OWNERSHIP STRUCTURE AND STOCK MARKET LIQUIDITY

Atulya Sarin Santa Clara University

Karen A. Shastri

University of Pittsburgh

Kuldeep Shastri University of Pittsburgh

Revised: November, 1999 JEL Classification: G10, G32 Keywords: Insider Ownership, Institutional Ownership, Bid-Ask Spread, Quoted Depth,

Information Asymmetry We are grateful to David Denis and Ken Lehn for their comments and suggestions at various stages of this project. We would also like to thank Linda Allen, Ranjan D'Mello, Bart Danielson, Lynn Doran, Craig Dunbar, Richard Ellsworth, Hanan Eytan, Jack Clark Francis, David Feldman, Vidhan Goyal, Chris Hessel, Kathy Kahle, Naveen Khanna, Sudha Krishnaswami, Beni Lauterbach, Alvin Marty, Ron Masulis, Howard Ross, Janet Smith, Richard Smith, René Stulz, Kishore Tandon and Oscar Varela for their comments and suggestions. Earlier versions of this paper have been presented at Baruch College (CUNY), Claremont Graduate University, Michigan State University, University of Miami, University of New Orleans, the European Finance Association Meetings, the Eastern Finance Association Meetings and the Financial Management Association Meetings. Sarin acknowledges financial support from the Dean Witter Foundation. Correspondence should be sent to Kuldeep Shastri, 340 Mervis Hall, Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA. Phone: 412-648-1708, Fax: 412-648-1693, Email: Kuldeep@Katz.Pitt.Edu

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OWNERSHIP STRUCTURE AND STOCK MARKET LIQUIDITY Abstract

This paper examines the relation between stock liquidity and the fractional ownership of insiders and institutions. We find that higher insider and institutional ownership are both associated with wider spreads and smaller quoted depth. We also find that information asymmetry faced by traders is positively related to insider ownership while there is no relation between adverse selection costs and institutional holdings. Our results indicate that the higher adverse selection costs in firms with higher fractional insider ownership are a consequence of the increased probability of an insider trade. We also document that the average transaction size is larger in firms with higher institutional ownership. Overall, our results show that stock liquidity decreases with concentrated ownership. However, for higher insider ownership this loss of liquidity is a consequence of higher adverse selection costs while for higher institutional ownership it is a result of higher inventory carrying costs.

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OWNERSHIP STRUCTURE AND STOCK MARKET LIQUIDITY

1. Introduction

A market is liquid if the cost of buying or selling a large number of shares on demand

is low. Amihud and Mendelson (1986) show that market participants are willing to pay for

liquidity. They measure liquidity by the quoted bid-ask spread and show that there is a

positive relation between expected returns and spread. This suggests that the costs of

acquiring capital are lower for firms with more liquid securities. Thus, liquidity in the stock

market has consequences for a firm's financing/investment policies.

There are a number of factors that can impact stock market liquidity. Glosten and

Milgrom (1985) argue that one cause of illiquidity is the presence of privately informed

traders. One such group of privately informed traders is the insiders of a firm. For example,

Seyhun (1986) shows that insider trades precede abnormal changes in the price of their

company's stock. This suggests that the level of insider ownership in a firm may influence

the liquidity of the stock.

Bhide (1993) further argues that active stockholders who reduce agency costs by

monitoring managers may also reduce stock liquidity by increasing informational

asymmetries. Along a similar vein, Kahn and Winton (1985) argue that higher liquidity

demands cause share prices to not fully reveal how much monitoring occurs. As a result

higher liquidity would be associated with decreased monitoring. In contrast, Maug (1998)

suggests that even though a liquid stock market reduces a large shareholder's incentive to

monitor because it allows them to exit the stock more easily, it also makes it easier to

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purchase additional shares and less costly to hold larger stakes. Based on the latter

possibility, he finds that liquidity has a positive impact on monitoring by making corporate

governance more effective.

Since it is frequently argued in the literature that institutional investors play an

important monitoring role, there may exist a relation between the institutional ownership in a

firm and the liquidity of the firm’s stock (cf. Wahal, 1996). The influence of institutional

investors on stock liquidity may be further reinforced by the influence of their trading

practices on stock price (cf. Lakonishok, Schleifer and Vishny, 1992).

This paper empirically examines the relation between the liquidity of a firm’s stock

and the fraction of the firm owned by insiders and institutions. The bid-ask spread is a direct

cost of transacting and we use it as one measure of stock liquidity. We find for our sample

of Value Line firms that after controlling for other determinants of spreads and ownership,

spreads are positively related to both insider and institutional ownership. Lee, Mucklow and

Ready (1993) point out that, in addition to the bid-ask spread, quoted depth is also part of

the stock market quote and that specialists actively manage adverse selection risk by

adjusting both spread and depth. Specifically, they show that specialists increase spreads

and decrease quoted depth in response to increases in perceived information asymmetry.

Consistent with our evidence on bid-ask spreads, we find that the quoted depth is negatively

related to the fraction of the firm's stock owned by insiders and institutions.

We also show that the liquidity loss in firms with high insider ownership is driven by

the fact that the information asymmetry faced by traders is positively related to insider

holdings. This higher adverse selection cost is a result of the increased probability of an

insider trade in firms with higher managerial ownership. This follows from the result that the

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information asymmetry component of spread is an increasing function of insider ownership

when we do not control for fact that the frequency of insider trading is positively related to

the level of insider ownership. In contrast, the positive relationship between the information

asymmetry component of spread and insider ownership does not persist after controlling for

the impact of insider trading.

In the final part of our analysis, we find that the impact of institutional holdings on

liquidity is not being driven by the perception that institutions are informed since we do not

find a positive relation between adverse selection costs and institutional holdings. Our

results suggest that the reduced liquidity is a consequence of higher inventory control costs

in the stock of firms with large institutional concentration. This conclusion follows from our

finding that there is a positive relation between average transaction size and institutional

holdings, suggesting that larger institutional holdings are associated with larger trades, thus

forcing the market maker to hold a larger inventory.

The rest of the paper is organized as follows. In section 2 we develop the empirical

prediction for the liquidity effects of ownership structure. We describe the sample and data

sources in section 3. The relations between ownership structure and quotes, information

asymmetry, insider trading and transaction size are examined in Sections 4, 5, 6, and 7

respectively. Section 8 concludes.

2. Liquidity Effects of Ownership Structure

Theory predicts a negative relation between stock market liquidity and insider ownership.

