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Surname, Initial(s). (2012) Title of the thesis or dissertation. PhD. (Chemistry)/ M.Sc. (Physics)/ M.A. (Philosophy)/M.Com. (Finance) etc. [Unpublished]: University of Johannesburg. Retrieved from: https://ujdigispace.uj.ac.za (Accessed: Date).
THE IMPACT OF DIVIDEND YIELD AND DIVIDEND PAY-OUT RATIOS ON SHARE
PRICE VOLATILITY IN SOUTH AFRICA
by
EVA LORRAINE UMWARI
MINOR DISSERTATION
submitted in partial fulfilment of the requirements for the degree
MAGISTER COMMERCII
in
FINANCIAL MANAGEMENT
in the
FACULTY OF ECONOMIC AND FINANCIAL SCIENCES
at the
UNIVERSITY OF JOHANNESBURG
SUPERVISOR: Professor NJ Smith
CO-SUPERVISOR: Professor I Botha
January 2015
i
Abstract
The relationship between the dividend policy of a company and the volatility of its share
price is significantly important to managers in order to optimise shareholders’ wealth.
Research on the relationship between dividend policy and share price volatility has
provided conflicting results in different parts of the world.
During the research for this study, the researcher addressed the question whether
dividend policy has an effect on share price volatility in South Africa. The FTSE Top 40
Index companies were used as sample for the period from 2009 to 2013.
A quantitative approach was applied to establish the relationship between the
dependent variable share price volatility (SPV) and independent variables dividend yield
(DY) and dividend pay-out ratio (DPR). The study also considered asset growth (AG),
leverage (L), earnings volatility (EV) and company size (FS) to account for other factors
that could affect share price volatility in companies. Secondary data from the FTSE Top
40 Index companies were used as a sample for the period from 2009 to 2013 and the
data were analysed by means of panel regression analysis.
It was concluded that both the dividend policy and asset growth and leverage did affect
the share price volatility of the sample of South African companies.
Results also indicate that specific dividend decisions can be manipulated by
management to get a more desired impact on share price volatility.
Key words
Dividend Yield; Dividend Pay-Out Ratio; Dividend Policy Measures; Share Price
Volatility; South Africa; Determinants of share price volatility; Determinants of
systematic risk; Stock price volatility;
ii
DECLARATION OF ORIGINAL WORK
I, Eva Lorraine Umwari, declare that this minor dissertation is my own unaided work.
Any assistance that I have received has been duly acknowledged in the dissertation. It
is submitted in partial fulfilment of the requirements for the degree of Master of
Commerce at the University of Johannesburg. It has not been submitted before for any
degree or examination at this or any other University.
___________________ _27th /02/2015_______________
Signature Date
iii
Acknowledgements
Firstly I would like to express my deepest gratitude to my supervisor, Professor NJ
Smith and my co-supervisor Professor I Botha for their consistent and invaluable
guidance despite their busy schedules, throughout the entire study.
I would also like to thank all my family members for their constant encouragement and
support during my completion of this study.
Most importantly I would like to thank God for providing me with everything I needed in
order to undertake and complete this study.
iv
TABLE OF CONTENTS
Chapter 1
Contextualisation ..................................................................................................... 1
1.1 Introduction ..................................................................................................... 1
1.2 Background ..................................................................................................... 2
1.3 Problem statement ........................................................................................ 3
1.4 Purpose of the study .................................................................................... 3
1.4.1 Goal of the study ........................................................................................... 3
1.5 Research question ........................................................................................ 4
1.5.1 Sub research questions............................................................................... 4
1.5.2 Delimitations of the research question ................................................... 4
1.6 Research methodology ................................................................................ 5
1.6.1 Scope ................................................................................................................ 6
1.6.2 Sampling strategy ......................................................................................... 6
1.7 Collecting and analysing data ................................................................... 6
1.8 Summary .......................................................................................................... 7
Chapter 2 8
A review of dividend policy and share price volatility ................................................ 8
2.1 Introduction ..................................................................................................... 8
2.2 Key dividend policy concepts .................................................................... 9
2.2.1 Types of dividend policies .......................................................................... 9
2.2.1.1 Constant pay-out ratio policy..................................................................... 9
2.2.1.2 Constant nominal payment dividend policy ........................................ 11
2.2.1.3 Low regular and extra dividend policy .................................................. 12
2.2.1.4 No dividend policy ...................................................................................... 13
2.2.1.5 Residual dividend policy ........................................................................... 13
2.2.2 Types of dividends ...................................................................................... 13
2.2.2.1 Cash dividends ............................................................................................ 14
2.2.2.2 Stock dividends ........................................................................................... 14
2.2.2.3 Stock splits ................................................................................................... 14
v
2.2.2.4 Ordinary stock split .................................................................................... 15
2.2.2.5 Reverse stock split ..................................................................................... 15
2.2.2.6 Share repurchases ...................................................................................... 15
2.2.3 Dividend dates ............................................................................................. 16
2.2.3.1 Date of declaration ...................................................................................... 16
2.2.3.2 Date of record ............................................................................................... 16
2.2.3.3 Ex-dividend date .......................................................................................... 17
2.2.3.4 Payment date ................................................................................................ 17
2.3 Theoretical perspective of dividend policies ....................................... 17
2.3.1 Bird in hand theory (1959)......................................................................... 18
2.3.2 Modigliani and Miller dividend irrelevance theory (1961) ................ 18
2.3.3 Residual dividend policy theory (1961) ................................................. 19
2.3.4 Dividend relevance theories: Walter’s model (1966) ......................... 19
2.3.5 Signalling/information effect theory (1973) .......................................... 20
2.3.5.1 Expectation theory ...................................................................................... 20
2.3.6 Agency cost theory (1976) ........................................................................ 21
2.3.7 Clientele effect (1977) ................................................................................. 21
2.3.8 Dividend relevance theory: Gordon’s model (1979) .......................... 22
2.3.9 Tax preference theory (1979) ................................................................... 22
2.4 Determinants of share price volatility .................................................... 23
2.4.1 Company size ............................................................................................... 23
2.4.2 Asset growth ................................................................................................. 24
2.4.3 Leverage ........................................................................................................ 24
2.4.4 Earnings volatility ....................................................................................... 25
2.4.5 Dividend yield ............................................................................................... 25
2.4.6 Dividend pay-out ratio ................................................................................ 26
2.5 Conclusion .................................................................................................... 27
Chapter 3 29
Research methodology ..................................................................................................... 29
3.1 Introduction ................................................................................................... 29
3.2 Problem statement ...................................................................................... 29
3.3 Goal of the study ......................................................................................... 29
3.4 Research question ...................................................................................... 30
vi
3.5 Research design .......................................................................................... 30
3.6 Research strategy ....................................................................................... 30
3.6.1 Research paradigm ..................................................................................... 31
3.6.2 Research method ........................................................................................ 31
3.7 Research instrument .................................................................................. 32
3.8 Sampling strategy ....................................................................................... 32
3.9 Data collection method .............................................................................. 33
3.10 Data analysis ................................................................................................ 33
3.10.1 Dependent variable ..................................................................................... 33
3.10.2 Independent variable .................................................................................. 34
3.10.2.1 Dividend pay-out ratio (DPR) ................................................................... 34
3.10.2.2 Dividend yield (DY) ..................................................................................... 34
3.10.3 Control variables ......................................................................................... 35
3.10.3.1 Leverage (L) .................................................................................................. 35
3.10.3.2 Company size (FS) ...................................................................................... 35
3.10.3.3 Asset growth (AG) ....................................................................................... 36
3.10.3.4 Earnings volatility (EV) .............................................................................. 36
3.11 Model description ........................................................................................ 36
3.11.1 Panel data analysis ..................................................................................... 37
3.11.2 Pooled regression model (ordinary least squares) ............................ 37
3.11.3 Fixed effect model (FEM) ........................................................................... 39
3.11.4 Random effects model (REM) .................................................................. 40
3.12 Validity and reliability of data................................................................... 42
3.12.1 Internal validity............................................................................................. 42
3.12.2 External validity ........................................................................................... 42
3.12.3 Face validity .................................................................................................. 43
3.12.4 Content validity ............................................................................................ 43
3.12.5 Reliability ....................................................................................................... 43
3.13 Ethical considerations ............................................................................... 44
3.14 Summary and conclusion ......................................................................... 45
Chapter 4 46
Data analysis and interpretation ..................................................................................... 46
4.1 Introduction ................................................................................................... 46
vii
4.2 Description of sample and variables ..................................................... 46
4.2.1 Share price volatility ................................................................................... 48
4.2.2 Independent variable .................................................................................. 48
4.2.3 Control variables ......................................................................................... 49
4.2.3.1 Leverage (L) .................................................................................................. 49
4.2.3.2 Company size (FS) ...................................................................................... 49
4.2.3.3 Asset growth (AG) ....................................................................................... 50
4.2.3.4 Earnings volatility (EV) .............................................................................. 50
4.3 Descriptive statistics .................................................................................. 50
4.4 Correlation analysis .................................................................................... 52
4.5 Model description ........................................................................................ 55
4.5.1 Panel regression .......................................................................................... 56
4.5.1.1 Pooled regression model .......................................................................... 56
4.5.1.2 Fixed effect model ....................................................................................... 58
4.5.1.3 Redundancy test for the fixed effects .................................................... 59
4.5.1.4 Hausman test ................................................................................................ 60
4.5.2 Detailed analysis of the core model ....................................................... 61
4.5.2.1 Asset growth ................................................................................................. 62
4.5.2.2 Leverage ........................................................................................................ 63
4.5.2.3 Dividend yield ............................................................................................... 63
4.5.2.4 Dividend pay-out ratio ................................................................................ 63
4.6 Summary and conclusion ......................................................................... 64
Chapter 5 66
Findings, conclusions and recommendations ........................................................... 66
5.1 Introduction ................................................................................................... 66
5.2 Reason for undertaking the research .................................................... 66
5.3 Research approach ..................................................................................... 67
5.4 Findings and conclusions on the study sample ................................. 68
5.6 Recommendations for further research ................................................ 70
5.7 Final remarks ................................................................................................ 70
References ............................................................................................................ 70
viii
LIST OF TABLES
Table 4.1: A list of the FTSE Top 40 Index companies and their respective
industries ........................................................................................... 46
Table 4.2: Statistical description of the study variables ................................. 50
Table 4.3: Correlation of study variables .......................................................... 52
Table 4.4: Pooled ordinary least square model (POLS) .................................. 56
Table 4.5: Fixed effect model ............................................................................. 57
Table 4.6: Fixed effect model excluding earnings volatility ............................ 57
Table 4.7: Fixed effect model excluding earnings volatility and company size
............................................................................................................ 58
Table 4.8: Redundant fixed effects test ............................................................ 59
Table 4.9: Hausman test ..................................................................................... 60
Table 4.10: Core model selected – the fixed effects model .............................. 61
ix
LIST OF FIGURES
Figure 2.1: Constant pay-out ratio policy ........................................................... 10
Figure 2.2: Constant nominal payment dividend policy ................................... 12
x
Abbreviations
SPV Stock price volatility
DY Dividend yield
DPR Dividend pay-out ratio
FS Company size
L Leverage
AG Asset growth
EV Earnings volatility
1
Chapter 1
Contextualisation
1.1 Introduction
The goal of financial management in private enterprises is to maximise shareholders’
wealth. In order to maximise shareholders’ wealth, investment decisions, financing
decisions and dividend decisions have to be made (Bhat, 2009:534). According to
Bhat (2009:22) these decisions must be considered carefully because they are
interrelated, and together they must maximise shareholders’ wealth.
The dividend decision is related to dividend policy (Bhat, 2009:534), which refers to
decisions the company makes with regard to the form of payment of a dividend (in
cash or shares or another form), the size of a dividend, and the frequency of the
dividend (Megginson & Smart, 2008:566).
The influence of the dividend policy is measured by dividend yield and dividend pay-
out ratio, which are dividend policy measures (Gaver & Gaver, 1993:125).
Throughout this study, the words ‘dividend policy measures’ therefore represent the
dividend policy as confirmed by the research of Allen and Rachim (1996:180);
Asghar, Shah, Hamid and Suleman (2011:47); Baskin (1989); Habib, Kiani and Khan
(2012:81); Hussainey, Mgbame and Chijoke-Mgbame (2011:7) ; Jecheche (2012:4);
Nazir, Nawaz, Waseem and Ahmed (2010:103); and Nazir, Abdullah and Nawaz
(2012:135).
Shareholders’ views on dividend policy vary depending on their desire to obtain or
enjoy their profits today and their desire to reinvest their gains to make more profits
in future (Fried, Shapiro & DeSchriver, 2008:271). Selecting a dividend policy is
therefore an important decision because it is interrelated with the investment and
financing decisions that influence the business success and wealth of shareholders
(Bhat, 2009:22).
2
The aim of this research was to determine the impact of the dividend policy on share
price volatility.
1.2 Background
Ambiguity with regards to the impact of dividend policy on share price volatility arises
from the conflicting results of research on the impact of dividend policy measures on
share price volatility.
It is expected that the dividend policy measures are inversely related to share price
volatility (Allen & Rachim, 1996:181–185; Harris & Mongiello, 2012:385; Hashemijoo,
Ardekani & Younesi, 2012:118; Hussainey et al., 2011:6; Nazir et al.,2010: 102).
Therefore, for every increase in the dividend policy measures there is a decrease in
share price volatility.
Results from research on the relationship between dividend policy measures and
share price volatility by Hussainey et al. (2011:10) in the UK, and Hashemijoo et al.
(2012:111) in Malaysia, concur with this conclusion that a negative relationship
exists between dividend policy measures and share price volatility.
However, results from research by Habib et al. (2012:82) in Pakistan, Asghar et al.
