JERA. 28.199 EI PUBLISHED. pdf
International Journal of Engineering Research in Africa
ISSN: 1663-4144, Vol. 28, pp 199-209
doi:10.4028/www.scientific.net/JERA.28.199
© 2017 Trans Tech Publications, Switzerland
Submitted: 2016-08-20
Revised: 2016-09-28
Accepted: 2016-09-29
Online: 2017-01-12
A Corporate Social Responsibility of Engineering the Liquidity-Adjusted
Capital Asset Pricing Modelling Sub-Sahara Africa:
Evidence from Ghana
Solomon Duduchogeab*,Yao Hongxinga, Benjamin Chris Ampimahc,
Prince Harvimc
a
School of Finance and Economics, Jiangsu University, 301 Xuefu Road, Zhenjiang, P.R. China,
b
Koforidua Technical University, Koforidua, Box 891,Koforidua Ghana,
c
Faculty of Science,Jiangsu University, 301 Xuefu Road, Zhenjiang, P.R. China
[email protected],[email protected], [email protected],
[email protected]
Keywords: Corporate Social Responsibility, Liquidity Risk, Asset Pricing, Emerging Market, Sub–
Sahara Africa.
Abstract. This paper estimates a conditional version of liquidity–adjusted capital asset pricing
model in an emerging market in line with the corporate social responsibility (CSR) of the Ghana
Stocks Exchange. We find out that for several years, Ghana stock market has been excluded from
the global financial watch and from empirical verification model for lack of transparency in the
performance of Exchange. Our evaluation concludes that illiquidity risk can be measured in the
local market and exhibit a strong trend of mix reactions from liquidity premia.While the effect of
the recent financial crisis do not show much difference between the different market conditions, the
effect is more stronger in the down market than the up market. Finally, we explore the size effect on
the market and conclude that the net beta as well as the systematic liquidity risk is pronounced in
the smaller market though insignificant.
1. Introduction
Globally, issues concerning corporate social responsibility (CSR), liquidity with its associated
marketability and trading cost has become the centre of attention to investors in the field of
financial market. According to a standard definition (Paul and Siegel 2006) corporate social
responsibility (henceforth CSR) defines a set of corporate practices which improve upon social and
environmental regulatory standards of the markets in which such corporations operate. The KPMG
International Survey on CSR reporting (KPMG 2008) documents that corporate responsibility
information is released (in stand-alone reports or integrated with annual financial reports) by 80%
of the constituents of the Global Fortune 250, up from 50% in 2005.In the modern world, a CSR is
a shift from the maximization of an investor profit to a shareholders welfare. The question is
whether the change in focus is the answer to the poor performance of stocks in Sub-Sahara Africa.
As an example, the GSE recently engineered the operation of what it termed as the Ghana
alternative Market in 2015 with a focus on businesses with a high potential for growth. The aim is
to accommodate these companies at various stages of their development, including start-ups and
existing ones, both small and medium with the aim of grooming them to become bigger in the
future (GSE, 2015).
An opportunity to examine the nexus between the role of corporate social responsibility and
corporate performance is to test the Liquidity-adjusted capital asset pricing modelthrough the
performance of the Ghana stock exchange from 2006 to 2015.
It is important to stress the role play by liquidity when investing with the aim of maximising returns
since it determines the marketability or otherwise of a stock. Liquidity is seen as one of the pivots
on which investment stand as it is of a bigger concern to both local and international investors in
emerging markets because of its cross-sectional and temporal variations (Bekaert et al., 2007).
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International Journal of Engineering Research in Africa Vol. 28
Although, many of the existing literature focus on the US stock market to the detriment of most of
the developing market, there is a an indication in recent times of many researchers focusing on
other developed and emerging markets(e.g. Karolyi et al., 2012) .The total value of African stocks
outside South Africa is only 0.6 per cent of all emerging-market stocks.
Sub-Saharan Africa (SSA) markets are the only ones that have not attracted the needed study,
though not surprising since these markets were established not long ago. It is dominated with
volatile but substantial returns which is crowded with different degrees of liquidity cost.(Wheeler,
1984; Mosley et al., 1995) document that Africa’s past problems were largely a function of
structural and international factors and, as such, they are likely to continue.
In recent years however, many of the African countries have seen the establishment of financial
stock markets as one of the surest way of galvanising the needed resources to embark on the
journey of economic emancipation. However (Hearn & Piesse, 2009) document that Sub-Sahara
Africa is a region made up of smaller, unregulated and a lack of proper governance system. These
shortcomings and other factors make it impossible for the markets to make it to the regional equity
market indices and is therefore excluded from the Global Emerging Market (GEM) portfolio funds
(Hearn&Piesse, 2009). However, with only 11 stock markets operating in Sub-Sahara Africa by the
end of 1997, the numbers have increased to more than 20 including one of the only sub regional
stock exchanges in the world, linking eight French-speaking West Africa countries(Sally,
2013).Ghana’s stock market which was established in 1989 and started operation in 1990 was
adjudged as the world’s best performing market at the end of 2004 with a year return of 144% in
US dollar terms compared with a 30% return by Morgan Stanley Capital International Global Index
(Databank Group, 2004).Even though the market is a G30 compliant, trade and prices are often
agreed informally and the market institutions are merely being used to announce pre-agreed
details(Akotey,2008).As stated by Bruce Hearn(2013),stock price, volatility, traded
volume and size (market capitalization) are all negatively associated with illiquidity in Ghana and
other 11 west African countries with the exception of Cape Verde where size has a positive
association with Lesmond zero (0.173) and Liu (0.520) measures.
Our paper contribute to the body of literature by looking at the effect of systematic liquidity risk
on the Ghanaian market using the liquidity–adjusted capital asset model (LCAPM) propended by
Acharya and Petersen (2005). To the best of our knowledge, our paper is the first to empirically
carry out this task in the entire West Africa sub-region. Second, the study intends to verify the
extent to which the price factor influences a smaller capitalist market like Ghana. Third, this paper
examines the illiquidity risk factors and how they affect stock returns in Ghana.
The rest of this paper is organized as follows; in section 2, we present various hypothetical
statements. Section 3 looks at the methodology and the research design. Section 4 discusses data
and report summary statistics for the market and section 5 serves as the conclusion of the study.
2. Hypothesis
It is imperative to say that liquidity risk is a factor which influences investors due to its
multifaceted effect on stock returns. The Ghanaian market though emerging has not been tested as
compare to the US, Europe and other parts of Asia due to its size and unattractiveness. We intend to
break this barrier by empirically investigating the relations between liquidity and asset pricing in
Ghana. The US market is a quoted- driven one which is different from the simple continuous
auction system in Ghana where bid and ask orders are written manually on a series of boards (Hearn
and Piesse, 2011).The difference between the developed approach in the US and the makeshift
approach in Ghana means that the impact of liquidity risk in both countries will not be the same.
However, whether this assertion is the case will have to be subject to empirical assessment. We
therefore assume the following hypothesis for the study:
1. Liquiditystocks both at the firm and market levels are positively related to stock returns.
2. The relation between firms (individual) level and market level liquidity stock is negatively
related to stock returns.
International Journal of Engineering Research in Africa Vol. 28
201
3. The relation between market liquidity and Individual stock returns is negatively related to
stock returns.
The three hypotheses stated above mainly relates to the liquidity risk in the LCAPM model of
Acharya and Pedersen which is an addition to the traditional CAPM model. This theoretical model
was empirically verified by Pastor and Stambaugh (2003).
