Institutional Ownership, Share Price Levels, and the Value of the Firm
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Institutional Ownership, Share Price Levels, and the Value of the Firm *
Chitru S. Fernando
Michael F. Price College of Business
University of Oklahoma
307 West Brooks
Norman, OK 73019
cfernando@ou.edu
(405) 325-2906
Vladimir A. Gatchev
Department of Finance
College of Business Administration
University of Central Florida
Orlando, FL 32816-1400
vgatchev@bus.ucf.edu
(407) 823-3694
Paul A. Spindt
A. B. Freeman School of Business
Tulane University
7 McAlister Drive
New Orleans, LA 70118
spindt@tulane.edu
(504) 865-5413
September 2007
Keywords: Share price level, firm value, institutional ownership, monitoring
JEL classification: C24, G12, G30
* We are grateful to Ed Dyl, Srini Krishnamurthy, Tom Noe, Russ Robins, Arturo Rodriguez, Wayne
Thomas, Martin Young, and seminar participants at Tulane University and the FMA European meetings for
useful discussions and comments. We remain responsible for any errors.Institutional Ownership, Share Price Levels, and the Value of the Firm
ABSTRACT
We theoretically and empirically examine the variation in institutional ownership and share price
levels across firms. By explicitly incorporating the role of institutional investors in monitoring
and information gathering, we show how they can substitute for costly information generation by
analysts. Ownership structure and share price levels emerge endogenously as an outcome of this
trade-off, in contrast to the causal relationship between price and institutional ownership that is
widely assumed in the literature. Holding size constant, our model predicts and empirical findings
confirm that higher valued firms have higher institutional ownership and higher share price
levels. We also show that analyst following and institutional ownership are inversely related and
that the positive association between share prices and institutional ownership exists independently
of liquidity and size considerations.The potential for institutional shareholders to affect the stock market performance of a firm by
monitoring its managers and generating information has been widely recognized in the finance
literature.1 A standard assumption is that a firm’s share price level serves as a proxy for the stock market
liquidity of its shares,2 which has also been used to explain the documented monotonic relationship
between institutional ownership and share price levels.3 The relegation of share price level to being a
liquidity proxy belies the considerable attention paid by firms and investors to share price levels and the
documented links between share price levels and several other important firm characteristics.4 In this
paper, we develop and test a unified theoretical framework to show how a firm’s institutional ownership
and share price level are both endogenously determined based on a firm’s fundamental characteristics,
which permits us to explain several observed empirical regularities and provide new insights.
Brennan and Hughes (1991) argue that the negative relation between brokerage commissions and
share prices provides an incentive for brokers to produce more information about low-priced stocks,
while Angel (1997) relies on the negative relation between share price and the relative tick size to
develop a model in which lower prices and higher relative tick sizes provide higher incentives for
analysts to promote the stock and help broaden the investor base of a firm. Merton (1987) predicts that
broadening the base of investors who are familiar with a stock will lower its required rate of return. Dyl
and Elliott (2006) empirically examine whether the cross-sectional variation of stock prices can be
explained by Merton’s (1987) model. They find that the share price level in their sample of firms
increases with both firm size and total equity per shareholder (a proxy for average investor size) and
1
See, for example, Shleifer and Vishny (1986), Brickley, Lease and Smith (1988), Agrawal and Mandelker
(1990), McConnell and Servaes (1990), Smith (1996), Carleton, Nelson and Weisbach (1998), Gillan and Starks
(2000), and Allen, Bernardo and Welch (2000).
2
See, for example, Brennan and Subrahmanyam (1996) and Gompers and Metrick (2001).
3
See, for example, Falkenstein (1996) and Gompers and Metrick (2001).
4
See, for example, Maloney and Mulherin (1992), Muscarella and Vetsuypens (1996), Falkenstein (1996), Angel
(1997), Seguin and Smoller (1997), Schultz (2000), Gompers and Metrick (2001), Fernando, Krishnamurthy and
Spindt (2004) and Dyl and Elliott (2006).
1argue that their findings support the notion that firms select the trading ranges for their shares to enlarge
their investor base.
Nevertheless, several questions remain. First, given the overwhelming evidence that lowering share
prices increases transactions costs,5 what is the trade-off between increased transactions costs and higher
information benefits associated with lower share prices? Second, what role do investors, specifically
institutional investors, play in substituting for the information generation role of stock market analysts?
While the aforementioned literature does not consider differences in investor characteristics, studies by
Falkenstein (1996) and Gompers and Metrick (2001) show that share price is significantly and
positively related to institutional ownership. Since institutions are considered to be more sophisticated
and better informed than retail investors, this finding appears inconsistent with the notion that firms who
have favorable private information maintain lower share prices to attract more analyst coverage and
promote their shares. It is possible that institutional ownership provides an alternative channel for firms
to disseminate information and increase their value through monitoring.6 Finally, the notion from
Merton (1987) that firms can increase their value by lowering share prices and broadening their
shareholder base seemingly conflicts with empirical evidence that suggests a positive association
between share price levels and the long term performance of the firm.7
We develop a model to examine these questions. In our model, firms benefit by both institutional
ownership and analyst coverage, with institutional ownership having the potential to substitute for costly
information generation by analysts. Firms that anticipate smaller benefits from institutional ownership
5
See, for example, Conroy, Harris and Benet (1990), McInish and Wood (1992), Stoll (2000), and Schultz (2000)
for evidence on the negative relation between price level and transactions costs. Benartzi et al. (2006) pose the
issue in stark terms by noting that if GE had not split its stock from 1935 to 2005, it would have saved investors
over $100 million in brokerage commissions in 2005 alone!
