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Determinants of the Voting Premium in
Swedish Listed Shares
Liquidity or Corporate Control
Author: Yu ZHENG
Supervisor: Jens Josephson Master’s Thesis in Financial Economics September 2011
Department of Economics
1
Abstract
This thesis investigates the impact of corporate control and liquidity on the voting premium in
Swedish dual class shares. The thesis uses the size of the largest shareholder and the dummy
variables as corporate control variables. The relative trading volume, the relative freely traded
shares and the relative free float are used as liquidity variables. Before the regression, a
Hausman test is performed to choose the panel regression model. The variance inflation factor
(VIF) is applied to show that no multicollinearity presents. Using a panel data set consists of 26
dual class shares listed on NASDAQ OMX Stockholm from 2005 to 2009, a random effects
model and a pooled OLS regression model are used to estimate the voting premium. The main
findings are two. First, the corporate control is a determinant for the voting premium via
ownership structure. The size of the largest shareholder has a negative impact on the voting
premium, and a majority ownership over 40 percent significantly reduces the voting premium.
Second, liquidity is not priced in the voting premium in Sweden.
Keywords: dual class shares, voting premium, corporate control, liquidity, private benefits of
control
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Contents
1 Introduction .......................................................................................................................... 2
2 Theoretical Background ...................................................................................................... 3
2.1 Private Benefits of Control ...................................................................................................... 3
2.2 Liquidity Theories .................................................................................................................... 6
3 Data ........................................................................................................................................ 7
3.1 Sample Selection....................................................................................................................... 7
3.2 Price Data and the Voting Premium ...................................................................................... 8
3.3 Corporate control Variables ................................................................................................. 11
3.4 Liquidity Variables ................................................................................................................ 12
3.5 Summary Statistics ................................................................................................................ 14
4 Empirical Methodology ..................................................................................................... 16
4.1 Regression Model Specification ............................................................................................ 16
4.2 Primary Tests and Choice of Model ..................................................................................... 17
4.3 Correlation and Multicollinearity Analysis ......................................................................... 18
4.4 Correction for Heteroskedasticity and Serial Correlation ................................................. 20
5 Statistical Analysis .............................................................................................................. 20
5.1 Regression Result Analysis .................................................................................................... 20
5.2 Endogenous Problems ........................................................................................................... 26
6 Conclusion and Prospective ............................................................................................... 27
7 Reference ............................................................................................................................. 29
8 Appendix ............................................................................................................................. 32
8.1 List of the Sample Firms ....................................................................................................... 32
8.2 The VIF of Dummy Variables .............................................................................................. 33
2
1 Introduction
In the thesis, dual class shares are two classes of common equities issued by a single firm with
the same cash flow right but different voting rights. According to standard asset pricing theory,
in which agency problem and liquidity compensation are not taken into consideration, shares
with equal payoff must have equal value and the same price. However, empirically there is a
price difference between dual class shares. Since the only difference for the two classes of
shares is voting rights, the price difference is defined as a voting premium. The existence of a
voting premium has been known by scholars for years. Lease et al. (1983) record a 5.44 percent
premium in the U.S.. Rydqvist (1987) finds the voting premium ranges between 2 percent and 6
percent from 1975 to 1985 in Sweden. Horner (1988) finds an over 10 percent premium in
Switzerland. Zingales (1994) finds an 81 percent premium in Italy.
Numerous literatures have linked the voting premium with corporate control. The logic behind it
is that the superior voting shares indicate a superior control power in a control contest. If control
is valuable, the superior voting shares would trade at a higher price than the inferior voting
shares. Lease et al. (1983) study 26 dual class firms in the U.S. and find positive and negative
voting premiums across firms. It suggests that there are both benefits and costs of corporate
control. Grossman and Hart (1988) develop a theoretical model to link the value of votes with
the private benefits of control. Following them, Zingales (1995) and Rydqvist (1996) have
derived further models. According to Zingales (1995), the voting premium reflects the
expectation that voting rights become valuable in a control contest, and the premium is a
function of the size of the private benefits of control. He studies the determinants of the value of
voting rights in U.S. corporations from 1984 to 1990. The study shows that the value of votes is
determined by the private benefits obtained from controlling the company, and ownership
structure has a major impact on the value of votes.
While lots of researches relate the voting premium to private benefits of control, fewer papers
relate it to liquidity. Large shareholders tend to have a stable holding of the superior voting
shares compared to the inferior voting shares, which makes the superior voting shares less liquid.
If liquidity is valued in the asset pricing, liquidity would be another determinant for the voting
premium and it would have a price discount effect on the superior voting shares. Evidence from
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Swiss dual class shares shows that both the degree of liquidity and the valuation of corporate
control benefits are major determinants for the relative stock price (Gardiol et al. 1997).
However, evidence from the Danish market from 1991 to 1999 shows that the voting premium
is negative for some firms for a long period and the premium is sensitive to liquidity but not to
corporate control (Neumann, 2003).
Given the fact that many firms in the Swedish stock market are dual class firms, this thesis aims
to investigate the voting premium in Swedish dual class shares, and to test the trade-off between
liquidity and corporate control. Based on a panel data regression model, the thesis provides
empirical evidence showing that whether corporate control and liquidity are valued in asset
pricing in the Swedish stock market.
Compared to other papers, the thesis puts more emphasis on liquidity. In the thesis, I try to use
free float to measure liquidity, a proxy noted by many investment banks in assessing the
acceptability for investment and liquidity of stock market during recent years (Chan et al., 2002).
The remainder of the paper is organized in the following way: In Section 2, a theoretical
background and previous empirical researches on corporate control and liquidity are presented.
Section 3 describes the data and the choice of variables. Descriptive statistics are also reported.
Section 4 explains the methodology and primary tests applied in the paper. Section 5 discusses
the empirical result. Section 6 presents the conclusions of the paper.
2 Theoretical Background
2.1 Private Benefits of Control
By holding company shares one owns the cash flow rights and voting rights. The voting rights
indicate the corporate control power. If control is valuable, the superior voting shares would
trade at a premium. Why is control valuable? The academic discussion starts from the agency
theory works by Berle and Means (1932) and Jensen and Meckling (1976). After that, a majority
literature has linked private benefits of control with the value of voting rights.
Early agency theory works by Berle and Means (1932) and Jensen and Meckling (1976) state
that agency costs exist with the separation of ownership and control. They suggest that the
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agency relationship is a contract under which the principles engage the agents to perform
services. There is a conflict of interest between these two parties. Since both parties are seeking
utility maximization, the agents might have incentive to act in self interest and might not act in
the best interest of the principles. As a result, the agents can acquire personal benefits at the
expense of the principles. The examples of the personal benefits ( so-called private benefits)
include appropriate corporate resources in the form of perquisites, the influence of electing the
Board of Directors, the ability to transfer assets on non-market terms to related parties (Nevona,
2003), self dealing, etc.