We intend to know the combine systematic effect of the individual liquidity risk on a marketwide basis in the Ghanaian market. To this end, our stated next hypothesis is that
4. The combined liquidity risk is priced in Ghana.
It has been posited that Sub-Saharan African market is small and risky. To this end, our study
intends to look into this using the Ghanaian market. This brings us to our next hypothesis which
states that;
5. It is risky to invest in smaller market than in bigger market.
3.Research Design
3.1. Measuring Liquidity
Bruce Hearn (2013) in studying the West African terrain including Ghana adopted three liquidity
measures to wit, the bid-ask spread of Jones (2002), zero daily return measure of Lesmond (1999)
and Liu (2006) illiquidity measure. For the purpose of this study, we employ the Amihud (2002)
illiquidity ratio as the basis for our measurement. This is in line with the price impact of Kyle
(1985).
The ratio is described as:
,
=
,
∑
,
(1)
, ,
, ,
Where , , denotes absolute stock return of i on day d and month t. , , is the volume of trading
for stock i on day d and of month t, and , is the sum of trading days for stock i and month t. The
Amihud illiquidity measurement is premise on everyday trading on the stock market and it is
measured on data from daily trading activities of returns on volume ratio. It is anticipated that a
higher ratio of the Amihud illiquidity measure is assumed to be preceded by a lower liquidity. This
means that investors will prefer to be compensated (normally called risk premium) for holding such
securities in period of insecurity.
This is a summary of the sample population data that was gathered during the period from the GSE
database showing the mean, median and the sum for the period under review.
year
N
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
21
21
21
24
25
30
29
32
35
35
Table 1 Sample Population
Mean
Median
0.45
0.47
0.56
0.66
0.76
0.69
0.73
0.72
0.75
0.85
0.04
0.04
0.03
0.03
0.05
0.03
0.03
0.04
0.05
0.05
Sum
333.51
384.19
460.34
530.01
644.05
640.17
705.92
662.64
757.54
948.35
3.2. Conditional LCAPM Model
We selected the Liquidity–adjusted capital asset model (LCAPM) of Acharya and Pedersen (2005)
as the foundation of our model for this study. One fundamental difference between the traditional
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International Journal of Engineering Research in Africa Vol. 28
CAPM and the LCAPM is the
introduction of liquidity cost such as cost of a round trip,
administrative cost etc. in the LCAPM as against a cost free CAPM. According to Acharya and
Pedersen (2005),the standard CAPM hold for expected net returns(that is net of the relative
illiquidity cost): (
−
).As a result, the conditional version of LCAPM is displayed at time
t as follows:
)=
(
+
+
,
(
+
(
(
,
)
+
(
(
,
)
+
(
(
(
,
)
(2)
Where
ℎ
Where is the gross return for stock i at month t, denotes gross risk-free rate, and represents
the trading cost for stock i at month t.
It is worth noting that, without the introduction of a cost element in the CAPM, equation 2 will be
akin to the CAPM. By assuming conditional covariance, variance and equal risk premium across the
different risk factors, an equivalent formulation of 3 is given
−
=
+
+
−
−
(3)
Where the ’s in equation 3 denotes
=
=
β3i=
β4i=
(
(
(
(
,
(
( ,
(
(
,
(4)
,
)
)
(5)
(6)
)
(7)
is the market return at month t,
Where is the return of stocks i at month t,
is the market aggregate liquidity cost at month t.
cost for stock i at month t, and
The net liquidity risk is given as
=
−
−
And the LCAPM net liquidity risk becomes:
E( − )=
+
+
And lastly, aggregate systematic risk is
=
+
−
−
And the LCAPM becomes:
E( − )=
+
+
The Amihud Illiquidity ratio then becomes
=
+
+
We transform the Amihud illiquidity ratio where
number of lags included in the equation.
−
= +
+
+
=
−
-
=
-
=
−
=
-
=
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
is the liquidity
(8)
(9)
(10)
(11)
(12)
+…+
is the measure of liquidity for stock , is the
+
+
+
+
+
+
+
+
+
+
+
+
(13)
+
+
+
+
(14)
(15)
(16)
(17)
(18)
International Journal of Engineering Research in Africa Vol. 28
−
=
+
+
+
+
+
+
+
203
(19)
+
Where
indicates individual stock excess returns at month t+1
are the liquidity
betas that are specified in equations (4) to (19), HLMt represents the High minus Low at month t,
Sizetdenotes market capitalization at month t and
is the cumulative returns over the past 12
months with a one lag.
Acharya and Pedersen, (2005); Lee, ( 2011) stated that equations (13) to (16) make it possible to
determine what influence each individual liquidity risk, and moderate the multi-collinearity
concerns for the betas. Equations (17) and (18) determine the aggregate liquidity risk effect and the
aggregate systematic risks. Lastly, equation (19) investigates the joint effects of the liquidity betas.
3.3.Measuring Liquidity-Adjusted Capital Asset Pricing Model
In the light of Lee (2011), we construct portfolios for the LCAPM basically on the Ghanaian
stock market with respect to market returns and illiquidity using panel regression. The intention for
using the panel regression over others such as the cross-sectional regression is to avoid statistical
bias that are known to be associated with it since it only accounts for correlation without accounting
for serial correlation.
For Table 2, the illiquidity betas are measured based on equation 4 to 7 for ten portfolios which
is basically in line with the Amihud (2002) illiquidity ratio by using the individual stocks and their
respective market returns. In the construction of the illiquidity betas, we sort stocks into 10 equal
parts and create 10 equally-weighted portfolios (deciles).Specifically, at the beginning of every
year, illiquidity betas are calculated using the individual liquidity stocks as well as their respective
market returns and create 10 equally weighted portfolios. The resultant outcome is the averages of
these betas for each portfolio over the ten year period.
Table 2 Summary of illiquidity portfolio betas
Returns
illiquidity betas
Lowest
0.001
2.2795
-0.0398
-0.0184
2.3376
2.3386
-0.14
1
0.001
2.2804
-0.0314
-0.0165
2.3283
2.3294
-0.74
2
0.0017
2.111
-0.01
-0.0002
2.1212
2.1229
-0.06
3
0.0019
2.2166
-0.0529
-0.0325
2.3021
2.304
-0.07
4
0.0019
2.1045
-0.011
-0.0007
2.1161
2.118
0.10
5
0.0022
2.1042
-0.0129
-0.0008
2.1179
2.1201
0.07
6
0.0036
2.1032
-0.0192
-0.0016
2.1239
2.1275
-0.05
7
0.0039
2.2548
-0.0093
-0.0015
2.2656
2.2694
0.89
8
0.0046
2.1001
-0.0168
-0.0015
2.1183
2.1229
-0.08
Highest
0.0191
0.1919
-0.0117
-0.0517
0.2553
0.2744
0.89
4. Empirical Results
4.1. Analytical Results
In our analysis, we present table 3 and discuss the dynamics of the Ghanaian stock market. We run
alternative ways of measuring the liquidity–adjusted capital asset pricing model from equation 4 to
7 to ascertain the true state of liquidity betas in respect to expected returns. In the light of this, we
first discuss the individual liquidity betas in our regression analysis and find out the outcome. Our
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International Journal of Engineering Research in Africa Vol. 28
findings indicate that which shows the co-movement between the individual liquidity and market
returns is highly insignificance in the presence of all the control variables such as the traditional ,
firm sizes, HML and momentum. This means that Hypothesis 1 which indicates that liquidity at the
level of the firm and the market are positively related to returns, is insignificant in the Ghanaian
situation as investors cannot rely on to predict the future outcome of events.