(2011:51) in Pakistan, and Jecheche (2012:7) in Zimbabwe indicate that a positive
relationship exists between dividend policy measures and share price volatility.
Although most researchers found either a negative or a positive relationship between
dividend policy measures and share price volatility, Rashid and Rahman (2008:7)’s
research in Bangladesh demonstrate no significant relationship between dividend
policy measures and share price volatility.
These conflicting research results in different countries regarding the relationship
between dividend pay-out ratio and share price volatility demonstrate that there is
uncertainty with regards to the relationship between dividend policy and share price
volatility.
3
It is therefore important to determine the impact of dividend policy measures on
share price volatility in South Africa to ensure that a selected dividend policy will
maximise shareholders’ wealth in the future.
1.3 Problem statement
Research regarding the influence of dividend policy measures on share price
volatility has produced different results in different countries. This conflicting
research indicates a knowledge gap in research regarding the impact of dividend
policy measures on share price volatility. The conflicting results also demonstrate
that despite the magnitude of international empirical and theoretical research, the
relationship between share price volatility and dividend policy measures in different
countries cannot be used as a proxy for other countries such as South Africa.
South Africa is an emerging market and therefore results from studies on developed
markets cannot be applied to South Africa. This is also confirmed by Asghar et al.
(2011:50) who states that results of a study on developing countries are not suitable
to apply in developed countries. The research problem identified was addressed by
determining the relationship between dividend policy measures and share price
volatility on the FTSE/JSE Top 40 Index companies on the JSE Limited by means of
a correlation analysis and a cross-sectional regression model.
1.4 Purpose of the study
The purpose of this study was to determine whether dividend policy influences share
price volatility and where there is a meaningful relationship, whether it can be
managed to limit share price volatility.
1.4.1 Goal of the study
The goal of this study was to establish the role of dividend policy measures on share
price volatility. The goal was divided into the following objectives:
4
Determining what kind of relationship exists between dividend policy and
share price volatility on the Johannesburg Stock Exchange Limited.
Establishing whether investors and managers can control share price volatility
through controlling dividend payments to influence share price volatility to
their advantage.
1.5 Research question
The primary research question was:
What is the relationship between dividend policy measures and share price volatility
on the Johannesburg Stock Exchange Ltd?
1.5.1 Sub research questions
Is there a relationship between dividend yield and share price volatility for the
FTSE Top 40 Index companies listed on the Johannesburg Stock Exchange?
Is there a relationship between pay-out ratio and share price volatility for the
FTSE Top 40 Index companies listed on the Johannesburg Stock Exchange?
1.5.2 Delimitations of the research question
The dividend policy measures in this study were regarded as dividend yield and pay-
out ratio (Gaver, 1993:125). These dividend policy measures represent the dividend
policy (Allen & Rachim, 1996:180; Asghar et al., 2011:47; Baskin, 1989; Jecheche,
2012:4; Habib et al., 2012:81; Hussainey et al., 2011:7; Nazir et al., 2010:103; Nazir
et al., 2012:135). As a result, throughout this study the words ‘dividend policy’ and
‘dividend policy measures’ are used interchangeably.
The research question was investigated based on data from years 2009 to 2013, and
the control variables of leverage, growth of assets, earnings volatility, and company
size were applied. These variables were identified by Allen and Rachim (1996:180-
181), Asghar et al. (2011:47), Hussainey et al. (2011:7) and Jecheche (2012:4) as
5
variables that may influence share price volatility, and will be discussed in detail in
the Research Methodology chapter of this study.
1.6 Research methodology
Following the guidelines of Hussainey et al., (2011:2) a quantitative research
methodology was followed in this study. According to Hartas (2010:66) quantitative
research involves either exploring relationships between variables or making group
comparisons. During this study quantitative research involved exploring the
relationships between dividend policy measures and share price volatility and factors
that influence them (control variables).
Time series and cross-sectional data (panel data) were found to be suitable for the
purpose of this research, and were used to explore these relationships (Hussainey et
al., 2011:2). The quantitative methodology applied involved running a panel
regression analysis and correlation analysis to identify the type of relationship
between the relevant panel data namely share price volatility, the dividend policy
measures and control variables.
Control variables are other variables that influence dependent variables, i.e. the
share price volatility (Nestor & Schutt, 2011:50). Four control variables, namely
earnings volatility, leverage, company size, and asset growth were included. These
four control variables were included due to the fact that they also influence share
price volatility (Allen & Rachim, 1996:180; Asghar et al., 2011:47; Baskin, 1989;
Habib et al., 2012:81; Hussainey et al., 2011:7; Jecheche, 2012:4; Nazir et al.,
2010:103; Nazir et al., 2012:135).
This consideration of control variables also involved identifying the relationship they
have with share price volatility and dividend policy measures through a correlation
matrix. The results of this research shed some light on the relationship between
dividend policy and share price volatility on the JSE and hence the research problem
was addressed in South Africa using this methodology.
6
1.6.1 Scope
The scope of this research was limited in terms of the size of the sample used, which
was the FTSE/JSE Top 40 Index companies. This sample was selected due to the
fact that the FTSE/JSE Top 40 represents the performance of South African
companies (Packard, 2009:957).
The relationship between share price volatility and the dividend policy measures of
dividend yield and dividend pay-out ratio and the control variables (company size,
leverage, earnings volatility and asset growth) and the question whether this
relationship is causal, were explored during this research. The research was only
based on a time period of five years from 2009 to 2013.
1.6.2 Sampling strategy
Secondary data from the McGregor database from 2009 to 2013 were used.
1.7 Collecting and analysing data
A quantitative analysis of data was used, which involved a statistical analysis of the
data. To achieve this, a demonstration of descriptive statistics of the data, a
correlation matrix of the data, and a panel regression between the variables were
performed.
The data were collected from McGregor database as secondary data or calculated
with secondary data from the McGregor database and were used to address the
research question.
7
1.8 Summary
Chapter 1 consists of an introduction and a discussion of the research problem,
research question, research hypothesis, and the purpose and delimitations of this
research. The rest of the study is structured as follows: Chapter 2 consists of a
review of dividend policies and share price volatility, whereas Chapter 3 contains the
research methodology applied. The data analysis and interpretation of the results are
contained in Chapter 4, and lastly conclusions and recommendations are made in
Chapter 5.
8
Chapter 2
A review of dividend policy and share
price volatility
2.1 Introduction
In the previous chapter the study was contextualised. In this chapter the relationship
between dividend policy and share price volatility is considered.
Share price volatility is the daily change in the market price of a share and can be
influenced by the dividend policy of the company (Fay, Knight & Thompson,
2001:379). Dividend policy is a decision the company makes with regard to payment
of dividends, the type of dividend (cash, shares or other), the size of a dividend, and
the frequency of the dividend (Megginson & Smart, 2008:566).
The dividend policy decision remains essential for managers to ensure maximisation
of shareholders’ wealth (Bhat, 2009:534). As a result of the characteristics of
shareholders’ wealth and share price volatility, a number of researchers have
investigated the relationship between dividend policy and share price volatility,
including Anjum, Suliman and Khan (2013:429), Hashemijoo et al. (2012:124), and
Ramadan (2013:17), among others.
Research regarding this relationship produced different results in different countries
and caused more uncertainty about whether dividend policy affects share price
volatility and shareholders’ wealth. As a result, managers are still continuously faced
with the decision whether to pay out dividends or to reinvest such amounts to
support the future growth of their companies and maximise shareholders’ wealth
(Bhat, 2009:534).
Should a company decide to pay out dividends, the uncertainty remains regarding
the amount and type of dividends to be paid, and when the dividends should be paid
in order to maximise shareholders’ wealth. Should this decision be unfavourable to
9
existing shareholders and potential investors, managers face the risk of
disinvestments by the existing shareholders and failure to attract new investors
(Keown, 2003:400).
To optimise shareholders’ wealth, an optimum dividend policy must be selected that
will maximise shareholders’ wealth and ensure favourable share price volatility
(University of California, 2008:26).
Optimum dividend policies, dividend policies and share price volatility are addressed
in Chapter 2. The chapter is divided into three main sections: key dividend policy
concepts; past dividend theories; and determinants of share price volatility.
2.2 Key dividend policy concepts
The different types of dividend policies, dividends, and dividend dates follow.
2.2.1 Types of dividend policies
There are five types of dividend policies namely the constant pay-out ratio policy, the
constant nominal payment policy, the low regular and extra policy, the no dividends
policy and the residual dividend policy (Bhat, 2009: 531). Irrespective of a company’s
long-term dividend policy, most companies select one of these five types of dividend
policies (Keown, 2003: 406). Each type of dividend policy is unique and will have a
unique impact on the share price and volatility of the share price.
Each type of dividend policy is furthermore explained and the different effects each
type of dividend policy have on share price are demonstrated.
2.2.1.1 Constant pay-out ratio policy
The constant pay-out ratio policy is a dividend policy where the company decides on
a percentage of the company’s earnings that will be paid to the shareholders
regardless of the size of its earnings (Megginson & Smart, 2008:570). This
percentage will be consistent even as the earnings change (increase or decrease).
10
Although the percentage paid as dividends in this policy is consistent, the earnings of
companies tend to vary, and therefore the actual amount of dividends of this
percentage will not be consistent. The dividends will be unstable and unpredictable
because of changes in earnings, and as a result only a few companies follow this
type of dividend policy. The policy provides volatile dividends, which is problematic
as some shareholders rely on dividends as a constant source of income (Baker &
Powell, 2009:405).
Despite the fact that the constant pay-out ratio dividend policy is only used by a few
companies, it has some advantages, including the likelihood of transmitting
inaccurate information to the public. The market also usually reacts negatively to a
cut in dividends but with the constant pay-out ratio dividend policy, volatile dividends
will not negatively affect the market because the percentage distributed as dividends
will stay the same (Baker & Powell, 2009:405).
The constant pay-out ratio dividend policy is also advantageous because investors
know dividends are a stable percentage. A number of stock exchanges require a
company to have stable dividends in order for them to be legally listed (Baker &
Powell, 2009: 405).
The constant pay-out ratio dividend policy is illustrated in Figure 2.1.
Figure 2.1: Constant pay-out ratio policy
Source: Researcher’s own deductions, October 2014
Rands Earnings per share
per share Dividends per share
2012 2013
Year
11
It is clear from the illustration in Figure 2.1 that as the earnings increase, the amount
of dividends will also increase. Hence the movement of the earnings per share is
similar to the movement of the dividend per share. The following is the constant pay-
out ratio policy equation (2.1) (Sheeba, 2011:353):
Dividend pay-out ratio = Dividend per share ÷ Earnings per share (2.1)
In the equation 2.1 (above) the dividend per share is divided by the earnings per
share to determine the dividend pay-out ratio (Sheeba, 2011:353).
Baker and Powell (2009:405) agree that a constant pay-out ratio dividend policy can
cause share price volatility.
2.2.1.2 Constant nominal payment dividend policy
The constant nominal payment dividend policy is a policy that involves paying a fixed
amount of a dividend every dividend cycle (Bhat, 2009:531). This implies that
whether earnings decrease or increase the dividend payment will remain constant.
This policy is most commonly used in companies that have stable earnings
(Megginson & Smart, 2008:570).
Companies following this policy hardly increase or decrease a dividend unless there
is a clear predicament that requires an increase or a decrease in dividends
(Megginson & Smart, 2008:570). The fixed movement of dividends, even as earnings
increase or decrease, is illustrated in Figure 2.2.
12
Figure 2.2: Constant nominal payment dividend policy
Source: Researcher’s own deductions, October 2014
The graph in Figure 2.2 illustrates that as earnings change per share the dividend
per share remains constant. Companies that use this policy can use it against a
target dividend pay-out ratio where a company pays a stated rand dividend, instead
of letting dividends fluctuate, and slowly adjusts it toward the target pay-out ratio as
earnings increase (Megginson & Smart, 2008:570).
This is referred to as a partial-adjustment strategy, which suggests that a company
can change between two dividend payment levels (Megginson & Smart, 2008:570).
One can assume that with this dividend policy, real dividends will steadily reduce
over time. Inflation will continuously erode the purchasing power of the dividends
(Estrada, 2005:n.p.).
2.2.1.3 Low regular and extra dividend policy
Low regular and extra policy is a dividend policy where a company pays the same
regular dividend and includes an additional dividend in the years where it has
performed unusually well (Mumba, 2013:344).
The constant dividend is referred to as the “regular dividend” while the extra dividend
is referred to as the “special or extra dividend” (Besley & Brigham, 2008:543).
Investors can rely on the company to receive at least a minimum dividend in times
Rands Earnings per share
per share Dividends per share
2012 2013
Year
13
when temporary changes in earnings occur (Megginson & Smart, 2008:570; Besley
& Brigham, 2008:543).
When a company therefore has volatile earnings this dividend policy is appropriate
because directors can set a low regular dividend that can be maintained even when
the company is making very low profits, in order to provide an extra dividend when
the company has abnormally high profits (Besley & Brigham, 2008:543).
2.2.1.4 No dividend policy
With this policy a company makes the decision not to pay any dividends to
shareholders of the company. Companies that do not pay dividends are usually
smaller, fast growing, and generate small cash flows (Anson, Fabozzi & Jones,
2010:23).
2.2.1.5 Residual dividend policy
This is a dividend policy that entails only paying a dividend if there are earnings left
after all lucrative investments have been made (Besley & Brigham, 2010:232). A
residual dividend policy is viewed as one where the default is “not to pay a dividend”,
yet a company will pay a special dividend whenever it fulfils certain conditions such
as the existence of lower investment opportunities than cash flow and no plan to
reduce debt or equity (Keown, 2003:399).
