Ownership structure and stock market liquidity

Ownership structure and stock market liquidity: evidence from Tunisia Nadia Belkhir Boujelbéne and Abdelfatteh Bouri Research Unit Corporate Finance Financial Theory – COFFIT, Faculty of Economics Sciences and Management, University of Sfax, 3018 Sfax, Tunisia Email: n.belkhirlaposte.net Email: abdelfettah.bourifsegs.rnu.tn Corresponding author JeanLuc Prigent Research Unit THéorie Economique, Modélisation et Applications – THEMA, University of Cergy, Pontoise. 33, boulevard du Port, F95011 CergyPontoise Cedex, France Email: JeanLuc.Prigentucergy.fr Abstract: The aim of this paper is to identify and analyse the influence of ownership concentration on stock market liquidity in general, and the adverse selection component of the spread in particular for a panel of Tunisian firms from 2001 to 2007. We document that firms with greater insider ownership display significantly lower liquidity. The negative relation between liquidity and insider ownership is attributable to adverse selection. We also find that the only negative correlation between blockholders and liquidity persists is that with turnover. Thus, it appears that blockholders decrease liquidity. We find that ownership effect depends on the owner identity. Our results suggest that state ownership is negatively related to spread, and positively related to market depth. Foreign ownership has no significant effect on liquidity measures. Keywords: insider ownership; INSID; institutional ownership; INST; foreign ownership; FORG; state ownership; STATE; liquidity; bidask spread; depth; turnover; price impact; PIMP; adverse selection costs; Tunisia. Reference to this paper should be made as follows: Boujelbéne, N.B., Bouri, A. and Prigent, JL. (2011) ‘Ownership structure and stock market liquidity: evidence from Tunisia’, Int. J. Managerial and Financial Accounting, Vol. 3, No. 1, pp.91–109. Biographical notes: Nadia Belkhir Boujelbéne is a PhD student in Corporate Finance Financial Theory (COFFIT) at the University of Economics and Management in SfaxTunisia. Abdelfatteh Bouri is a Professor in Corporate Finance Financial Theory (COFFIT) at the University of Economics and Management in SfaxTunisia. JeanLuc Prigent is a Professor in Théorie Economique, Modélisation et Applications (THEMA) at the University of Cergy, Pontoise.

Ownership structure and stock market liquidity:

evidence from Tunisia

Nadia Belkhir Boujelbéne* and Abdelfatteh Bouri

Research Unit Corporate Finance Financial Theory – COFFIT, Faculty of Economics Sciences and Management,

University of Sfax, 3018 Sfax, Tunisia E-mail: n.belkhir@laposte.net

E-mail: abdelfettah.bouri@fsegs.rnu.tn

*Corresponding author Jean-Luc Prigent Research Unit THéorie Economique, Modélisation et Applications – THEMA,University of Cergy,

Pontoise 33, boulevard du Port, F-95011 Cergy-Pontoise Cedex, France E-mail: Jean-Luc.Prigent@u-cergy.fr

Abstract: The aim of this paper is to identify and analyse the influence

of ownership concentration on stock market liquidity in general, and the adverse selection component of the spread in particular for a panel of Tunisian firms from 2001 to 2007 We document that firms with greater insider ownership display significantly lower liquidity The negative relation between liquidity and insider ownership is attributable to adverse selection We also find that the only negative correlation between blockholders and liquidity persists is that with turnover Thus, it appears that blockholders decrease liquidity We find that ownership effect depends on the owner identity Our results suggest that state ownership is negatively related to spread, and positively related to market depth Foreign ownership has no significant effect on liquidity measures

Keywords: insider ownership; INSID; institutional ownership; INST; foreign

ownership; FORG; state ownership; STATE; liquidity; bid-ask spread; depth;

turnover; price impact; PIMP; adverse selection costs; Tunisia

Reference to this paper should be made as follows: Boujelbéne, N.B.,

Bouri, A and Prigent, J-L (2011) ‘Ownership structure and stock market

liquidity: evidence from Tunisia’, Int J Managerial and Financial Accounting,

Vol 3, No 1, pp.91–109

Biographical notes: Nadia Belkhir Boujelbéne is a PhD student in Corporate

Finance Financial Theory (COFFIT) at the University of Economics and Management in Sfax-Tunisia