From table 3, we observe that
is significant at the 1% level but with the wrong sign and thus
rejecting Hypothesis 2 and rejecting the assertion by Acharya and Pedersen(2005) who documents
that stocks whose returns are sensitive to market illiquidity are riskier and that investors require a
higher return as a compensation for holding such assets. We belief that this assertion is not
supported in the case of Ghana due to information asymmetry and the fact that most investors do
not have access to the right information in the market. The result points to a phenomenon in which a
decrease in liquidity level does not deter prospective investors from investing due to the lack of
information concerning the true nature of the market. Table 3 also show the regress
to be
positive with 1% significant level. In the Ghanaian case, this again does not give support to the
highly regarded Acharya and Pedersen (2005) assertion that investors are willing to accept lower
return during period of meltdown. The results reject Hypothesis 3 which indicates that the relation
between market liquidity and individual stock returns is negative. Having dealt with the
implications of our analysis on the individual liquidity betas, we now turn our attention on the net
as well as the aggregated liquidity betas. Both net liquidity and the systematic liquidity are all
positively related to stock returns and more importantly, the systematic liquidity risk is priced
though insignificant. In the final analysis, we can conclude that the Ghanaian market is full of
variations and inconsistent with the literature even though systematic liquidity risk is insignificantly
priced.
Table 3 The Panel regression fixed effect
Variables
Expected (1)
sign
+
0.0540 *
(158.346)
+
(2)
(3)
(4)
(5)
0.3461
(451.315)
0.0067
***
(147.117)
0.0002
***
(318.476)
0.5927
(259.380)
0.5276
(2.294)
_
_
0.0053
***
(60.952)
+/-
0.0009
***
(59.121)
+/Constant
0.7950
(10.601)
0.8046
(10.845)
0.1767
(22.265)
SIZE
-
0.9717
(0.00004)
0.6435
(−0.0007)
0.3558
(−0.0002)
MOM
+
0.1824
(3.142)
0.2152
(3.64)
HML
+
0.8235
(−6.739)
0.7286
(−12.190)
0.0089
***
(4.207)
0.2173
(−14.905)
0.0473
**
(22.529)
0.0339
**
(−0.0004)
0.0014
***
(4.546)
0.0530 *
(−16.285)
(6)
(7)
0.9718
(315.337)
0.9983
(0.957)
0.9920
(40.042)
NA
(18.800)
0.8321
(0.803)
0.848
(9.012)
0.0695 *
(−1.218)
0.7284
(14.967)
0.4454
(22.585)
0.8974
(−.0002)
0.6539
(0.0005)
0.9948
(−0.0005)
0.2525
(3.226)
0.2020
(3.061)
0.9848
(4.947)
0.8349
(−7.412)
0.8243
(−7.090)
0.9961
(−17.607)
4.2. The Size Effect
Very little is documented on stocks in emerging market such as that of Ghana in relation to size
and it effect on market returns. Most of the firms in Ghana are made of smaller sizes and most
International Journal of Engineering Research in Africa Vol. 28
205
studies documented so far have proved that expected stock returns is negatively related to size
(Fama and French,1992,Banz,1981,Chordia,2000).Limkriangkrai et al (2008) mentioned liquidity
as being priced only in the smaller markets. He indicated that for smaller markets, the effect of
liquidity is submerged in the size effect due to the higher cost of operation. However, others also
defer in opinions on this issues. Fabre and Frino (2004) find that commonality in liquidity is mainly
a large firm phenomenon. Having this in mind, we set out to find out the effect of size on liquidity
in Ghana which is a lower middle income country. To do this, we sorted our data into three different
groups with a ratio 30: 40:30 base on their market capitalization concurrently for each month.
The results from the analyses indicate that the market in Ghana is significant with respect to
small size firms as oppose to the large as well as medium size groups as seen from table 4. From
table 4, it is observe that the net liquidity
and the market-wide liquidity
are all carrying
positive signs for the small size firms but with varying significant levels. In the case of the small
firms, the net liquidity is highly significant with a 1% level of significance with the systematic
liquidity β6 though significant but at the 10% level. However, in the case of the medium as well as
the large size firms, only systematic liquidity carries a 10% level of significance. Regressing for
smaller size firms produce some level of significance. It is also observe that and which are both
positive and significant at 1% level in table 3 has it significance level varying to 10% for both
medium and small firms in table 4. The conclusion that can be drawn from the net liquidity β5 and
the aggregated liquidity β6 is that both remain significant in the smaller size group though the
impact is minimal for the systematic liquidity β6. In effect, we can say that illiquidity risk is priced
in the smaller firms in Ghana and confirm Hypothesis 4. The unit cost of production in smaller
firms in Ghana carry some form of risk and investors need some form of compensation in order to
invest their portfolios in such a risky market.
4.3. Illiquidity Effect during Different Market Situations
Asset pricing plays an important role during different market situations and may not exhibit
either same or similar tendencies. Research documented so far indicates that stock returns behave
differently during up and down market situations (Chiang and Zheng, 2010).The fact remains that,
during down markets, price factors command more returns premiums as illiquidity is incorporated
into asset pricing model(Brennan et al.,2011)
As a result, we decided to test the illiquidity risk with respect to returns in Ghana during
different market scenario and see whether the resultant outcome will show some form of
asymmetric effect during the period of up and down market situations. To do this, we grade the
upward period from the year 2006 to 2008 and the meltdown period which span from 2009 to 2015.
The outcome in the Ghanaian market is reported in table 5. In the face of the world financial
crisis in March, 2008, many Sub-Saharan African countries suffered substantial financial
damagesduring this period especially those whose economic investment is tied to foreign investors.
We decided to dual on the net
and
for comparison purposes. The observation from
and
is very interesting. Indications are the coefficients for the Ghanaian market are highly
insignificant in both down and up markets. Even though the market does not produce any
significance, during the down market period, both beta and
gives coefficient values of 0.8109
and 0.8044 respectively as oppose to the up market values of 0.5243 and 0.5128indicating that
excess stock returns is positively correlated with market illiquidity though insignificant to command
a change in the market situation. We can see from the results that during the up market period the
performance of the market illiquidity risk in respect of and β6. We can say that since both betas
and are positive in the down market and stronger than the up market, it effect on the market
may be more pronounce than the up market.This is an indication that the effect of illiquidity risk is
almost twice stronger in the down market than in the up market and that systematic illiquidity risk is
much felt during the down market than in the up situation.