6
Institutional investors employ their own buy-side analysts or are able to generate information through other
means without relying on sell-side analysts. Additionally, institutional investors can also add value to firms they
own by monitoring. See, for example, Shleifer and Vishny (1986), McConnell and Servaes (1990), Smith (1996),
Carleton, Nelson and Weisbach (1998), Gillan and Starks (2000), and Allen, Bernardo and Welch (2000) for a
discussion of the benefits to a firm brought about by institutional ownership.
7
See, for example, Seguin and Smoller (1997) and Fernando, Krishnamurthy and Spindt (2004).
2set lower share prices to increase the brokerage revenue associated with trading their shares and thereby
induce more information generation by analysts. In contrast, firms that anticipate large benefits from
institutional ownership place a lower value on analyst following and set higher share prices to decrease
the cost to investors of owning their shares. In equilibrium, higher priced firms have a higher
institutional ownership than lower priced firms. Additionally, our model predicts that higher priced
firms will have a higher value than lower priced firms.
The association between institutional ownership and price documented in previous studies has been
justified as evidence that institutional investors favor high priced stocks to avoid transaction costs. This
explanation raises the question of why retail investors are more willing than institutional investors to
accept higher transactions costs. In contrast, while establishing a theoretical basis for the empirically
observed positive relationship between share prices and institutional ownership, we show that this
relationship exists independently of liquidity considerations.
As in Brennan and Hughes (1991) and Angel (1997), in our model too lower share prices increase
the incentive for brokers to generate information and promote stocks. However, unlike Brennan and
Hughes (1991) and Angel (1997), who assume homogeneous populations of investors, we allow for
investor heterogeneity by separating investors into two clienteles, institutional and retail, and explicitly
account for the well-documented possibility that institutional investors can add value by generating
information about and monitoring the activities of firms they own. Our model is consistent with Merton
(1987) and the ensuing literature in the sense that, holding all else constant, lowering share prices will
increase a firm’s value provided the increased benefits of lower prices exceed the higher transactions
costs. Nonetheless, when the possibility that institutional ownership might also increase the value of the
firm is factored in, we show that in equilibrium the value of the firm will actually increase with share
price, holding all else constant. This theory of investor clienteles and share price determination is new to
the literature.
In addition to providing new insights into the relationships among and endogeneity associated with
several key firm specific variables such as institutional ownership, analyst coverage, share price levels,
3stock market liquidity and the value of the firm, our model also yields several other new findings and
empirical implications. First, by extending the previous literature to incorporate the role of institutional
investors, our model predicts that analyst coverage and institutional ownership will be inversely related.
Second, while establishing a theoretical basis for the empirically observed positive relationship between
share prices and institutional ownership, we show that this relationship exists independently of liquidity
considerations, which contradicts the widely-held belief that higher liquidity is what drives institutions
to hold higher-priced stocks. Additionally, our model also implies that the share price level will be an
indicator of a firm’s value. Therefore, we would expect to find a greater propensity for institutions to
invest in higher priced stocks even in the absence of “prudent-man” rules that constrain them to do so.8
Additionally, firms with higher levels of institutional ownership and higher values will choose higher
split-to prices when they split their shares. Surprisingly though, our model predicts that analyst coverage
of such firms will be lower than their coverage of other similar-sized firms.
Our empirical findings strongly support our theoretical predictions. In particular, consistent with
Brennan and Hughes (1991), Angel (1997) and our theory, we find that while analyst coverage increases
with a firm’s market capitalization, it actually declines with the share price level after controlling for
size. Extending the work by Brennan and Hughes (1991) and Angel (1997), we show that the number of
analysts following a firm will decrease with the firm’s information quality (as measured by its S&P
quality rank) and with institutional ownership. We find that a firm’s share price level rises with
institutional ownership even after controlling for differences in stock market liquidity. We also find that
a firm’s value as measured by Tobin’s Q is positively related to institutional ownership or its share price
level, and that higher-valued firms will target higher price levels when they split their shares. Tobin’s Q
8
See, for example, Del Guercio (1996). The argument here is similar to the “ownership clientele” effects
discussed by Allen, Bernardo, and Welch (2000), where higher quality firms attract relatively less-taxed
institutional investors. As noted by Allen, Bernardo and Welch (2000), such investors have a relative advantage in
ensuring that the firms they invest in are well managed.
4is also positively related to the precision of analyst forecasts. Furthermore, share prices of firms with
higher information precision are less sensitive to broker information precision and vice versa.
The rest of our paper is organized as follows. We develop our model in Section I and also discuss
its empirical implications. Section II describes our sample and methodology. Section III presents the
empirical results. Section IV concludes.
I. The Model and Empirical Implications
In this section, we develop a model that explains how firms optimally select their share price levels
to maximize firm value, taking into account the benefits of institutional ownership and the costs and
benefits of information generation by market intermediaries. We conclude the section by developing
several empirical implications of our model.