Abdelfatteh Bouri is a Professor in Corporate Finance Financial Theory (COFFIT) at the University of Economics and Management in Sfax-Tunisia

Jean-Luc Prigent is a Professor in Théorie Economique, Modélisation

et Applications (THEMA) at the University of Cergy, Pontoise

1 Introduction

Stock market liquidity is one of the fundamental components of market microstructure and has been viewed as an issue of interest in the financial literature The adverse selection component of bid-ask spreads, which is due to information asymmetry between dealers and informed traders, is well founded in the financial literature A fundamental objective of the present study is to explore how ownership level affects the stock market liquidity and the adverse selection component in emerging stocks markets This study has three distinct features that differentiate it from existing studies First, we enlarge the market-level database from emerging market economies Second, we gather transaction data from pure order driven market, while previous studies are collected transaction data from quote-driven markets The third feature of our study is that we introduce a set of better measures of liquidity such as quoted spread, effective spread, market depth, turnover, and price impact (PIMP)

Tunisia corresponds to an ideal setting to examine these issues In fact, Tunisian listed companies have similar ownership characteristics to publicly traded companies

in most countries around the world They are characterised by a high degree of ownership in general and are predominantly family-controlled The Tunisian Financial system is fragmented, dominated by banks Besides, financial institutions, including insurance, investment and securities companies own important proportions of the shares

in listed companies and are often among the five largest block holders The main characteristic of ownership is that it is highly concentrated According to the study of Omri (2001), the percentage of shares held by top five shareholders, in Tunisia, should exceed 88%

A lot of theoretical studies argue that the market liquidity will facilitate the exit

of large shareholders and thus reduce the intervention from those shareholders (Bolton and Thadden, 1998) Holmström and Tirole (1993) derive a theoretical model for investigating the negative relationship between ownership concentration and market liquidity The model suggests that the liquidity increased when the ownership

by a large owner decreased However, Maug (1998) and Kahn and Winton (1998) argue that the large shareholders tend to increase their holdings in a more liquid stock market

Previous studies are instigated for developed capital markets, those are quote-driven markets and most liquid in the world as the USA (Brockman et al 2009; Rubin, 2007; Heflin and Shaw, 2000), where the institutional environments differ greatly from that in Tunisia It is well identified that emerging financial markets are not as liquid as those of developed economies The lack of liquidity is considered as a key factor for the high volatility in emerging markets and an important obstacle to financial market development This study combines corporate governance research with market microstructure research by examining a link between a corporate governance variable, ownership structure, and a market microstructure variable, stock market liquidity

We divide our empirical analysis into two main sections In the first section, we investigate the effect of ownership structure on the firm’s market liquidity, including bid-ask spread, depths, turnover and PIMP We find that insider ownership (INSID)

significantly increases the firm’s quoted and effective bid-ask spread and PIMP measure

INSID also significantly decreases the depth The block ownership significantly reduces the turnover Thus, our results suggest that blockholder ownership (BLC) is associated with reduced market liquidity

In our second section, we examine the relation between the ownership structure and adverse selection component We construct estimates of the portion of the spread due to adverse selection using the Lin et al (1995) decomposition model, and find that the information component of bid-ask spread increases as the level of ownership by insiders increases

The remainder of the paper is organised as follows The next section gives a brief review of the literature Section 3 of the paper describes the data and the methodology

The following Section 4 presents the main results and their interpretation The article ends, in a last section with a brief summary of conclusions

2 Literature review

The relation between liquidity and ownership has received considerable attention in financial economics from a variety of perspective Researchers have considered both the effect of block ownership on liquidity as well as the effect of owner identity on liquidity

Previous studies have proposed two hypotheses to investigate the relation between ownership structure and market liquidity: the adverse selection hypothesis and the trading hypothesis The first hypothesis suggests that while informed shareholders have private information about firm value, a level of information asymmetry increases, which reduces liquidity (Grossman and Stiglitz, 1980; Glosten and Milgrom, 1985; Kyle, 1985) The trading hypothesis posits that when a firm’s ownership is concentrated, the free float is limited, there are fewer trades, and therefore the liquidity is decreased (Demsetz, 1968;