206
Variables
International Journal of Engineering Research in Africa Vol. 28
Table 4 Panel regression results for different size groups Large Stocks
Expected
sign
+
+
1
2
3
4
5
0.0558 *
(−66.545)
0.1421
(−73.831)
0.7825
(00.401)
0.1613
(−74.559)
0.3899
(−75.121)
0.1630
(−74.714)
_
_
0.7963
(−2.502)
+/-
0.9095
(−1.409)
+/Constant
SIZE
-
MOM
+
HML
+
0.9235
(−3.345)
0.5353
(0.001)
0.1939
(−3.028)
0.7817
(7.568)
Medium Stock
Variables
Expected
sign
+
+
0.9180
(−4.167)
0.5601
(0.002)
0.2594
(−3.3)
0.7896
(8.484)
0.9114
(−4.528)
0.5618
(0.002)
0.2665
(−3.32)
0.7831
(8.823)
0.9089
(−4.926)
0.5960
(0.002)
0.2678
(−3.061)
0.7958
(8.719)
0.7959
(0.298)
0.9133
(−4.430)
0.5646
(0.002)
0.2612
(−3.268)
0.7871
(8.638)
3
4
5
0.1848
(−144.758)
0.2452
(−67.436)
0.0763 *
(−10.908)
0.2221
(−74.401)
0.8653
(−7.872)
0.1842
(−85.300)
_
0.2819
(1.897)
+/-
-
MOM
+
HML
+
Small Stock
Variables
Expected
sign
+
+
_
_
0.0410 **
(−492.305)
0.0572 *
(−848.910)
0.0535 *
(−1020.20)
0.7350
(−388.133)
0.0310 **
(−0.028)
0.2744
(2.652)
0.0409 **
(374.169)
0.0899 *
(−0.036)
0.2594
(−1.833)
0.0564 *
(642.985)
0.0938 *
(−0.038)
0.2609
(−1.955)
0.0528 *
(772.785)
0.1372
(−0.013)
0.1070
(3.114)
0.7343
(294.877)
2
3
4
5
0.0183 **
(268.586)
3.00e-05
***
(1026.36)
5.66e-05
***
(7.566)
0.0107 **
(315.464)
0.0880 *
(212.245)
0.0001
***
(913.606)
0.3093
2.93e-06 ***
(140.620)
3.20e-06 ***
(0.001)
2.33e-06 ***
(15.916)
3.08e-06 ***
(−100.158)
7
0.0002 ***
(−115.670)
0.0002 ***
(37.831)
0.0002 ***
(196.368)
0.0002 ***
(2.427)
0.0967 *
(−6.632)
0.0772 *
(−681.22
8)
0.1019
(−0.028)
0.5920
(−0.949)
0.0763 *
(516.300)
0.0694 *
(−846.07
1)
0.1034
(−0.037)
0.3781
(−1.428)
0.0685 *
(641.184)
1
0.0835 *
(−43.476)
6
0.1020
(−5.894)
+/-
SIZE
0.3246
(−1.135)
0.8706
(8.908)
0.8570
(−0.0001)
0.9273
(0.192)
0.9053
(−5.061)
2
0.0892 *
(59.975)
7
2.58e-06 ***
(559.183)
1.91e-06 ***
(133.708)
1.86e-06 ***
(858.910)
4.42e-06 ***
(42.355)
1
_
Constant
6
6
0.0002 ***
(−3341.94)
0.0001 ***
(−0.019)
0.0002 ***
(2.553)
0.0002 ***
(2533.38)
7
3.43e-05
***
(4650.78)
3.62e-05
***
(58.104)
3.67e-05
***
(623.776)
3.39e-05
International Journal of Engineering Research in Africa Vol. 28
207
(−23.9702)
+/-
0.0003
***
(6.579)
+/Constant
SIZE
-
MOM
+
HML
+
***
(−236.753)
0.0068
***
(−389.223)
0.0082
***
(−0.359)
0.0227 **
(3.333)
0.0067
***
(293.275)
4.33e-06
***
(−429.212)
5.73e-06
***
(−0.395)
0.0001
***
(2.805)
4.46e-06
***
(318.602)
0.0038
***
(−515.691)
0.0047
***
(−0.487)
0.0163 **
(3.583)
0.0243 **
(−479.533)
0.0037
***
(387.329)
0.0240 **
(360.742)
0.0242 **
(−0.437)
0.0271 **
(3.577)
1.69e-05
***
(−473.232)
2.16e-05
***
(−0.438)
0.0005
***
(2.817)
1.69e-05
***
(352.017)
0.0779 *
(−2.111)
0.0274 **
(−378.399)
0.0303 **
(−0.354)
0.0630
(3.305)
*
0.0265 **
(286.575)
7.19e-05
***
(−177.919)
0.0005 ***
(0.036)
6.39e-05
***
(−4.932)
9.67e-05
***
(107.470)
Table 5 Liquidity risk in up and down market
Variables
Expected
Sign
+
+/+/-
Constant
SIZE
-
MOM
+
HML
+
Down Market
1
2
Up Market
3
0.4102
(720.473)
0.8109
(−182.701)
0.8044
(189.262)
0.0010 ***
(−470.926)
0.0009 ***
(−0.436)
0.0064 ***
(2.787)
0.0010 ***
(350.296)
0.7055
(−24.123)
0.5243
(51.622)
0.5128
(−051.975)
0.5935
(−869.895)
0.1632
(−0.018)
0.2985
(25.073)
0.5929
(660.094)
0.0211 **
(−425.639)
0.0215 **
(−0.417)
0.1648
(3.073)
0.0208 **
(320.774)
4
0.0023 ***
(−208.576)
0.0032 ***
(−0.012)
0.0454 **
(3.46)
0.0022 ***
(158.644)
4.4. Alternative Proxy of Liquidity
Several researchers often use different measures of liquidity to investigate the relations between
liquidity and excess returns. This indicate that liquidity cannot be explain from one proxy.Brennan
and Subrahmanyam(1996) use transaction cost as a measure of liquidity with Datar and
Radcliffe(1998) using trading volume turnover as a proxy for the measurement of liquidity. The
Amihud illiquidity ratio relies on the assumption that the percentage of the non-trading days is
relatively low. However, when studying the West African terrain, Hearn and Piesse (2011)
document that the greatest degree of illiquidity in the region can be seen in the BRVM and Ghana
with Ghana having a percentage of daily zero returns of 77% for the entire market. Due to this, we
use the Lesmond et al (1999) zero- return measurement as our proxy for this study. The zero return
ratio explain and addresses the inherent concern of the Amihud ratio since it is able to capture the
zero trading days in the Ghana situation.
Table 6 present the estimation of theregression on the zero return proxy. For the purpose of
conservation of space, we sought to report only the net liquidity
and the systematic
liquidity .The overall outcome from the analysis is that net liquidity and the systematic
liquidity are positively correlated with the market liquidity riskat the 10% level of significance
.the magnitude of this results is however insignificant in the Ghanaian case as compare with other
results obtain in some of the existing literature. This go to confirm the multi-faceted nature of
liquidity which indicates that liquidity cannot be studied from one dimensional way.
208
International Journal of Engineering Research in Africa Vol. 28
Variables
Table 6 Panel regression results for zero returns
Expected Sign
Zero Returns
1
+
0.47
(1.78)
+/-
0.15*
(-0.32)
+/Constant
Size
-
HLM
+
MOM
+
2
0.08**
(0.99)
0.32
(1.03)
0.13*
(-0.30)
0.06**
(0.92)
0.11*
(1.40)
0.55
(0.26)
0.32
(0.14)
0.45
(0.30)
0.21
(0.17)
5.0. Conclusion
The entirety of this study examined the role play by liquidity in the pricing of asset in Ghana, an
emerging market situated in West Africa which for several years has been excluded from the global
market watch and from empirical valuation model. The Acharya and Pedersen LCAPM model is
seen as one of the few models that can be used to study many forms of liquidity risk showing the
relations between excess stock returns and illiquidity. Due to the volatility of the stock market in
Ghana and the consistent high risk nature of the market, many investors do not have confidence in
investing in the market and therefore demand a high form of compensation for the investment they
make in the economy in the smaller and fragile market. We also find out that both the net liquidity
beta and the systematic liquidity betas though positive are less stronger but have a greater influence
in the down market than in the up market.We also find out that the effect of liquidity are not the
same but keep varying across the different liquidity risk used in the analysis. The cost of equity in
the market is also seen to be very high and the result makes it clear the depth and level of adherence
to the proper regulations the entire stock market in the world and the volatile nature of the region.
Overall, this study she some light and support for analysis.
Acknowledgement
The authors are very grateful for the financial support from the National Science Foundation of
China with grant no. 71271103, 71371087. This work would not have been possible without their
support. We are also grateful to the staff of the Ghana Stocks Exchange who provided the data for
entire the work to be carried out. We also extend our appreciation to our colleagues whose
suggestions enhanced the work. Finally, we cannot end without appreciating all those who
contributed in divers ways in making the work complete.
References
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[2] Akotey, A.J.(2007),Interview with Armah Akotey,Vice President of Investment, Data Brokerage
Ltd, Accra, Ghana.23 December,2008.
[3] Amihud, Y. (2002), 'illiquidity and stock returns: cross-section and time –series effects', Journal
of Financial Markets, 5, 31-56.
[4] Banz, R.W., 1981, the relationship between returns and market value of common stock, Journal
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[5] Bekaert, G., Harvey, C.R., Lundblad, C., 2007.Liquidity and expected returns: lessons from
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[6] Brennan, M.J., W. Huh and A. Subrahmanyam(2011), 'An analysis of the Amihud Illiquidity
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Financ. Econ.105,82-112.