Demsetz and Lehn (1985) and Denis and Denis (1994) argue that the benefit of higher

ownership is greater in firms where the profit potential of managers' actions is less

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observable and they show that firms facing a more uncertain environment have larger

insider ownership. Since the level of information asymmetry concerning the value of a firm

is an increasing function of this uncertainty, this suggests a positive cross-sectional relation

between information asymmetry and insider ownership. The higher level of information

asymmetry should, in turn, lead to wider bid-ask spreads. Also, higher levels of insider

ownership may be associated with higher probabilities of insider trading.1 Since the insiders

are expected to be informed, the market maker would incorporate a larger adverse selection

component into the quoted bid-ask spread and depth leading to wider spreads and a

smaller depth.2

The extant empirical evidence on the relation between spreads and insider

ownership is inconclusive. Chiang and Venkatesh (1988) examined this relation in 1973 for

a limited sample of 56 New York Stock Exchange (NYSE) stocks and found the predicted

positive relation. In contrast, Glosten and Harris (1988) reported an insignificant relation

between spreads and insider holdings for a sample of 250 NYSE stocks over the period

1981 to 1983. This study improves on previous work by (a) examining the relation between

quoted depth and insider ownership, (b) analyzing the impact of insider ownership on

1It could also be argued that in situations that insiders receive private benefits

from control, they may not trade. The evidence on this issue is mixed. For example, Demsetz (1986) finds a positive relation between insider trading and insider holdings, while Kahle (1996) finds no relation between the two.

2The adverse selection component of the spread arises in a market that consists of informed and liquidity (uniformed) traders. In this framework, the market maker expects to lose on trades with the informed traders and sets the bid-ask spread to maximize the difference between the expected gain from transactions with liquidity traders and the expected loss from transactions with informed traders. See Bagehot (1971), Copeland and Galai (1983), and Glosten and Milgrom (1985) for more details.

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adverse selection costs and (c) controlling for potential joint determinants of spread, depth

and insider ownership in the analysis.

With respect to institutional holdings, the predicted impact on liquidity is ambiguous.

On one hand, institutional traders are viewed as possessing private information about the

firm since they are exposed to a variety of analyses on the firm. Thus, increased institutional

participation should lead to wider bid-ask spreads and higher adverse selection costs. In

addition, since average trade size may be an increasing function of institutional holdings,

the market maker would be forced to hold a larger inventory position in stocks with higher

institutional holdings. This suggests that bid-ask spreads would be wider for firms with larger

institutional participation. In this case, however, the adverse selection costs would not

necessarily increase. A similar prediction would result from the argument that institutions

exhibit herding behavior or are involved in positive feedback. Alternatively, if increased

institutional holdings increases the likelihood that their buy and sell orders may be self

equating, the market maker would then be required to carry a smaller inventory and bid-ask

spreads would be a decreasing function of institutional holdings. This argument follows from

the view that institutions are negative feedback traders or that they are heterogeneous and

use a wide variety of portfolio strategies that offset each other.

A number of previous studies have examined the empirical relation between spreads

and institutional holdings. Tinic (1972) and Hamilton (1978) report a negative relation

between institutional ownership and bid-ask spreads for a sample of NYSE and National

Association of Securities Dealers Automated Quotation System (Nasdaq) stocks,

respectively. In contrast, Fabozzi (1979) and Chiang and Venkatesh (1988) find no

significant relation for a sample of Nasdaq and NYSE stocks, respectively. More recently,

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Kothare and Laux (1995) report a nonnegative relation for a sample of Nasdaq stocks, while

Jennings, Schnatterly and Seguin (1995) find that bid-ask spreads and the information

asymmetry component of spread decrease with institutional holdings for their sample of

Nasdaq stocks. Again, to our knowledge this is the first study that examines the relation

between depth and institutional ownership. In addition, we analyze the impact of institutional

ownership by controlling for potential joint determinants of spread, depth and institutional

ownership.

3. Sample Description

Our sample is extracted from firms listed in Value Line as of year-end 1984. The sample

is restricted to the 786 firms that satisfy the following criteria: (a) the firm is listed on the

American Stock Exchange (AMEX) or NYSE in 1985, (b) we could obtain data on ownership

of officers and directors from the proxy statement, (c) the firm’s stock has trade and quote

data available on the Institute for the Study of Securities Markets (ISSM) transactions

database from April to December 1985, (d) the stock has valid measures of information

asymmetry and (e) the firm is not a financial entity.

3.1 The Estimation of Spread, Depth and Ownership Structure

To evaluate the relation between ownership structure and the bid-ask spread, we first obtain

the daily weighted average bid-ask spread as in McInish and Wood (1985). For each stock,

the relative bid-ask spread, defined as the difference in the ask and bid prices divided by

the average of the bid and ask prices, is calculated for every quote. The daily weighted

average bid-ask spread is then estimated as the weighted average of the relative bid-ask

spread. The weight for each quote is the number of seconds the quote was outstanding

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divided by the total number of seconds for which quotes were outstanding in the trading

day.3 Then for each stock in our sample, we estimate the median weighted average bid-ask

spread (SPREAD) over the April to December 1985 period. All quote data are obtained from

the ISSM database. The quoted depth (DEPTH) for each stock is calculated using the same

technique used to obtain the spread.4

The ownership structure of a firm in our sample is defined in terms of two variables -

INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS. INSIDER HOLDINGS is defined as

the percentage of the outstanding shares owned by all officers and directors as specified in

the 1985 proxy statements. INSTITUTIONAL HOLDINGS is the percentage of outstanding

shares held by institutions as defined in the 1985 Value Line.

Table 1 contains some descriptive statistics on spreads, depth and ownership

structure. The mean and median spreads for the 786 stocks are 1.26 and 1.05 percent,

respectively. The mean and median quoted depth is 30.20 and 19.49 round lots,

respectively. The insider ownership in the sample averages 9.49 percent which is similar to

the averages reported in Brickley, Lease and Smith (1988), McConnell and Servaes (1990),

and Morck, Shleifer and Vishny (1988). For our sample, institutions own an average of

34.74 percent of the firm's shares, similar to the average of 32.9 percent reported in

Brickley, Lease and Smith (1988).

3We discard all quotes before and after the close of the market. 4Quoted depth is defined as the average number of shares the specialist is

willing to trade at a given price. Specifically, for each quote, depth is the average of the depth at the ask plus the depth at the bid.