Merton, 1987) Given these two hypotheses, examining the empirical relationship between liquidity and ownership is complicated as various ownership proxies may differ

in their suitability for detaining these two costs with adverse selection costs on the one side, and trading frictions on the other

Most of empirical studies were conducted on developed markets; particularly in the American market show that block ownership impairs the firm’s market liquidity (Brockman et al., 2009; Rubin, 2007; Heflin and Shaw, 2000) The large blockholders have access to private information and consequently they acquire superior information about firm value thus potential benefits from blockholder monitoring might be partially compensate by reduced liquidity attributable to wider spreads

Heflin and Shaw (2000) examine the relation between block ownership and market liquidity They find that both relative and effective spreads are larger in the firm with higher BLC They also find adverse selection component estimates increase as the ownership by blockholders increases These results show that blockholders increase liquidity costs because of their access to private information However, Brockman et al

(2009) find that the reduced trading activity has a real friction effect on the firm’s liquidity After controlling for this real friction effect, they find little evidence that block ownership has a negative impact on informational friction Their findings show that block ownership affects market liquidity essentially through its effect on real frictions and not informational frictions

Naes (2004) argue that the ownership concentration, measured by the aggregate holdings of the five largest owners, increases the spread This result is in conformity with the theoretical predictions Comerton-Forde and Rydge (2006) report, on a sample of firm listed on the Australian Stock Exchange, a positive effect between ownership concentration and illiquidity

Market microstructure theory predicts a negative relationship between stock market liquidity and INSID The insiders have access to privileged information about the firm, and they trade based on this information The empirical evidence on the relation between stock market liquidity and INSID is inconclusive Sarin et al (2000) argue that the presence of insiders increase the probability of informed trading and the cost of transaction Thus, this contributes to higher level of information asymmetry and reduces liquidity Using a simultaneous equations approach, Sarin et al (2000) find that INSID is positively related to bid-ask spreads and negatively related to depth But, Dennis and Weston (2001) find that spread is negatively related to the level of INSID Kini and Mian (1995), who examine whether ownership structure affects the specialist’s choice of bid-ask spread on the NYSE, document a no positive relation between bid-ask spread and INSID The relation between liquidity and INSID in Norwegian market is studied in Naes (2004) A significant positive relationship is found between the spread measures and the holdings of the primary insiders Primary insiders comprise company managers and members of the Board of Directors Rubin (2007) finds that insider’s ownership of US firms is negatively associated with trade-based measures (volume and turnover), but positively associated with order-driven liquidity measures

The predicted impact of institutional ownership (INST) on liquidity is not clear

On the one hand, institutional investors obtain private information about the firm because they have resources to make any analyses on the firm and acquire information

The market makers are brought to widen spreads Thus, increased INST should lead to wider spreads and higher adverse selection costs On the other hand, institutional investors are heterogeneous and hold diversified portfolios Many studies focus on the negative relation between liquidity and INST (Sarin et al., 2000; Fehle 2004);

however other studies have noted positive effect of INST on liquidity (Dennis and Weston, 2001)

Sarin et al (2000) treat both ownership structure and spreads as endogenous and they show that greater INST leads to larger spreads, the adverse selection components of the spread, and smaller quoted depths These results contradict those obtained by Dennis and Weston (2001), who find that the relative spread is negatively associated to the INST

They suggest that institutional investors prefer stocks with narrower spreads since they are more liquid The results corroborate those obtained by Tinic (1972) and Hamilton (1978) These authors found a relation negative between INST and spread for a sample of NYSE and NASDAQ stocks, respectively Fehle (2004) examines the relation between bid-ask spread, measured both as effective and specialist spreads and INST He found that spreads are negatively related to INST share Barabanov and Mc Namara (2002) show that the relative bid-ask spreads are negatively related to the level of INST Rubin (2007) finds a two-sided relation between INST and liquidity On the one hand, liquidity

is positively related with the level of INST; on the other hand, liquidity is negatively related with the concentration of INST Agarwal (2007) proves that market liquidity increases with INST but begins to decline once it arrives at to 40%