A. The Economy
We consider a multi-stage economy with J risky assets (firms), a riskless asset, and a continuum
of risk-averse institutional and retail investors. Risky asset payoffs are independently distributed, with
each risky asset j having a random terminal payoff of Vj that is normally distributed with mean μ j and
precision h j . The riskless asset’s return is normalized at zero. All investors in the economy have
identical constant absolute risk aversion (CARA) preferences with CARA parameter γ .9 We examine
the investment decision of a representative investor of each type, assuming for convenience that in
aggregate both investor types are endowed with equal amounts of the riskless asset prior to trading in
the risky asset.
Investment in the risky assets is facilitated by brokers. Brokers can produce signals of the final
payoff of each firm with precision hB . Conditional on the number of brokers producing information, N j ,
9
Our model can be easily extended to the case where institutional and retail investors have different risk
preferences without altering its main predictions.
5the precision of final payoff for each firm j becomes h j + N j hB . This specification assumes that broker
reports are independent of each other. As in Brennan and Hughes (1991), we assume that all brokers are
risk neutral, have identical abilities, have the same number of clients, and are perfectly competitive,
which implies that they will charge identical commissions. As suggested by the discussion and findings
in Benartzi, et al. (2006) and Goldstein, et al. (2006), we assume a commission structure where brokers
charge a fixed commission, c , per share.10
In our model, institutional investors are differentiated from retail investors by their ability to affect
the ex ante payoff distribution of the firms they invest in by producing information about these firms
and/or monitoring their performance. For each firm j we aggregate these effects in a single institutional
influence parameter m j > 0 . Institutional investment modifies the precision of firm pay offs by the
multiplicative factor, (1 + m j θ I ) , where θI is the fraction of the firm held by institutions, resulting in a
precision of the distribution of the firm’s value of ( h j + N j hB )(1 + m j θ I ) . The precision increases with
both m j and θI , reflecting the increase in the benefits of institutional ownership with both the
institutional influence parameter and institutional ownership.
B. Stages
The model consists of four stages. In the first stage firms determine the number of shares
outstanding to maximize the market value of the firm, which also determines the price per share. As
observed by Brennan and Hughes (1991) and Angel (1997), lower share prices (and a higher number of
shares outstanding) provide greater incentive for market makers to promote the firm’s shares since the
returns to market makers increase with lower share prices under prevailing transactions cost structures.
Therefore, a higher number of shares (and lower share price) will increase the information generated by
brokers about a firm but also the cost to shareholders of investing in the firm due to the higher
10
Our results remain qualitatively unchanged when we assume that institutional and retail investors have different
commission structures.
6brokerage commissions they will be required to pay. In the second stage, brokers produce information
about each firm’s terminal pay off. In the third stage, investors make their investment decisions
regarding each firm, which in turn determines the commissions earned by brokers and each firm’s
market-clearing valuation. In the final stage, firms’ payoffs are realized and firms liquidate. The four
stages are summarized below. We solve the model recursively.
s=1 s=2 s=3 s=4
• Firm j chooses • N j brokers produce • Ownership by • Payoff is
number of shares investor type realized.
information about
outstanding S j . firm payoff. i ∈ { I , R} is • Firm
liquidates.
• Each broker determined.
• Payoff Vj normally produces • Firm market-
distributed with independent clearing value Q j
mean μ j and information with
is determined.
precision hB .
precision hj . • Broker
• Payoff precision commissions are
becomes determined.
h j + N j hB
C. Optimal Investor Holdings and Equilibrium Valuations
An investor of type i ∈ { I ,R} has an initial endowment of the riskless asset denoted by W0 ,i . The
investor of type i ∈ { I ,R} solves:
max E ⎡⎣ −e− γW ⎤⎦ ,
i
θij
(1)
where Wi = W0 ,i + ∑ j =1 θijVj − ∑ j=1 θij ( Q j + cS j ) . Q j is the market valuation of firm j at the trading
J J
stage and c is the per-share trading commission for all investors.
In a perfectly competitive market for broker services, brokers will use all their commission
revenues to produce information. The total commission revenues earned by brokers from trading the
7shares of firm j are equal to ( θ I + θ R ) cS j , and each broker incurs a cost of f per firm to produce
information with precision hB about that firm. Noting that ( θ I + θ R ) = 1, the expression for the optimal
number of brokers can be stated as follows:
Lemma 1 (Brennan and Hughes, 1991): Under perfect competition, the number of brokers
producing information about firm j , N j , is given by:
cS j
Nj = . (2)
f
Therefore, firms with more shares outstanding will have a larger number of brokers producing
information about them.
Given the previous assumptions regarding the distribution of final firm payoffs, the portfolio
optimization problem of each investor i can be stated as follows:
γ2
θ ij
( )
max γE Wi − Var Wi ,
2
( ) (3)
where
( )
Var Wi = ∑ j=1 θij2
J
(h
1
+ N j hB )(1 + m j θI )
. (4)
j
The expression in (4) indicates that all else equal, firms with more precise initial information ( h j ),
higher broker coverage ( N j ), more precise information produced by brokers ( hB ), and a higher benefit
from institutional investor participation ( m j ) have a lower variance of future values.