This dividend policy is in line with popular past studies and theories such Modigliani
and Miller’s (1961) dividend policy irrelevance theory (Keown, 2003:399). The
different types of dividend policies mentioned above are distributed in different ways
and therefore affect the share price in different ways. The different ways in which
dividends are distributed are mentioned in the next section.
2.2.2 Types of dividends
There are four types of dividends namely cash dividends, stock dividends, stock
splits and share repurchases (Nikolai, Bazley & Jones, 2009:855). These dividends
14
can affect share price, which in turn affects shareholders’ wealth (Mayo, 2013:370).
The different types of dividends is explained in the following section.
2.2.2.1 Cash dividends
Cash dividends are dividends paid in cash (Mayo, 2013:370). Cash dividends can
cause share price volatility because they affect the share price and this in turn
affects the shareholders’ wealth (Nikolai et al., 2009:855).
2.2.2.2 Stock dividends
Stock dividends are dividends paid in the form of additional shares to shareholders,
rather than cash (Mayo, 2013: 370). The following are different aspects of stock
dividends that were relevant to this study.
When a stock dividend is substituted for a cash dividend, the stock dividend will not
necessarily increase a company’s capability to grow. However, stock dividends tend
to increase the after-tax returns to investors or reduce investors’ tax liability (Baker,
2009:393).
Some companies prefer not to issue stock dividends because of the high costs of
issuing new shares. The costs of new share issues also cause institutional investors
to avoid investing in companies that offer several stock dividends (Baker, 2009:393).
Although stock dividends differ from cash dividends, they affect the share price
volatility in a similar manner (Strong, 2008:276).
2.2.2.3 Stock splits
Stock splits are similar to stock dividends (Spinella, 2004:176). When a company
wants to sell its ordinary shares at a lower price it usually implements a stock split.
Companies also use a stock split to keep a share price in a range expected by
investors. This is referred to as the “trading range” (Moyer, 2012:239).
15
Although the number of shares in a stock split increases by a specific multiple, the
rand value of the total shares remains the same as the pre-split amounts. Therefore
the stock split does not affect the total real value of shares and the capital structure
of a company (Spinella, 2004:176).There are two types of stock splits, namely an
ordinary stock split and a reverse stock split (Sheeba, 2011:350).
2.2.2.4 Ordinary stock split
An ordinary stock split occurs when a company distributes more shares to its
shareholders than they owned. In a stock split of 2-for-1 an investor receives two
shares for one share he/she owns in a company (Sheeba, 2011:350).
2.2.2.5 Reverse stock split
A reverse stock split occurs when companies would like to increase the market price
of its shares. It is also done when some companies want to remove small
shareholders (Sheeba, 2011:350).
The following is an example of a reverse stock split. A share worth one rand is split
in the ratio of one to 10. A new share will be worth ZAR10. In this case, a
shareholder will need to sell 10 shares in order to receive one share (Sheeba,
2011:350).
2.2.2.6 Share repurchases
Share repurchases occur when a company buys back its own shares from the
market (Frino, Hill & Chen, 2012: 343). Similar to cash dividends, share repurchases
are announced, but unlike cash dividends that are declared, the offer to purchase is
not legally binding (Moles, Parrino & Kidwell, 2011:674).
Although a share repurchase seems more advantageous than cash dividends,
managers have been known to abuse it. Managers have more knowledge about the
performance of the company than investors and as a result can take advantage of
this knowledge to the detriment of shareholders (Jalilvand & Malliaris, 2013:101).
16
An explanation of the different types of dividend dates and their impact on the share
price is presented in the next section.
2.2.3 Dividend dates
Another aspect that influences share price volatility is dividend dates. An
announcement of dividends is usually linked to changes in share prices (Puxty,
Dodds & Wilson, 1988:209). There are four types of dividend dates namely the date
of declaration, the record date, the ex-dividend date and the payment date.
2.2.3.1 Date of declaration
The date of declaration (also known as the announcement date) is the date on which
the board of directors officially declares that it will pay a dividend (Needles, Powers &
Crosson, 2010:538). On this date, the company indicates the amount of the dividend
it will distribute to the shareholders and the date of the dividend payment (Albrecht,
Stice & Stice, 2010:518).
Once a dividend is declared it is referred to as a declared dividend and the company
is legally compelled to pay it. As a result of the legal obligation that arises on the
declaration date, the liability of dividends payable is recorded and the dividends
account is debited on the declaration date (Needles, Powers & Crosson, 2010:538).
In the process of accounting, the retained earnings decrease due to the total
dividends declared during the period (Needles, Powers & Crosson, 2010:538).
Depending on how the stock market perceives the declared dividend, the date of
declaration of a dividend can cause the share price of the dividend declaring
company to fluctuate (Cotterell, 2011:1).
2.2.3.2 Date of record
The date of record is the date on which the list of shareholders who will receive the
next dividend payment, is finalised (Warren, Reeve & Duchac, 2011:493). It is
referred to as the date the right to obtain a dividend is decided (Needles, Powers &
17
Crosson, 2010:538). The record date falls between the declaration date and the
payment date.
Unlike the declaration date this date does not require a journal entry in the books of
a company (Mladjenovic, 2005:50). According to Sferra (2013:48) the share price
changes before the record date.
2.2.3.3 Ex-dividend date
The ex-dividend date is the day after the record date on which an investor purchased
a stock (Davis, 2003:65). Unlike the record date, this date is determined by the stock
exchange on which a company is listed not by the company (Frino, Hill & Chen,
2012: 343). The ex-dividend date is also the date on which the right to receive a
dividend no longer applies (Besley & Brigham, 2011:578).
If a shareholder buys a share before the ex-dividend date, the dividend belongs to
the buyer and is reported by the buyer. However, if the shareholder buys the share
on or after the ex-dividend date, the dividend belongs to the seller (JK Lasser
Institute, 2007:4).The share price could decrease slightly on the ex-dividend date
(Baker & Powell, 2009:405).
2.2.3.4 Payment date
This payment date is the date that management selects to pay the declared
dividends to the shareholders (Wilmott, 2007:140). The share price usually declines
on this date (Sferra, 2013:48).
2.3 Theoretical perspective of dividend policies
The impact of dividend policy on share price volatility plays an important role in the
type of dividend policy a company adapts (Bhat, 2009:534). Dividend policy
theories and their perspectives on the relationship between dividend policy and
share price volatility are described in the next section.
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2.3.1 Bird in hand theory (1959)
In 1959, the bird in hand theory was proposed. It is stated in this theory that if the
dividend pay-out ratio of a company decreases, investors will become concerned
about the future earnings or capital gains of the company (Frankfurter, Wood &
Wansley, 2003:72).
It is proposed that certain investors would rather receive their dividend in the present
than wait for capital gains in the future to avoid agency costs and public scrutiny.
When most of the company profits are distributed to the shareholders as dividends,
there is a decreased risk of managers misusing company cash, i.e. agency costs are
reduced as a result of high dividend pay-outs (Ehrhardt & Brigham, 2008:554).
Theorists of the bird in hand theory claim that there is a positive relationship between
the share price of a company and its dividends (Ehrhardt & Brigham, 2008:642).
2.3.2 Modigliani and Miller dividend irrelevance theory (1961)
In 1961, Modigliani and Miller proposed the dividend irrelevance theory, which
contradicts the bird in hand theory. The bird in hand theory states that dividends are
relevant in determining a company’s share price, while the Modigliani and Miller
theory states that dividends are irrelevant in determining a company’s share price
(Arkadi, 2010:8).
The dividend irrelevance theory states that the dividend policy a company chooses
will not affect the market value or the share price of a company. It is implied that
share price volatility is not caused by a change in dividend policy (Arkadi, 2010:8)
and that the value of a company only depends on the income it gains from its assets
(McMurry, 2011:276).
It should be noted that the assumptions made by the Modigliani and Miller dividend
irrelevance theory are unrealistic. These assumptions include perfect capital market,
no taxes, no brokerage costs, uniform information, available information to all
investors and that all investors are rationale (Mac & Bhaird, 2010:139).
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As a result, some researchers reject this theory, although its propositions that a
company that has invested in bad projects cannot hope to resurrect its image with
shareholders by offering them higher dividends, are valuable (Mac & Bhaird,
2010:139).
2.3.3 Residual dividend policy theory (1961)
The residual dividend policy theory states that a company should only pay a dividend
if there are retained earnings left after funding all lucrative investments, i.e.
investments with a positive net present value (NPV) (Megginson & Smart, 2008:590).
Therefore, this dividend policy is influenced by companies’ investment opportunities
and the availability of a balance of internally generated funds after all lucrative
investments have been made. The policy has limited direct influence on the market
share price of ordinary shares (Keown, 2003:399).
2.3.4 Dividend relevance theories: Walter’s model (1966)
Walter’s model proposes that the dividend policy of a company is linked to its
investment policy because the selection of a dividend policy affects the company
value. Walter justifies this suggestion by comparing the relationship between the cost
of capital (required rate of return) and the return on the company’s investment
(internal rate of return) (Sheeba, 2011:318).
The optimum dividend policy is calculated based on cost of capital and the return on
the company’s investment (Periasamy, 2009:17). If the cost of capital is greater than
the internal rate of return, all profits should be distributed as dividends, but if the
internal rate of return is greater than the cost of capital, all profits should be retained
in the company for reinvestment within the company (Sheeba, 2011:319).
According to Walter the company’s dividend policy affects the value of the company
and its share price (Sheeba, 2011:318).
20
2.3.5 Signalling/information effect theory (1973)
In a perfect market, a change in the financing, investment and dividend decision
does not affect the value of the company. However, in reality, an unpredictable
change in dividends can have a significant effect on the share price and the value of
a company (Lasher, 2007:644). Managers can use the dividend policy as a signal of
the company’s financial status, especially its earnings (Keown, 2003:401).
When a company offers a dividend that is larger than expected, it can be a signal to
investors that the management expects that it will earn significantly higher earnings
in the future. A decrease in dividends or lower than expected dividends can also
signal that management is expecting an unfavourable change in future earnings
(Keown, 2003:401).
Due to this signalling effect, management of companies are unwilling to cut
dividends even when there is a decrease in earnings (Sheeba, 2011:354). The
signalling effect theory was deemed essential to this study because it demonstrates
that the share price of a company could change due to the signalling effect of
dividend policies (Bhattacharya, 1979:265).
2.3.5.1 Expectation theory
The expectation theory states that investors have expectations of what the next
dividend of a company will be. These expectations can affect the share price
negatively or positively (Lasher, 2007:645).
This theory states that, as the announcement date of dividends comes closer,
investors create expectations in their minds about what the next dividend will be
(Keown, 2003:402).These expectations arise from current earnings, future earnings,
past dividend policies, financing and investment decisions, etc.
When the dividend is finally announced, investors compare their expectations to the
actual dividend announced (Keown, 2003: 402). If the actual dividend announced is
as expected the market price of the company will remain constant (Lasher,
21
2007:645). However, if the dividend is lower or higher than expected, investors will
reconsider their views of the company (Keown, 2003:402).
2.3.6 Agency cost theory (1976)
The agency cost theory arose when there was a problem with separating the control
of the company and the ownership of the company (Pirmin, 2007:2). The theory
states that if the functions of agents (managers of the company) and the owners
(shareholders of the company) are united, both parties will profit because each party
will work to the advantage of both parties (Fama & Jensen, 1983:320).
However, if the functions of the agents and owners are not united, agency costs will
have to be incurred by the owners (shareholders) to ensure that managers work in
the best interest of the company and not themselves (Shleifer & Vishny, 1997:740).
The agency cost theory is relevant because managers can influence the distribution
of dividends in a way that is not in the best interest of the shareholders which can
affect share prices (Shleifer & Vishny, 1997:740).
2.3.7 Clientele effect (1977)
The clientele effect theory was proposed during 1977 (Keown, 2003:400). This
theory states that investors select which company to invest in based on the policies
the company has in place, for instance the dividend policy (Pettit, 1977:995).
This theory also states that older and low income investors tend to prefer investing in
companies with cash dividends while young investors prefer to invest in the more
aggressive and risky portfolios with potential for earning larger returns (Ehrhardt &
Brigham, 2008:446).
The clientele effect theory is important because it identifies the factors that play a
role in investors’ decisions to invest in a shares (Ehrhardt & Brigham, 2008:568).
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2.3.8 Dividend relevance theory: Gordon’s model (1979)
Similar to Walter’s model, the Gordon relevance model proposes that dividends are
relevant and affect the share price of a company. The Gordon model is based on the
assumptions that a company is financed by equity only, and that the cost of capital
and the internal rate of return do not change, the company has a continuous life, the
percentage of net income that is retained to grow the business (retention ratio)
selected remains constant, and the company growth rate is constant and is less than
the internal rate of return (Khan & Jain, 2005:30).
Considering these assumptions, Gordon suggested that investors are rational and
risk averse, and hence prefer to receive dividends immediately rather than in future
(Khan & Jain, 2005:30). Gordon also created a dividend calculation model in line
with his assumptions. It is expressed as follows:
P= (E(1-b))/ (ke – br) (2.1)
Where P is the price per share, b is the percentage of retained earnings (retention
ratio), ke is the cost of capital and br is growth rate. This equation indicates that the
value of a share is influenced by the future dividends of the company (Sheeba,
2011:320).
2.3.9 Tax preference theory (1979)
The tax preference theory arose from the increased tax consciousness of investors
to tax on dividends and capital gains tax, and states that rational investors prefer
companies to distribute dividends only when tax on dividends is low and retain profits
in the company when tax on dividends is high (Cordes, Ebel & Gravelle, 2005:300).
The preceding section on dividend theories and the impact of dividend policy on
share price volatility, is followed by section 2.4, which includes the main factors that
have an impact on share price volatility. These factors are referred to as
determinants of share price volatility.