Foreign investors invest to acquire gains from diversification; so they have an informational disadvantage vis-a-vis domestic investors There are various potential reasons for the negative liquidity effect from foreign institutions Many studies demonstrate that institutional trading is more possible information-driven (e.g., Ali et al., 2004; Bushee and Goodman, 2007), and INST rises the degree of information asymmetry (e.g., Dennis and Weston, 2001; Agarwal, 2007; Rubin, 2007) In emerging markets, foreign investors are better traders since they are better informed (Grinblatt and Keloharju, 2000; Seasholes, 2004) Using a sample of Indonesian firms, Rhee and Wang (2009) find that foreign ownership (FORG) has a negative impact on liquidity They show that a 10% increase in FORG is associated with approximately 2% increase in the bid-ask spread, 3% decrease in depth, and 4% rise in price sensitivity Naes (2004) show that FORG is negatively related to spreads and positively related to depth

The inefficiency of state ownership (STATE) is claimed to follow from factors such

as a slow decision making process and conflicts between the dual role of being an owner and the governing authority Therefore a negative effect from inefficient STATE should perhaps influence performance directly But, it may also be reflected in the liquidity

Attig et al (2003, 2006) find that government is associated with a lower spread

Naes (2004) finds that STATE is positively associated with adverse selection cost and negatively associated with market depth

3 Data and methods of analysis

3.1 Data and sample description

Our data are kindly provided by the Tunisian Stock Exchange (BVMT) The trading system on the BVMT is an electronic limit order market The orders are submitted by brokers on the behalf of investors and executed through an automated trading system, using a computerised limit-order book, known as SUPERCAC

Trading is executed from 8:30 am to 11:30 am from Monday to Friday It commenced

by a pre-opening session (from 9:00 am to 10:00 am) through which investors can deposit, change or cancel orders but no trades are permitted A theoretical opening price

is displayed in real time to show the market tendency There are two main trading methods: call auction and continuous trading The market opens by a call auction for all stocks at some point of time during the first five-minute opening period For the more actively traded stocks, this is followed by a continuous market until 11:30 am but, for illiquid stocks, a second call is set at 10:15 am for securities not traded at the open call and a last call takes place at 11:00 am

The BVMT is a pure order driven market where investors can choose between market and limit orders, so as liquidity is only provided by limit order traders Market orders have no limit on prices and look for immediate execution while limit orders specify a price either above the current asks or below the current bid and offer price improvement relative to market orders A market order is matched with the best opposite quote of the order book Limit orders are held in the limit order book until they are matched with incoming market orders to produce trades; otherwise, they are annulled or modified

A limit order faces the risk of non-execution whereas a market order executes with certainty At the end of each month, all orders are purged from the limit order book

Table 1 Number of companies listed on Tunisian Stock Exchange

2001 2002 2003 2004 2005 2006 2007

Market capitalisation MD* 6,527 5,490 3,840 3,085 2,976 2,842 3,275 Notes: *MD: millions de dinars This table presents the number of companies listed on

the Tunisian Stock Exchange (BVMT) at year-end and the market capitalisation values Our sample includes the 6-year period beginning in 2001 and ending in

2007

Table 1 shows the number of companies listed on the Tunisian Stock Exchange (BVMT)

at year-end and the market capitalisation values The sample period is from 1 January

2001 to 31 December 2007

The first dataset is the proportion of shares held by different types of investors There

is no electronic database on Tunisian firm ownership including relevant information on corporate governance characteristics Data of ownership are collected manually from firm’s annual reports available on the Tunisian Stock Exchange, from the leaflets of issue

of shares and from financial statements published in the official bulletins of the Tunisian Stock Exchange (BVMT) for seven years The ownership structures are those available

on the 31st December of each year

Our second dataset is the daily stock trading summary, including high, low, closing prices, trading volumes, end-of-day best bid prices, volume available at bid, end-of-day best ask price, volume available at ask for each stock and market capitalisation These data are obtained from the Tunisian Stock Exchange (BVMT) and are used to construct a variety of liquidity measures We exclude firms for which trading and liquidity data are not available

