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

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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)

Test Sectors

Fisher ADF Test Fisher PP Test

Level First

Difference

Level First

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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