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2.4 Determinants of share price volatility
Share price volatility is regarded as systematic risk faced by portfolio holders in the
market (Guo, 2002:75), and entails volatility in the market or stock exchange that is
not diversifiable (Harris & Mongiello, 2012:385).
The main factors that determine share price volatility include company size, asset
growth, leverage, earnings volatility, dividend yield and dividend pay-out ratio
(Ilaboya & Aggreh, 2013:114; Ramadan, 2013:13-14; Sadiq et al., 2013:428).
2.4.1 Company size
Company size refers to the magnitude of a company in various forms such as
capitalisation (market value of shares), assets, sales and employment among others
(Kuo, 2007:97). It is expected that the larger the company is the less the share price
volatility will be. Harris and Mongiello (2012:389) argue that larger companies are
capable of minimising the effect of political, economic and social factors.
Studies on the relationship between company size and share price volatility have
contrasting results in different countries. Some indicate a negative relationship as
expected, while others indicate an unexpected positive relationship.
Researchers that found a negative relationship between company size and share
price volatility include Ramadan (2013:16) who found a significant negative
relationship through correlation analysis (-0.39) and regression analysis (-0.0536) on
the Jordan stock exchange, and Hussainey et al., (2011:10) who found a significant
negative relationship through correlation analysis (-0.1823) and regression analysis
(-0.3130) on the London Stock Exchange.
However, on the Karachi Stock Exchange, Nishat and Irfan (2001:15) found a
significant positive relationship through correlation analysis (0.034) and regression
analysis (0.001). Allen and Rachim (1996:182) also found a significant positive
relationship through correlation analysis (0.298) and regression analysis (0.021) on
the Australian Stock Exchange.
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2.4.2 Asset growth
Asset growth is the change in total assets from one year to the next (Lipson, Mortal
& Schill, 2009:3), which is expected to increase the share price volatility of a
company. There is a positive relationship between asset growth and share price
volatility (Idol, 1978:55), due to fast-growing companies experiencing a high level of
competition, which necessitates a fast reaction to economic changes (Harris &
Mongiello, 2012:385).
Researchers who studied this positive relationship between asset growth and share
price volatility include Ilaboya and Aggreh (2013:114) from Nigeria, who found a
significant positive relationship between asset growth and share price volatility
through correlation analysis (0.129) and regression analysis (0.222) on the Nigerian
Stock Exchange. Ullah (2010:28) also discovered a significant positive relationship
through correlation analysis (0.012) and regression analysis (1.07) on the Karachi
Stock Exchange.
2.4.3 Leverage
Leverage relates to the percentage of a company’s assets that is financed by debt
(Mayo, 2010:436), and is expected to have a positive relationship with share price
volatility due to the fact that leverage exposes a company to higher financial risk and
therefore a higher systematic risk (Harris & Mongiello, 2012:387).
Researchers who indicated a positive relationship between leverage and share price
volatility during their studies include Ullah (2010:28) from Pakistan, who found a
significant positive relationship through correlation analysis and regression analysis
on the Karachi Stock Exchange, and Hussainey et al. (2011:10) who also
determined a significant positive relationship through correlation analysis (0.1528)
and regression analysis on the London Stock Exchange.
25
2.4.4 Earnings volatility
Earnings volatility is an unpredictable change in earnings over a certain time period
(El Harizi, El Sayed, Bettina & Shields, 2007:1). Some investors are willing to pay a
premium for companies with stable earnings, and therefore these companies have
stable earnings as strategic thrust.
However, Koller, Goedhart and Wessels (2010:347) indicate that rational investors
focus on the company’s return and risk associated with cash flows, not only the
earnings. It is expected that a positive relationship exists between earnings volatility
and share price volatility due to the fact that companies with high earnings volatility
tend to have volatile share prices (Martin & Tyson, 2009:113-114).
Studies regarding the relationship between earnings volatility and share price
volatility have contrasting results in different countries. Some indicate a positive
relationship as expected, while others indicate an unexpected negative relationship.
Researchers who found a positive relationship between earnings volatility and share
price volatility during their studies include Hashemijoo et al. (2012:124) in Malaysia,
who found a significant positive relationship through correlation analysis and
regression analysis. In the United Kingdom, Hussainey et al. (2011:6) also found a
significant positive relationship through correlation analysis and regression analysis.
However, some studies found an unexpected negative relationship between
earnings volatility and share price volatility. Ilaboya and Aggreh (2013:114) found a
significant negative relationship through correlation analysis and regression analysis
on the Nigerian Stock Exchange.
2.4.5 Dividend yield
Dividend yield consists of two components, namely dividend and capital gains
(Croushore, 2006:189). If investors expect either of these components to increase,
the market price of the share will increase (Besley & Brigham, 2008:292).
The dividend issued by a company is usually expressed as a percentage of the
market price per share (Taylor, 2007:375). A number of researchers have indicated
26
that an inverse relationship between share price volatility and dividend yield should
be expected (Carter & Shawn-Schmidt, 2008:94).
This inverse relationship is expected because share prices of companies that offer
large dividends tend to be less volatile (Martin & Tyson, 2009:181). Studies on the
relationship between dividend yield and share price volatility indicate contrasting
results in different countries. Some indicate a negative relationship as expected
while others indicate an unexpected positive relationship. The following researchers
agree with the expectations that there is a negative correlation between dividend
yield and share price volatility.
In Jordan, Ramadan (2013:17) found a significant negative relationship through
correlation analysis and regression analysis on the Amman Stock Exchange. In
Malaysia, Hashemijoo et al. (2012:124) also found a significant negative relationship
through correlation analysis and regression analysis on the Malaysian Stock
Exchange.
However, some researchers also found a positive correlation between dividend yield
and share price volatility. On the Kenyan Stock Exchange, Kenyoru, Kundu and
Kibiwott (2013:120) found a significant positive relationship through correlation
analysis and an insignificant positive relationship through regression analysis.
2.4.6 Dividend pay-out ratio
Dividend pay-out ratio is a measure of the proportion of the company’s earnings that
are distributed to the shareholders as dividends and total return (Rich, Jones,
Heitger & Hansen, 2011:695). This ratio demonstrates the percentage of a
company’s earnings that is paid out to the ordinary shareholders as dividends.
It is expected that the pay-out ratio has a negative relationship with share price
volatility (Harris & Mongiello, 2012:388) due to the fact that share prices of
companies that offer large dividends tend to be less volatile (Martin & Tyson,
2009:181).
27
Studies on the relationship between dividend pay-out ratio and share price volatility
derived contrasting results in different countries. Some indicate a negative
relationship as expected while others indicate an unexpected positive relationship.
On the Amman Stock Exchange, Ramadan (2013:17) found a significant negative
relationship through correlation analysis and regression analysis In Pakistan, Sadiq
et al. (2013:429) also found a significant negative relationship through correlation
analysis.
Despite these findings that were in line with expectations, on the Karachi Stock
Exchange, Asghar (2012: 49) found a significant positive relationship through
correlation analysis and an insignificant positive relationship through regression
analysis. These contradicting research results demonstrate that there is still
uncertainty with regards to the relationship between dividend pay-out ratio and share
price volatility.
The research on determinants of share price volatility indicates that all the major
determinants of share price volatility have an uncertain relationship with share price
volatility. This points to a gap in literature that legitimates a study on this terrain.
2.5 Conclusion
The literature reviewed demonstrates that there have been a number of studies and
theories internationally regarding the relationship between dividend policy and stock
price volatility. The research is however inconclusive on what the relationship
between the dividend policy and the share price volatility is.
Literature on key dividend policy concepts, including the most relevant past dividend
theories and the most relevant determinants of share price volatility, was reviewed.
The key dividend policy concepts discussed included types of dividend policies
(constant pay-out ratio policy; constant nominal payment policy; and low regular and
extra policy) and aspects that influence share price volatility (types of dividends;
dividend dates; date of declaration; ex-dividend date; date of record; payment date;
and stock reactions to dividend dates). It was demonstrated that these key concepts
have an influence on share price volatility.
28
The theoretical basis of this study was broadened by an analysis of specific models
that were previously applied to determine the influence of the dividend policy on
share price volatility. These include the bird in hand theory; Modigliani and Miller
dividend irrelevance theory (1961); clientele effect; tax preference theory; residual
dividend policy theory; signalling effect theory; and agency cost theory.
Lastly, the most relevant determinants of share price volatility were considered,
namely the effect of company size on share price volatility; the effect of asset growth
on share price volatility; the effect of leverage on share price volatility; the effect of a
company’s earnings volatility on share price volatility; the effect of dividend yield on
share price volatility; and the effect of dividend pay-out ratio on share price volatility.
It was demonstrated that there have been a number of international studies on the
relationship between dividend policy and share price volatility. The research results
were however not consistent, which demonstrate that every country is unique. This
literature review demonstrated that a specific SA study on the relationship between
dividend policy and share price volatility would make a significant contribution on
how the South African financial market should view the relationship between
dividend policy and share price volatility.
29
Chapter 3
Research methodology
3.1 Introduction
In Chapter 2 literature regarding dividend policy concepts, dividend policy theories
and the determinants of share price volatility was reviewed. In this chapter, the
researcher elaborates on the research methodology that was applied to determine
the relationship between dividend policy and share price volatility. To contextualise
the research methodology applied in this study, the problem statement, the goal and
the research question of the study are briefly reiterated.
3.2 Problem statement
Although the influence of dividend policy on share price volatility has been under
study before, past study results were not consistent and varied amongst different
countries. The past study results of developed countries cannot be used as proxy for
developing countries such as South Africa (Asghar et al., 2011:50). As a result, there
is a need for research on the impact of dividend policy on share price volatility in
South Africa specifically.
3.3 Goal of the study
The goal of this study was to establish the role of dividend policy measures on share
price volatility in South Africa.
30
3.4 Research question
The primary research question of the study was: What is the relationship between
dividend policy measures and share price volatility on the Johannesburg Stock
Exchange Ltd?
To support the primary research question the following supporting research
questions were formulated:
Is there a relationship between dividend yield and share price volatility for the
FTSE Top 40 Index companies listed on the Johannesburg Stock Exchange?
Is there a relationship between pay-out ratio and share price volatility for the
FTSE Top 40 Index companies listed on the Johannesburg Stock Exchange?
Based on this background, the research design of the study was considered.
3.5 Research design
A research design is the blue print or the plan of a study (Kumar, 2011:30). Due to
the nature of this study, a quantitative research approach was followed. According to
Blaxter, Hughes and Tight (2010:64) quantitative research is research that deals with
numerical data. The numerical data applied in this study were secondary in nature
and were obtained from reliable data sources, namely McGregor, Bureau of
Financial Analysis (BFA), Reuters and the Johannesburg Stock Exchange Limited
(JSE Ltd).
Based on the research design, the following section contains an explanation of the
research strategy.
3.6 Research strategy
A strategy is a set of deliberately intended courses of action to deal with a situation.
In terms of this definition, strategies have two essential characteristics: they are
made in advance of the actions to which they apply, and they are developed
31
consciously and purposefully (Franzen & Moriarty, 2008:49). The research paradigm
within the research strategy of this study is explained in the following section.
3.6.1 Research paradigm
Research paradigms are patterns of beliefs and practices that regulate inquiry within
a discipline by providing lenses, frames and processes through which investigation is
accomplished (Weaver & Olson, 2006:460). Taylor, Kermode and Roberts (2007:5)
state that a paradigm is “a broad view or perspective of something”.
There are three forms of research paradigms, namely positivism, post-positivism and
interpretivism (Kasi, 2009:95).
When a researcher believes that when the right research questions are asked, the
required facts in research will be obtained, it refers to positivism (Somekh & Lewin,
2005:207). When a researcher focuses on understanding relationships rather than
explaining them, it refers to interpretivism (Cryer, 2006:78). Post-positivism involves
having an explanation and an understanding of research (Kasi, 2009:95).
During this study, it was aimed to have both an understanding and an explanation of
the research. As a result, the post-positivism paradigm was followed. For a research
paradigm to be implemented a research method is required. In this regard, an
explanation of the research method that was applied, is presented in the next
section.
3.6.2 Research method
There are four types of research methods, namely the descriptive qualitative,
descriptive quantitative, correlation/regression analysis and quasi-experimental
research methods (Gropper & Smith, 2012:567).
A descriptive qualitative research method involves studying a phenomenon
comprehensively to determine patterns and themes regarding events that the
researcher has precise questions about (Parse, 2001:57). Quasi-experimental
research methods involve manipulating an independent variable using an integral
32
group of constituents instead of constituents assigned to experimental treatments
(Mitchell, Crosby, Wonderlich & Adson, 2005:16). A descriptive quantitative research
method involves a simplified description of a phenomenon using numbers, e.g.
frequency histogram, mean and standard deviation (Partington, 2002:101). Lastly,
correlation and regression analysis assist to determine if a relationship exists
between two variables and if it does exist, to what extent (Madrigal, 2012:179).
During this study, a descriptive quantitative research approach was applied, and
correlation and regression analysis were utilised to determine the relationship
between variables.
The research instruments that were used in the research method are mentioned in
the next section.
3.7 Research instrument
The research instrument refers to measurement tools used to analyse data (Ariola,
2006:140). In this study descriptive statistics, correlation matrices and panel
regression models were used to analyse data.
3.8 Sampling strategy
The sample of this study included all the companies in the FTSE/JSE Top 40 Index
during a five-year period from January 2009 to December 2013. This sample was
selected because the FTSE/JSE Top 40 Index companies represent the overall
performance of the largest shares traded on the JSE (Bureau of National Affairs,
2002:1112).