Solving for the optimal ownership weights and the equilibrium valuation of the firm, subject to
market clearing, we can state our results for the equilibrium ownership structure of the firm as follows:
8PROPOSITION 1: The equilibrium ownership structure for each firm j is given by
1 2 3 + (1 + m j ) − ( 4 + m j )
2
θ Ij = + (5)
2 6m j
2 3 + (1 + m j ) − ( 4 + m j )
2
1
θ Rj = − (6)
2 6m j
(See Proof in Appendix A)
2 3 + (1 + m j ) − ( 4 + m j )
2
Since it is easily seen that
6m j
(
lies in the interval 0 , 1
6 ) for m j > 0 , we
can conclude that both institutional and retail ownership will be positive and institutional ownership will
be in the interval ( 1 2 , 2 3 ) . Nonetheless, the institutional ownership share will increase with m j and
the retail ownership share will be correspondingly reduced. It is interesting to note that trading
commissions, which are the outcome of the number of shares/share price decision of the firm, do not
directly affect the manner in which ownership of any given firm is shared between institutional and
retail investors. Therefore, in our model, there is no causal relation between trading commissions and
ownership structure. As noted previously, this result remains unchanged when we assume that
institutional and retail investors have different commission structures, provided the differential is
applied uniformly across all firms. Nonetheless, as we will show later, since firms optimize their share
price decision to maximize any potential benefit of institutional ownership, firms that have high (low)
values of m j will also have high (low) share prices in equilibrium, thereby leading to a positive
association between institutional ownership and share prices. These results are in stark contrast to the
existing empirical literature which assumes that the higher costs of trading low priced shares causes a
lower institutional ownership of these shares relative to high priced shares.
9We can state our result for the market valuation of each firm at the trading stage, conditional on the
number of shares outstanding, as follows:
Lemma 2: The market valuation of each firm j at the trading stage is given by
(1 + m ) 3 + (1 + m j ) − (1 + m j ) − 1
2 2
γf j
Q j = μ j − cS j − (7)
( fh j
+ cS j hB ) mj
(See Proof in Appendix A)
Conditional on the choice of number of shares outstanding, firm value increases with the benefit of
institutional ownership ( m j ), with the ex ante precision of the firm’s payoff distribution ( h j ), and the
precision of broker information ( hB ), while it decreases with investor risk aversion ( γ ).
D. The Choice of Shares Outstanding 11
In the first stage, firms choose the number of shares outstanding so as to maximize their value at
the trading stage. In doing so, firms have to balance offsetting effects. On the one hand, setting a low
number of shares outstanding (leading to high share prices) reduces the cost of brokerage commissions
and increases the net returns to shareholders. On the other hand, a lower number of shares outstanding
induces less information production by brokers, which results in higher uncertainty and lower valuations
for the firm. Proposition 2 states the equilibrium result for the optimal number of shares that arises from
these tradeoffs.
11
Since the mechanism by which firms select a share price is setting the number of shares outstanding, we pose
the firm’s optimization problem as one of selecting the number of shares rather than the share price, noting that
share price equals the firm’s value divided by the number of shares.
10PROPOSITION 2: The optimal number of shares outstanding, S *j , for each firm j , is given by
⎛ ⎞
γfhB m j ⎜ (1 + m j ) 3 + (1 + m j ) − (1 + m j ) − 1⎟ − fh j m j
2 2
⎝ ⎠
S *j = (8)
chB m j
(See Proof in Appendix A)
As firm specific quality ( h j ) and institutional benefits ( m j ) increase, the optimal number of shares
outstanding decreases. Additionally, for a high precision of broker information, the number of shares
outstanding decreases as the precision of broker information ( hB ) increases. For low precision of broker
information, however, as hB increases the optimal number of shares outstanding increases.12 We analyze
the relation between equilibrium share prices and these variables in Proposition 4 below.
Solving the expression in (5) for m j we obtain that
(
m j = 2 ( 2θ Ij − 1) θ Ij ( 2 − 3θ Ij ) . ) (9)
Replacing for m j in the expression for Proposition 2 and for θ Ij in the interval 1 , 2 ( 2 3 ) , we find
that
S *j =
⎛
1 ⎜ γf
×
(
2 − ( 5 − 3θ Ij ) θ Ij )
−
⎞
fh j ⎟
. (10)
c ⎜⎜ hB θ Ij hB ⎟⎟
⎝ ⎠
As we have shown previously, equilibrium values of institutional ownership ( θ Ij ) lie in the interval
( 1 2 , 2 3 ) and it is easily observed that for values of θ Ij in this interval, the optimal number of shares
outstanding decreases with institutional ownership. As we prove later, this result gives rise to a positive
⎛ ⎛ γf ⎛ ⎞⎞⎞
The point at which the relation changes is hB = 4 ⎜ ( fh j ) ⎜ (1 + m j ) 3 + (1 + m j ) − (1 + m j ) − 1⎟ ⎟⎟ ⎟⎟ .
2 2 2
⎜⎜
12
⎜
⎝ ⎝ mj ⎝ ⎠⎠⎠
11association between share prices and institutional ownership. We should note, however, that this
association is not causal.