The final sample consists of 210 firm-year observations Table 2 present the variable definitions for the main variables used in the study

3.2.1 Ownership structure

The ownership structure of a firm in our sample is defined in terms of five variables:

blockholdings, INSID, INST, STATE and FORG

Our measure for ownership by blockholders (BLC) is the percentage of shares held

by the large blockholders, whose own more than 5% INSID is defined as the percentage

of the firm’s shares held by officers, directors and all other investors who may be related

to the management We compute the total number of shares held by institutional investors

as a percentage of shares by INST We considered as institutional investors, the banks, the investment firms, the insurance companies, pension funds, and mutual funds STATE is the percentage of shares held by the government FORG is defined as the percentage of

shares held by the foreign investors

Table 2 Variable definitions

Variable Definition

BLC The percentage of the firm’s shares held by the large blockholders, whose own

more than 5%

INSID The percentage of the firm’s shares held by officers, directors and all other

investors who may be related to the management

INST The percentage of the firm’s shares held by the institutional investors (the banks,

the investment firms, the insurance companies, pension funds, and mutual funds)

STATE The percentage of the firm’s shares held by the government

FORG The percentage of the firm’s shares held by the foreign investors

AQS The absolute quoted bid-ask spread is defined as the quoted ask price minus the

quoted bid price

RQS The relative quoted bid-ask spread is defined as the quoted ask price minus the

quoted bid price scaled by their midpoint

AES The absolute effective bid-ask spread is defined as two times the absolute value of

the difference between the transaction price and the quoted midpoint

RES The relative effective bid-ask spread is defined as two times the absolute value of

the difference between the transaction price and the quoted midpoint, scaled by the quoted midpoint

DEPTH The quoted depth is calculated as the number of shares at quoted bid and ask

multiplied by their respective prices

TURN The turnover is defined as the number of shares traded divided by the number of

shares outstanding

PIMP The price impact is the ratio of the daily absolute return to the daily dinar volume

PRICE The price is as the average of daily closing price

VOLUME The trading volume is defined as total trading volume divided by of trading days

VOL The volatility is measured as the standard deviation of daily close-to-close returns

SIZE The firm size is the natural logarithm of the market value of the firm’s equity,

calculated at the end of each trading day and averaged over the year

AS The adverse selection component of spread is estimated follow the method of Lin,

Sanger and Booth (LSB, 1995)

Notes: Table 2 reports variable definitions for the variables used in the study Our sample includes the 6-year period beginning in 2001 and ending in 2007 Our sample included stocks traded on the Tunisian Stock Exchange (BVMT)

3.2.2 Liquidity measures

In addition to the ownership data, we construct various dependent variables from the Tunisian Stock Exchange (BVMT) Because liquidity has many dimensions, we use seven liquidity measures that are usual in the literature (see, e.g., Aitken and Comerton-Forbe, 2003; Goyenko et al., 2009; Fang et al., 2009) The first measure is the

absolute quoted bid ask spread (AQS) defined as the quoted ask price minus the quoted

bid price, AQS t =Ask t−Bid t The second measure is the relative quoted bid-ask spread

(RQS) defined as the quoted ask price minus the quoted bid price scaled by their

midpoint,

2

t

RQS

−

=

+

The third measure is the absolute effective bid-ask

spread (AES) defined as two times the absolute value of the difference between the

transaction price and the quoted midpoint, 2

2

Ask Bid

measure is the relative effective bid-ask spread (RES) It is defined as two times the

absolute value of the difference between the transaction price and the quoted midpoint,

scaled by the quoted midpoint,

2

2 2

t t

Ask Bid p

RES

Ask Bid

+

−

=

+ Our fifth measure is the

quoted depths (DEPTH) calculated as the number of shares at quoted bid and ask

multiplied by their respective prices,

2

t

task ask tbid tbid t

measure is the turnover (TURN), defined as the number of shares traded divided by the number of shares outstanding The seventh measure is the PIMP is the measure

developed by Amihud (2002) and later used by Acharya and Pedersen (2005) It is the ratio of the daily absolute return to the daily dinar volume,1 t

t

t

R PIMP

VOLUME

We identify the impact of liquidity measures while controlling other factors that may affect ownership structure Stoll (1978) shows that relative spreads are negatively related with trading volume and share price, and positively related with returns volatility