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3.2. The Estimation of Information Asymmetry

For the purposes of this analysis we use three proxies for information asymmetry. The first

proxy is the adverse selection component of the bid-ask spread estimated using the

procedure in George, Kaul and Nimalendran (1991). Specifically, for each security we

estimate the relative adverse selection component (INFO) as Quoted Spread - Estimated

Spread, where Quoted Spread is the ratio of the difference between ask and bid prices and

the average of the bid and ask prices, and Estimated Spread is COV-2 , where COV is the

serial covariance of the difference between the return based on the last transaction price at

1:00 p.m. on each day and the return based on the bid price quoted subsequently to the

time of this transaction.5,6

A second proxy for information asymmetry is related to the model developed in

Glosten and Harris (1988). In this model, the change in transaction price at time t (∆pt) is

related to the signed order flow at t (qt), the public information innovation (εt) and the change

in the sign of the order at time t relative to that at t-1 (Dt-Dt-1).7 Specifically the equation

estimated for each stock j is:

5The GKN methodology to estimate effective spreads is an improvement over

Roll's methodology. Roll (1984) uses the serial covariance of returns, while GKN use the serial covariance of the difference in trade-to-trade returns and subsequent bid-to-bid returns. The latter methodology purges the effect of serial covariance in expected returns from the serial covariance of observed returns. Thus, the serial covariance of return differentials is purely a result of the bid-ask bounce.

6The use of the 1:00 p.m. transaction price avoids any problems associated with end-of-day trading biases. However, the results are not qualitatively different when closing quotes and prices are used.

7The classification of a buy or a sell follows that used in Lee and Ready (1991).

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jtjtjtjjtjjt DDqp εββ +−+=∆ − )( 121 (1)

The level of information asymmetry is measured by the coefficient β1j.

A third proxy used for information asymmetry is based on a model for intra-day

security price movements developed by Madhavan and Smidt (1991). In this model, market

makers use Bayesian rules to update their beliefs about the expected value of the stock. In

this framework, the expected stock value is represented as a combination of the prior mean

(based on public information) and a revision (due to a noisy signal based on private

information contained in the current order flow. We estimate the following version of the

Madhavan-Smidt model of the revision in transaction price using trade-by-trade data:

11321 −− ++−+=∆ jtjjtjtjjtjjtjjt zDDqp εεβββ (2)

where all variables have been defined previously.8 The level of information asymmetry is

measured by the coefficient β1j. Madhavan and Smidt (1991) argue that in a market with a

lower level of information asymmetry, each trade will convey less new information to the

specialist and, therefore, will have a lower impact on the price revision. This lower level of

information asymmetry results in a lower value of β1j.

As shown in Brennan and Subrahmanyam (1998) and Huang and Stoll (1997), the

three techniques used to measure information asymmetry are related to each other. In the

form being used here, all three models assume that the proportion of the spread that is

attributable to inventory cost is zero. In contrast to Glosten-Harris and Madhavan-Smidt,

George-Kaul-Nimalendran also assume no serial dependence in transaction type. The

8The original model in Madhavan and Smidt (1991) also includes terms for the

specialist's current and lagged inventory as two additional explanatory variables.

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Madhavan-Smidt model differs from Glosten-Harris in one major aspect. Specifically, the

Glosten-Harris framework does not allow orders to depend on price, while Madhavan and

Smidt allow informed investors to condition their order flow on the price.

4. Ownership Structure, Bid-Ask Spread and Quoted Depth

4.1 Bid-Ask Spread

Several studies have shown that price, return volatility and volume explain a

significant portion of the cross-sectional variation in spread.9 To account for this relation,

we use a multivariate framework to analyze the relation between bid-ask spreads and

ownership structure. Specifically, we regress spread on stock price (PRICE), trading volume

(VOLUME) and the standard deviation of daily returns (VOLATILITY), in addition to

INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS. 10,11

The parameter estimates are presented in the second column of Table 2. The

estimates of the coefficients of PRICE, VOLUME and VOLATILITY are consistent with

results reported in previous studies. Specifically, we find that spreads decrease in price and

volume and increase in volatility. The results also indicate that spreads increase with

insider holdings. In particular, we find that a 1 percent increase in fractional insider

However, they find that the coefficients on these variables are generally insignificant.

9See for example, Barclay and Smith (1988), Benston and Hagerman (1974), Choi and Subrahmanyam (1993), Choi and Shastri (1989), and Stoll (1978b).

10In all our regressions, we use the natural logarithms of all variables.

11Price is calculated using the same technique as spread and depth. Volatility is the standard deviation of the daily close-to-close returns and volume is the median daily trading volume in the sample period.

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ownership is associated with a 0.4 percent increase in spreads. In addition, we find a

positive relation between institutional ownership and bid-ask spreads. In this case, a 1

percent increase in fractional institutional ownership is associated with a 6 percent increase

in spreads. Our results also indicate that the addition of the ownership structure variables

causes the adjusted R2 to increase by 0.08 at the margin.

One problem with the above specification is that it ignores the fact that ownership

structure and spreads may be simultaneously determined by the same variables. To

account for this possibility, we follow a two-step procedure. We first regress INSIDER

HOLDINGS and INSTITUTIONAL HOLDINGS on the market value of equity (SIZE),

VOLATILITY, the ratio of research and development expenses to sales (R&DtoSales), the

age of the firm (AGE) and a dummy variable if the firm under consideration is a utility

(UTIL).12 We, then, use the residuals from these regressions as alternate measures of

ownership. We refer to these measures as INSIDER RESIDUAL and INSTITUTIONAL

RESIDUAL.

The results from the estimation of the regression of spreads on PRICE, VOLUME,

VOLATILITY, INSIDER RESIDUAL and INSTITUTIONAL RESIDUAL are reported in the

third column of Table 2. The results from this estimation are similar to those reported

12The choice of independent variables is based, in part, on previous work on the

determinants of ownership structure (cf. Demsetz and Lehn, 1985). A firm is classified as a utility if the first two digits of its SIC code are 34. Our sample contains 98 utilities. The estimated regression parameters are, in general, consistent with previous work. Specifically, we find that insider ownership increases in volatility and research and development expenditures to sales and decreases in firm size, firm age and the utility dummy. In the case of institutional holdings, we find that it increases with firm size and age and decreases with the other three variables.

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earlier. Specifically, we find that INSIDER RESIDUAL and INSTITUTIONAL RESIDUAL are

both positively related to spread

4.1.1 A Simultaneous Equation System for Spreads and Ownership Structure

There is anecdotal evidence that suggests that institutions tend to purchase stocks

with low spreads and high trading volume. To account for the possibility that spread and

trading volume affect institutional ownership, and that institutional ownership simultaneously

also affects spreads, we estimate a system of three equations with bid-ask spread, insider

ownership and institutional ownership as the dependent variables. The independent

variables in the spread and insider ownership equations are the same as those used in the

previous subsection. In the institutional holdings equation, we include two additional

independent variables - SPREAD and VOLUME. The results of this estimation are

presented in Table 3. The second column in this table provides the results for the SPREAD

equation. The corresponding results for the INSIDER HOLDINGS equation are in the third

column, while the fourth column contains the results for the INSTITUTIONAL HOLDINGS

equation.