We can now extend the predictions of Brennan and Hughes (1991) presented in Lemma 1. We use
expression (8) to substitute for the shares outstanding in Lemma 1, which gives the equilibrium number
of brokers producing information.
PROPOSITION 3: The number of brokers producing information is:
⎛ ⎞
(1 + m j ) 3 + (1 + m j ) − (1 + m j ) − 1⎟
2 2
γ ⎝ ⎜
N *j = × ⎠ − hj
(11)
fhB mj hB
Replacing for m j in the expression for Proposition 3, we find that:
N *j =
γ
×
( )
2 − ( 5 − 3θ Ij ) θ Ij
−
hj
(12)
fhB θ Ij hB
The number of brokers producing information is a decreasing function of firm specific information
precision ( h j ) and of institutional ownership ( θ Ij ). Institutional ownership, however, should not be
viewed as causing brokerage coverage. Expression (12) simply shows that firms with higher monitoring
benefits will have higher institutional ownership and fewer brokers producing information as the
equilibrium outcome.
The model also relates institutional ownership and firm characteristics to equilibrium firm value. By
substituting the optimal number of shares in (8) for the value of the firm expressed in Lemma 2, we
obtain the expression for the equilibrium value of the firm, which we state in Proposition 4:
PROPOSITION 4: The equilibrium value of the firm is given by
⎛ ⎞
(1 + m j ) 3 + (1 + m j ) − (1 + m j ) − 1⎟
2 2
γf ⎝ ⎜
Qj = μ j +
*
fh j
−2 × ⎠
(13)
hB hB mj
12Replacing for m j in the expression for Proposition 3, we find that:
Q*j = μ j +
fh j
−2
γf
×
(
2 − ( 5 − 3θ Ij ) θ Ij )
hB hB θ Ij (14)
It is evident from (14) that the maximized firm value is an increasing function of firm specific
information precision ( h j ) and of institutional ownership ( θ Ij ). Note that (14) does not imply that firms
can increase their values by increasing institutional ownership. What it shows is that firms with higher
monitoring benefits will have higher institutional ownership and higher values as the equilibrium
outcome.
Our model also allows us to examine the relation between a firm’s share price and its expected cash
flows, institutional benefits, and parameters related to information precision of the firm. Proposition 5
provides the expression for a firm’s equilibrium share price (expression (14) divided by expression
(10)).
PROPOSITION 5: The equilibrium share price of the firm is given by
⎛ ⎞
⎜ ⎟
⎜ μ j − fh j ⎟
Pj* = c ⎜ − 2⎟ (15)
γf ( 2 − ( 5 − 3θ ) θ )
⎜ ⎟
⎜ Ij Ij ⎟
⎜ × − fh j ⎟
⎝ hB θ Ij ⎠
The equilibrium share price increases with the expected cash flows of the firm ( μ j ), institutional
ownership ( θ Ij , which measures institutional benefits), and the precision of the initial information
available about the firm ( h j ).13 Furthermore, a firm’s equilibrium value will increase with the precision
of broker information ( hB ) but the relation between the precision of broker information ( hB ) and share
13 * *
These relations hold for positive firm value ( Q j ) and positive number of shares outstanding ( S j ).
13price is not necessarily monotonic. For high precision of broker information, as precision of broker
information ( hB ) increases shares outstanding will decrease and share prices will increase. For low
precision of broker information, however, as the precision of broker information ( hB ) increases the
value of the firm increases but the number of shares outstanding also increases and no clear relation
exists unless one makes further assumptions about the remaining parameters. Further examination,
however, reveals that for sufficiently high μ j the cross-derivative of share price with respect to the
precision of broker information ( hB ) and the firm’s initial precision of information ( h j ) is negative.
Putting together the comparative statics results for the value of the firm and its share price, we can
conclude that if the parameters in the model are not perfectly measured in practice (as we expect) there
will be a positive association between the value of the firm and its share price level. This is not because
share price levels directly affect firm value but because the factors that lead to a higher firm value also
lead to a higher price per share.
E. Empirical Implications
Our theoretical framework gives rise to several empirical hypotheses. The first hypothesis pertains
to the analyst coverage of the firm.
Hypothesis 1: The number of analysts following a firm will be positively related to the number of shares
outstanding. Alternatively, the number of analysts following a firm will be inversely related to share
price and positively related to firm market capitalization. Furthermore, the number of analysts
following a firm will be inversely related to the firm’s quality of information ( h j ) and institutional
ownership ( θ Ij ).Finally, as the precision of broker information ( hB ) increases, the effect of the firm’s
quality of information ( h j ) on the number of analysts following the firm will be less negative.
14Hypothesis 1 follows from Lemma 1 (based on Brennan and Hughes, 1991) and the results
presented in Proposition 3. Empirically, we expect the number of analysts (the dependent variable) to
decrease as share price (the independent variable) increases. In addition, keeping share price fixed, we
expect the number of analysts to increase as the market capitalization of the firm increases. This is
because, for a given share price, increasing firm value means increasing the number of shares
outstanding. The model also predicts that the number of analysts will decrease with institutional
ownership and a firm’s quality of information -- predictions that extend Brennan and Hughes (1991). At
the same time, the model predicts that the interaction between a firm’s quality of information and broker
information precision will be positively related to the number of analysts following the firm.