A five-year data sample was selected because it has been successfully used in a
number studies such as Nazir et al. (2010:106), and Hussainey et al. (2011:7).
33
3.9 Data collection method
Sources of data and the methods used to collect and analyse data must be reliable.
The following is an indication of data sources used in this study.
The data applied in this study were annual data from January 2009 to December
2013. The data set consisted of an annual dependent variable (share price volatility)
and independent variables (dividend yield, dividend pay-out ratio, asset growth,
earnings volatility, company size, and leverage).
As indicated in the previous sections, data were obtained from the following reliable
sources: Reuters, JSE and McGregor/BFA (Van & Robertson, 2003:8).
3.10 Data analysis
The following section consists of an analysis and explanation of the sources of the
relevant variables in this study.
3.10.1 Dependent variable
In this study, the share price volatility (SPV) is regarded as the dependant variable to
determine the relationship between dividend policy measures and share price
volatility on the JSE Ltd. Share price volatility is the systematic risk faced by portfolio
holders in the market (Guo, 2002:75).
Khawaja, Bhutto, Butt and Anwar (2012:142) calculated SPV by squaring the
standard deviation of the share price, while Nazir et al. (2012:135) calculated SPV as
the difference between the highest and lowest share price in a year divided by the
mean of the highest and lowest prices and then squaring the result. This method was
successfully applied in a number of other studies, including Asghar et al. (2011:47),
Hashemijoo et al. (2012:120) and Hussainey et al. (2011:7).
34
3.10.2 Independent variable
According to Asghar et al. (2011:47), Nazir et al. (2012:135) and Hussainey et al.
(2011:7), dividend policy is regarded as the independent variable in determining the
relationship between dividend policy measures and share price volatility.
Dividend policy is represented by two measures, namely dividend pay-out ratio and
dividend yield (Asghar et al., 2011:46; Hussainey et al., 2011:6; Nazir et al., 2012:
135). The manner in which the dividend yield and dividend pay-out ratio were
determined in this study is indicated in the next section.
3.10.2.1 Dividend pay-out ratio (DPR)
Dividend pay-out ratio is a measure of the proportion of the profits of a company that
is distributed to the shareholders as dividends (Rich et al., 2011:793).
Nazir et al. (2012:135) calculate DPR by dividing cash dividends paid in a year by
the total earnings after tax, while Allen and Rachim (1996:180) and Jecheche
(2012:4) calculate DPR by dividing total cumulative dividends for all years for each
company by total cumulative earnings for each company in each year.
For the purposes of this research DPR was calculated using the approach of
Hussainey et al. (2011:7). The rationale was that it would be better to obtain dividend
per share expressed as a percentage of the share price directly from the McGregor
database than to calculate every DPR manually like Allen and Rachim (1996:180)
did.
3.10.2.2 Dividend yield (DY)
Dividend yield is the rate of return on shares that arises from cash dividends
shareholders receives (Warren et al., 2011). Allen and Rachim (1996:180) calculate
DY by adding annual cash dividends paid and then dividing the total by the market
value of the shares at the start of the year. Hussainey et al. (2011:7) calculate DY as
dividend per share expressed as a percentage of the share price. Dividend yield is
35
already calculated in the McGregor database and was therefore be obtained directly
from the McGregor database.
3.10.3 Control variables
Leverage, company size, asset growth, and earnings volatility were also included in
the regression model to determine whether dividend policy (DY and DPR) influences
share price volatility. These control variables were included because they also have
an influence on SPV and dividend policy (Allen & Rachim, 1996:180; Jecheche,
2012:5; Khawaja et al., 2012:143). An explanation of the control variable and how it
was calculated in during this study, is presented in the next section.
3.10.3.1 Leverage (L)
Leverage is the percentage of the assets of a company that is financed by debt
(Mayo, 2010:436). Leverage has been calculated by Hussainey et al., (2011:7),
Jecheche (2012:5) and Khawaja et al. (2012:143) as the sum of long-term debt
divided by total assets per annum.
This study was also based on leverage calculated as the ratio of total long-term debt
to total assets. This method was used because it has been used successfully in
number of studies before such as Allen and Rachim (1996:180), Hashemijoo et al.
(2012:120) and Hussainey et al. (2011:7) among others.
3.10.3.2 Company size (FS)
Company size refers to the size of a company as measured by various criteria such
as capitalisation (market value of shares), assets, sales and employment among
others (Kuo, 2007:97).
According to Rashid and Rahman (2008:4) the company size is calculated as the
natural logarithm of the market value of equity at the beginning of the year. However,
according to Jecheche (2012:5) the company size is calculated as the average
market value of shares.
36
Lastly, Habib et al. (2012:81) uses total assets as a proxy for company size.
Because it is clear that there is different approaches to determine company size, this
approach was followed during this study.
3.10.3.3 Asset growth (AG)
Asset growth is the change in total assets of a company over a specific period
(Asghar et al., 2011:48; Khawaja et al., 2012:142; Nazir et al., 2010:103). Asset
growth is calculated as the ratio of the change in total assets at the end of the year to
the level of total assets at the beginning of the year. The researcher of this study
followed a similar approach and calculated asset growth as the ratio of the change in
total assets at the end of the year to the level of total assets at the beginning of the
year.
3.10.3.4 Earnings volatility (EV)
Earnings volatility refers to the changes in earnings over a specific period
(Organisation for Economic Co-operation and Development, 2011:157). Hussainey
et al. (2011:7) calculates EV as the standard deviation of the earnings (before
interest and taxes).
In this study EV was calculated similar to the manner in which Hashemijoo et al.
(2012:121), Hussainey et al., (2011:6), Jecheche (2012:4) and Khawaja et al.
(2012:142) have done it, but only as the ratio of earnings before interest and tax to
total assets, due to missing data in certain years.
The model that was used to analyse the relationship between the compiled variables
is elaborated upon in the next section.
3.11 Model description
During this study a panel regression model was applied. The following regression
equation was applied to determine the relationship between dividend policy and
share price volatility.
37
SPV= α+ β1DY + β2DPR + β3FS + β4EV+ β5L + β6AG + e (Allen & Rachim,
1996:181)
Where SPV is stock price volatility, α is the intercept, β is the coefficient of the
variable, DY is the dividend yield, DPR is the dividend pay-out ratio, FS is the
company size, EV is the earnings volatility, L is the leverage, AG is the asset growth
variable and e is the error term.
The dependent variable share price volatility (SPV) was regressed against the
independent variables dividend yield and dividend pay-out ratio and the control
variables company size, earnings volatility, leverage and asset growth.
The models that were applied is further explained below.
3.11.1 Panel data analysis
Panel data were used in a panel regression model in this study. Panel data were
used because among other benefits, panel data improve the ability of the model to
control the impact of missing variables (Hsiao, 2003:7).
Panel data are available in two dimensions, namely cross section and time series
(Hsiao, 2003:7). In order to establish a panel regression, the following three panel
data analysis methods were used: the pooled regression model (ordinary least
square (OLS)), the fixed effects model (FEM) and the random effects model (REM).
Each of these models were analysed and the most suitable model was selected.
3.11.2 Pooled regression model (ordinary least squares)
The pooled regression model consists of a linear regression where the coefficient (β)
and the intercept (α) are estimated. This linear equation is based on the following
assumptions: the residuals are normally distributed, the mean is zero, the variance is
constant and error terms are independent from the explanatory variables (Gujarati,
2004:636-652).
38
According to Gujarati (2004:636-652), the linear pooled regression model is based
on:
Yit= αi + βX1it + βX2it + βX3it + βX4it+ βX5it+ βX6it + eit (3.1)
Where Y is the dependent variable (share price volatility), and βx1, βx2, βx3 βx4, βx5
and βx6 are explanatory variables (dividend pay-out ratio, dividend yield, firm size,
leverage and asset growth). The error term is e it and the intercept is α i. This model
restricts the coefficients of the explanatory variables to be common across the units
(i) and time periods (t) (Gujarati, 2004:636-652).
Khosa (2012:71) suggests that the pooled regression method is a simple way to
determine the relationship between the variables. However, despite its simplicity, the
pooled regression model has some disadvantages, one of which arises when there
is a correlation between the values of a time series and previous values of the same
series. This could result in model specification errors (Khosa, 2012:71).
Another disadvantage is that the pooled regression model is also based on the
assumption that the intercept of the different companies in the sample is the same
(Kanninen, 2007:84). This is an unrealistic assumption because the companies in
the sample of this study were different.
Another concern of the pooled regression model is that it assumes that the slope
coefficients of the independent variables are the same for all companies
(Anandajayasekeram, Rukani & Babu, 2007:166). This is another unrealistic
assumption because the companies in the sample were different and therefore had
individual specific effects.
As a result of these disadvantages, the pooled regression model could have
produced incorrect results for this study. Therefore, the fixed effect model was also
considered because it allows intercepts to vary according to cross section units
(Sahai & Ojeda, 2005:4).
39
3.11.3 Fixed effect model (FEM)
In contrast to the pooled regression model, the fixed effect model considers that the
intercepts of the companies differ because of unique company effects such as
industry differences, differences in the market and other differences in companies
(Allen & Rachim, 1996:180; Asghar, et al., 2011:48).
The fixed effect model is based on the following assumptions: the slope coefficients
are constant, i.e. they do not vary across companies or overtime (Sahai & Ojeda,
2005:4); and the coefficients of the slopes are constant, i.e. they do not vary across
the different industries in the sample (Sahai & Ojeda, 2005:4).
Sahai and Ojeda (2005:4) state that the fixed effect model is based on:
Y it= αi + βX1it + βX2it + βX3it + βX4it+ βX5it+ βX6it + eit (3.2)
Where Y is the dependent variable (share price volatility), and βx1, βx2, βx3 βx4, βx5
and βx6 are explanatory variables (dividend pay-out ratio, dividend yield, firm size,
leverage and asset growth). The error term is e it and the intercept is αi.
The i on the intercept α i demonstrates that the intercept of the companies can be
different because of the individual company effects (Miller & Yang, 2007:584). These
effects can be industry differences, differences in the market and other differences in
companies (Allen & Rachim, 1996:180; Asghar et al., 2011:48; Miller & Yang,
2007:584).
Although this method was advantageous to this study, it has some disadvantages.
The fixed effect model assumes homogeneity, yet company-specific
heteroscedasticity and autocorrelation may exist and misrepresent the estimation
(Demidenko, 2013:353). As a result of this disadvantage of the fixed effect model,
the random effect model was also considered in order to select the most appropriate
core model for this study.
40
3.11.4 Random effects model (REM)
The random effects model assumes that there are no individual specific effects of
companies. However, the individual effect it considers is a random variable, which is
assumed to be uncorrelated with the explanatory variables (Cooper, Hedges &
Valentine, 2009:316).
This model also requires estimation of the mean value of the intercept and its
variance (Gujarati, 2004:636-652). One of the advantages of this model is that it is
unnecessary to estimate N cross-sectional intercepts, which makes the model more
economical in the use of degrees of freedom.
The random effect model is constructed as follows (Sahai & Ojeda, 2005:5):
Yit = αi + βX1 it + βX2 it + βX3 it + βX4 it + βX5 it + βX6 it + eit (3.3)
Where Y is the dependent variable (share price volatility), and βx1, βx2, βx3 βx4, βx5
and βx6 are explanatory variables (dividend pay-out ratio, dividend yield, firm size,
leverage and asset growth). The error term is e it and the intercept is αi.
The intercept (αi) is randomly determined. As a result, the intercept in the equation
(3.3) is expressed as follows;
α i= α + εi (3.4)
Where the error term is eit and the intercept is αi.
The εi is the random error term with a zero mean and variance of σ2 εi.. This
demonstrates that the mean of the intercept α is the same across companies and
that the difference in the intercept for each company is demonstrated in the error
term εi (Cooper, Hedges & Valentine, 2009:316). The equation 3.3 is combined with
equation 3.4 to produce the following random effect model 3.5:
Yit = α + βX1 it + βX2 it + βX3 it + βX4 it + βX5 it + βX6 it + eit + εi (3.5)
In the equation (3.5) above, the error term εi for individual specific effects and the
error term for combined time series and cross-section eit can be combined and
denoted as Eit as follows:
41
Eit = εi + eit (3.6)
The equation 3.6 above, can be combined with the equation 3.5 to produce the
following equation.
Yit = α + βX1 it + βX2 it + βX3 it + βX4 it + βX5 it + βX6 it + Eit (3.7)
In the random effect model equation 3.7 above, the intercept (α) represents the
mean of all the intercepts (cross-sectional) and the error component εi represents the
random deviation of individual intercepts from this mean value (Gujarati, 2004:636-
652).
All three models discussed above were considered in this study (ordinary least
square (OLS), fixed effects model (FEM) and random effects model (REM)). The
most appropriate model was selected based on the redundant likelihood and the
Hausman tests.
The redundant likelihood test was used to test if there were similarities between the
companies in the sample of the study. The null hypothesis of the redundant test is
that the effects of each company are redundant, i.e. the intercepts of the companies
are the same (Hayter, 2012:661).
When the redundant likelihood test cross-section F and Chi-square are significant,
the null hypothesis can be rejected because, the intercepts of the companies would
not be the same, i.e. companies in the sample are different with different specific
effects (Hayter, 2012:661).
The Hausman test is based on comparisons between two different estimators. It
tests the null hypothesis that the coefficients estimated random effects model are the
same as the estimated by the fixed effects model (Baum, 2006:230-231).
If they are the same the P value of the Chi square statistic of the Hausman test is
insignificant and it will be safe to use the random effect model. However, if the P-
value of the Hausman test is significant, the fixed effects model should be used
because it will be safer to use the fixed effects model (Baum, 2006:230-231).
42
Having selected a core model and determining the variables that influence share
price volatility, the validity and reliability of the study also had to be considered.