Rydqvist (1987) suggests that control is valuable because shareholders usually disagree on the
choice of the production plan. A shareholder with majority control could ensure the firm is
efficiently operated according to his point of view, while shareholders with minority control
have to accept the strategy even if they perceive the strategy to be inefficient.
Grossman and Hart (1988), Rydqvist (1996) and Zingales (1994, 1995) develop theoretical
models to link the value of votes with private benefits of control through ownership structure.
Grossman and Hart (1988) try to model the connection between agency problem and the voting
structure. They build a model in which there are two candidates for the management position,
the incumbent team and the rival team. The two candidates would hold a corporate control
contest. Different scenarios are analyzed to show how the structure of voting influences the
outcome of the control contests. Following their model, Zingales (1995) develops a model to
determine the optimal bidding strategy. Eq. (1) is a measurement of private benefits of control
given by Zingales (1995).
( ) is the price of the voting (nonvoting) share, is the private benefits of control
obtained by the winner in the control contest, is the proportion of voting share outstanding.
The equation shows that the voting premium is a function of the relative size of the private
benefits of control. In conclusion, according to Grossman and Hart (1988) and Zingales (1995),
when there is a control contest, the superior voting shares should receive a premium which is a
function of the private benefits of control (as is shown in Eq. (1)). On the other hand, the
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probability of a control contest is influenced by the ownership structure. Therefore, the
ownership structure has an impact on the voting premium.
Similar with the model above, Rydqvist (1987) proposes an oceanic games model to describe
the control power distribution among shareholders. The idea is that in a model of control test,
there are a few major shareholders and an ocean of an infinite number of minor shareholders.
The Shapley value per share of the voting game, called the oceanic power ratio, gives the
probability that small shareholders are pivotal in forming a majority coalition (Rydqvist, 1987).
He suggests that it could predict the voting premium. Following Rydqvist (1987), Zingales
(1994, 1995) and Neumann (2003) use the Shapley ratio as a proxy for control to predict voting
premium.
The existence of the private benefits of control has been shown by many empirical papers.
Previous researches also imply that there is a linkage between private benefits of control and the
voting premium. Lease et al. (1983) test the hypothesis that control is valued by capital market.
They suggest that there are both benefit and cost of corporate control. In the oceanic game
model, Rydqvist (1987) finds that the Shapley value for a control contest could predict the
voting premium in the Stockholm Stock Exchange. Barclay and Holderness (1989) argue that 20
percent premium of NYSE reflects private benefits that accrue exclusively to the block holder.
Rydqvist (1996) finds that the value of voting right depends on the ownership distribution.
Specifically, the voting premium is larger in the firms where two largest shareholders have more
equal shares than in the firms where there is only one dominant shareholder. Interestingly,
Rydqvist (1996) also finds that the voting premium is larger during periods of frequent takeover
activity. Zingales (1994) studies the value of voting rights in the Milan Stock Exchange (MSE)
and finds that the influence of voting rights in Italy is as large as dividend rights. The voting
premium on the MSE is intrinsically related to the value of control via ownership structure,
whereas the liquidity is difficult to judge. Zingales (1995) studies the determinants of the value
of voting rights in US corporations from 1984 to 1990. The study shows that the value of vote is
determined by the private benefit obtained from controlling the company, and the ownership
structure has a major impact on the value of votes. Doidge (2004) finds a lower voting premium
of firms that are cross-listings in the U.S. and he suggests that cross-listing leads to lower
private benefits of control. Nevona (2003) studies 661 dual class firms across 18 countries and
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finds that the value of the control block vote vary among countries. Guadalupe and González
(2010) investigate the impact of competition on private benefits of control in 16 countries,
where private benefits of control are presented as the voting premium. The result shows that
competition significantly reduces the private benefits of control.
2.2 Liquidity Theories
The impact of liquidity on the voting premium for dual class shares is less mentioned than
corporate control in previous studies. However, the impact liquidity to general asset pricing has
been discussed by lots of literatures.
Kyle (1985) builds a theoretical model to demonstrate that information asymmetry is correlated
with liquidity, and the liquidity could capture some pricing features of trading. He suggests that
market liquidity refers to three elements: tightness, depth and resiliency. In Kyle‟s (1985) model,
he use as the liquidity parameter and the model shows is an increasing function of
information asymmetry. Amihud and Mendelson (1986) use bid-ask spreads to measure
liquidity and find that asset returns increase significantly with bid-ask spreads, which indicate
liquidity is priced in the asset return. Liquidity based asset pricing models are developed by an
increasing number of papers. Brennan and Subrahmanyam (1996) research asset pricing on
compensation for illiquidity and find that there is a significant return premium for illiquidity due
to information asymmetry in the NYSE market. Holmström and Tirole (1998) develop an asset
pricing model based on industrial and financial corporations‟ desires for liquidity to fulfill future
cash needs. Acharya and dersen (2005) develop an equilibrium model with liquidity risk. They
find empirical evidence that liquidity risk affects asset pricing through various channels. Liu
(2006) builds a two-factor (market and liquidity) model that successfully describes the stock
returns. He finds empirical evidence that a significant liquidity risk premium exists in the
NASDAQ market.
According to the theories above, if dual class shares have different liquidity features, liquidity
would also be a determinant for the voting premium. Doidge (2003) argues that if the low voting
class is more liquid than the high voting class, it will have a negative impact on voting premium.
He states that, for example, foreign ownership restrictions on the superior voting shares exist in
some countries may reduce the liquidity of the superior voting shares, which leads to a
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downward bias in the voting premium. Neumann (2003) and Gardiol et al. (1997) discuss the
possible interaction of control and liquidity. Neumann (2003) finds that blockholders typically
concentrate their ownership in stock with superior rights. Their holding tends to be stable over
time that shares with inferior voting rights are expected to be more liquid. Gardiol et al. (1997)
argue that if control is really valued by the shareholders, there will be various mechanisms to
prevent control dilution and hostile takeover. Such mechanisms will reduce the liquidity of
shares with superior voting rights. If such liquidity differences occur, it is reasonable to observe
a price discount for the superior voting shares due to liquidity.
Smith and Amoako-Adu (1995) find that both higher liquidity in the restricted share class and
concentrated ownership reduce the price premium in dual class shares in Canada. Doidge (2003)
finds that the liquidity proxy is insignificant in the U.S. cross listing firms. His findings are
similar to Zingales (1994) and Zingales‟s (1995) findings, which state that liquidity doesn‟t have
an impact on the price premium in Italy and the U.S.. However, using a different liquidity
measure (implied liquidity risk estimated over 20 trading days), Neumann (2003) finds a
negative and significant impact in Denmark. Defining liquidity level as the relative proportion
of freely negotiable shares, Gardiol et al. (1997) also find that liquidity has an influence on the
voting premium in Switzerland.
