.e. the total dividend amount paid to
equity shareholders upon the number of equity shares outstanding is used. Dividend per share
is deflated by the wholesale price index. Profit after tax and preference dividend is the
earnings available to the equity shareholders. Firms utilize these earnings to distribute
dividends to shareholders. Thus, higher the profits, higher are the dividend payments, which
in turn enhances the market price of the stocks. A positive relationship is thereby expected
between share prices and profitability. As a measure of profitability, the ratio of profit after

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tax to total assets i.e. return on assets (ROA) is used. Price-earning (PE) ratio indicates the
price that investors are willing to pay for the net profit per share earned by the firm. It is
computed as the market price per equity share upon earnings per share of the firm. Since
price-earning ratio reflects the market expectations about the firm’s future performance, a
high PE ratio denotes the investors’ expectations that the firm will have higher earnings in the
future. Investors would therefore be willing to pay more for the shares of firms with higher
PE ratio. A positive relationship is therefore expected between share prices and price-earning
ratio.

Leverage measured as debt-equity ratio, indicates the proportion of a firm’s assets that is
financed by debt as against equity. Raising capital via debt involves periodic interest
payments on part of firms; increased use of debt by a firm would therefore result in higher
interest payments and this lowers the earnings available to equity shareholders. Investors
therefore generally prefer firms with lower debt. This way a negative relation between share
prices and leverage is expected.
Prior to testing for cointegration, the data needs to be tested for stationarity. We employ two
panel unit root tests, viz., Fisher type Augmented Dickey-Fuller (Fisher-ADF) and Phillips-
Perron (Fisher-PP) tests to test the unit root properties of the data. These tests accommodate
individual member specific unit root process. The results of the panel unit root tests for the
chosen variables, both in level and first difference are reported in table 2.

Table 2: Panel Unit Root Test Results (Null Hypothesis : Series has Unit Root)
Fisher ADF Test Fisher PP Test
Test Sectors Level First Level First
Difference Difference
Share price
Food and Allied 11.81(0.34) 39.87(0.00) 10.12(0.58) 47.23(0.00)
Fuel and Power 14.20(0.24) 81.24(0.00) 17.92(0.35) 58.12(0.00)
Engineering 19.81(0.81) 102(0.00) 31.20(0.91) 162.12(0.00)
Pharmaceuticals and Chemicals 14.29(0.78) 87.95(0.00) 17.98(0.61) 90.87(0.00)
Healthcare 21.30(0.56) 69.75(0.00) 24.17(0.19) 89.76(0.00)
Dividend per share
Food and Allied 15.51(0.34) 59.78(0.00) 10.12(0.28) 61.25(0.00)
Food and Allied 34.30(0.20) 73.44(0.00) 29.90(0.13) 68.15(0.00)
Fuel and Power 29.85(0.71) 102(0.00) 41.25(0.50) 112.52(0.00)
Engineering 95.24(0.68) 27.55(0.00) 27.58(0.11) 92.89(0.00)
Pharmaceuticals and Chemicals 31.31(076) 50.23(0.00) 39.19(0.10) 70.76(0.00)
Healthcare 19.61(0.75) 42.87(0.00) 21.52(0.28) 13.53(0.00)
Return on Assets
Food and Allied 12.21(0.44) 45.70(0.00) 50.22(0.80) 17.53(0.00)
Food and Allied 52.10(0.34) 75.54(0.00) 77.72(0.20) 78.22(0.00)
Fuel and Power 28.21(0.71) 92(0.00) 51.70(0.91) 62.20(0.00)
Engineering 19.21(0.56) 25.35(0.00) 27.78(0.93) 95.57(0.00)
Healthcare 21.78(0.54) 69.45(0.00) 21.78(0.65) 32.20(0.00)
Price-Earning ratio
Food and Allied 71.51(0.34) 50.87(0.00) 25.20(0.58) 37.23(0.00)
Food and Allied 18.50(0.24) 78.25(0.00) 24.05(0.78) 48.32(0.00)

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Fuel and Power 31.81(0.81) 92(0.00) 51.30(0.91) 192.18(0.00)
Engineering 17.30(0.30) 90.05(0.00) 15.78(0.69) 55.77(0.00)
Healthcare 17.51(0.04) 36.80(0.00) 16.22(0.40) 40.20(0.00)
Debt-Equity ratio
Food and Allied 25.95(0.89) 56.77(0.00) 40.62(0.68) 93.33(0.00)
Fuel and Power 17.20(0.24) 70.20(0.00) 10.90(0.30) 50.22(0.00)
Engineering 45.31(0.71) 152(0.00) 72.20(0.81) 132.10(0.00)
Healthcare 22.40(0.96) 29.75(0.00) 14.15(0.35) 99.06(0.00)
Note: Values in (#) are P-values.
As shown in table 2, the Fisher ADF test result for share price in level fails to reject the null
hypothesis that share price in level is nonstationary. Similarly the result of Fisher PP test
indicates that share price in level is nonstationary. Hence, we test for stationarity of share
price in first difference. Both the Fisher ADF test and Fisher PP test results indicate that share
price in first difference is stationary. This implies that, for all the sectors under consideration,
the variable share price follows an I (1) process. Next, we examine whether the variable
dividend per share is stationary. The results of both Fisher ADF and Fisher PP tests indicate
that dividend per share in level is nonstationary. When tested for stationarity in first
difference, the results of Fisher ADF and Fisher PP tests reject the null hypothesis that
dividend per share in first difference is nonstationary. Therefore, for all the sectors, dividend
per share becomes stationary upon first differencing and it follows an I (1) process. For the
variable return on assets, both the Fisher ADF and Fisher PP test reveal that return on asset in
level is nonstationary. In first difference form, return on assets is found to be stationary as
indicated by the test results. Thus, the data pertaining to the variable return on assets, for all
the sectors, follow an I (1) process.