As can be seen from the fourth column in this table, our results support the argument

that institutions tend to migrate to stocks with lower spreads and larger trading volume. This

follows from the fact that the coefficient of spread in the institutional holdings equation is

negative while that for volume is positive. On the other hand, consistent with our earlier

results, the spread equation indicates that, at the margin, higher insider and institutional

ownership is associated with higher spreads. As a matter of fact, a comparison of the

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second column in Tables 2 and 3 indicates that the elasticity of spread with respect to

insider and institutional ownership changes very little across the two specifications.

4.2 Quoted Depth

It has been argued by Lee, Mucklow and Ready (1993) that the spread is only one

dimension of market liquidity. A second measure that also impacts liquidity is the number of

shares market makers is willing to purchase or sell at the quoted bid and ask prices,

respectively. Moreover, Lee, Mucklow and Ready (1993) suggest that the bid-ask spread

and the quoted depth are jointly determined with increased depth, ceteris paribus, indicating

an improvement in liquidity. If the depth of the quotes is simultaneously determined with the

spread, then the determinants of the spread should also be related to the depth. Therefore,

to determine if ownership structure is related to quoted depth, we estimate a regression of

DEPTH on PRICE, VOLUME, VOLATILITY and ownership. In view of the results on

spreads, we would expect depth to be negatively related to both insider ownership and

institutional ownership. We find, in the fourth and fifth columns of Table 2, that the

coefficients of insider and institutional ownership are negative and significant in all

specifications. In particular, we find that a 1 percent increase in fractional insider ownership

is associated with a 1 percent decrease in depth while a 1 percent increase in fractional

institutional ownership is associated with a 4 percent decrease in depth. 13

Finally, given the argument that spread and depth and jointly determined, we also

estimate a regression of spread per unit of quoted depth (ratio of spread to depth) on

13The marginal contribution of the ownership variables to the adjusted R2 of the

depth equation is 0.01.

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PRICE, VOLUME, VOLATILITY and ownership. In view of the results on spreads and

depth, we would expect the spread-depth ratio to be positively related to both insider

ownership and institutional ownership. We find, in the sixth and seventh columns of Table

2, that the coefficients of insider and institutional ownership are positive and significant in all

specifications. Thus, our results provide support for the hypothesis that concentrated

ownership adversely impacts stock liquidity.

5. Ownership Structure and Information Asymmetry

One possible reason for wider spreads and narrower quoted depth in firms with

higher levels of insider/institutional ownership is that adverse selection costs faced by the

specialists are higher in these firms. These costs could be a consequence of a higher

probability of informed trading or a greater expected loss from an informed trade. We test

this hypothesis using the three different proxies for information asymmetry that were

detailed in section 3.2 - George, Kaul and Nimalendran’s measure of the adverse selection

component of spread, the Glosten-Harris measure of the price impact of a trade and the

Madhavan-Smidt price impact measure (INFO, IMPACTGH and IMPACTMS, respectively).

Specifically, we regress the three measures on INSIDER HOLDINGS and INSTITUTIONAL

HOLDINGS. Since we would expect INFO, IMPACTGH and IMPACTMS to increase with

information asymmetry, the hypothesis that adverse selection costs faced by the specialists

are higher in the firms with a higher insider and institutional ownership would receive

support if all three proxies increase in INSIDER HOLDINGS and INSTITUTIONAL

HOLDINGS. The results of this analysis are presented in Table 4.

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As can be seen from the second column in this table, INFO is an increasing function

of INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS. Replacing the two ownership

variables by INSIDER RESIDUAL and INSTITUTIONAL RESIDUAL does not affect this

conclusion (see the third column of table 4). This result suggests that higher insider and

institutional ownership is associated with more information asymmetry.

The results from the regressions of IMPACTGH and IMPACTMS confirm these

conclusions for insider ownership but provide the opposite results for institutional ownership.

The fourth and fifth columns of table 4 indicate that IMPACTGH is positively related to

INSIDER HOLDINGS and negatively related to INSTITUTIONAL HOLDINGS. Along the

same lines, IMPACTMS is positively related to INSIDER HOLDINGS and negatively related

to INSTITUTIONAL HOLDINGS. This is consistent with the argument that adverse selection

costs might be lower for firms with higher institutional ownership and/or that institutions

migrate to firms with lower informational asymmetry.

6. Ownership Structure, Insider Trading and Information Asymmetry

In the previous section, we found a positive relation between insider holdings and

information asymmetry. As argued earlier, this positive relation can be attributed to two

factors. First, if higher insider holdings are associated with a larger perceived probability of

insider trading, the specialist would compensate for the expected loss to informed traders by

quoting a wider spread. On the other hand, even if the probability of insider trading is not

expected to be higher, the market maker would quote a higher spread if the expected loss

from an informed trade is larger for firms with higher insider ownership. This hypothesized

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relation would hold if, as argued by Demsetz and Lehn (1985) and Denis and Denis (1994),

managers in firms with higher insider ownership have larger informational advantages. In

this section, we attempt to distinguish between these two possible explanations for the

positive relation between insider ownership and information asymmetry.

To determine if there is a relation between spreads and the perceived probability of

insider trading, we need a proxy for the latter variable. We use the actual fraction of trades

accounted for by insiders (INSIDER TRADING) from April to December 1985 as the proxy.

Trades accounted for by insiders are defined as open market purchases or sales by insiders

as reported in the SEC’s Official Summary of Securities Transactions and Holdings.

First, to confirm that insider trading is related to ownership structure, we estimate a

regression of insider trading on insider and institutional ownership. As can be seen from the

results in Table 5, INSIDER TRADING is positively related to INSIDER HOLDINGS and not

related to INSTITUTIONAL HOLDINGS. This suggests that the positive relation between

spreads (information asymmetry) and insider ownership could be attributable to higher

levels of insider trading for firms with larger insider ownership.