3 Data
3.1 Sample Selection
The sample of firms in this paper consists of all the dual class firms from Nasdaq OMX
Stockholm except those that lack ownership structure data. In total there are 30 dual class firms
listed in Stockholm OMX, with 26 A/B shares, 3 A/C shares (Hufvudstaden AC, Industrivärden
AC, SEB AC) and 1 A/R share (Stora Enso AR). The basic information (industry sector, size) of
the samples is collected from Nasdaq OMX website1. The price data are collected from
Datastream. The ownership structure data are collected from the book “Ägarna och makten i
Sveriges” (Owners and Power in Sweden‟s Listed Companies) by Fristedt and Sundqvist (2005-
1For more information about the website see http://www.nasdaqomxnordic.com/shares/
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2009). The book publishes annually and provides detailed data of the share holdings of the 25
largest shareholders in Swedish listed firms.
In my sample of firms, I excluded TranscomWorldWide AB, Metro International AB and Stora
Enso AR because of the lack of ownership data. Hufvndstaden AC is excluded because its
voting arrangement is different from other dual class shares. Hufvndstaden C share has a higher
voting right than A share, and it is 1: 100 (10: 1 for others). Besides, the price data of Husqvarna
AB and the ownership data of Midsona AB are only available from 2007.
The 26 firms chosen can be seen in Appendix 8.1. The firms cover 7 industries (Industrials 8,
Financial 7, Consumer discretionary 3, Materials 3, IT 2, Health care 1, Telecommunication 1,
Consumer staples 1) with 19 large firms, 2 mid firms and 5 small firms. The information of
industry classification is collected from the Nordic list on Nasdaq OMX1, and it is in accordance
with the Global Industry Classification Standard (GICS).
The time period is from 2005 to 2009. This time period is chosen because before 2005, there is a
lack of free float data, and ownership structure data of 2010 hasn‟t published in the magazine
used in the study when the research is done. In all, there are 126 observations for an unbalanced
panel.
3.2 Price Data and the Voting Premium
Daily close prices of the firms from 03 January 2005 to 31 December 2009 are collected from
Datastream. The voting arrangements of A share and B2 share are collected from the book by
Fristedt and Sundqvist (2005-2009). The voting rights of the A shares and B shares of the
sample are 10: 1 from 2005 to 2009, where A shares are superior voting shares and B shares are
inferior voting shares3. A shares and B shares of the sample firms have equal cash flow each
1 For more information about the Nordic list see
http://www.nasdaqomxnordic.com/digitalAssets/76/76084_thenordiclistsep232011.xls
2 Note: C shares and R shares are referred as B shares in this paper for simplicity.
3 Before Sweden joined the EC in 1995 the Swedish Companies Act allowed proportions up to 1000 votes‟ a share.
Today, the highest allowed proportion is 10 votes a share (Jonnergård and Larsson, 2009).
9
year.
The most common measurement of the voting premium is . The voting premium could
also be simply calculated as the price premium of dual class shares, as done in Lease et al. (1983)
and Neumann (2003):
(2)
Zingales (1995), Doidge (2004) and Guadalupe and González (2010) adjust the simple voting
premium to relative number of votes of an inferior voting share versus a superior voting share:
(3)
where is the price of the superior voting shares and is the price of the inferior voting
shares, and is the relative number of votes of the inferior voting shares compared to the
superior voting shares.
In this thesis, the voting arrangements across companies are constant. The voting premium
could be comparable without adjusting for relative votes. However, I consider it is necessary to
adjust the simple voting premium to the voting arrangement. Because if we need to set up model
with a larger sample, or need to compare the voting premium with that in other countries, the
voting premium adjusted to relative votes would be a more reliable measurement. Following
Zingales (1995), I measure the voting premium as in Eq. (3). Since the ownership structure data
are on an annual basis, I calculate the average of the daily voting premium annually and get the
voting premium on an annual basis.
Figure 3-1 shows the daily average voting premium from 2005 to 2009. From the figure we
could see that the average voting premium of all the samples has a positive value all the time. It
fluctuates at 4 percent level. The fluctuation of the voting premium is small across the years.
Only in the last half of 2008, the voting premium shows a higher fluctuation.
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Figure 3-1 Daily Average Voting Premium 2005 - 2009
Table 3-1 Description Statistics of the Voting Premium
The Voting Premium is calculated as , where ( ) is the price of the
superior (inferior) voting shares, is the relative number of votes of the inferior voting shares compared
to the superior voting shares. Data are based on the daily close price in Datastream. Two outliers (1724
percent from Ortivus 30-31 Oct 2006) are excluded.
Year Equally weighted mean Min Median Max Negative
2005 3.55% -3.68% 1.01% 35.45% 7
2006 3.33% -4.97% 1.33% 12.69% 4
2007 4.92% -2.51% 5.64% 15.42% 4
2008 6.47% -19.23% 4.08% 32.59% 4
2009 3.97% -31.84% 2.22% 36.45% 9
Table 3-1 presents the summary statistics for the annual voting premium of the samples. The
annual voting premium is calculated as the annual average of the daily premium for each firm.
From 2005 to 2009, the equally weighted mean of the voting premium each year remains
positive and is relatively stable. The value of the voting premium is relatively low in 2005 and
2006, and it increases a little in the year 2007 and 2008, but decreases in 2009. On average the
voting premium is quite low in Sweden compared to other countries. The table also shows that
in 2009, 9 out of 26 firms have a negative voting premium, implying a price discount for the
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superior voting shares. The number is 4 from 2006 to 2008, and 7 in 2005.
3.3 Corporate control Variables
In order to investigate the influence the corporate control on the voting premium, several
corporate control variables are used in the thesis. Based on previous theory (Grossman and Hart,
1988), the ownership distribution decides the probability of a control contest, so that the value
of control is linked with ownership structure. I use the size of the largest shareholder, which is
the percentage of votes held by the largest shareholder, as one of the corporate control variables.
The shareholder data are complex because there are cross holdings and family holdings. Before
collecting the corporate control variables, it is necessary to identify the principles for grouping
shareholders into coalition first. We assume one coalition (e.g. Family members) cooperates and
makes the same votes, so their holdings of votes could be added together as the holding of one
shareholder.
Family holdings are considered as the fraction of shares owned by family members or a close
group of individuals who do not belong to the same family like co-founders (Cronqvist and
Nillsson, 2003). Following Rydvist (1987), the holdings of the various members of the family
are added together. Besides, the holdings of the company that is majority controlled by the
family are added into the family holdings. For example, Wallenberg family is the largest family
owners in the sample of firms. The family has a controlling holding for their investment
company Investor. When the Wallenberg family members and Investor have shares of one
company, the family members and Investor will be considered as one coalition. Their votes will
be added together to be the voting power of one shareholder. Other family owners in the sample
of firms include Stenbeck family, Lundberg family and Söderberg family.