Empirically, we can also examine how the value of the firm is affected by the precision of
information and institutional monitoring benefit, which is our next empirical hypothesis.
Hypothesis 2: The value of the firm will increase with the firm’s quality of information ( h j ), the
precision of broker information ( hB ), and institutional ownership ( θ Ij ).
Hypothesis 2 follows directly from Proposition 3 and expression (12).
Our model predicts a positive relation between value and institutional ownership, and between
share price and institutional ownership (see also Hypothesis 4 below). This suggests a positive
association between value and share price that gives rise to our next empirical hypothesis:
Hypothesis 3: The value of the firm and its share price will be positively associated.
It should be noted that the relation in Hypothesis 3 is not causal. It stems from the fact that share
price levels as well as firm value are both positively affected by monitoring benefits and the precision of
initial information about the firm.
15Proposition 4 allows us to make empirical predictions about the cross-sectional differences in share
price levels of firms and gives rise to Hypothesis 4.
Hypothesis 4: A firm’s share price will increase with institutional ownership ( θ Ij ) and the precision of
the information available about the firm ( h j ). This relation should hold even after controlling for
measures of market liquidity.
Examining expression (13), we do not find a clear relation between the share price of the firm and
the precision of broker information ( hB ). While it is true that the value of the firm monotonically
increases with the precision of broker information, the optimal number of shares outstanding first
increases and then decreases as the precision of broker information increases. However, we can propose
the following hypothesis about the interaction of the precision of broker information ( hB ) and the firm’s
information quality ( h j ) as far as firm share price is concerned.
Hypothesis 5: For sufficiently high firm cash flows ( μ j ), the precision of broker information ( hB ) and
the firm’s information quality ( h j ) act as substitutes in determining the share price of the firm.
Empirically, we expect a negative interaction effect between the precision of broker information
( hB ) and the firm’s information quality ( h j ) in explaining the split-to price of the firm. This is because
as the precision of broker information increases the precision of information about the firm becomes
less important. As a direct consequence, split-to prices will be less (more) sensitive to firm information
precision for higher (lower) levels of broker information precision.
We empirically examine these hypotheses in Section III after describing our sample and variable
definitions in the next section.
16II. Sample and Variables
Our sample covers the years from 1985 to 2005 and includes all firms with common stock (CRSP
share codes 10 or 11) listed on NYSE/AMEX/Nasdaq (CRSP exchange codes 1, 2, or 3). We also
require that firms have data available in the Center for Research in Security Prices (CRSP) daily and
monthly files and the Compustat database.
Share price levels and shares outstanding for year t are based on values at the end of June in year t
and come from the CRSP monthly files.14 Also from the CRSP monthly files we identify firms that split
their shares in year t and we use their split-to prices for that year as another measure of the firm’s
preferred share price. As measures of firm size we use firm market capitalization (June of year t closing
price times shares outstanding from CRSP) and total assets (Compustat item 6 for fiscal year t). In order
to control for trends in firms’ market capitalizations (asset sizes) over time, we divide firm market
capitalization (total assets) by median NYSE market capitalization (total assets).
We use the S&P common stock ranking of the firm as a measure of firm specific information
precision ( h j ), which we obtain for each fiscal year from the Compustat annual files (Compustat item
282, available from 1985 onward). Each month, in its Security Owner's Stock Guide, S&P publishes
common stock rankings of firms that are listed on the NYSE/AMEX or are among the most active
Nasdaq firms. Firm rankings are based on historic (past ten years) stability and growth of earnings and
dividends, which makes the S&P common stock rankings a straightforward measure of firm-specific
uncertainty. The range of scores is aligned with the following rankings: A+ (Highest); A (High); A–
(Above Average); B+ (Average); B (Below Average); B– (Lower); C (Lowest); D (in Reorganization).
For the purposes of our quantitative analyses we translate the S&P rankings into the following scores:
(A+: 9), (A: 8), (A–: 7), (B+: 6), (B: 5), (B–: 4), (C: 3), (D: 2). Appendix B describes the S&P common
stock ranking methodology.
14
Using CRSP average share prices for year t and Compustat share prices leads to similar results.
17We measure the number of analysts producing information about the firm ( N j ) by the number of
analysts providing one year earnings forecasts from I/B/E/S. As a measure of the precision of analyst
information ( hB ), on the other hand, we use the inverse of the mean absolute error of one year earnings
forecasts from I/B/E/S. This variable is not available for a significant part of our sample. In order to
maximize sample size when estimating the coefficients for the remaining variables, in the subsequent
regressions we make this measure of analyst precision equal to zero while at the same time we also
include a dummy variable indicating whether analyst precision is missing. This approach effectively
allows us to estimate the coefficient for analyst precision only for firms with an available measure of
analyst precision. We use the standard deviation of daily returns for year t from the CRSP daily files as
a measure of overall firm risk.
The model in the previous section shows that institutional ownership increases monotonically with
and is solely determined by institutional benefits ( m j ), which permits us to use institutional ownership
as a direct measure of institutional benefits ( m j ). We collect end-of-June percentage institutional
ownership data from the CDA Spectrum database of Thomson Financial, which consists of institutional
13F filings.
We use two measures of Tobin’s Q, both from the existing literature, to measure firm value ( Q j ).