3.12 Validity and reliability of data
It is essential for a study research instrument to be valid and reliable. A research
instrument that is reliable may not necessarily be valid (Grinnell & Unrau, 2010:192).
As a result, both the validity and reliability of this study are noted and discussed
separately in the following section.
Validity determines if the research instrument is measuring what it is meant to be
measuring (Bailey, 2008:69). The following are four types of validity.
3.12.1 Internal validity
Internal validity refers to whether the results of a study can be generalised to the
population (Bless, Higson-Smith & Kagee, 2006:93). It indicates whether the causal
relationships between two or more variables are justifiable and not caused by
something else (Maree & Van Der Westhuizen, 2008:29).
Since this study was based on variables considered to be the main variables that
affect share price volatility by a number writers, such as Allen and Rachim
(1996:180), Asghar et al. (2011:47), Hashemijoo et al. (2012:120) and Hussainey et
al. (2011:7), among others. The internal validity of this study can be regarded as
satisfactory.
3.12.2 External validity
External validity refers to whether the results of a study can be generalised to a
larger population than the target population (Blanche, Blanche, Durrheim & Painter,
2006:165).
The findings of this study cannot be generalised to a wider population than the target
population. Results from similar studies in other countries were not consistent and
43
study results varied amongst different countries. This indicates that past study
results of developed countries cannot be used as proxy for developing countries
such as South Africa and vice versa (Asghar et al., 2011:50).
3.12.3 Face validity
Face validity refers to the extent to which a research instrument is viewed as capable
of covering the concept it is meant to measure (Combs & Falletta, 2000:n.p.).
According to the Bureau of National Affairs (2002:1112) the FTSE Top 40 Index
companies represent the overall performance of the largest shares traded on the
JSE Ltd. As a result of the reliability of the sample (FTSE Top 40 Index companies)
and the data analysis techniques applied in this study, the level of face validity are
regarded as satisfactory.
3.12.4 Content validity
Content validity refers to the appropriateness and extent of representation of the
content of the sample used in a study (Connolly & Montgomery, 2005:26). The
sample of the study consists of the FTSE/JSE Top 40 Index companies. These
provide a good representation of the performance of South African listed companies
(Packard, 2009:957). Panel regression is also a widely accepted model to determine
the impact of dividend policy measures on share price volatility on the JSE Ltd. It is
therefore concluded that content validity is acceptable.
3.12.5 Reliability
The methodology applied in this study is standardised and has been successfully
applied to determine the relationship between dividend policy and share price
volatility by among others Asghar et al. (2011:47) in Pakistan, Hashemijoo et al.
(2012:120) in Malaysia, Hussainey et al. (2011:7) in the United Kingdom. This study
is therefore regarded as reliable. The next section contains the ethical considerations
the researcher had to keep in mind during this study.
44
3.13 Ethical considerations
Research ethics involves the adherence to fundamental ethical principles while
conducting studies. The design and implementation of research activities must
preserve the dignity, safety and rights of both direct and indirect research
participants (Shrader, 1994:2).
During this study all the required research ethics were considered and anonymity
and confidentiality were maintained at all times.
Anonymity refers to neither the researchers nor the readers of the results being able
to identify a response with a certain respondent. Confidentiality refers to the
researcher being able to identify responses of people but promising and choosing
not to reveal them publicly (Babbie, 2012:67).
This study is based on data retrieved from the McGregor database, therefore it is
secondary in nature. In this regard it is not anonymous as the readers and
researchers can identify with a respondent. However, this research is confidential
because the researcher can identify the variables of a company but will not reveal
them publicly.
45
3.14 Summary and conclusion
In this chapter the various aspects of research methodology and how they were
applied in this study were considered. These aspects included scope, goal of the
study, research question, design, paradigm, method, target population, data sources,
analysis, ethical considerations and limitations of the study. It was concluded that
due to the nature of this study, a quantitative research approach would be followed.
Because the researcher made use of secondary numerical data during this study, it
was decided to make use of McGgregor/Bureau of Financial Analysis (BFA) and the
JSE Ltd data sources. It was also decided that a post-positivism research paradigm
would be followed because it involved both an understanding and an explanation of
the research.
The sample of this study was all the shares in the FTSE/JSE Top 40 Index
companies during a five-year period from January 2009 to December 2013. A
descriptive quantitative research method was applied. Share price volatility (SPV)
was regarded as the dependant variable and dividend policy measures as the
independent variables.
Panel data were used in a panel regression model in this study, and were used
because among other benefits panel data improved the ability of the model to control
the impact of missing variables. Panel data are available in two dimensions, namely
cross section and time series (Hsiao, 2003:7). In order to establish a panel
regression, the following three panel data analysis methods were used: the pooled
regression model (ordinary least square (OLS)), the fixed effects model (FEM) and
the random effects model (REM).
The findings of this study are mainly applicable to South Africa. This does however
not erode the fact that countries that have similar economic and financial attributes
as South Africa, may find the findings useful.
46
Chapter 4
Data analysis and interpretation
4.1 Introduction
The research methodology used to establish the relationship between dividend
policy and share price volatility was explained in Chapter 3. Chapter 4 supplements
this explanation with an analysis of the study data.
The impact of dividend policy (represented by dividend policy measures) on share
price volatility was investigated, while considering the main control variables, namely
company size, leverage, asset growth and earnings volatility.
Firstly, a description of the sample is provided. Thereafter, a statistical description of
the study variables is presented. This is followed by an analysis of the study data
through correlation analysis and regression analysis with Eviews (statistical
package) to determine the relationship between the variables. The chapter is
concluded with a summary of the main findings.
4.2 Description of sample and variables
The sample selected for this study consisted of the FTSE/JSE Top 40 Index
companies. This sample represented the performance of the Top 40 South African
companies (Packard, 2009:957). Table 4.1 presents the FTSE/JSE Top 40 Index
companies and their respective industries as listed on Reuters.
47
Table 4.1: A list of the FTSE Top 40 Index companies and their respective
industries
Source: Reuters, April 2014
Company Industry
1 AGL - Anglo American Plc Energy and Materials
2 AMS - Anglo American Plat Ltd Energy and Materials
3 AngloGold Ashanti Ltd Energy and Materials
4 APN - Aspen Pharmacare Holdings Limited Health Care
5 ARI - African Rainbow Minerals Limited Energy and Materials
6 ASR - Assore Limited Energy and Materials
7 BIL - Bhp Billiton Plc Energy and Materials
8 BTI - British American Tobacco Plc Consumer directories and staples
9 BVT - The Bidvest Group Limited IT & Telecom
10 CCO - Capital & Counties Properties Plc Financials
11 CFR - Compagnie Financiere Richemont Sa Consumer directories and staples
12 DSY - Discovery Limited Financials
13 EXX - Exxaro Resources Limited Energy and Materials
14 FSR - Firstrand Limited Financials
15 GRT - Growthpoint Properties Limited Financials
16 IMP - Impala Platinum Holdings Limited Energy and Materials
17 INL - Investec Limited Financials
18 INP - Investec Plc Financials
19 IPL - Imperial Holdings Limited IT & Telecom
20 ITU - Intu Properties Plc Financials
21 KIO - Kumba Iron Ore Limited Energy and Materials
22 LHC - Life Healthcare Group Holdings Ltd Health Care
23 MDC - Mediclinic International Limited Health Care
24 MND - Mondi Limited Energy and Materials
25 MNP - Mondi Plc Energy and Materials
26 MTN - Mtn Group Limited IT & Telecom
27 NED - Nedbank Group Limited Financials
28 NPN - Naspers Limited Consumer directories and staples
29 OML - Old Mutual Plc Financials
30 REI - Reinet Investments S.C.A Financials
31 REM - Remgro Limited Financials
32 RMI - Rand Merchant Insurance Hldgs Ltd Financials
33 SAB - Sabmiller Plc Consumer directories and staples
34 SBK - Standard Bank Group Limited Financials
35 SHF - Steinhoff International Holdings Ld Consumer directories and staples
36 SHP - Shoprite Holdings Limited Consumer directories and staples
37 SLM - Sanlam Limited Financials
38 SOL - Sasol Limited Energy and Materials
39 TBS - Tiger Brands Limited Consumer directories and staples
40 VOD - Vodacom Group Limited IT & Telecom
41 WHL - Woolworths Holdings Limited Consumer directories and staples
48
The purpose of this study was to determine the impact of dividend policy on share
price volatility from 2009 to 2013. For each company in the sample listed in Table
4.1 variables that influenced the share price volatility were selected based on studies
by Allen and Rachim (1996:180); Asghar et al. (2011:47-48); Habib et al. (2012:80-
81); Hussainey et al. (2011:7); Jecheche (2012:1); Khawaja et al. (2012:142-143);
and Nazir et al. (2012:135).
The variables used in this study to determine the impact of dividend policy on share
price volatility were share price volatility (SPV), asset growth (AG), earnings volatility
(EV), company size (FS), leverage (L), dividend yield (DY) and dividend pay-out ratio
(DPR). To calculate the value of variables data were collected from 2009 to 2013.
The calculation of the value for each variable is discussed in the next section.
4.2.1 Share price volatility
Share price volatility (SPV) was the dependant variable in this study. It was
calculated as the difference between the highest and lowest share price in a year,
divided by the mean of the highest and lowest share prices and then squaring the
result (Khawaja et al., 2012:142; Nazir et al., 2012:135).
4.2.2 Independent variable
Dividend policy was the independent variable in this study to determine the
relationship between dividend policy and SPV on the JSE Ltd. (Asghar et al.,
2011:47; Hussainey et al., 2011:7; Jecheche, 2012:4; Nazir et al., 2012:135).
Dividend policy is represented by dividend pay-out ratio and dividend yield (Asghar
et al., 2011:46; Jecheche, 2012:1; Hussainey et al., 2011:6).
Dividend pay-out ratio and dividend yield were calculated as follows in this study.
Dividend pay-out ratio was calculated based on Hussainey et al. (2011:7)’s
approach, which states that dividend pay-out ratio is determined by dividing the
dividend yield by earnings yield.
49
According to the McGregor and company certified public accountants website
(McGregor and Company, 2014:n.p) the dividend yield in the McGregor data base is
calculated as the dividend per share divided by the market price per share. The
dividend yield is already calculated in the McGregor database and was therefore
obtained directly from McGregor database.
4.2.3 Control variables
Leverage, company size, asset growth, and earnings volatility were included in the
panel regression model as control variables to determine whether dividend policy
(dividend yield and dividend pay-out ratio) influenced share price volatility.
These control variables were included because they also have an influence on share
price volatility. The following studies indicated that these variables also affect share
price volatility: Allen and Rachim (1996:180), Hussainey et al. (2011:7), Jecheche
(2012:5) and Khawaja et al. (2012:143).
The control variables were calculated as follows in this study.
4.2.3.1 Leverage (L)
Leverage was obtained directly from the McGregor database. It was calculated as
the ratio of total debt to total assets (Habib et al., 2012:79).
4.2.3.2 Company size (FS)
Total assets were used as a proxy for company size as was done by Habib et al.
(2012:81). Total assets were retrieved directly from the McGregor database.
50
4.2.3.3 Asset growth (AG)
Asset growth was calculated as the percentage change in total assets per year
(Asghar et al., 2011:47). Asset growth was retrieved directly from the McGregor
database.
4.2.3.4 Earnings volatility (EV)
Earnings volatility was calculated as the ratio of earnings before interest and tax to
total assets (Asghar et al., 2011:47). The ratio of earnings before interest and tax to
total assets was retrieved directly from the McGregor database.
After calculating the value of each variable, a statistical description of the variables
data set was designed. Descriptive statistics of the independent variables and
dependent variables were calculated in Eviews and Palisade tools, both statistical
packages. A demonstration and explanation of the descriptive statistics results is
presented in the next section.
4.3 Descriptive statistics
In order to establish the basic characteristics of the variables in this study, the
following descriptive statistics were calculated for each variable: mode, mean,
median, standard deviation and skewness. The statistics from 2009 to 2013 for all
the variables in this study is presented in Table 4.1.
51
Table 4.2: Statistical description of the study variables
Source: Eviews output, August 2014
An explanation of the descriptive statistical results of the dependent variable, share
price volatility, follows.
Skewness refers to the degree of the unevenness of a probability distribution of a
dataset and is determined through a comparison of the measures of central
tendency, namely the mean, the mode and the median (Kumar, 2008:236). It is also
used to determine the normality of a dataset (Black, 2011:49; Healey, 2012:94).
When the mean is equal to the median and the mode, the dataset is normally
distributed. When the mean is lower than the median, the dataset is negatively
skewed. However, when the mean is higher than the median, the data set is
positively skewed (Black, 2011:49; Healey, 2012:94).
The mean of share price volatility is greater than the mode and median, therefore the
share price volatility dataset is positively skewed. Hence, the mean will not present
an accurate picture of the centre of a distribution of the share price volatility dataset.
In this regard, the median is the preferred measure of central tendency because it
presents a value between the mode and the mean (Healey, 2012:94). The median
for the share price volatility dataset is ZAR 0.13.
Standard deviation is the best measure of dispersion (Siddiqui, 2011:187). It is
defined as a statistic that presents the average distance of variables from the mean
(Macfie & Nufrio, 2006: 87).The higher the variable’s average distance from the
mean, the higher the risk of the variables (Mun, 2010:43; Fontanills & Gentile,
2003:41).