Except from family holdings, the holdings of different companies if they are majority controlled
by the same company are combined together. For example, in 2009, SCA has 29.8 percent of
votes held by Industrivärden, 6.4 percent of votes held by SHB pensionsstiftelse. However, SHB
group has majority holdings for Industrivärden. Therefore the holdings of Industrivärden and
SHB pensionsstiftelse are added together as the holdings of the SHB group in the case of SCA.
The SEB group is another shareholder coalition in the sample of firms. The grouping principles
are consistent with the data source by Fristedt and Sundqvist.
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Using the grouping principles above, the percentage of votes held by the largest shareholder is
collected from the book by Fristedt and Sundqvist (2005–2009) from 2005 to 2009. This
corporate control variable is called the size of the largest shareholder. When the size of the
largest shareholder is large, the voting power is concentrated to one shareholder or one
shareholder coalition. The control contest is less likely to occur and the voting rights become
less valuable. However, when the votes are diversely held by different small shareholders,
according to the oceanic theory by Rydqvist (1987) and the competition theory, the private
benefits and the value of voting rights will increase.
In addition, I introduce three dummy variables to describe different levels of corporate control.
The similar methodology is applied by Neumann (2003) and Gardiol et al. (1997). The value of
the dummy D40 (D30, D20) equals one when the largest shareholder holds more than 40 (30, 20)
percent of votes. The smallest dummy size I choose is D20, because for most of the firms in the
sample, there is at least one shareholder that holds more than 10 percent of votes. Dummy
variables are used as a supplement to the size of the largest shareholder, for dummies better
distinct the level of corporate control.
3.4 Liquidity Variables
The liquidity variables I use in the thesis include the daily trading volume and the free float.
Free float is defined as the total shares excluding shares held by strategic investors such as
governments, corporations, controlling shareholders, and board members (Chan et al., 2002). It
is the portion of the listed share capital that freely trades on the market. In asset allocation
literature, market capitalism is a proxy for liquidity. However, in continental European
companies, because of partial privatizations, family controlled companies, cross holding, etc.,
the difference between market capitalism and free float is larger (Hamon and Jacquilat, 1999).
Similar conclusion is found by Gingliner and Hamon (2007). Their findings suggest that the free
float is an effective proxy for liquidity in the continental European market. When a company has
a small free float, the availability of trading capital on the market is small, which suggest a low
level of liquidity. In the case of dual class shares, if the strategic investors tend to have a stable
holding of the superior voting shares, the free float of the superior voting shares would be
smaller than that of the inferior voting shares. I collect the annual free float data from the book
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by Fristedt and Sundqvist (2005-2009) from 2005 to 2009.
I calculate the ratio between the free float of A shares divided by the free float of B shares as
one liquidity variable. The ratio is defined as the relative free float. If the relative free float is
smaller than 1, A shares are less liquid than the B shares.
The free float is the portion of the freely traded shares on the market. In order to get the number
of the shares that is freely traded on the market, I collect the total share outstanding of each dual
class share from the same source as the free float. By multiplying the free float with the total
share outstanding, I obtain the number of the freely traded shares on the market.
I calculate the ratio between the freely traded A shares divided by the freely traded B shares as
another liquidity variable. The ratio is defined as the relative free float*, and it is shown in Eq.
(4)
Similar to the relative free float ratio, a ratio smaller than 1 indicate that there are less freely
traded A shares on the market, and A shares are less liquid than B shares.
However, there are some drawbacks to use the free float as liquidity proxy. Firstly, since the
concept of the strategic shareholder is obscure, the value of the free float may vary with
different definitions for the strategic shareholder. In the thesis, I collect the free float data from
one source to make sure that the definition of the strategic shareholder is constant within the
sample of firms. Secondly, the idea of the free float is related to ultimate owners and ownership
structure, which may infer a potential correlation between the liquidity variable and the
corporate control variable. In the thesis, the correlation between the liquidity variable and the
corporate control variable is tested before the regression.
The trading volume also reflects the liquidity of the shares. I collect the daily trading volume of
both classes of shares for all the sample of firms from Nasdaq OMX from 2005 to 2009.
Following Zingales (1994, 1995), I calculate the average of the daily trading volume of each
class of share annually, and define the ratio between the average trading volume of A shares
divided by the average trading volume of B shares as the relative volume. This is the third
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liquidity variable. When the relative volume is smaller than 1, the trading volume of A shares is
lower than the trading volume of B shares, and A shares are less liquid. The three liquidity
variables will be used respectively as independent variables to estimate voting premium.
3.5 Summary Statistics
Table 3-2 Summary Statistics
The voting Premium is calculated as , where ( ) is the price of the
superior (inferior) voting share, is the relative number of votes of the inferior voting shares compared
to the superior voting shares. The relative volume is the ratio average daily trading volume of A shares
divided by the average daily trading volume of B shares. Relative Free Float* is the ratio the freely
traded A shares divided by the freely traded B shares. Relative Free Float is the ratio the free float of A
shares divided by the free float of B shares. Size Largest shareholder is the percentage of votes held by
the largest shareholder. D40 (30, 20) is a dummy variable that equals 1 if one largest shareholder holds
over 40 (30, 20) percent of votes. Three free float outliers are excluded. Two voting premium outliers
(1724 percent from Ortivus 30-31 Oct 2006) are excluded. The time period is from 2005 to 2009.
Mean Std.Dev Min Max Median Obs
Voting Premium 4.48% 8.53% -31.84% 36.45% 1.72% 126
Relative Volume 10.880 39.713 0.0001 262.100 0.011 126
Relative Free Float* 3.085 9.543 0.000 43.670 0.110 123
Relative Free Float 0.498 0.332 0.050 1.590 0.410 123
Size Largest shareholder 36.73 16.27 14.40 72.80 31.80 126
D40 - - - - - 46
D30 - - - - - 64
D20 - - - - - 115
Table 3-3 presents summary statistics of the panel data set. The median of the annual average of
the daily premium is 1.72 percent. The mean voting premium is 4.48 percent. The level of the
voting premium in the data set for Sweden is comparable to the price premium 5.44 percent in
U.S. estimated by Lease et al. (1983), ranging between 2 percent to 6 percent from 1975 to 1985
in Sweden (Rydqvist, 1987), 5.8 percent in Denmark (Neumann, 2003) and 3.9 percent in
Norway (Doidge, 2004). It is lower than the value 10.47 percent in U.S. (Zingales, 1995), 16.2
percent in Switzerland (Doidge, 2004), 32.8 percent for OECD countries between 1990 and
2003 (Guadalupe and González, 2010), 19.31 percent in Canada between 1988 and 1992 (Smith
15
and Amoako-Adu, 1995) and 81 percent in Italy (Zingales, 1994). Generally the voting premium
in Sweden is lower than that of other countries except Norway.