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Kyle, A.S. (1985), 'Continuous auctions and insider trading', Econometrica, 5 3(6), 1315-1335.
[16] Lee, K.H.(2011), 'The World price of liquidity risk', J.Financ.Econ.,99(1),136-161.
Lesmond,D.A., J.P. Orden and C.A. Trzcinka (1999), 'A new estimate of transaction
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ISSN: 1663-4144, Vol. 28, pp 199-209
doi:10.4028/www.scientific.net/JERA.28.199
© 2017 Trans Tech Publications, Switzerland
Submitted: 2016-08-20
Revised: 2016-09-28
Accepted: 2016-09-29
Online: 2017-01-12
A Corporate Social Responsibility of Engineering the Liquidity-Adjusted
Capital Asset Pricing Modelling Sub-Sahara Africa:
Evidence from Ghana
Solomon Duduchogeab*,Yao Hongxinga, Benjamin Chris Ampimahc,
Prince Harvimc
a
School of Finance and Economics, Jiangsu University, 301 Xuefu Road, Zhenjiang, P.R. China,
b
Koforidua Technical University, Koforidua, Box 891,Koforidua Ghana,
c
Faculty of Science,Jiangsu University, 301 Xuefu Road, Zhenjiang, P.R. China
[email protected],[email protected], [email protected],
[email protected]
Keywords: Corporate Social Responsibility, Liquidity Risk, Asset Pricing, Emerging Market, Sub–
Sahara Africa.
Abstract. This paper estimates a conditional version of liquidity–adjusted capital asset pricing
model in an emerging market in line with the corporate social responsibility (CSR) of the Ghana
Stocks Exchange. We find out that for several years, Ghana stock market has been excluded from
the global financial watch and from empirical verification model for lack of transparency in the
performance of Exchange. Our evaluation concludes that illiquidity risk can be measured in the
local market and exhibit a strong trend of mix reactions from liquidity premia.While the effect of
the recent financial crisis do not show much difference between the different market conditions, the
effect is more stronger in the down market than the up market. Finally, we explore the size effect on
the market and conclude that the net beta as well as the systematic liquidity risk is pronounced in
the smaller market though insignificant.
1. Introduction
Globally, issues concerning corporate social responsibility (CSR), liquidity with its associated
marketability and trading cost has become the centre of attention to investors in the field of
financial market. According to a standard definition (Paul and Siegel 2006) corporate social
responsibility (henceforth CSR) defines a set of corporate practices which improve upon social and
environmental regulatory standards of the markets in which such corporations operate. The KPMG
International Survey on CSR reporting (KPMG 2008) documents that corporate responsibility
information is released (in stand-alone reports or integrated with annual financial reports) by 80%
of the constituents of the Global Fortune 250, up from 50% in 2005.In the modern world, a CSR is
a shift from the maximization of an investor profit to a shareholders welfare. The question is
whether the change in focus is the answer to the poor performance of stocks in Sub-Sahara Africa.
As an example, the GSE recently engineered the operation of what it termed as the Ghana
alternative Market in 2015 with a focus on businesses with a high potential for growth. The aim is
to accommodate these companies at various stages of their development, including start-ups and
existing ones, both small and medium with the aim of grooming them to become bigger in the
future (GSE, 2015).
An opportunity to examine the nexus between the role of corporate social responsibility and
corporate performance is to test the Liquidity-adjusted capital asset pricing modelthrough the
performance of the Ghana stock exchange from 2006 to 2015.
It is important to stress the role play by liquidity when investing with the aim of maximising returns
since it determines the marketability or otherwise of a stock. Liquidity is seen as one of the pivots
on which investment stand as it is of a bigger concern to both local and international investors in
emerging markets because of its cross-sectional and temporal variations (Bekaert et al., 2007).
All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans
Tech Publications, www.ttp.net. (#72950785, jiangsu university, zhenjian, China-12/01/17,12:17:16)
200
International Journal of Engineering Research in Africa Vol. 28
Although, many of the existing literature focus on the US stock market to the detriment of most of
the developing market, there is a an indication in recent times of many researchers focusing on
other developed and emerging markets(e.g. Karolyi et al., 2012) .The total value of African stocks
outside South Africa is only 0.6 per cent of all emerging-market stocks.
Sub-Saharan Africa (SSA) markets are the only ones that have not attracted the needed study,
though not surprising since these markets were established not long ago. It is dominated with
volatile but substantial returns which is crowded with different degrees of liquidity cost.(Wheeler,
1984; Mosley et al., 1995) document that Africa’s past problems were largely a function of
structural and international factors and, as such, they are likely to continue.
In recent years however, many of the African countries have seen the establishment of financial
stock markets as one of the surest way of galvanising the needed resources to embark on the
journey of economic emancipation. However (Hearn & Piesse, 2009) document that Sub-Sahara
Africa is a region made up of smaller, unregulated and a lack of proper governance system. These
shortcomings and other factors make it impossible for the markets to make it to the regional equity
market indices and is therefore excluded from the Global Emerging Market (GEM) portfolio funds
(Hearn&Piesse, 2009). However, with only 11 stock markets operating in Sub-Sahara Africa by the
end of 1997, the numbers have increased to more than 20 including one of the only sub regional
stock exchanges in the world, linking eight French-speaking West Africa countries(Sally,
2013).Ghana’s stock market which was established in 1989 and started operation in 1990 was
adjudged as the world’s best performing market at the end of 2004 with a year return of 144% in
US dollar terms compared with a 30% return by Morgan Stanley Capital International Global Index
(Databank Group, 2004).Even though the market is a G30 compliant, trade and prices are often
agreed informally and the market institutions are merely being used to announce pre-agreed
details(Akotey,2008).As stated by Bruce Hearn(2013),stock price, volatility, traded
volume and size (market capitalization) are all negatively associated with illiquidity in Ghana and
other 11 west African countries with the exception of Cape Verde where size has a positive
association with Lesmond zero (0.173) and Liu (0.520) measures.
Our paper contribute to the body of literature by looking at the effect of systematic liquidity risk
on the Ghanaian market using the liquidity–adjusted capital asset model (LCAPM) propended by
Acharya and Petersen (2005). To the best of our knowledge, our paper is the first to empirically
carry out this task in the entire West Africa sub-region. Second, the study intends to verify the
extent to which the price factor influences a smaller capitalist market like Ghana. Third, this paper
examines the illiquidity risk factors and how they affect stock returns in Ghana.
The rest of this paper is organized as follows; in section 2, we present various hypothetical
statements. Section 3 looks at the methodology and the research design. Section 4 discusses data
and report summary statistics for the market and section 5 serves as the conclusion of the study.
2. Hypothesis
It is imperative to say that liquidity risk is a factor which influences investors due to its
multifaceted effect on stock returns. The Ghanaian market though emerging has not been tested as
compare to the US, Europe and other parts of Asia due to its size and unattractiveness. We intend to
break this barrier by empirically investigating the relations between liquidity and asset pricing in
Ghana. The US market is a quoted- driven one which is different from the simple continuous
auction system in Ghana where bid and ask orders are written manually on a series of boards (Hearn
and Piesse, 2011).The difference between the developed approach in the US and the makeshift
approach in Ghana means that the impact of liquidity risk in both countries will not be the same.
However, whether this assertion is the case will have to be subject to empirical assessment. We
therefore assume the following hypothesis for the study:
1. Liquiditystocks both at the firm and market levels are positively related to stock returns.
2. The relation between firms (individual) level and market level liquidity stock is negatively
related to stock returns.
International Journal of Engineering Research in Africa Vol. 28
201
3. The relation between market liquidity and Individual stock returns is negatively related to
stock returns.