A more direct test of this hypothesis would be to re-estimate the regression in table 4

after accounting for the effects of insider trading. If the positive relation between information

asymmetry and insider ownership is due to higher levels of insider trading for firms with

more concentrated insider ownership, we would expect insider holdings not to be related to

information asymmetry after accounting for insider trading. One of the problems with this

approach is that a regression of measures of information asymmetry on INSIDER

HOLDINGS and INSIDER TRADING would suffer from multi-collinearity problems. To avoid

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these problems, we use a two-step approach. We first regress the proxies for information

asymmetry on VOLUME, VOLATILITY, PRICE and INSIDER TRADING.14 We, then, use

the residual from this estimation as the dependent variable in a regression with independent

variables related to ownership structure. The results of this estimation are provided in Table

6.

The results in this table indicate that after controlling for insider trading, we observe

an insignificant relation between information asymmetry and insider ownership. This

suggests that the higher levels of information asymmetry in firms with larger insider

ownership are a result of higher perceived probabilities of insider trading.

14These regressions indicate that INFO, IMPACTGH and IMPACTMS increase in

INSIDER TRADING.

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7. Transaction Size and Ownership Structure

The results reported earlier suggest that higher institutional holdings are associated with

wider spreads, but there is a no relation between information asymmetry and institutional

holdings. One possible reason for these results is that firms with higher institutional holdings

could pose larger inventory control costs for the market maker. This hypothesis would

receive support if there is a positive relation between INSTITUTIONAL HOLDINGS and

average transaction size and follows from the argument that larger transactions would force

the market maker to hold a larger inventory, thus increasing the inventory cost component

of the spread. To test this hypothesis, we estimate a regression relating average transaction

size to insider and institutional ownership. The results of the estimation are reported in

Table 7.

As can be seen from this table, average transaction size decreases with insider

ownership but increases with institutional ownership. This suggests that for firms with higher

institutional holdings, transactions are associated, on average, with a larger number of

shares being traded. This, in turn, would imply that inventory costs may be an alternative

explanation for the possible relation between spreads and institutional ownership and the

non-positive relation to information asymmetry.

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8. Conclusion

The purpose of this paper is to examine the relation between liquidity and ownership

structure. We find that insider ownership is positively related to both bid-ask spreads and

the information asymmetry component of the spread and negatively related to quoted depth.

These results suggest that traders face higher adverse selection costs with larger insider

holdings. The higher adverse selection costs are a consequence the increased probability of

an insider trades in firms with higher managerial ownership. We also find a positive

(negative) relation between the bid-ask spread (quoted depth) and the level of institutional

ownership. However, this relation is not being driven by the specialist expecting to lose on

informed trades, but because they are required to maintain larger inventories.

Overall, our results suggest that stock liquidity decreases with concentrated

ownership. This decrease in liquidity is a factor that should be considered when evaluating

the effect of ownership structure on firm value.

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23

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24

TABLE 1 Descriptive Statistics Descriptive statistics for ownership of all officers and directors (INSIDER HOLDINGS) and institutions (INSTITUTIONAL HOLDINGS), market value of equity (SIZE), price per share (PRICE), firm age (AGE), research and development expenses to sales (R&D to SALES), standard deviation of daily returns (VOLATILITY), relative bid-ask spread (SPREAD), quoted depth (DEPTH), daily trading volume (VOLUME), average trade size (TRADE SIZE) and the fraction of trading volume accounted for by insider trades (INSIDER TRADING) for a sample of 786 firms. The sample satisfies the following requirements: (a) the firm was followed by Value Line in 1985, (b) the stock has trade and quote data on the Institute for the Study of Securities Market (ISSM) transaction database from April to December 1985, (c) measures of information asymmetry could be estimated from the quote and trade data on the ISSM database (d) 1985 corporate proxy statements are available for the firm and (e) the firm was not a financial. The data on ownership of officers and directors is obtained from 1985 corporate proxy statements, institutional ownership is obtained for 1985 from Value Line, the number of shares outstanding, firm age, research and development expenses and sales are obtained for 1985 from COMPUSTAT, insider trading information is obtained from the Securities and Exchange Commission’s Official Summary of Securities Transactions and Holdings for 1985 and all remaining variables are based on data from the ISSM database from April to December 1985.

Variable

Mean

Median

First

quartile

Third

quartile INSIDER HOLDINGS (%)

9.49

3.93

0.20

13.00

INSTITUTIONAL HOLDINGS (%)

34.74

34.76

19.38

49.32

SIZE ($000,000)

1542.5

457.58

155.29

1344.36

PRICE ($)

29.03

25.63

16.00

36.13

AGE (years)

18.79

23.00

15.00

23.00

R&D to SALES (%)

6.51

2.22

0.61

4.46

VOLATILITYa (%)

2.01

1.63

1.30

2.12

SPREADb (%)

1.26

1.05

0.73

1.53

DEPTHc

30.20

19.49

11.01

36.84

VOLUME (round lots)

633.49

214.00

67.00

720.00

TRADE SIZE (round lots)

10.05

8.82

6.00

13.18

INSIDER TRADING (%)

0.69

0.32

0.10

0.75

aVolatility is measured as the standard deviation of close-to-close returns. bSpread is measured as the ratio of the difference between the ask and bid prices to the average of the ask and bid prices. cQuoted depth is (depth at ask + depth at bid)/2 where depth is the number of shares the specialist is willing to buy (bid) or sell

(ask).

25

TABLE 2 Bid-Ask Spreads, Quoted Depth and Ownership Structure

Coefficient estimates of ordinary least square regressions relating the natural logarithm of bid-ask spread, quoted depth and the spread-depth ratio on the natural logarithms of percentage ownership of all officers and directors, percentage ownership of institutions, daily trading volume, standard deviation of daily returns and price per share. The percentage ownership of all officers and directors and the percentage ownership of institutions are measured in two ways -the actual percentage (INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS) and the residuals (INSIDER RESIDUAL and INSTITUTIONAL RESIDUAL) from regressions of INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS on market value of equity), standard deviation of daily returns, research and development expenses to sales, firm age and a dummy variable to indicate that the firm is a utility.a The sample is described in Table 1. p-values are reported in parenthesis below. All tests of statistical significance are based on White’s heteroskedasticity-consistent estimates of variances.