The mean of the relative volume is 10.88, which indicates that the average trading volume of A
shares are larger than the average trading volume of B shares. It is not in accordance with the
hypothesis that A shares trade less than B shares. However, the median of the relative volume is
0.011. The distribution of relative volume is left skewed. A majority of the observations have a
relative volume smaller than 1. The mean of the relative volume in Sweden is larger than the
value 0.44 in the U.S. (Zingales, 1995), and 0.344 in Danmark (Neumann, 2003). The mean and
the median of the relative free float are 0.498 and 0.41 respectively, which indicates that the
superior voting shares are less liquid than the inferior voting shares. As to the relative freely
traded shares on market, the mean and the median of the relative free float* are 3.085 and 0.11
respectively. This is similar to the relative volume. It indicates that a majority of the sample
have less freely traded A shares on the market. Generally, most of the samples are consistent
with the hypothesis that the superior voting shares are less liquid than the inferior voting shares.
As to the corporate control variables, descriptive statistics show a high ownership concentration
in the firms. The average size of the largest shareholder is 36.73 percent, with the minimum size
14.4 percent, the maximum size 72.8 percent and the median size 31.8 percent. The mean and
the median are comparable to 32.33 and 28.38 in the U.S., while the minimum is much bigger
than 0.77 in U.S. (Zingales, 1995). This result is in accordance with the empirical finding by
Bergström and Rydqvist (1990) that a larger shareholder coalition holds more than 50 percent of
equity in many listed firms in Sweden. In 46 out of 126 observations, the largest shareholder
controls over 40 percent of the votes. Only in 11 out of 126 observations, no shareholder
controls over 20 percent of the votes.
16
4 Empirical Methodology
4.1 Regression Model Specification
To investigate the influence corporate control and liquidity on the voting premium, the
following equations are modeled:
(5)
(6)
The description of the variables is shown in Table 4-1.
Table 4-1 Description of the variables
Notation Variable Description
VP Voting premium
Size.L Size of largest
shareholder The percentage of votes held by the largest shareholder
D40 (30,20) Dummy variables Dummy variables that equals 1 if the largest shareholder holds
more than 40 (30, 20) percent of votes, zero otherwise
FreeFloat Relative free float The free float of A shares divided by the free float of B shares
FreeFloat* Relative free float* The freely traded A shares divided by the free traded B shares
RelVol Relative trading
volume
The average trading volume of A shares divided by the average
trading volume of B share
In Eq. (5) and Eq. (6), Size. L and Dummy variables are the corporate control variables;
FreeFloat, FreeFloat* and RelVol are the liquidity variables. According to the theory, the
coefficient is expected to be negative, because ownership concentration is expected to have a
negative effect on the voting premium. The coefficients in Eq. (5) and in Eq. (6) are
expected to be positive, because the lower value of the liquidity variable, the higher price
discount for A shares, and the lower value of the voting premium.
The dummy variables cannot be estimated together with the size of the largest shareholder,
because there is multicollinearity within the variables. The coefficients would lose their
17
significance when estimated together. The three liquidity variables cannot be used together
because of the same reason.
4.2 Primary Tests and Choice of Model
There are several considerations before proceeding to the regression. First of all, when choosing
between the panel data techniques, the fixed effects model and the random effects model, I
consider the fixed effects model is not appropriate for the analysis. The fixed effects model
allows the intercept to be a group specific term, but it cannot be used to investigate time-
invariant causes of the dependent variables, because time invariant characteristics of the
individuals are collinear with the individual dummies (Torres-Reyna, 2010, Doidge, 2004). In
my regression model, the corporate control variable (the size of the largest shareholder) does not
vary much across time for each firm in most cases, and some of the dummy variables D40 (30,
20) are invariant for one firm across the years. The individual dummies in the fixed effects
model would be collinear with them. The fixed effects model is more suitable to study the
impact of variables that vary over time, which is not the purpose of my study.
The random effects model, however, is able to capture the variation across the entities and
include the time invariant variables. Furthermore, Hausman test between the fixed effects model
and the random effects model suggests that the preferred model is the random effects model.
The null hypothesis of Hausman test is that the unique errors are not correlated with the
regressors. Table 4-2 presents the test statistics of Hausman test. The random effects model
controls unobserved individual effects for each specification.
For a general random effects model:
(7)
where is composite error, , is the idiosyncratic error.
To establish a random effects model, Eq. (5) becomes:
(8)
is a random error term and are uncorrelated with other regressors.
18
Breusch-Pagan Lagrange multiplier (LM) test is performed to test if the variance across entities
is zero ( . If
, there is no presence of an unobserved effect. In this case, OLS
pooled regression is valid as the random effects model (Woolridge, 2002). Breusch-Pagan
Lagrange multiplier (LM) test rejects the null hypothesis , which indicates that the
random effects model would be better than OLS pooled regression. Test statistics are presented
in Table 4-2.
The preference to the random effects method is not in accordance with Zingales‟s (1995) that
presents a fixed effect model for U.S. data, but is similar to Doidge‟s (2004) that uses a random
effects model. Considering that the method of OLS pooled regression has been adopted by many
previous researches (Zingales, 1994 1995; Rydqvist, 1987; Neumann, 2003). I adopt both
pooled OLS model and the random effects model in this thesis to confirm that that result is
robust with the methods of estimation.
Table 4-2 test results from Hausman test and Breusch-Pagan LM test
The tests are based on the equation
(8)
Estimation Eq. (8) P value(Hausman test) P value(Breusch-Pagan LM test)
Size.L & RelVol 0.4004 (accept) 7.72E-04 (reject)
Size.L & Free Float* 0.5257 (accept) 8.22E-04 (reject)
Size.L & Free Float 0.2176 (accept) 7.52E-04 (reject)
Null hypothesis Hausman test: the unique errors are not correlated with the regressors
Null hypothesis Breusch-Pagan LM test: variance across entities is zero (
4.3 Correlation and Multicollinearity Analysis
Table 5-1 shows the Pearson correlation coefficients with the corresponding t values. The voting
premium is negatively correlated to the size of the largest shareholder at 5 percent significant
level, and it is not significantly correlated to the liquidity variables. The correlation between the
size of the largest shareholder and the relative free float* is -0.283 at 0.1 percent significant
level, which means that a concentrate ownership is weakly correlated to a low level of freely
traded A shares on the market. Similar correlation coefficients are found with the size of the
largest shareholder and the other liquidity variables. It has been discussed in the theory part
19
about the interaction between control and liquidity, and now the significant correlation
coefficients have verified this interaction. However, this correlation may lead to a potential
multicollinearity problem.