The three hypotheses stated above mainly relates to the liquidity risk in the LCAPM model of
Acharya and Pedersen which is an addition to the traditional CAPM model. This theoretical model
was empirically verified by Pastor and Stambaugh (2003).
We intend to know the combine systematic effect of the individual liquidity risk on a marketwide basis in the Ghanaian market. To this end, our stated next hypothesis is that
4. The combined liquidity risk is priced in Ghana.
It has been posited that Sub-Saharan African market is small and risky. To this end, our study
intends to look into this using the Ghanaian market. This brings us to our next hypothesis which
states that;
5. It is risky to invest in smaller market than in bigger market.
3.Research Design
3.1. Measuring Liquidity
Bruce Hearn (2013) in studying the West African terrain including Ghana adopted three liquidity
measures to wit, the bid-ask spread of Jones (2002), zero daily return measure of Lesmond (1999)
and Liu (2006) illiquidity measure. For the purpose of this study, we employ the Amihud (2002)
illiquidity ratio as the basis for our measurement. This is in line with the price impact of Kyle
(1985).
The ratio is described as:
,
=
,
∑
,
(1)
, ,
, ,
Where , , denotes absolute stock return of i on day d and month t. , , is the volume of trading
for stock i on day d and of month t, and , is the sum of trading days for stock i and month t. The
Amihud illiquidity measurement is premise on everyday trading on the stock market and it is
measured on data from daily trading activities of returns on volume ratio. It is anticipated that a
higher ratio of the Amihud illiquidity measure is assumed to be preceded by a lower liquidity. This
means that investors will prefer to be compensated (normally called risk premium) for holding such
securities in period of insecurity.
This is a summary of the sample population data that was gathered during the period from the GSE
database showing the mean, median and the sum for the period under review.
year
N
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
21
21
21
24
25
30
29
32
35
35
Table 1 Sample Population
Mean
Median
0.45
0.47
0.56
0.66
0.76
0.69
0.73
0.72
0.75
0.85
0.04
0.04
0.03
0.03
0.05
0.03
0.03
0.04
0.05
0.05
Sum
333.51
384.19
460.34
530.01
644.05
640.17
705.92
662.64
757.54
948.35
3.2. Conditional LCAPM Model
We selected the Liquidity–adjusted capital asset model (LCAPM) of Acharya and Pedersen (2005)
as the foundation of our model for this study. One fundamental difference between the traditional
202
International Journal of Engineering Research in Africa Vol. 28
CAPM and the LCAPM is the
introduction of liquidity cost such as cost of a round trip,
administrative cost etc. in the LCAPM as against a cost free CAPM. According to Acharya and
Pedersen (2005),the standard CAPM hold for expected net returns(that is net of the relative
illiquidity cost): (
−
).As a result, the conditional version of LCAPM is displayed at time
t as follows:
)=
(
+
+
,
(
+
(
(
,
)
+
(
(
,
)
+
(
(
(
,
)
(2)
Where
ℎ
Where is the gross return for stock i at month t, denotes gross risk-free rate, and represents
the trading cost for stock i at month t.
It is worth noting that, without the introduction of a cost element in the CAPM, equation 2 will be
akin to the CAPM. By assuming conditional covariance, variance and equal risk premium across the
different risk factors, an equivalent formulation of 3 is given
−
=
+
+
−
−
(3)
Where the ’s in equation 3 denotes
=
=
β3i=
β4i=
(
(
(
(
,
(
( ,
(
(
,
(4)
,
)
)
(5)
(6)
)
(7)
is the market return at month t,
Where is the return of stocks i at month t,
is the market aggregate liquidity cost at month t.
cost for stock i at month t, and
The net liquidity risk is given as
=
−
−
And the LCAPM net liquidity risk becomes:
E( − )=
+
+
And lastly, aggregate systematic risk is
=
+
−
−
And the LCAPM becomes:
E( − )=
+
+
The Amihud Illiquidity ratio then becomes
=
+
+
We transform the Amihud illiquidity ratio where
number of lags included in the equation.
−
= +
+
+
=
−
-
=
-
=
−
=
-
=
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
is the liquidity
(8)
(9)
(10)
(11)
(12)
+…+
is the measure of liquidity for stock , is the
+
+
+
+
+
+
+
+
+
+
+
+
(13)
+
+
+
+
(14)
(15)
(16)
(17)
(18)
International Journal of Engineering Research in Africa Vol. 28
−
=
+
+
+
+
+
+
+
203
(19)
+
Where
indicates individual stock excess returns at month t+1
are the liquidity
betas that are specified in equations (4) to (19), HLMt represents the High minus Low at month t,
Sizetdenotes market capitalization at month t and
is the cumulative returns over the past 12
months with a one lag.
Acharya and Pedersen, (2005); Lee, ( 2011) stated that equations (13) to (16) make it possible to
determine what influence each individual liquidity risk, and moderate the multi-collinearity
concerns for the betas. Equations (17) and (18) determine the aggregate liquidity risk effect and the
aggregate systematic risks. Lastly, equation (19) investigates the joint effects of the liquidity betas.
3.3.Measuring Liquidity-Adjusted Capital Asset Pricing Model
In the light of Lee (2011), we construct portfolios for the LCAPM basically on the Ghanaian
stock market with respect to market returns and illiquidity using panel regression. The intention for
using the panel regression over others such as the cross-sectional regression is to avoid statistical
bias that are known to be associated with it since it only accounts for correlation without accounting
for serial correlation.
For Table 2, the illiquidity betas are measured based on equation 4 to 7 for ten portfolios which
is basically in line with the Amihud (2002) illiquidity ratio by using the individual stocks and their
respective market returns. In the construction of the illiquidity betas, we sort stocks into 10 equal
parts and create 10 equally-weighted portfolios (deciles).Specifically, at the beginning of every
year, illiquidity betas are calculated using the individual liquidity stocks as well as their respective
market returns and create 10 equally weighted portfolios. The resultant outcome is the averages of
these betas for each portfolio over the ten year period.
Table 2 Summary of illiquidity portfolio betas
Returns
illiquidity betas
Lowest
0.001
2.2795
-0.0398
-0.0184
2.3376
2.3386
-0.14
1
0.001
2.2804
-0.0314
-0.0165
2.3283
2.3294
-0.74
2
0.0017
2.111
-0.01
-0.0002
2.1212
2.1229
-0.06
3
0.0019
2.2166
-0.0529
-0.0325
2.3021
2.304
-0.07
4
0.0019
2.1045
-0.011
-0.0007
2.1161
2.118
0.10
5
0.0022
2.1042
-0.0129
-0.0008
2.1179
2.1201
0.07
6
0.0036
2.1032
-0.0192
-0.0016
2.1239
2.1275
-0.05
7
0.0039
2.2548
-0.0093
-0.0015
2.2656
2.2694
0.89
8
0.0046
2.1001
-0.0168
-0.0015
2.1183
2.1229
-0.08
Highest
0.0191
0.1919
-0.0117
-0.0517
0.2553
0.2744
0.89
4. Empirical Results
4.1. Analytical Results
In our analysis, we present table 3 and discuss the dynamics of the Ghanaian stock market. We run
alternative ways of measuring the liquidity–adjusted capital asset pricing model from equation 4 to
7 to ascertain the true state of liquidity betas in respect to expected returns. In the light of this, we
first discuss the individual liquidity betas in our regression analysis and find out the outcome. Our
204
International Journal of Engineering Research in Africa Vol. 28
findings indicate that which shows the co-movement between the individual liquidity and market
returns is highly insignificance in the presence of all the control variables such as the traditional ,
firm sizes, HML and momentum. This means that Hypothesis 1 which indicates that liquidity at the
level of the firm and the market are positively related to returns, is insignificant in the Ghanaian
situation as investors cannot rely on to predict the future outcome of events.