The first measure of Tobin’s Q is the ratio of the market value of assets to the book value of assets,
where asset market value is the sum of the book value of assets (Compustat item 6) and the market
value of common stock (Compustat item 199 times item 25) less the book value of common stock
(equity (Compustat item 216, or item 60 plus item 130, or item 6 minus item 181) minus preferred stock
(Compustat item 10, or item 56, or item 130)) and deferred taxes when available (Compustat item 35)
net of post-retirement benefits when available (Compustat item 330). Because of its ease of calculation
and because it is available for a large set of firms, this measure of Q is widely used in existing literature
(see, for example, Kaplan and Zingales, 1997; Gompers, Ishii, and Metrick, 2003; and the references
therein).
18Lewellen and Badrinath (1997) show that using book values of assets in the denominator of the Q
ratio has shortcomings (e.g., downward biased Q ratios and incorrect ordering of Q ratios across firms).
They propose a different measure of Q that calculates and uses replacement costs (rather than book
values) of fixed assets and inventory. For our second measure of Tobin’s Q we calculate replacement
values using the approach of Lewellen and Badrinath (1997) as modified by Lee and Tompkins
(1999).15 This second measure of Tobin’s Q is equal to the market value of the firm’s common stock
plus the book values of preferred stock, short-term debt, and long-term debt divided by the replacement
value of firm assets. Asset replacement values are calculated as book value of total assets minus book
values of fixed assets and inventory plus replacement values of fixed assets and inventory minus all
liabilities other than long-term and short-term debt. The replacement value of fixed assets in year t
requires the estimation of historic investments in fixed assets year by year. Then the method uses
specific depreciation and inflation estimates to determine the replacement value in year t of each year’s
investment “vintage.” The replacement value of fixed assets is the sum of the replacement values of
historic investments. The calculation of inventory replacement values is simpler. If firms use FIFO to
account for inventory, then inventory replacement value is equal to its book value. If firms use a
different method to account for inventory (such as LIFO) they usually report what adjustment to make
to obtain the current (FIFO) value of inventory (Compustat item 240).16 Lewellen and Badrinath (1997)
and Lee and Tompkins (1999) provide further details on the calculation of asset replacement values.
Both measures of Q are adjusted for the median Q of the firm’s industry, where industries are defined as
in Fama and French (1997).
The existing literature frequently uses share price level as a proxy for stock market liquidity. To
control for liquidity differences in our sample while also studying the relation of share price levels to
15
To increase sample size, Lee and Tompkins (1999) propose an approach to calculate replacement values of fixed
assets for firms for which the Lewellen and Badrinath (1997) procedure leads to missing observations.
16
Our results remain unchanged if we measure firms’ Q ratios based on Lindenberg and Ross (1981). Papers that
calculate Q ratios based on Lindenberg and Ross (1981) include McConnell and Servaes (1990) and Lang and
Stulz (1994).
19other firm-specific variables, we employ the share turnover of the firm to control for market liquidity.
Our measure of liquidity is the natural logarithm of 0.01 plus the annualized share turnover for June of
year t. To address the overstatement of trading volume on Nasdaq compared to trading volume on
NYSE/AMEX and to also control for other differences between NYSE/AMEX and Nasdaq, in our
regressions we also use a dummy variable equal to one if an issue is traded on NYSE/AMEX and equal
to zero if it is traded on Nasdaq.
Existing research has shown that firm value, as measured by the industry-adjusted Q ratio, is
positively related to the growth opportunities of the firm and S&P 500 membership. We control for
growth opportunities using the change in assets (Compustat item 6) from year t-1 to year t relative to
year t assets and the R&D expenses relative to total assets (Compustat item 46 divided by item 6).
Similar to our measure of the precision of analyst earnings forecasts, R&D expenses are not available
for a significant part of our sample. We again make R&D expenses equal to zero when not available and
at the same time we include a dummy variable indicating whether or not R&D expenses are missing.
This allows us to estimate the coefficient for R&D expenses relative to assets only for firms with
available R&D expenses and to estimate the other coefficients using all data for the remaining variables.
As an additional control for Q and to also control for a possible endogeneity problem due to indexing
affecting both firm value and institutional ownership, we also include a variable indicating whether the
stock is in the S&P 500 index (Compustat item 276 equals 0.1).
We include two additional control variables when we analyze stock prices: the gross stock returns
for year t-1 (from June of year t-1 to June of year t) and for year t-2 (from June of year t-2 to June of
year t-1). We take the natural logarithm of both returns, which gives us two measures of past returns.
The rationale for controlling for recent stock returns is that a stock may have a high (low) price due to a
recent run-up (decline) rather than a systematic preference for high (low) price levels. Stock returns data
comes from the CRSP monthly files.
Table I reports the mean, median, standard deviation, and number of observations for the variables
discussed above. The average (median) firm in our sample has approximately 7 (4) one-year analyst
20earnings forecasts. The median firm has a share price of around $12 per share and a split-to price of
around $22 per share. The median firm in our sample is small relative to NYSE firms: its market
capitalization is approximately 13% of median NYSE market capitalization and its asset size is
approximately 12% of median NYSE asset size. Both average and median S&P common stock rankings
are around 5 (or B) while institutional ownership is 30% for the average firm and 24% for the median
firm.