Table 4-3 Correlation Statistics
Premium Size.L Free Float Free Float* Rel.Vol
Premium 1.00 -0.20*(-2.28) 0.12(1.28) 0.04 (0.48) 0.08 (0.90)
Size.L -0.20*(-2.28) 1.00 -0.49 ***(-6.25) -0.28 ***(-3.24) -0.26 ***(-3.05)
Free Float 0.12(1.28) -0.49 ***(-6.25) 1.00 0.57***(7.71) 0.57***(7.71)
Free Float* 0.04 (0.48) -0.28 ***(-3.24) 0.57***(7.71) 1.00 0.94 ***(29.84)
Rel.Vol 0.08 (0.90) -0.26 ***(-3.05) 0.57***(7.71) 0.94 ***(29.84) 1.00
Significant level: * 5% **1% ***0.1%
If multicollinearity exists in a regression, the variance of the coefficients would increase; the
variables would lose their significance even with a significant F value; the sign of the
coefficients would be incorrectly estimated; small changes in the data would produce large
change in the estimation (Belsley et al., 1980; Greene, 1993, O‟Brien, 2007). In order to control
on potential multicollinearity, the variance inflation factor (VIF) is applied to detect
multicollinearity before the regression. The VIF is a statistic to measure the degree of
multicollinearity in a regression model. It shows how much the variance of the coefficient
estimate is being inflated by multicollinearity. Most commonly the rule of 10 associated with
VIF are regarded as a sign of severe multicollinearity (O‟Brien, 2007). Table 4-2 presents the
VIF statistics. From the table, we can see that the VIF statistics range from 1.0 to 1.3, which
means that the variance of the coefficient hasn‟t been inflated by multicollinearity, and there is
no multicollinearity problem between the control variable and liquidity variables. We could
conclude that although there is an interaction between liquidity and control, the correlation is
not high enough to cause multicollinearity. Therefore the regression results are not biased
because of multicollinearity.
The correlation between the two liquidity variables is as high as 0.938 at 0.1 percent significant
level. This confirms that the liquidity variables cannot be estimated together or there would be a
multicollinearity problem. There is no multicollinearity between the dummy variables and the
liquidity variables as well. The VIF statistics of the dummy variables are shown in Appedix 8.2.
20
Table 4-4 test results from the Variance Inflation Factor (VIF)
The tests are based on the equation
(5)
Note: a pooled OLS model is used here, because the vif function is not applicable to the random effects model
VP VIF VIF VIF
Size.L 1.323 1.087 1.075
Free Float 1.323 - -
Free Float* - 1.087 -
Rel.Vol - - 1.075
4.4 Correction for Heteroskedasticity and Serial Correlation
To avoid potential bias in the estimation, I use the robust covariance matrix estimator vcovHC
to account for heteroskedasticity and serial correlation in the regression. Similar to the Newey-
West estimator for time series models, vcovHC, according to Croissant and Milo (2008), is an
estimator for panel models doing White-Arellano covariance matrix (White 1980, Arellano
1987), which is robust with heteroskedasticity and serial correlation (when clustered by groups).
The standard errors of the random effects model in the result part are corrected to account for
heteroskedasticity and serial correlation across the observations of the same firms in different
years.
For the OLS pooled regression model, the standard errors are corrected to account for
heteroskedasticity and serial correlation using Newey-West HAC covariance matrix estimator.
5 Statistical Analysis
5.1 Regression Result Analysis
21
Table 5-1 Random effects regression model
The voting Premium is calculated as , where ( ) is the price of the
superior (inferior) voting share, is the relative number of votes of the inferior voting shares compared
to the superior voting shares. The relative volume is the ratio average daily trading volume of A shares
divided by the average daily trading volume of B shares. Relative Free Float* is the ratio the freely
traded A shares divided by the freely traded B shares. Relative Free Float is the ratio the free float of A
shares divided by the free float of B shares. Size Largest shareholder is the percentage of votes held by
the largest shareholder. D40 (30, 20) is a dummy variable that equals 1 if one largest shareholder holds
over 40 (30, 20) percent of votes. Three free float outliers are excluded. Two voting premium outliers
(1724 percent from Ortivus 30-31 Oct 2006) are excluded. The time period is from 2005 to 2009.
Voting
Premium (1) (2) (3) (4) (5) (6) (7) (8) (9)
Intercept 7.380** 8.247** 6.828‟ 5.599*** 5.115** 8.237‟ 5.715*** 5.442** 8.759‟
(2.697) (2.949) (1.765) (3.724) (2.855) (1.678) (3.818) (2.994) (1.831)
Relative 0.014 - - 0.014 0.018 0.014 - - -
Volume
(0.751) - - (0.751) (0.935) (0.770) - - -
Relative - -0.006 - - - - -0.001 0.009 0.018
Free
Float* - (-0.062) - - - - (-0.016) (0.094) (0.239)
Relative - - 1.602 - - - - - -
Free
Float - - (0.504) - - - - - -
Size.L -0.084‟ -0.109* -0.092 - - - - - -
(-1.667) (-2.068) (-1.569) - - - - - -
D40 - - - -3.667* - - -4.132* - -
- - - (-2.301) - - (-2.550) - -
D30 - - - - -1.693 - - -2.424 -
- - - - (-0.810) - - (-1.164) -
D20 - - - - - -4.290 - - -4.976
- - - - - (-0.904) - - (-1.072)
R Square
1.9
2.4
2.6
3.0
1.2
2.3
3.3
1.1
2.5
(%)
Significant level: „10% *5% **1% ***0.1%
22
Table 5-2 OLS pooled regression model
The voting Premium is calculated as , where ( ) is the price of the
superior (inferior) voting share, is the relative number of votes of the inferior voting shares compared
to the superior voting shares. The relative volume is the ratio average daily trading volume of A shares
divided by the average daily trading volume of B shares. Relative Free Float* is the ratio the freely
traded A shares divided by the freely traded B shares. Relative Free Float is the ratio the free float of A
shares divided by the free float of B shares. Size Largest shareholder is the percentage of votes held by
the largest shareholder. D40 (30, 20) is a dummy variable that equals 1 if one largest shareholder holds
over 40 (30, 20) percent of the votes. Three free float outliers are excluded. Two voting premium outliers
(1724 percent from Ortivus 30-31 Oct 2006) are excluded. The time period is from 2005 to 2009.
Voting
Premium (1) (2) (3) (4) (5) (6) (7) (8) (9)
Intercept 8.125* 8.797** 8.493‟ 5.833** 5.437** 10.757‟ 5.839*** 5.677* 10.962‟
(2.446) (2.632) (1.800)' (3.326) (2.783) (1.822) (3.395) (2.847) (1.927)
Relative 0.006 - - 0.007 0.010 0.007 - - -
Volume
(0.302) - - (0.351) (0.477) (0.397) - - -
Relative - -0.020 - - - - -0.011 -0.004 0.007
Free
Float* - (-0.203) - - - - (-0.112) (-0.040) (0.095)
Relative - - 0.143 - - - - - -
Free
Float - - (0.047) - - - - - -
Size.L -0.101 -0.123‟ -0.118 - - - - - -
(-1.544) (-1.897) (-1.578) - - - - - -
D40 - - - -4.010* - - -4.346* - -
- - - (-2.019) - - (-2.248) - -
D30 - - - - -2.094 - - -2.783 -
- - - - (-0.780) - - (-1.087) -
D20 - - - - - -6.957 - - -7.356
- - - - - (-1.165) - - (-1.279)
R Square 4.1 5.1 5.1 5.6 2.0 5.8 5.9 2.7 6.2
(%)
Significant level: „10% *5% **1% ***0.1%
23
Table 5-1 and table 5-2 present the results from the random effects model and the OLS pooled
regression model1. The standard errors of the random effects model in the result part are
corrected to account for heteroskedasticity and serial correlation across the observations of the
same firms in different years.