From table 3, we observe that
is significant at the 1% level but with the wrong sign and thus
rejecting Hypothesis 2 and rejecting the assertion by Acharya and Pedersen(2005) who documents
that stocks whose returns are sensitive to market illiquidity are riskier and that investors require a
higher return as a compensation for holding such assets. We belief that this assertion is not
supported in the case of Ghana due to information asymmetry and the fact that most investors do
not have access to the right information in the market. The result points to a phenomenon in which a
decrease in liquidity level does not deter prospective investors from investing due to the lack of
information concerning the true nature of the market. Table 3 also show the regress
to be
positive with 1% significant level. In the Ghanaian case, this again does not give support to the
highly regarded Acharya and Pedersen (2005) assertion that investors are willing to accept lower
return during period of meltdown. The results reject Hypothesis 3 which indicates that the relation
between market liquidity and individual stock returns is negative. Having dealt with the
implications of our analysis on the individual liquidity betas, we now turn our attention on the net
as well as the aggregated liquidity betas. Both net liquidity and the systematic liquidity are all
positively related to stock returns and more importantly, the systematic liquidity risk is priced
though insignificant. In the final analysis, we can conclude that the Ghanaian market is full of
variations and inconsistent with the literature even though systematic liquidity risk is insignificantly
priced.
Table 3 The Panel regression fixed effect
Variables
Expected (1)
sign
+
0.0540 *
(158.346)
+
(2)
(3)
(4)
(5)
0.3461
(451.315)
0.0067
***
(147.117)
0.0002
***
(318.476)
0.5927
(259.380)
0.5276
(2.294)
_
_
0.0053
***
(60.952)
+/-
0.0009
***
(59.121)
+/Constant
0.7950
(10.601)
0.8046
(10.845)
0.1767
(22.265)
SIZE
-
0.9717
(0.00004)
0.6435
(−0.0007)
0.3558
(−0.0002)
MOM
+
0.1824
(3.142)
0.2152
(3.64)
HML
+
0.8235
(−6.739)
0.7286
(−12.190)
0.0089
***
(4.207)
0.2173
(−14.905)
0.0473
**
(22.529)
0.0339
**
(−0.0004)
0.0014
***
(4.546)
0.0530 *
(−16.285)
(6)
(7)
0.9718
(315.337)
0.9983
(0.957)
0.9920
(40.042)
NA
(18.800)
0.8321
(0.803)
0.848
(9.012)
0.0695 *
(−1.218)
0.7284
(14.967)
0.4454
(22.585)
0.8974
(−.0002)
0.6539
(0.0005)
0.9948
(−0.0005)
0.2525
(3.226)
0.2020
(3.061)
0.9848
(4.947)
0.8349
(−7.412)
0.8243
(−7.090)
0.9961
(−17.607)
4.2. The Size Effect
Very little is documented on stocks in emerging market such as that of Ghana in relation to size
and it effect on market returns. Most of the firms in Ghana are made of smaller sizes and most
International Journal of Engineering Research in Africa Vol. 28
205
studies documented so far have proved that expected stock returns is negatively related to size
(Fama and French,1992,Banz,1981,Chordia,2000).Limkriangkrai et al (2008) mentioned liquidity
as being priced only in the smaller markets. He indicated that for smaller markets, the effect of
liquidity is submerged in the size effect due to the higher cost of operation. However, others also
defer in opinions on this issues. Fabre and Frino (2004) find that commonality in liquidity is mainly
a large firm phenomenon. Having this in mind, we set out to find out the effect of size on liquidity
in Ghana which is a lower middle income country. To do this, we sorted our data into three different
groups with a ratio 30: 40:30 base on their market capitalization concurrently for each month.
The results from the analyses indicate that the market in Ghana is significant with respect to
small size firms as oppose to the large as well as medium size groups as seen from table 4. From
table 4, it is observe that the net liquidity
and the market-wide liquidity
are all carrying
positive signs for the small size firms but with varying significant levels. In the case of the small
firms, the net liquidity is highly significant with a 1% level of significance with the systematic
liquidity β6 though significant but at the 10% level. However, in the case of the medium as well as
the large size firms, only systematic liquidity carries a 10% level of significance. Regressing for
smaller size firms produce some level of significance. It is also observe that and which are both
positive and significant at 1% level in table 3 has it significance level varying to 10% for both
medium and small firms in table 4. The conclusion that can be drawn from the net liquidity β5 and
the aggregated liquidity β6 is that both remain significant in the smaller size group though the
impact is minimal for the systematic liquidity β6. In effect, we can say that illiquidity risk is priced
in the smaller firms in Ghana and confirm Hypothesis 4. The unit cost of production in smaller
firms in Ghana carry some form of risk and investors need some form of compensation in order to
invest their portfolios in such a risky market.
4.3. Illiquidity Effect during Different Market Situations
Asset pricing plays an important role during different market situations and may not exhibit
either same or similar tendencies. Research documented so far indicates that stock returns behave
differently during up and down market situations (Chiang and Zheng, 2010).The fact remains that,
during down markets, price factors command more returns premiums as illiquidity is incorporated
into asset pricing model(Brennan et al.,2011)
As a result, we decided to test the illiquidity risk with respect to returns in Ghana during
different market scenario and see whether the resultant outcome will show some form of
asymmetric effect during the period of up and down market situations. To do this, we grade the
upward period from the year 2006 to 2008 and the meltdown period which span from 2009 to 2015.
The outcome in the Ghanaian market is reported in table 5. In the face of the world financial
crisis in March, 2008, many Sub-Saharan African countries suffered substantial financial
damagesduring this period especially those whose economic investment is tied to foreign investors.
We decided to dual on the net
and
for comparison purposes. The observation from
and
is very interesting. Indications are the coefficients for the Ghanaian market are highly
insignificant in both down and up markets. Even though the market does not produce any
significance, during the down market period, both beta and
gives coefficient values of 0.8109
and 0.8044 respectively as oppose to the up market values of 0.5243 and 0.5128indicating that
excess stock returns is positively correlated with market illiquidity though insignificant to command
a change in the market situation. We can see from the results that during the up market period the
performance of the market illiquidity risk in respect of and β6. We can say that since both betas
and are positive in the down market and stronger than the up market, it effect on the market
may be more pronounce than the up market.This is an indication that the effect of illiquidity risk is
almost twice stronger in the down market than in the up market and that systematic illiquidity risk is
much felt during the down market than in the up situation.