Spread

Depth

Spread-Depth Ratio

Independent variables

(1)

(2)

(1)

(2)

(1)

(2)

Intercept

-1.892 (0.00)

-1.754 (0.00)

2.423 (0.00)

2.276 (0.00)

-4.315 (0.00)

-4.029 (0.00)

VOLUME

-0.132 (0.00)

-0.129 (0.00)

0.534 (0.00)

0.536 (0.00)

-0.668 (0.00)

-0.665 (0.00)

VOLATILITY

0.057 (0.00)

0.052 (0.00)

-0.223 (0.00)

-0.227 (0.00)

0.280 (0.00)

0.279 (0.00)

PRICE

-0.686 (0.00)

-0.674 (0.00)

-0.643 (0.00)

-0.643 (0.00)

-0.043 (0.20)

-0.032 (0.32)

INSIDER HOLDINGS

0.004 (0.02)

-

-0.010 (0.00)

-

0.014 (0.00)

-

INSIDER RESIDUAL

-

0.003 (0.07)

-

-0.009 (0.03)

-

0.012 (0.02)

INSTITUTIONAL HOLDINGS

0.060 (0.00)

-

-0.040 (0.04)

-

0.100 (0.00)

-

INSTITUTIONAL RESIDUAL

-

0.059 (0.00)

-

-0.055 (0.01)

-

0.115 (0.00)

Adjusted R2

0.96

0.96

0.87

0.87

0.89

0.89

aThe estimated equations are: ln(INSIDER HOLDINGS) = 6.67 - 0.89ln(SIZE) + 0.42ln(VOLATILITY) + 0.47ln(R&DtoSALES) - 0.33ln(AGE) - 3.24UTIL ln(INSTITUTIONAL HOLDINGS) = 1.87 + 0.21ln(SIZE) - 0.15ln(VOLATILITY) - 0.19ln(R&DtoSALES) + 0.12ln(AGE) - 0.67 UTIL The adjusted R2 are 0.35 and 0.28, respectively. In the insider holdings equation, the coefficients of SIZE and UTIL are significant at the 1 percent level. The p-values for VOLATILITY, R&DtoSALES and AGE are 0.06, 0.44 and 0.09, respectively. In the institutional holdings equation, all coefficients except for that of R&DtoSALES are significant at the 1 percent level. The coefficient of R&DtoSALES has an associated p-value of 0.22 in this equation.

26

TABLE 3 Bid-Ask Spreads and Ownership Structure Coefficient estimates of a simultaneous equation system relating (a) the natural logarithm of bid-ask spread to the natural logarithms of percentage ownership of all officers and directors, percentage ownership of institutions, daily trading volume, standard deviation of daily returns and price per share, (b) the natural logarithm of the percentage ownership of all officers and directors to the natural logarithms of market value of equity, standard deviation of daily returns, research and development expenses to sales, firm age and a dummy variable to indicate that the firm is a utility and (c) the percentage ownership of institutions on market value of equity, standard deviation of daily returns, research and development expenses to sales, firm age and a dummy variable to indicate that the firm is a utility, the bid-ask spread and daily trading volume. The sample is described in Table 1. p-values are reported in parenthesis below.

Dependent variables

Independent variables

SPREAD

INSIDER

HOLDINGS

INSTITUTIONAL

HOLDINGS Intercept

-1.895 (0.00)

6.462 (0.00)

0.399 (0.13)

VOLUME

-0.133 (0.00)

-

0.139 (0.00)

VOLATILITY

0.056 (0.00)

0.416 (0.00)

-0.143 (0.01)

PRICE

-0.687 (0.00)

-

-

SIZE

-

-0.892 (0.00)

-0.070 (0.05)

AGE

-

-0.248 (0.20)

0.088 (0.06)

R&DtoSALES

-

0.909 (0.16)

0.030 (0.84)

UTIL

-

-3.223 (0.00)

-0.636 (0.00)

SPREAD

-

-

-0.555 (0.00)

INSIDER HOLDINGS

0.004 (0.00)

-

-

INSTITUTIONAL HOLDINGS

0.061 (0.00)

-

-

Adjusted R2

0.96

0.36

0.34

27

TABLE 4 Information Asymmetry and Ownership Structure

Coefficient estimates of ordinary least square regressions relating the natural logarithm of three measures of information asymmetry on the natural logarithms of percentage ownership of all officers and directors, percentage ownership of institutions, daily trading volume, standard deviation of daily returns and price per share. The three measures of information asymmetry are the dollar amount of the spread attributable to information asymmetry (INFO), the price impact of a trade as measured in Glosten and Harris (1988) (IMPACTGH), and the price impact of a trade as measured in Madhavan and Smidt (1991) (IMPACTMS). All three measures of information asymmetry are normalized by price per share. The percentage ownership of all officers and directors and the percentage ownership of institutions are measured in two ways - the actual percentage (INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS) and the residuals (INSIDER RESIDUAL and INSTITUTIONAL RESIDUAL) from regressions of INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS on market value of equity, standard deviation of daily returns, research and development expenses to sales, firm age and a dummy variable to indicate that the firm is a utility.a The sample is described in Table 1. p-values are reported in parentheses below. All tests of statistical significance are based on White’s heteroskedasticity-consistent estimates of variances.

Dependent variable

INFOb

IMPACTGH

c

IMPACTMSd

Independent variables

(1)

(2)

(1)

(2)

(1)

(2)

Intercept

1.359 (0.00)

1.836 (0.00)

-6.237 (0.00)

-6.491 (0.00)

-7.968 (0.00)

-8.260 (0.00)

VOLUME

-0.311 (0.00)

-0.320 (0.00)

-0.803 (0.00)

-0.880 (0.00)

-0.526 (0.00)

-0.596 (0.00)

VOLATILITY

0.128 (0.08)

0.120 (0.10)

0.527 (0.00)

0.638 (0.00)

0.472 (0.00)

0.559 (0.00)

PRICE

-1.487 (0.00)

-1.523 (0.00)

-0.083 (0.21)

-0.154 (0.02)

-0.221 (0.01)

-0.336 (0.00)

INSIDER HOLDINGS

0.028 (0.01)

-

0.049 (0.00)

-

0.057 (0.00)

-

INSIDER RESIDUAL

-

0.023 (0.05)

-

0.021 (0.07)

-

0.030 (0.09)

INSTITUTIONAL HOLDINGS

0.087 (0.05)

-

-0.251 (0.00)

-

-0.294 (0.00)

-

INSTITUTIONAL RESIDUAL

-

0.074 (0.09)

-

-0.256 (0.00)

-

-0.317 (0.00)

Adjusted R2

0.74

0.69

0.73

0.73

0.46

0.44

aThe estimated equations are:

ln(INSIDER HOLDINGS) = 6.67 - 0.89ln(SIZE) + 0.42ln(VOLATILITY) + 0.47ln(R&DtoSALES) - 0.33ln(AGE) - 3.24UTIL ln(INSTITUTIONAL HOLDINGS) = 1.87 + 0.21ln(SIZE) - 0.15ln(VOLATILITY) - 0.19ln(R&DtoSALES) + 0.12ln(AGE) - 0.67 UTIL