In table 5-1, the estimated coefficients of the size of the largest shareholder always have the
expected sign (-0.084, -0.109 and -0.092), and they are significantly different from zero at 10
percent level and 5 percent level in the first two regressions. The result shows that the corporate
control influences the value of votes via ownership structure. The ownership concentration has a
negative effect on the voting premium. The significant negative sign of the size of the largest
shareholder is consistent with the theory of private benefits of control. When control is allocated
to one major shareholder, there is less probability of a corporate control contest, and the value of
control becomes less valuable. This negative relationship has explained the low voting premium
in Sweden in the summary statistics, for Sweden has a higher level of ownership concentration
compared to other countries. This result is similar to Rydqvist‟s (1996) findings that the voting
premium is small when there is only one dominant shareholder in the firm for the Stockholm
Stock Exchange. Zingales‟s (1994) uses the size of the largest shareholder as a proxy for the
private benefits that outside shareholder expect to receive. He finds a similar result that the
coefficient is negatively significant in the Milan Stock Exchange. The coefficient of Size.L from
Zingales (1994) is -0.77, which is more negative than the coefficient of Sweden. It is
understandable because the Italian market has high private benefits of control according to his
argument.
The coefficients of corporate control which are measured by the dummy variables are shown in
regression 4-9. The majority hold dummy D40 is significant (t test = -2.55) at 5 percent level.
The value of the coefficient is -3.667 in regression (4), -4.13 in regression (7). This finding is
the same with the expectation, and is in accordance with the result of the size of the largest
shareholder. It means that when a firm is controlled by one shareholder who has over 40 percent
1 Two voting premium outliers (1724 percent from Ortivus 30-31 Oct 2006) are excluded in the regression.
The free float of A share of Modern Times Group MTG, SWECO and Tele2 are zero in 2009, while that data
are 87.2, 50.5 and 75.5 in 2008. I dropped these three free float observations in 2009.
24
voting power, the voting rights would lose the value at the speed of 4.13 percent. This is because
when there is a majority shareholder, it becomes impossible for small shareholders to make
decisions through the voting rights to peruse private benefits (Gardiol et al., 1997). The
coefficients for dummy variables D30 and D20 are also negative but are insignificant. It
indicates that 40 percent could be a benchmark for the power of control to influence the value of
votes. This result is similar to the finding by Zingales (1994) who finds a majority owned
dummy have a strong negative effect on the voting premium in Italy. However, the coefficient
found in Italy has a larger absolute value -30.2. Gardiol et al. (1997) also find the significant
negative effects from the dummy variables D50, D20 and D10 in Switzerland. The coefficients
have value ranging from -10 to -15 which are also higher than that in Sweden. As it is shown in
the summary statistics, the Swedish listed firms have high levels of ownership concentration.
The largest shareholder holds over 10 percent of votes in all the sample of firms. Compared to
Sweden, the ownership distribution in Switzerland is more dispersed. That may be the reason
why the dominant holding of 10 percent and 20 percent control power has an influence on the
voting premium in Switzerland (Gardiol et al., 1997), but only a dominant holding of over 40
percent of control power has an impact on the voting premium in Sweden. On the other hand,
Neumann (2003) finds corporate control dummy variables don‟t affect the voting premium in
Denmark.
Overall, the coefficients of the corporate control variables infer that corporate control has an
impact on the voting premium via ownership structure in Sweden. This result also confirms the
theoretical model developed by Grossman and Hart (1988), Zingales (1995) that there is a
linkage between the value of votes and private benefits of control. Besides, the level of private
benefits in Sweden is relatively small because the coefficients of the corporate control variables
are relatively low.
The liquidity variable measured as the relative trading volume does not relate to the voting
premium in all the regressions, though all the coefficients have the expected signs. This means
that liquidity with the measurement of relative trading volume is not a determinant for the
voting premium for the sample. Using the same proxy for liquidity, similar insignificant results
are found by Zingale (1994, 1995) and Doidge (2004). A different result is found by Nevona
(2003) that relative turnover is negative and significant. Doidge (2004) argues that it is not a
25
serious concern because there is no strong theoretical prediction about the impact of relative
turnover on the voting premium.
The liquidity variable measured as relative free float*, which is the relative freely traded shares,
is insignificant in all the regressions. However, when corporate control variable is estimated
with relative free float*, the corporate control variable has a more significant t value. Besides,
despite of the insignificancy, the coefficients have the expected signs. The similar insignificant
coefficients are found when using relative free float as liquidity variable, and the coefficients
don‟t have the expected signs.
Overall, all the liquidity variables are not relevant to the voting premium in dual class shares in
Sweden. This is consistent with Zingales‟s (1994, 1995) findings for Italy and U.S. market, and
Doidge‟s (2004) finding for non-US firms listed in U.S. market. However, the result is different
from the findings by Gardiol et al. (1997) for Switzerland, Nevona (2003) for a cross-country
study, and Neumann (2003) for Danmark.
Why are the liquidity variables insignificant in the regressions? Although there is a weak
interaction between the liquidity variables and the control variables, the multicollinearity
analysis has already ruled out the bias caused by this correlation relationship. The insignificance
does not originate from multicollinearity. One explanation is that Nevona (2003) has invented a
different measurement to calculate liquidity which he finds significant. Gardiol et al. (1997)
uses the book value of inferior shares in the capital structure as a proxy for liquidity. Besides,
from the summary statistics we could see that not all the A shares are less liquid than B shares.
The liquidity differences between A shares and B shares are varying across firms. It is possible
that the phenomenon that the large shareholder tends to have a stable holding of the superior
voting shares is not significant in Sweden. Bergström and Rydqvist (1990) show that in Sweden,
largest shareholder coalition often invests in a large number of B shares which add little voting
power. In all, the insignificant liquidity variables demonstrate that liquidity is not a determinant
for the voting premium in Swedish firms.
The OLS pooled regression results in table 5-2 are similar to the random effects model. The
coefficients for the corporate control variables are significant and negative. The significant
levels of the corporate control variables have decreased slightly in the OLS pooled regression,
26
and the coefficients have higher negative values. The liquidity variables are not significantly
relevant to the voting premium in the OLS pooled regression. The result from the OLS pooled
regression has confirmed the robust of the estimation.