206
Variables
International Journal of Engineering Research in Africa Vol. 28
Table 4 Panel regression results for different size groups Large Stocks
Expected
sign
+
+
1
2
3
4
5
0.0558 *
(−66.545)
0.1421
(−73.831)
0.7825
(00.401)
0.1613
(−74.559)
0.3899
(−75.121)
0.1630
(−74.714)
_
_
0.7963
(−2.502)
+/-
0.9095
(−1.409)
+/Constant
SIZE
-
MOM
+
HML
+
0.9235
(−3.345)
0.5353
(0.001)
0.1939
(−3.028)
0.7817
(7.568)
Medium Stock
Variables
Expected
sign
+
+
0.9180
(−4.167)
0.5601
(0.002)
0.2594
(−3.3)
0.7896
(8.484)
0.9114
(−4.528)
0.5618
(0.002)
0.2665
(−3.32)
0.7831
(8.823)
0.9089
(−4.926)
0.5960
(0.002)
0.2678
(−3.061)
0.7958
(8.719)
0.7959
(0.298)
0.9133
(−4.430)
0.5646
(0.002)
0.2612
(−3.268)
0.7871
(8.638)
3
4
5
0.1848
(−144.758)
0.2452
(−67.436)
0.0763 *
(−10.908)
0.2221
(−74.401)
0.8653
(−7.872)
0.1842
(−85.300)
_
0.2819
(1.897)
+/-
-
MOM
+
HML
+
Small Stock
Variables
Expected
sign
+
+
_
_
0.0410 **
(−492.305)
0.0572 *
(−848.910)
0.0535 *
(−1020.20)
0.7350
(−388.133)
0.0310 **
(−0.028)
0.2744
(2.652)
0.0409 **
(374.169)
0.0899 *
(−0.036)
0.2594
(−1.833)
0.0564 *
(642.985)
0.0938 *
(−0.038)
0.2609
(−1.955)
0.0528 *
(772.785)
0.1372
(−0.013)
0.1070
(3.114)
0.7343
(294.877)
2
3
4
5
0.0183 **
(268.586)
3.00e-05
***
(1026.36)
5.66e-05
***
(7.566)
0.0107 **
(315.464)
0.0880 *
(212.245)
0.0001
***
(913.606)
0.3093
2.93e-06 ***
(140.620)
3.20e-06 ***
(0.001)
2.33e-06 ***
(15.916)
3.08e-06 ***
(−100.158)
7
0.0002 ***
(−115.670)
0.0002 ***
(37.831)
0.0002 ***
(196.368)
0.0002 ***
(2.427)
0.0967 *
(−6.632)
0.0772 *
(−681.22
8)
0.1019
(−0.028)
0.5920
(−0.949)
0.0763 *
(516.300)
0.0694 *
(−846.07
1)
0.1034
(−0.037)
0.3781
(−1.428)
0.0685 *
(641.184)
1
0.0835 *
(−43.476)
6
0.1020
(−5.894)
+/-
SIZE
0.3246
(−1.135)
0.8706
(8.908)
0.8570
(−0.0001)
0.9273
(0.192)
0.9053
(−5.061)
2
0.0892 *
(59.975)
7
2.58e-06 ***
(559.183)
1.91e-06 ***
(133.708)
1.86e-06 ***
(858.910)
4.42e-06 ***
(42.355)
1
_
Constant
6
6
0.0002 ***
(−3341.94)
0.0001 ***
(−0.019)
0.0002 ***
(2.553)
0.0002 ***
(2533.38)
7
3.43e-05
***
(4650.78)
3.62e-05
***
(58.104)
3.67e-05
***
(623.776)
3.39e-05
International Journal of Engineering Research in Africa Vol. 28
207
(−23.9702)
+/-
0.0003
***
(6.579)
+/Constant
SIZE
-
MOM
+
HML
+
***
(−236.753)
0.0068
***
(−389.223)
0.0082
***
(−0.359)
0.0227 **
(3.333)
0.0067
***
(293.275)
4.33e-06
***
(−429.212)
5.73e-06
***
(−0.395)
0.0001
***
(2.805)
4.46e-06
***
(318.602)
0.0038
***
(−515.691)
0.0047
***
(−0.487)
0.0163 **
(3.583)
0.0243 **
(−479.533)
0.0037
***
(387.329)
0.0240 **
(360.742)
0.0242 **
(−0.437)
0.0271 **
(3.577)
1.69e-05
***
(−473.232)
2.16e-05
***
(−0.438)
0.0005
***
(2.817)
1.69e-05
***
(352.017)
0.0779 *
(−2.111)
0.0274 **
(−378.399)
0.0303 **
(−0.354)
0.0630
(3.305)
*
0.0265 **
(286.575)
7.19e-05
***
(−177.919)
0.0005 ***
(0.036)
6.39e-05
***
(−4.932)
9.67e-05
***
(107.470)
Table 5 Liquidity risk in up and down market
Variables
Expected
Sign
+
+/+/-
Constant
SIZE
-
MOM
+
HML
+
Down Market
1
2
Up Market
3
0.4102
(720.473)
0.8109
(−182.701)
0.8044
(189.262)
0.0010 ***
(−470.926)
0.0009 ***
(−0.436)
0.0064 ***
(2.787)
0.0010 ***
(350.296)
0.7055
(−24.123)
0.5243
(51.622)
0.5128
(−051.975)
0.5935
(−869.895)
0.1632
(−0.018)
0.2985
(25.073)
0.5929
(660.094)
0.0211 **
(−425.639)
0.0215 **
(−0.417)
0.1648
(3.073)
0.0208 **
(320.774)
4
0.0023 ***
(−208.576)
0.0032 ***
(−0.012)
0.0454 **
(3.46)
0.0022 ***
(158.644)
4.4. Alternative Proxy of Liquidity
Several researchers often use different measures of liquidity to investigate the relations between
liquidity and excess returns. This indicate that liquidity cannot be explain from one proxy.Brennan
and Subrahmanyam(1996) use transaction cost as a measure of liquidity with Datar and
Radcliffe(1998) using trading volume turnover as a proxy for the measurement of liquidity. The
Amihud illiquidity ratio relies on the assumption that the percentage of the non-trading days is
relatively low. However, when studying the West African terrain, Hearn and Piesse (2011)
document that the greatest degree of illiquidity in the region can be seen in the BRVM and Ghana
with Ghana having a percentage of daily zero returns of 77% for the entire market. Due to this, we
use the Lesmond et al (1999) zero- return measurement as our proxy for this study. The zero return
ratio explain and addresses the inherent concern of the Amihud ratio since it is able to capture the
zero trading days in the Ghana situation.
Table 6 present the estimation of theregression on the zero return proxy. For the purpose of
conservation of space, we sought to report only the net liquidity
and the systematic
liquidity .The overall outcome from the analysis is that net liquidity and the systematic
liquidity are positively correlated with the market liquidity riskat the 10% level of significance
.the magnitude of this results is however insignificant in the Ghanaian case as compare with other
results obtain in some of the existing literature. This go to confirm the multi-faceted nature of
liquidity which indicates that liquidity cannot be studied from one dimensional way.
208
International Journal of Engineering Research in Africa Vol. 28
Variables
Table 6 Panel regression results for zero returns
Expected Sign
Zero Returns
1
+
0.47
(1.78)
+/-
0.15*
(-0.32)
+/Constant
Size
-
HLM
+
MOM
+
2
0.08**
(0.99)
0.32
(1.03)
0.13*
(-0.30)
0.06**
(0.92)
0.11*
(1.40)
0.55
(0.26)
0.32
(0.14)
0.45
(0.30)
0.21
(0.17)
5.0. Conclusion
The entirety of this study examined the role play by liquidity in the pricing of asset in Ghana, an
emerging market situated in West Africa which for several years has been excluded from the global
market watch and from empirical valuation model. The Acharya and Pedersen LCAPM model is
seen as one of the few models that can be used to study many forms of liquidity risk showing the
relations between excess stock returns and illiquidity. Due to the volatility of the stock market in
Ghana and the consistent high risk nature of the market, many investors do not have confidence in
investing in the market and therefore demand a high form of compensation for the investment they
make in the economy in the smaller and fragile market. We also find out that both the net liquidity
beta and the systematic liquidity betas though positive are less stronger but have a greater influence
in the down market than in the up market.We also find out that the effect of liquidity are not the
same but keep varying across the different liquidity risk used in the analysis. The cost of equity in
the market is also seen to be very high and the result makes it clear the depth and level of adherence
to the proper regulations the entire stock market in the world and the volatile nature of the region.
Overall, this study she some light and support for analysis.
Acknowledgement
The authors are very grateful for the financial support from the National Science Foundation of
China with grant no. 71271103, 71371087. This work would not have been possible without their
support. We are also grateful to the staff of the Ghana Stocks Exchange who provided the data for
entire the work to be carried out. We also extend our appreciation to our colleagues whose
suggestions enhanced the work. Finally, we cannot end without appreciating all those who
contributed in divers ways in making the work complete.
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