28

TABLE 4 (continued) Information Asymmetry and Ownership Structure

The adjusted R2 are 0.35 and 0.28, respectively. In the insider holdings equation, the coefficients of SIZE and UTIL are significant at the 1 percent level. The p-values for VOLATILITY, R&DtoSALES and AGE are 0.06, 0.44 and 0.09, respectively. In the institutional holdings equation, all coefficients except for that of R&DtoSALES are significant at the 1 percent level. The coefficient of R&DtoSALES has an associated p-value of 0.22 in this equation. Coefficient estimates of ordinary least square regressions relating the natural logarithm of three measures of information asymmetry on the natural logarithms of percentage ownership of all officers and directors, percentage ownership of institutions, daily trading volume, standard deviation of daily returns and price per share. The three measures of information asymmetry are the dollar amount of the spread attributable to information asymmetry (INFO), the price impact of a trade as measured in Glosten and Harris (1988) (IMPACTGH), and the price impact of a trade as measured in Madhavan and Smidt (1991) (IMPACTMS). All three measures of information asymmetry are normalized by price per share. The percentage ownership of all officers and directors and the percentage ownership of institutions are measured in two ways - the actual percentage (INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS) and the residuals (INSIDER RESIDUAL and INSTITUTIONAL RESIDUAL) from regressions of INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS on market value of equity, standard deviation of daily returns, research and development expenses to sales, firm age and a dummy variable to indicate that the firm is a utility.a The sample is described in Table 1. p-values are reported in parentheses below. All tests of statistical significance are based on White’s heteroskedasticity-consistent estimates of variances.

bINFO is defined as (quoted spread - effective spread). The effective spread is calculated using the formula 2√-Cov, where Cov

is the serial covariance of the difference between returns based on transaction prices and the returns based on bid-to-bid prices. See George, Kaul and Nimalendran (1991) for more details.

cIMPACTGH is estimated as the coefficient β1 obtained from the following equation for revisions in transaction price + D - D + q = p t1-tt2t1t εββ )(∆

where qt is the signed transaction size, and Dt equals + 1 for a buy and -1 for a sell. The classification of a buy or a sell follows that used in Lee and Ready (1991). See Glosten and Harris (1988) and Brennan and Subrahmanyam (1995) for more details

dIMPACTMS is estimated as the coefficient β1 obtained from the following equation for revisions in transaction price εεβββ 1-tjt1-t3t2t1t z + + D - D + q = p∆

where all variables are defined in footnote (c). See Madhavan and Smidt (1991) and Brennan and Subrahmanyam (1995) for more details.

29

TABLE 5

Insider Trading and Ownership Structure

Coefficient estimates of ordinary least square regressions relating the natural logarithm of the fraction of daily trading that is accounted for by insiders on the natural logarithms of percentage ownership of all officers and directors and percentage ownership of institutions. The percentage ownership of all officers and directors and the percentage ownership of institutions are measured in two ways - the actual percentage (INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS) and the residuals (INSIDER RESIDUAL and INSTITUTIONAL RESIDUAL) from regressions of INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS on market value of equity, standard deviation of daily returns, research and development expenses to sales, firm age and a dummy variable to indicate that the firm is a utility.a The sample is described in Table 1. p-values are reported in parenthesis below. All tests of statistical significance are based on White’s heteroskedasticity-consistent estimates of variances.

Independent variables

(1)

(2) Intercept

-1.141 (0.00)

-1.126 (0.00)

INSIDER HOLDINGS

0.179 (0.00)

-

INSIDER RESIDUAL

-

0.107 (0.00)

INSTITUTIONAL HOLDINGS

-0.002 (0.97)

-

INSTITUTIONAL RESIDUAL

-

0.102 (0.24)

Adjusted R2

0.18

0.04

a The estimated equations are:

ln(INSIDER HOLDINGS) = 6.67 - 0.89ln(SIZE) + 0.42ln(VOLATILITY) + 0.47ln(R&DtoSALES) - 0.33ln(AGE) - 3.24UTIL ln(INSTITUTIONAL HOLDINGS) = 1.87 + 0.21ln(SIZE) - 0.15ln(VOLATILITY) - 0.19ln(R&DtoSALES) + 0.12ln(AGE) - 0.67 UTIL The adjusted R2 are 0.35 and 0.28, respectively. In the insider holdings equation, the coefficients of SIZE and UTIL are significant at the 1 percent level. The p-values for VOLATILITY, R&DtoSALES and AGE are 0.06, 0.44 and 0.09, respectively. In the institutional holdings equation, all coefficients except for that of R&DtoSALES are significant at the 1 percent level. The coefficient of R&DtoSALES has an associated p-value of 0.22 in this equation..

30

TABLE 6 Information Asymmetry, Insider Trading and Ownership Structure

Coefficient estimates of ordinary least square regressions relating the three measures of information asymmetry residuals on the natural logarithms of percentage ownership of all officers and directors and the percentage ownership of institutions The information asymmetry residuals are obtained from regression of the natural logarithms of the three measures of information asymmetry on the fraction of daily trading that is accounted for by insiders. The three measures of information asymmetry are the dollar amount of the spread attributable to information asymmetry (INFO), the price impact of a trade as measured in Glosten and Harris (1988) (IMPACTGH), and the price impact of a trade as measured in Madhavan and Smidt (1991) (IMPACTMS). All three measures of information asymetry are normalized by price per share. The percentage ownership of all officers and directors and the percentage ownership of institutions are measured in two ways - the actual percentage (INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS) and the residuals (INSIDER RESIDUAL and INSTITUTIONAL RESIDUAL) from regressions of INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS on market value of equity, standard deviation of daily returns, research and development expenses to sales, firm age and a dummy variable to indicate that the firm is a utility.a The sample is described in Table 1. p-values are reported in parenthesis below. All tests of statistical significance are based on White’s heteroskedasticity-consistent estimates of variances.