5.2 Endogenous Problems
One of the potential biases in my regression result is that the explanatory variables are likely to
be endogenous. According to Wooldridge (2002), endogeneity usually arises in one of the three
ways: Omitted Variables, Measurement Error and Simultaneity. In my regression model:
(5)
The voting premium probably has an impact on liquidity and control variables, because the
prices of the dual class shares could affect the trading volume and the holding of shares by the
shareholders. This is endogeneity caused by simultaneity. Moreover, we would worry that
control and liquidity variables are correlated from unobserved factors. As is shown in Breusch-
Pagan LM test, there are presents of unobserved effects in the panel data. Possibly that both
control and liquidity variables are correlated with legislation and the takeover bid rules, which
also affects the value of votes. This is endogeneity caused by omitted variables. The control
variable and the liquidity variable are weakly correlated to each other as well. At last,
measurement error in control and liquidity is always a possibility.
The random effects model employed may capture the unobserved effects and could deal with the
endogeneity caused by omitted variables for panel data. However, it is necessary to note that the
random effects model is a panel technique that is in developing and it may not be capable of
capturing all the unobserved effects. Another way to enhance the model is to take more
variables that may lead to the unobserved effects into the regression. These variables include
dummies for legislation, size of the firm, conversion rights, etc. A more robust regression model
could be estimated by testing these variables together with the control and liquidity variables.
Regarding the fact that the liquidity variables are weakly correlated with the control variables, a
better liquidity proxy that is uncorrelated with control variable may improve the estimation.
Instrumental variables method, such as two-stage least squares method (2SLS) regression, could
be used to deal with Endogeneity caused by omitted variables as well.
27
Endogeneity caused by simultaneity is a more complicated econometric problem, because all the
variables (dependent and independent) may influence on each other. Wooldridge (2002)
proposed simultaneity equation models (SEMs) to deal with such endogenous. In SEMs, two or
more variables are jointly determined by a system of equations. The equations system could be
estimated by 2SLS or 3SLS. However, Wooldridge (2002) states that system procedures are
efficient if all equations are correctly specified, but single-equation methods are more robust.
6 Conclusion and Prospective
This thesis investigates the impact of corporate control and liquidity on the voting premium in
the Nasdaq OMX Stockholm from 2005 to 2009. This thesis uses the size of the largest
shareholder and the dummy variables as corporate control variables. The relative trading volume,
the relative freely traded shares and the relative free float are used as liquidity variables. The
thesis finds that the Swedish listed dual class shares have an average voting premium of 4.48
percent, which is relatively low compared to other countries. The voting power is quite
concentrated to one largest shareholder across the firms. For most of the firms, A shares are less
liquid than B shares. The Pearson correlation shows that there is a weak negative interaction
between corporate control and liquidity, but the correlation would not produce multicollinearity.
The estimations of the random effects regression and the pooled OLS regression show that
corporate control is a determinant for the voting premium in Swedish listed shares via
ownership structure. The size of the largest shareholder has a negative impact on the voting
premium. The intuition behind it is that the concentration of control power reduces the
probability of a control contest, which makes the value of votes (value of control) less valuable.
This negative coefficient has explained that the low voting premium in Sweden is a result of the
concentrated ownership structure. Besides, a majority ownership over 40 percent significantly
reduces the voting premium while the majority ownership below 30 percent is not recorded to
be significant. The empirical findings in Sweden confirm the theoretical models developed by
Grossman and Hart (1988) and Zingales (1995) that there is a linkage between the value of votes
and private benefits of control. The level of private benefits in Sweden is relatively small
because the coefficients of the corporate control variables are relatively low compared to other
empirical papers (Zingales 1994, Gardiol et al. 1997).
28
On the other hand, the thesis finds that liquidity is not a determinant for the voting premium in
Sweden. All the liquidity variables I use do not have an impact on the voting premium. This
may result from the tendency of stable holding of the superior voting shares varies across the
firms in the Swedish market. As suggest by Bergström and Rydqvist (1990) that in Sweden,
largest shareholder coalition often invests in a large number of B shares which add little voting
power.
There is a potential endogenous problem that may influence the regression results. I used the
random effect model to control for the endogeneity caused by omitted variables for panel data.
However, the endogeneity caused by simultaneity (the voting premium has an impact on control
and liquidity) is not controlled in the thesis. To enhance the model, simultaneity equation
models estimated by 2SLS could be built for the further research. Other dynamic liquidity
proxies could be tried to estimate the voting premium. Another possible way to enhance the
model is to include more independent variables such as legislation and takeover bid rules to
capture the unobserved effects.
Acknowledgement
I would like to thank Jens Josephson and Bo Larsson, for helpful suggestions and insightful
comments.
29
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8 Appendix
8.1 List of the Sample Firms
Sources: http://www.nasdaqomxnordic.com/digitalAssets/76/76084_thenordiclistsep232011.xls the
Nordic list, 2011, 3rd
Jan
Name Sector Size Voting Rights(A : B)
ACAP Invest AB Industrials SMALL 10 : 1
Atlas Copco AB Industrials LARGE 10 : 1
Electrolux AB Consumer Discretionary LARGE 10 : 1
Ericsson AB IT LARGE 10 : 1
Holmen AB Materials LARGE 10 : 1
Industrial Financial Systems AB IT MID 10 : 1
Industrivärden, AB Financials LARGE 10 : 1
Investor AB Financials LARGE 10 : 1
Kinnevik AB Financials LARGE 10 : 1
Midway Holding AB Industrials SMALL 10 : 1
Modern Times Group MTG AB Consumer Discretionary LARGE 10 : 1
NCC AB Industrials LARGE 10 : 1
Ortivus AB Health Care SMALL 10 : 1
Ratos AB Financials LARGE 10 : 1
SCA AB Industrials LARGE 10 : 1
SCANIA AB Materials LARGE 10 : 1
SEB AB Financials LARGE 10 : 1
SKF AB Industrials LARGE 10 : 1
SSAB AB Materials LARGE 10 : 1
Svenska Handelsbanken Financials LARGE 10 : 1
Svolder AB Financials SMALL 10 : 1
SWECO AB Industrials MID 10 : 1
Tele 2 AB Telecommunication
Services
LARGE 10 : 1
Volvo AB Industrials LARGE 10 : 1
Hufvudstaden AB Consumer Discretionary LARGE 10 : 1
Midelfart Sonesson AB Consumer Staples SMALL 10 : 1
33
8.2 The VIF of Dummy Variables
Table 4-1 test results from the Variance Inflation Factor (VIF)
The tests are based on the equation
(6)
Note: a pooled OLS model is used here, because the vif function is not applicable to the random effects model
VP VIF VIF VIF VIF VIF VIF
D40 1.055 - - 1.044 - -
D30 - 1.093 - - 1.084 -
D20 - - 1.021 - - 1.046
Free Float* 1.055 1.093 1.021 - - -
Rel.Vol - - - 1.044 1.084 1.046
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