Descriptive
Statistic
Stock Price
Volatility
Dividend
Pay-out
ratio
Dividend
Yield
Earnings
Volatility
Firm
Size Leverage
Asset
Growth
Unit ZAR % % ZAR ZAR ZAR %
Mean 0.20 0.523 4.002 0.12 266,000,000.00 0.50 12.181
Median 0.13 0.406 2.739 0.10 63,391,500.00 0.51 10.525
Mode 0.03 0.000 0.000 -0.01 505,016,794.28 0.70 -7.867
Skewness 3.98 7.518 13.079 2.59 2.66 0.26 0.609
Standard Deviation 0.21 1.183 13.867 0.14 437,000,000.00 0.28 17.305
52
The standard deviation of the share price in this study was ZAR 0.21. This value
indicated that the share price volatility was high.
4.4 Correlation analysis
After the statistical description of the dependent variable, share price volatility, an
examination of the relationship between the share price volatility and the
independent variables was required to determine the relationship between variables
and share price volatility. In this regard, a correlation analysis was made on the
relationships between share price volatility, the dependent variable and the
independent variables.
The expected relationship of variables with share price volatility based on previous
studies follows. The results of the correlation analysis of the study variables is
thereafter presented.
It was expected that dividend pay-out ratio, dividend yield and company size were
inversely related to share price volatility (Allen & Rachim, 1996:181-185; Hashemijoo
et al., 2012:118; Hussainey et al., 2011:6; Nazir et al., 2010:102). Therefore, for
every increase in dividend pay-out ratio or dividend yield or company size there is a
decrease in share price volatility.
The inverse relationship between company size and share price volatility exists due
to the fact that large companies tend to be considered less risky. Large companies
usually have larger earnings and reserves to survive economic downturns.
Therefore, the reaction of shareholders and prospective investors to changes in
large companies is reduced, leading to less volatile share prices of large companies
(Morris & Morris, 2007:113).
Companies that have larger earnings tend to offer larger dividends, and therefore
tend to be considered low risk. This causes limited reaction of shareholders and
prospective investors to changes in these companies, hence the share price volatility
in these companies is limited. In this regard, an inverse relationship is expected
between the dividend policy (represented by dividend yield and pay-out ratio) and
the share price volatility (Morris & Morris, 2007:113).
53
Leverage, asset growth and earnings volatility are expected to have a positive
relationship with share price volatility (Allen & Rachim, 1996:181-185; Harris &
Mongiello, 2012:385; Hashemijoo et al., 2012:118; Hussainey et al., 2011:6; Nazir et
al., 2011:102). Therefore, the more leverage or earnings volatility or asset growth a
company has the more volatile its share price will be.
When most of the earnings of a company are invested in assets, there are fewer
reserves for the company to survive in an economic downturn. This indicates that a
positive relationship between asset growth and share price volatility can be expected
(Martin & Tyson, 2009:113-114).
According to Martin and Tyson (2009:113-114) there is a positive relationship
between leverage and share price volatility. Companies with large debt are
considered to be risky. This risky perception can lead to share price volatility in
companies with large debt.
Lastly, a positive relationship between earnings volatility and share price volatility is
expected because companies with volatile earnings are regarded as risky. Volatile
earnings causes instability and volatility in share prices volatility (Martin & Tyson,
2009:113-114).
A correlation analysis of the variables of this study is presented in the next section.
The correlation matrix of the variables in this study is presented in Table 4.3.
Table 4.3: Correlation of study variables
Source: Eviews output, August 2014
The correlations presented in Table 4.3 indicate the relationship between the
selected variables. The results indicate that except for earnings volatility and
Variable
Stock Price
Volatility
Dividend
Pay-out ratio
Dividend
Yield
Earnings
Volatility
Firm
Size Leverage
Asset
Growth
Stock Price Volatility 1.000
Dividend Pay-out ratio -0.036 1.000
Dividend Yield -0.005 0.809* 1.000
Earnings Volatility -0.006 0.069 -0.018 1.000
Firm Size -0.047 0.013 0.013 -0.333* 1.000
Leverage -0.036 -0.089 -0.114 -0.202* 0.148** 1.000
Asset Growth -0.254* -0.194* -0.224* 0.159** -0.100 -0.060 1.000
* , **, ***,**** indicate statistically significant coefficients at 1% , 5%, 10%,15% level of confidence respectively.
54
leverage, all the relationships with share price volatility were as expected (Harris &
Mongiello, 2012:385).
The highest correlation existed between the dividend pay-out ratio and dividend
yield, which had a value of 0.809 and was significant on a 95% confidence level.
This high correlation causes multicollinearity, which was ignored, because it did not
affect the estimations in a model (Filler & DiGabriele, 2012:93). The rest of the
correlation coefficients wer low with signs that were in line with expectations (Harris
& Mongiello, 2012:385).
The second significant correlation was the expected negative correlation (-0.333)
between earnings volatility and company size. This was expected due to the fact that
small or growing companies tend to have volatile earnings.
Another significant correlation was the expected positive correlation between
company size and leverage (0.148). This was expected because large companies
tend to be more differentiated, have more physical assets, are more subject to
market examination and can sustain more debt (Allen & Rachim, 1996:82).
The expected negative correlation between share price volatility and asset growth (-
0.254) was also significant. This again followed expectations due to the fact that the
higher the company investments in assets, the less earnings it has left to distribute
dividends (Harris & Mongiello, 2012:385).
The associations between asset growth and the dividend policy measures of
dividend pay-out ratio (-0.194) and dividend yield (-0.224) were also as expected and
both significant as stated by Harris and Mongiello (2012:385). These associations
were as expected due to the fact that the more a company pays dividends the more
it could reduce its ability to buy physical assets because most of its income is
distributed as dividends (Harris & Mongiello, 2012:385).
The correlation between earnings volatility and leverage (-0.202) was also significant
and as expected. This association was expected, because a company that increases
its leverage and makes use of it appropriately could have less volatile earnings
(Allen & Rachim, 1996:181-185; Hussainey et al., 2011:6).
55
Lastly, although insignificant, the dividend pay-out ratio had an expected positive
correlation with earnings volatility. This association was expected because
companies with volatile earnings are presumed to pay lower dividends and viewed
as being more perilous (Allen & Rachim, 1996:83).
In order to validate the correlation results found, a panel regression of the study
variables was performed and is presented in the next section.
4.5 Model description
A panel regression was used to produce a model for this study. This type of
regression was selected mainly because panel data analysis is suitable for data that
has cross sections and a time dimension, which was the case with this study (Hsiao,
2003:7).
This study followed the standard share price volatility equation adopted in a number
of studies such as Allen and Rachim (1996:180), Asghar et al. (2011:47-48),
Hussainey et al. (2011), Jecheche (2012:1), Nazir et al. (2010:103) and Nazir et al.
(2012:135) among others.
The dependent variable is share price volatility (SPV), which is explained by the
independent variables of dividend pay-out ratio (DPR), dividend yield (DY), asset
growth (AG), earnings volatility (EV), company size (FS) and leverage (L).
According to Allen and Rachim (1996:181), the general panel regression model for
this study is estimated as follows:
SPVit = α1 + α2 DPRit + α3 DYit+ α4Lit+ α5FSit+ α6AGit+α7EVit + eit (4.1)
Where the dependent variable SPV is share price volatility for ith cross-sectional
company during the tth period. The sign α1 is the constant while α2, α3, α4, α5, and α6
are the coefficients of the independent variables, namely dividend pay-out ratio
(DPR), dividend yield (DY), asset growth (AG), earnings volatility (EV), company size
(FS) and leverage (L).This regression equation was used in the panel regression,
which is presented in the following section.
56
4.5.1 Panel regression
Panel data analysis was used in this study because the sample data of this study
had cross sections and a time dimension (Hsiao, 2003:7).
This analysis started with a pooled regression model (Ordinary Least Square (OLS),
which was the benchmark model to test the two panel models, the Fixed Effects
Model (FEM) and the Random Effects Model (REM) against.
Details on how the models were estimated were discussed in Chapter 3. The
empirical results that were obtained from these estimations are presented in the next
section. The best empirical results were selected and compared to expected results
and results from other studies.
4.5.1.1 Pooled regression model
The pooled regression model estimates a linear relationship between the dependent
variable (share price volatility) and the independent variables (dividend pay–out ratio,
dividend yield, company size, leverage and asset growth) (Gujarati, 2004:636-652).
In this regard it was assumed that dividend pay-out ratios, dividend yield, asset
growth, leverage and earnings volatility would explain the share price volatility.
The pooled regression model assumes a normally distributed residual constant
variance and the value of the error term is zero. The pooled regression model also
estimates a linear relationship between the dependent variable and the explanatory
variables (Gujarati, 2004: 636-652). In this case, earnings volatility, dividend pay-out
ratio, dividend yield, company size, asset growth and leverage could explain the
volatility in the share price in the pooled regression model.
The pooled regression model results are presented in Table 4.4.
57
Table 4.4: Pooled ordinary least squares model (POLS)
Source: Eviews output, August 2014
The pooled regression model results in Table 4.4 demonstrate that the only
significant variable explaining share price volatility is asset growth. As a result, only
the asset growth coefficient is explained. This coefficient demonstrates that there is a
significant negative relationship between the share price volatility and asset growth.
The relationship demonstrated by the coefficient was in line with the weak negative
correlation of -0.254 that was found between asset growth and share price volatility
in the correlation matrix (in Table 4.2).
This significant coefficient was interpreted as follows: for a unit increase in asset
growth in a company there was a decrease in share price volatility of ZAR 0.003, if
all other variables were constant.
In the pooled regression model (Table 4.4) only one variable, namely asset growth
out of all the variables in the model was significant. This significant variable was the
only good predictor of share price volatility, while the rest of the insignificant
variables were poor predictors of share price volatility.
Unlike a pooled regression model, the fixed effects model assumes that the slope of
the coefficients is constant but allows the intercept to vary across the individual
companies in the sample (Woodridge, 2012:496). This is a more realistic assumption
for the different companies in the sample of this study. As a result, the fixed effect
model was investigated to determine whether it’s a better estimator than the pooled
regression model presented above.
Variable Coefficient Std. Error t-Statistic Prob.
Constant 0.256382 0.032748 7.829013 0
Dividend pay-out ratio-0.015427 0.017737 -0.869741 0.3856
Dividend yield 0.00016 0.001507 0.105866 0.9158
Leverage -0.030172 0.045282 -0.666319 0.506
Company size -2.39E-11 2.95E-11 -0.810932 0.4185
Earnings volatility 0.020077 0.092804 0.216335 0.829
Asset Growth -0.002808 0.000734 -3.82597 0.0002*
*,**,*** indicate statistically significant coefficients at 1%,5% and 10% respectively
58
4.5.1.2 Fixed effect model
The results of fixed effect estimations is presented in Table 4.5.
Table 4.5: Fixed effect model
Source: Eviews output, August 2014
The outcome of the fixed effect estimations in Table 4.5 demonstrates that all the
variables were significant, except earnings volatility and company size, which were
significant at 89% confidence level. In order to select a model for this study the two
insignificant variables, earnings volatility and company size, were excluded next.
Table 4.6 presents the model, excluding earnings volatility.
Table 4.6: Fixed effect model excluding earnings volatility
Source: Eviews output, August 2014
The outcome of the fixed effect estimations in Table 4.6 demonstrates that all the
variables were significant, except company size, which was significant at 84%
Variable Coefficient Std. Error t-Statistic Prob.
Constant 0.067013 0.121868 0.549877 0.5832
Dividend pay-out ratio-0.074377 0.036695 -2.026906 0.0445**
Dividend yield 0.005073 0.002675 1.896208 0.0599***
Leverage 0.589559 0.204622 2.881211 0.0046*
Company size -2.85E-10 1.76E-10 -1.620743 0.1072
Earnings volatility -0.298361 0.259326 -1.150528 0.2518
Asset Growth -0.00275 0.000864 -3.182506 0.0018*
*,**,*** indicate statistically significant coefficients at 1%,5% and 10% respectively
Variable Coefficient Std. Error t-Statistic Prob.
Constant -0.005 0.105 -0.048 0.962
Dividend pay-out ratio -0.068 0.036 -1.880 0.0621***
Dividend yield 0.005 0.003 1.758 0.0809***
Leverage 0.643 0.200 3.220 0.0016**
Asset Growth -0.003 0.001 -3.454 0.0007*
Company size 0.000 0.000 -1.417 0.159
*,**,*** indicate statistically significant coefficients at 1%,5% and 10% respectively
59
confidence level. In this regard, company size was also excluded from the model
next. Table 4.7 presents the model, excluding all the insignificant variables, namely
earnings volatility and company size.
Table 4.7: Fixed effect model excluding earnings volatility and company size
Source: Eviews output, August 2014
All the variables in the fixed effects model (Table 4.7) were significant. Therefore,
this fixed effects model was better than the fixed effects models including the
insignificant variables (Tables 4.5 and 4.6). The appropriateness of the fixed effect
model should be tested using the fixed effect redundancy test (Brooks, 2014:506).
This test compares the fixed effect model and the pooled regression model in order
to establish if the fixed effects are significant in comparison to the pooled regression
model. It indicates whether to incorporate heterogeneity in the model (Brooks,
2014:507). In this regard, the following the redundancy test of this study is presented
in the next section.
4.5.1.3 Redundancy test for the fixed effects
In order to determine if the pooled regression model was a more appropriate model
for this study than a fixed effect model, the redundant fixed effects test was done.
The results are presented in Table 4.8.
Variable Coefficient Std. Error t-Statistic Prob.
Constant -0.050 0.100 -0.499 0.619
Dividend pay-out ratio -0.072 0.036 -1.984 0.0492**
Dividend yield 0.005 0.003 1.810 0.0723***
Leverage 0.614 0.199 3.081 0.0025*
Asset Growth -0.003 0.001 -4.366 0*
*,**,*** indicate statistically significant coefficients at 1%,5% and 10% respectively
60
Table 4.8: Redundant fixed effects test
Source: Eviews output, August 2014
The redundant test tests whether the fixed effects are redundant, i.e. whether the
intercepts of the companies are the same (Hayter, 2012:661). The results of this test
demonstrate that cross-section F and Chi-square were both significant at 99%
confidence level. This outcome indicates that according to the test statistic and p
values, the null hypothesis could be rejected at a 1% level of significance.