In addition, Glosten and Harris (1988) suggest that spreads may be influenced by factors such as share price, trading volume, return volatility and firm size

We use a number of control variables defined in the pervious literature to account for any effects of external factors in our analysis Our control variables include share price, trading volume, volatility, and firm size Price (PRICE) is the average of daily closing price, trading volume (VOLUME) is defined as total trading volume divided by of trading days, volatility (VOL) (Heflin and Shaw, 2000) is measured as the standard deviation of daily close-to-close returns, and firm size2 (SIZE) is the logarithm of the market value of the firm’s equity, calculated at the end of each trading day and averaged over the year We use log transformation of market capitalisation values to reduce skewness This variable was also used by Rubin (2007) and Comerton-Forde and Rydge (2006) We anticipate spreads to be negatively associated to price, trading volume, and firm size, and positively associated to volatility If little spreads are accompanied by high depth and vice versa, we would anticipate depth to be positively associated to trading volume, and firm size, and negatively associated to volatility We define and present spread decomposition model in the next section

3.2.3 Adverse selection model

We compute an estimate of adverse selection component following the method introduced by Lin et al (1995)

We obtain adverse selection spread component estimates from estimating the following regression for each firm using ordinary least squares:

where Q t = (A t + B t )/2 is the quoted bid-ask spread midpoint at time t, z t = P t – Q t , P t is the transaction price prior to quoted spread at time t The coefficient λ is the LSB adverse

selection component of the bid-ask spread attributable to informed trading, and e t + 1 is a normally distributed error term

4 Empirical results

4.1 Descriptive statistics and univariate tests

After having defined the different variables, we suggest to present the descriptive statistics of ownership, liquidity, and other variables in the following table

Table 3 reports the summary statistics for liquidity measures and control variables relating to dimensions of the liquidity and the asymmetry of information selected, with the explanatory variables namely those concerning the ownership structure

[Blockholdings (BLC), INSID, INST, STATE, FORG] like those concerning the variables

of control (price, volume, volatility and size) over the period 2001–2007

In Table 3, we report summary statistics (mean, standard deviation, minimum value, maximum value, the coefficient of skewness, the coefficient of kurtosis, the Jarque and Bera statistics) for the variables used in our analysis Since our sample consists of

30 Tunisian companies, the distribution of ownership shows that the block ownership varies from a low of 7.48% to a maximum of 80.15% The mean proportion of shares outstanding held by blockholders is 37.292% The level of INSID varies between 0% and 74.5% with an average value of 6.193% The descriptive statistics also show that of institutional investors consists of banks, investment firms, insurance companies, pension funds, and mutual funds, hold on average 23.548% of a firm Similarly, Jennings et al

(2002) found that this value is equal to 23.21% On average, the level of STATE in our sample is 10.511% State is present for only a small portion of the sample We find that foreign investors control 11.670% of company share on average The descriptive statistics for ownership shows the importance of concentration in Tunisia

The statistics for the liquidity measures are calculated for each stock, and then averaged across stocks Given that the liquidity measures are highly skewed, we employ the log transformation of some measures The average relative quoted bid-ask spread is 2.0893% The values of skewness and kurtosis of this variable demonstrate that the distribution of spread is leptokurtic (kurtosis > 3) and asymmetric (skewness > 0)3 The average relative effective bid-ask spread is 2.3288% The average quoted depth is 325.901 The average asymmetric information cost is 0.3184 Our sample firms have an average market capitalisation of 1.34108 dinars The mean volatility is 0.0201

Table 4 presents correlation matrix of the variables included in the tests Panel A reports correlation between all ownership structure Panel B exhibits correlation between all liquidity measures: spreads, depth, turnover, PIMP and adverse selection costs Panel

C shows correlation between liquidity measures, ownership structure and control variables

Table 3 Descriptive statistics for ownership, liquidity and control variables