Similarly for the variables price earning ratio and debt equity ratio, the results of both Fisher
ADF and Fisher PP tests fail to reject the null hypothesis that the variable in level is
nonstationary. Upon first differencing, both these variables turn out to be stationary. The
results thus indicate that the variables price earning ratio and debt equity ratio for all the
sectors under consideration follow an I (1) process. Overall, for the chosen sectors, the
variables share price, dividend per share, return on assets, price earning ratio and debt equity
ratio are nonstationary in level and stationary in first difference. Since all these variables
follow I (1) process, we next proceed to test whether there exists cointegration between these
variables. To test for cointegration, we employ panel cointegration test proposed by Pedroni
(1999), the results of which are reported in table 3.
Table 3: Panel cointegration test results (Null Hypothesis : no cointegration)
Name of Sectors Group ADF test statistics
Food and Allied -4.89(0.00)
Fuel and Power -9.12(0.01)
Engineering -3-08(0.00)
Pharmaceuticals and Chemicals -7.89(0.00)
Healthcare -6.23(0.02)
Note: Values in (#) are P-values.

From table 3 it is evident that, for all the sectors under consideration, the Group ADF test
statistics rejects the null hypothesis that there is no cointegration between the variables. This

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implies that the variables share price, dividend per share, return on assets, price earning ratio
and debt equity ratio are cointegrated and that there exists a long run equilibrium relationship
between them. Having identified that the variables are cointegrated, we proceed to estimate
the model specified in equation (1) in order to identify the share price determinants. For this
purpose, we employ the group mean panel FMOLS method proposed by Pedroni (2000), and
the results are reported in table 4.

Table 4: Group mean panel FMOLS results
Sectors Slope Coefficients

Food and Allied 11.23(17.11)*** 2.48(3.72)*** 3.66(10.23)*** -2.54(-3.08)***
Fuel and Power 46.86(9.23)*** 8.17(-6.12) 8.45(7.12)*** -1.87(-2.18)*
Engineering 59.75(22.10)*** -3.59(4.26)* 6.12(3.16)*** -0.58(-7.71)***
Pharmaceuticals and Chemicals 19.87(3.49)*** 2.58(-0.04) 2.98(8.72)*
-1.58(-0.75)***
Healthcare 24.36(5.18)*** 6.59(0.34)*** 5.38(9.72)* -0.89(-4.23)***
Note: Values in (#) are t-values. *** and * denote significance at 1% and 10% level
respectively; , , and are the slope coefficients for DPS, ROA, PE and DE
respectively.

From the results of table 4 it is evident that the variable dividend per share is a significant
determinant of share prices for all the sectors under consideration. As expected, dividend per
share is positively related to share price. This means that share price would rise with an
increase in dividend per share. This finding indicates that investors attach more value to those
firms that pay dividends and therefore, a consistent and liberal dividend policy would enable
firms enhance their market value. Similar evidence of dividend being a significant
determinant of share prices is reported in Zahir and Khanna (1982), Karathanassis &
Philippas (1988) and Zahir (1992). Next, we examine the influence of return on assets on
share prices. As is evident from table 4, return on assets is found to significantly influence
share prices in the case of Food and Allied, Engineering, and Healthcare sectors respectively.
As expected, return on assets bear a positive relation with share prices. For the remaining two
sectors, Fuel and Power, and Pharmaceuticals and Chemicals, return on assets does not
influence share prices. This finding implies that investors do not attach much importance to
profitability of a firm. Instead, what matters to the investors more is the portion of earnings
that is paid to them in the form of dividend.
Zahir (1992) and Somoye et al (2009) have also found evidence of profitability being a
significant determinant of share prices. The variable price earning ratio is found to be a
significant factor influencing share prices for all the five sectors under consideration. It is
found to be positively related to share prices. This indicates that the shares with higher PE
ratio will be better valued in the market as it reflects the investors’ expectations that the firm
will have good prospects in the future. The finding of price earning ratio as a significant
determinant of share prices is in line with Mehta and Turan (2005).The results further indicate
that debt-equity ratio is a significant determinant of share prices for all the five sectors and
that it exerts a negative relation with share price. This implies that as the debt content in the
capital structure of a firm decreases, its share price rise and vice versa. This finding indicates

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that investors prefer firms with lower debt content, since increased use of debt by a firm
lowers the earnings available for equity shareholders and investors become apprehensive
about their returns. In a nutshell, the FMOLS test results reported in table 4 reveal that the
variables dividend, price-earning ratio and leverage are significant determinants of share
prices for all the sectors under consideration. Further, profitability is found to be a significant
factor influencing share price only in the case of Food and Allied, Engineering, and
Healthcare sectors respectively.

CONCLUSION
The present study attempted to identify the factors that influence share prices for the selected
sectors of Bangladeshi Stock market (only DSE is studied). Panel data pertaining to the
sectors
Food and Allied, Fuel and Power, Engineering, Pharmaceuticals and Chemicals, and
Healthcare sectors undertaking over the period 2006-2010 is used. The study has chosen
dividend, profitability, price-earning ratio and leverage as possible determinants of share
prices and employs the fully modified ordinary least squares method to identify the share
price determinants. The results indicate that the variables dividend, price earning ratio and
leverage are significant determinants of share prices for all the sectors under consideration.
Further, in the case of Food and Allied, Engineering, and Healthcare sectors respectively,
profitability is also found to be a factor influencing share prices.

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Determinants of stock prices in dhaka stock exchange (dse),

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