Therefore, the intercepts of the companies were not the same, which is true because
the companies in the sample for this study were different, i.e. they had different
specific effects because they belong to different industries. This therefore confirmed
heterogeneity among the cross-sections.
In this regard, the pooled regression model could be rejected for this study. The
relevance of the random effects model to this study also had to be determined. As a
result, the relevance of the random effects model to this study was determined with
the Hausman test next.
4.5.1.4 Hausman test
The Hausman test was performed in order to test the null hypothesis that the random
effects model is preferred due to higher efficiency, i.e. it is not correlated with the
error (Culyer, 2014: 429). This null hypothesis could be accepted if the P-value of the
Chi square statistic of the Hausman test was greater than 0.05. Therefore it would
be safe to use the random effects model instead of the fixed effects model (Brooks,
2014:509)
However, this null hypothesis is rejected if the P-value of the Hausman is less than
0.05. This indicates that the random effects is correlated with the error, i.e. it is not
Test cross-section fixed effects
Effects Test Statistic d.f. Prob.
Cross-section F 1.754 -39145.000 0.0092*
Cross-section Chi-square 73.823 39.000 0.0006*
* , **, ***,**** indicate statistically significant coefficients at 1% , 5%, 10% level of confidence respectively.
61
efficient. In this case, the fixed effects model was more efficient than the random
effects model and would be used instead of the random effects model (Brooks,
2014:509).The Hausman test results of this study is presented in Table 4.9.
Table 4.9: Hausman test
Source: Eviews output, August 2014
The Hausman test results of this study (Table 4.9) demonstrated that the P-value
was significant (99% confidence level), which implies that the null hypothesis should
be rejected. This indicates that the fixed effects model was the most appropriate for
this study.
The redundant test indicated that the fixed effects model was suitable for this study,
while the Hausman test also indicated that the fixed effects model was suitable for
this study. As a result the core model selected for this study was the fixed effects
model, which contained only significant variables.
4.5.2 Detailed analysis of the core model
Based on the tests in the previous sections, the fixed effects model with only
significant variables was selected as the core model for this study. The variable
analysis with this model is presented in Table 4.10.
Test cross-section random effects
Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross-section random 22.511 6.000 0.001*
* , **, ***,**** indicate statistically significant coefficients at 1% , 5%, 10% level of confidence respectively.
62
Table 4.10: Core model selected – the fixed effects model
Source: Eviews output, August 2014
The fixed effects model results (in Table 4.10) demonstrate four significant variables
explaining share price volatility, namely dividend yield, pay out-ratio, asset growth
and leverage. These significant variables are the good predictors of share price
volatility and is explained in the following section.
4.5.2.1 Asset growth
A positive relationship is expected between asset growth and share price volatility
(Harris & Mongiello, 2012:388; Martin & Tyson, 2009:113 -114). According to
financial theory a positive relationship between asset growth and share price
volatility is expected. Findings in literature are conflicting regarding this relationship,
although it leans more toward a positive relationship. In this study, a negative
significant influence was evident, in line with findings from Profilet and Bacon
(2013:229) which was based on an emerging market.
The reason for a negative relationship, especially in this study, is the fact that the
JSE sample (FTSE Top 40 Index companies) consisted of growing companies,
which had high investments in assets. The higher a company’s investments in
assets, the less earnings it has left to distribute dividends, which negatively affects
the share price volatility (Morris & Morris, 2007:113).
Variable Coefficient Std. Error t-Statistic Prob.
Constant -0.050 0.100 -0.499 0.619
Dividend pay-out ratio -0.072 0.036 -1.984 0.0492**
Dividend yield 0.005 0.003 1.810 0.0723***
Leverage 0.614 0.199 3.081 0.0025*
Asset Growth -0.003 0.001 -4.366 0*
*,**,*** indicate statistically significant coefficients at 1%,5% and 10% respectively
63
4.5.2.2 Leverage
According to financial theory a positive relationship is expected between leverage
and share price volatility (Harris & Mongiello, 2012:387). A positive relationship was
also found in this study. These findings are correspond with findings from Ilaboya
and Aggreh (2013:116) and Rashid and Rahman (2008:6); studies that are also
based on emerging markets like this study.
The existence of this positive relationship is because the JSE Ltd. consists of a
significant number of growing companies, which tend to have high leverage. The
higher the leverage a company has, the more volatile is its share price (Morris &
Morris, 2007:113).
4.5.2.3 Dividend yield
The findings in most literature regarding the relationship between dividend yield and
share price volatility differ from the findings of this study. Most studies find a negative
relationship in line with financial theory where a negative relationship is expected
between dividend yield and share price volatility (Morris & Morris, 2007:113).
A positive relationship was determined between dividend yield and share price
volatility, corresponding with findings from Nazir et al. (2012:137), a study based on
an emerging market like this study. This positive relationship exists, especially in this
study because of the signalling of higher future earnings of companies on the JSE
Ltd. This sensitivity leads to an increase in share price volatility when there is an
increase in distribution of dividends, hence the positive relationship (Fracassi,
2008:1).
4.5.2.4 Dividend pay-out ratio
In financial theory, a negative relationship is expected between dividend pay-out
ratio and share price volatility (Morris & Morris, 2007:113). This study also found a
negative significant relationship between dividend pay-out ratio and share price
volatility, corresponding with findings from Kenyoru, Kundu and Kibiwott (2013:120)
64
and Nishat and Irfan (2001:16); studies that were also based on emerging markets
like this study. This negative relationship was found in this study because
companies on emerging markets that offer large dividends tend to be considered
less risky, because they tend to have larger earnings, hence the inverse relationship
(Morris & Morris, 2007:113).
The primary research question of the study what the relationship between dividend
policy measures and share price volatility on the JSE Ltd was. This research
question was answered with the core model discussed and analysed in Table 4.9.
This core model indicates that the dividend policy measures, namely dividend pay-
out ratio and dividend yield have a significant relationship with share price volatility.
The relationship between dividend pay-out ratio and share price volatility was as
expected and significant, while the relationship between dividend yield and share
price volatility was unexpected yet significant. Due to the significant results and the
fact that the JSE Ltd. is an emerging market, it was concluded that dividend policy
negatively affected share price volatility on the JSE Ltd. This is confirmed by Allen
and Rachim (1996:183) and Nazir et al. (2012:137) who produced similar results and
came to a similar conclusion.
The discussion on the main variables in the core model that influence share price
volatility and the response to the research question is followed by a summary and
conclusion of the chapter.
4.6 Summary and conclusion
The relationship between dividend policy and share price volatility was analysed for
this chapter. The variables used in this study to determine the impact of dividend
policy on share price volatility was the dependent variable, share price volatility
(SPV); the independent variables for dividend policy, dividend yield (DY) and
dividend pay-out ratio (DPR); and the control variables, asset growth (AG), earnings
volatility (EV), company size (FS) and leverage (L).
Panel data models were used to determine the nature of the relationship between
the variables, with the FTSE Top 40 Index companies as a sample. Using the core
65
model evidence of a negative relationship between share price volatility and the
dividend policy could be found. The model demonstrated that dividend policy has a
negative effect on share price volatility, which was in line with expectations and most
similar studies.
The negative impact of dividend policy on share price volatility suggests that
companies with large pay-out ratios have less volatile share prices and can be
regarded as less risky.
66
Chapter 5
Findings, conclusions and
recommendations
5.1 Introduction
In the previous chapter the relationship between dividend policy and share price
volatility was investigated. The reasons for undertaking the study, a summary of the
study findings, conclusions and recommendations are presented in Chapter 5.
The wealth of shareholders depends on dividend policy (dividend decision),
investment decisions and financing decisions (Bhat, 2009:534). These three
decisions are interrelated and contribute to the maximisation of shareholders’ wealth.
This study focussed specifically on the impact of the dividend policy; the dividend
decision on share price volatility, which in turn affects shareholders’ wealth.
The reasons for undertaking of this study are reiterated below.
5.2 Reason for undertaking the research
One of the reasons this study was undertaken, was because the influence of
dividend policy on share price volatility were found to be inconsistent and varied
amongst different countries (Asghar et al., 2011:50). These deferring results indicate
that past results from other countries could not be used as proxy for South Africa.
Another reason for this study was to determine whether dividend policy affected
share price volatility. If there was a relationship between dividend policy and share
price volatility, this relationship could be managed to decrease share price volatility
and to maximise shareholders’ wealth.
67
Lastly, this research was undertaken to present investors and managers with a basis
to manage systematic risk (share price volatility). If a relationship was found
between share price volatility and dividend policy, a dividend policy could be
adjusted to influence systematic risk to investors and managers advantage.
The following section briefly presents a summary of the research approach.
5.3 Research approach
The aim of this study was to determine the impact of dividend policy on share price
volatility in South Africa to enhance better management of share price volatility.
The research question of this study was: what is the relationship between dividend
policy measures and share price volatility on the JSE Ltd.?
The following sub-research questions were investigated:
Is there a relationship between dividend yield and share price volatility for the
FTSE Top 40 Index companies listed on the JSE Ltd.?
Is there a relationship between pay-out ratio and share price volatility for the
FTSE Top 40 Index companies listed on the JSE Ltd.?
The sample of this study consisted of the FTSE Top 40 Index companies listed on
the JSE Ltd. This sample was subjected to a panel regression analysis.
The panel regression estimation process as proposed by Brooks (2008:489-498)
was applied to perform the data analysis. It included three possible main models: the
pooled ordinary least square model, the fixed effects model and the random effects
model.
In order to establish the validity and accuracy of the best model, econometric
evaluation tests such as the fixed redundancy test and the Hausman test were
applied. The tests indicated that the fixed effects cross-sectional model was the most
suitable model compared to all the models investigated to explain the influence of
the independent variables namely dividend yield, pay-out ratio, asset growth and
leverage on share price volatility.
68
The fixed effects cross-sectional model was therefore applied to answer the research
question. The following section present a summary of the findings of the fixed effects
cross-sectional model results.
5.4 Findings and conclusions on the study sample
The application of the fixed effects cross-sectional model revealed important
relationships between the key variables representing dividend policy; dividend yield
and pay-out ratio. The results on the control variables are presented first.
Company size and earnings volatility were expected to have an effect on share price
volatility (Harris & Mongiello, 2012:389; Martin & Tyson, 2009:114). It was however
found to have no effect on share price volatility.
An unexpected negative significant influence of asset growth on share price volatility
was found. These results are in line with findings of Profilet and Bacon (2013:229) in
a study based on emerging markets, like this study. The results imply that a
company can increase its assets to reduce share price volatility (Morris & Morris,
2007:113).
A positive relationship was found between leverage and share price volatility. These
findings were in agreement with findings of studies on emerging markets by Ilaboya
and Aggreh (2013:116) and Rashid and Rahman (2008:6). The findings indicated
that the higher the leverage of a company, the higher the share price volatility tend to
be (Morris & Morris, 2007:113).
The relationship between dividend pay-out ratio and share price volatility was
negative as expected, while the relationship between dividend yield and share price
volatility was unexpectedly positive (Harris & Mongiello, 2012:389).
The significant negative relationship between dividend pay-out ratio and share price
volatility was expected and in line with findings by Kenyoru et al. (2013:120) and
Nishat and Irfan (2001:16). This relationship implied that the higher the dividend
pay-out ratio a company applies, the lower the share price volatility tends to be.
69
The positive significant relationship between dividend yield and share price volatility
was unexpected but in line with findings of Allen and Rachim (1996:183) and Nazir et
al. (2012:137).
This unexpected positive relationship between dividend yield and share price
volatility was possibly caused by the signalling of higher future earnings of growing
companies (Fracassi, 2008:1).
Similar to this study, a study on an emerging market by Nazir et al. (2012:137) also
found a negative relationship between dividend pay-out ratio and share price
volatility and a positive relationship between dividend yield and share price volatility.
It is concluded in this study that there is a relationship between dividend yield and
share price volatility as well as between dividend pay-out ratio and share price
volatility for the FTSE Top 40 Index companies listed on the JSE Ltd.
Because dividend yield and dividend pay-out ratio represent dividend policy, it is
concluded that there is a relationship between dividend policy and share price
volatility. Therefore, if managers manipulate the correct elements of their dividend
policy, it can influence their share price volatility in a desired way.
5.5 Contributions of the study
This study was undertaken because the influence of dividend policy on share price
volatility was found to be inconsistent in different countries. This research makes a
contribution to this gap in knowledge in the South African literature (Asghar et al.,
2011:50).
The results of this study also demonstrated that there is a relationship between
dividend policy and share price volatility. The particulars of this finding will enable
managers to select a dividend policy that will influence share price volatility in a
desired way.
70
5.6 Recommendations for further research
This study focused mainly on the relationship between dividend policy (represented
by the dividend policy measures) and share price volatility. Stock exchanges are
rapidly changing and therefore the determinants of share price volatility are also
changing. Future studies on the determinants of share price volatility should analyse
other variables that were not fully covered or excluded in this study.
Further studies could also include other determinants of share price volatility, such
as differences in the market, cost structures and regulatory restrictions.
5.7 Final remarks
The researcher managed to achieve her goal to determine the impact of dividend
policy on share price volatility. Based on the methodology applied, it could be
concluded that dividend policy measures influence share price volatility based on the
sample of selected FTSE Top 40 Index companies.
It is also concluded that dividend decisions can be manipulated by management to
get a more desired impact on share price volatility.
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