Dependent variable

Residual of INFOb

Residual of IMPACTGH

c

Residual of IMPACTMS

d Independent Variables

(1)

(2)

(1)

(2)

(1)

(2)

Intercept

5.905 (0.00)

6.097 (0.00)

4.851 (0.00)

4.238 (0.00)

4.235 (0.00)

3.753 (0.00)

VOLUME

-0.201 (0.00)

-0.198 (0.00)

-0.518 (0.00)

-0.571 (0.00)

-0.339 (0.00)

-0.388 (0.00)

VOLATILITY

-0.004 (0.96)

-0.026 (0.72)

0.229 (0.02)

0.283 (0.00)

0.262 (0.02)

0.309 (0.00)

PRICE

-1.586 (0.00)

-1.609 (0.00)

-0.307 (0.00)

-0.397 (0.00)

-0.407 (0.00)

-0.540 (0.00)

INSIDER HOLDINGS

0.004 (0.74)

-

0.006 (0.65)

-

0.030 (0.05)

-

INSIDER RESIDUAL

-

0.009 (0.50)

-

-0.007 (0.61)

-

0.016 (0.75

INSTITUTIONAL HOLDINGS

0.028 (0.54)

-

-0.345 (0.00

-

-0.338 (0.00)

-

INSTITUTIONAL RESIDUAL

-

0.042 (0.40)

-

-0.277 (0.00)

-

-0.302 (0.00)

Adjusted R2

0.69

0.69

0.51

0.50

0.33

0.32

aThe estimated equations are: ln(INSIDER HOLDINGS) = 6.67 - 0.89ln(SIZE) + 0.42ln(VOLATILITY) + 0.47ln(R&DtoSALES) - 0.33ln(AGE) - 3.24UTIL

ln(INSTITUTIONAL HOLDINGS) = 1.87 + 0.21ln(SIZE) - 0.15ln(VOLATILITY) - 0.19ln(R&DtoSALES) + 0.12ln(AGE) - 0.67 UTIL The adjusted R2 are 0.35 and 0.28, respectively. In the insider holdings equation, the coefficients of SIZE and UTIL are significant at the 1 percent level. The p-values for VOLATILITY, R&DtoSALES and AGE are 0.06, 0.44 and 0.09, respectively. In the institutional holdings equation, all coefficients except for that of R&DtoSALES are significant at the 1 percent level. The coefficient of R&DtoSALES has an associated p-value of 0.22 in this equation.

31

TABLE 6 (continued) Information Asymmetry, Insider Trading and Ownership Structure

Coefficient estimates of ordinary least square regressions relating the three measures of information asymmetry residuals on the natural logarithms of percentage ownership of all officers and directors and the percentage ownership of institutions The information asymmetry residuals are obtained from regression of the natural logarithms of the three measures of information asymmetry on the fraction of daily trading that is accounted for by insiders. The three measures of information asymmetry are the dollar amount of the spread attributable to information asymmetry (INFO), the price impact of a trade as measured in Glosten and Harris (1988) (IMPACTGH), and the price impact of a trade as measured in Madhavan and Smidt (1991) (IMPACTMS). All three measures of information asymetry are normalized by price per share. The percentage ownership of all officers and directors and the percentage ownership of institutions are measured in two ways - the actual percentage (INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS) and the residuals (INSIDER RESIDUAL and INSTITUTIONAL RESIDUAL) from regressions of INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS on market value of equity, standard deviation of daily returns, research and development expenses to sales, firm age and a dummy variable to indicate that the firm is a utility.a The sample is described in Table 1. p-values are reported in parenthesis below. All tests of statistical significance are based on White’s heteroskedasticity-consistent estimates of variances.

bINFO is defined as (quoted spread - effective spread). The effective spread is calculated using the formula 2√-Cov, where Cov is the serial covariance of the difference between returns based on transaction prices and the returns based on bid-to-bid prices. See George, Kaul and Nimalendran (1991) for more details. Residuals of this variable are obtained from the regression ln(INFO) = -4.315 + 0.234ln(INSIDER TRADING). The coefficient of insider trading has an associated t-value of 5.82.

cIMPACTGH is estimated as the coefficient β1 obtained from the following equation for revisions in transaction price + D - D + q = p t1-tt2t1t εββ )(∆

where qt is the signed transaction size, and Dt equals + 1 for a buy and -1 for a sell. The classification of a buy or a sell follows that used in Lee and Ready (1991).. See Glosten and Harris (1988) and Brennan and Subrahmanyam (1995) for more details. Residuals of this variable are obtained from the regression ln(IMPACTGH) = -10.885 + 0.533ln(INSIDER TRADING). The coefficient of insider trading has an associated t-value of 13.75.

dIMPACTMS is estimated as the coefficient β1 obtained from the following equation for revisions in transaction price εεβββ 1-tjt1-t3t2t1t z + + D - D + q = p∆ where all variables are defined in footnote (c) See Madhavan and Smidt (1991) and Brennan and Subrahmanyam (1995)

for more details. Residuals of this variable are obtained from the regression ln(IMPACTMS) = -11.964 + 0.35ln(INSIDER TRADING). The coefficient of insider trading has an associated t-value of 8.19.

32

TABLE 7

Average Transaction Size and Ownership Structure

Coefficient estimates of ordinary least square regressions relating the natural logarithm of the average transaction size on the natural logarithms of percentage ownership of all officers and directors and percentage ownership of institutions. The percentage ownership of all officers and directors and the percentage ownership of institutions are measured in two ways - the actual percentage (INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS) and the residuals (INSIDER RESIDUAL and INSTITUTIONAL RESIDUAL) from regressions of INSIDER HOLDINGS and INSTITUTIONAL HOLDINGS on market value of equity, standard deviation of daily returns, research and development expenses to sales, firm age and a dummy variable to indicate that the firm is a utility. The sample is described in Table 1. p-values are reported in parenthesis below. All tests of statistical significance are based on White’s heteroskedasticity-consistent estimates of variances.

Independent variables

(1)

(2) Intercept

1.092 (0.00)

2.171 (0.00)

INSIDER HOLDINGS

-0.038 (0.00)

-

INSIDER RESIDUAL

-

-0.017 (0.02)

INSTITUTIONAL HOLDINGS

0.325 (0.00)

-

INSTITUTIONAL RESIDUAL

-

0.186 (0.00)

Adjusted R2

0.29

0.22

aThe estimated equations are:

ln(INSIDER HOLDINGS) = 6.67 - 0.89ln(SIZE) + 0.42ln(VOLATILITY) + 0.47ln(R&DtoSALES) - 0.33ln(AGE) - 3.24UTIL ln(INSTITUTIONAL HOLDINGS) = 1.87 + 0.21ln(SIZE) - 0.15ln(VOLATILITY) - 0.19ln(R&DtoSALES) + 0.12ln(AGE) - 0.67 UTIL The adjusted R2 are 0.35 and 0.28, respectively. In the insider holdings equation, the coefficients of SIZE and UTIL are significant at the 1 percent level. The p-values for VOLATILITY, R&DtoSALES and AGE are 0.06, 0.44 and 0.09, respectively. In the institutional holdings equation, all coefficients except for that of R&DtoSALES are significant at the 1 percent level. The coefficient of R&DtoSALES has an associated p-value of 0.22 in this equation.