[Insert Table I about here]
III. Empirical Findings
In this section we present the results of our empirical tests of the hypotheses developed in Section
I.E. In Section II.A we examine how analyst coverage is related to share prices and other variables. We
examine the relation between the value of the firm and several key variables in Section II.B. Finally, in
Section III.C we study the determination of share price levels.
A. Analyst Coverage
Table II reports our estimates from regressions to explain the number of analysts providing
earnings forecasts. When the measure of share price is the actual share price of the firm then the
regression uses 66,568 firm-years. The adjusted R-square of the model is 63%, which is relatively high.
We find strong support for Hypothesis 1. The number of analysts is negatively related to the share
price and positively related to the firm’s market capitalization, with both relations significant at the 0.01
level. The coefficient on share price shows that a firm that increases its share price by one percent, with
its market capitalization fixed, will incur a reduction in the number of its analysts by around 0.017
analysts or approximately 0.25% (0.43%) reduction in analyst coverage for the average (median) firm.
Alternatively, if a firm doubles its share price, the number of analysts declines by around 25% for the
average firm and 43% for the median firm. The results remain similar when we use the split-to price as
our measure of the firm’s target price level despite a significant reduction in sample size. In this case we
21find that a one percent increase in the split-to price of the firm leads to a decline in the number of
analysts by around 0.3% for the average firm and by around 0.5% for the median firm. The adjusted R-
square is again around 64%. These findings are robust to controlling for the liquidity of a firm’s stock
market trading activity, measured by share turnover.
Extending the work of Brennan and Hughes (1991), our model also makes predictions about the
relation between analyst coverage, and firm information quality and ownership structure. As predicted
in Hypothesis 1, we expect that the number of analysts following the firm will decrease with firm
information quality (as measured by S&P quality rank) and with institutional ownership. We further
expect that the effect of firm information quality on analyst coverage will be less negative as the broker
information precision increases. In other words, we expect a positive coefficient for the interaction
between S&P firm quality rank and the measure of broker information precision. The regression also
controls for the market capitalization of the firm, trading activity, standard deviation of returns, and
historic returns. The relevant coefficients are even larger in magnitude and have the same signs and
statistical significance if we do not control for standard deviation of returns and historic returns.
Consistent with Hypothesis 1, institutional ownership is negatively and significantly (at the 0.01 level)
related to the number of analysts following the firm. An increase in institutional ownership by one
percentage point is associated with a reduction in the number of analysts following the firm by around
0.09% for the average firm and by around 0.16% for the median firm. We also find that firms with
higher quality of information (as measured by their S&P quality rank) have fewer analysts following
their stock. The coefficient estimate for S&P quality rank in model (5) is significant at the 0.10 level and
implies that an increase in S&P rank by 1.00 leads to a 0.53% (0.92%) reduction in analyst coverage for
the average (median) firm in our sample. As predicted, the interaction term between S&P quality rank
and analyst information precision has a positive coefficient significant at the 0.01 level. The coefficient
for analyst information precision (model (5)) is significant at the 0.01 level and positive. Our theory
does predict a positive relation between analyst information precision and analyst coverage, but only for
sufficiently low analyst information precision.
22[Insert Table II about here]
B. The Value of the Firm
Table III reports our findings on the links between firm value (as measured by Tobin’s Q) and
other key variables of interest, such as the firm’s quality of information (measured by S&P quality
rank), the precision of analyst forecasts, institutional ownership, size, and share price levels. In Panel A
we compute Q as the ratio of the market value of assets to the book value of assets whereas in Panel B
we compute Q as the ratio of the market value of assets to the replacement value of assets.
We obtain broadly consistent results across the two panels. Our findings provide strong support for
our theory in general and Hypothesis 2 in particular. We find that Tobin’s Q is positively related to
information quality as measured by the S&P rank. In Panel A this relation is significant at the 0.01 level
in models (1) to (3) while in Panel B it is significant at the 0.01 level in models (1), (2), and (4) and at
the 0.05 level in model (3). For example, model (2) of Panel A shows that a one unit increase in the
S&P ranking (e.g., from C to B) results in a 7.09 percent increase in firm value. When the share price is
included as an explanatory variable in the regressions, the relation between Tobin’s Q and S&P rank
remains significant although the economic effect of S&P rank on firm value is lower, with a unit
increase in S&P rank resulting in a 1.53 percent increase in firm value. For model (4), this relationship
remains significant at the 0.01 level in Panel B but is insignificant in Panel A despite having the correct
sign.
Tobin’s Q is also positively related to the precision of analyst forecasts, a finding also supporting
Hypothesis 2. In both panels this relation is significant at the 0.01 level for all regression specifications.
For an economic interpretation of the coefficients we note that Table I shows that the standard deviation
of the measure of broker information precision is 1.53. Combined with the coefficient estimates in
model (2) of Panel A, for example, we find that a one standard deviation increase in the precision of
broker information leads to a 16.52 percent increase in firm value.
23Further support for Hypothesis 2 is provided by the relation between Q and institutional ownership.
We find a positive and significant (at the 0.01 level) relation for all regression specifications in both
panels. Using the standard deviation of institutional ownership from Table I and the estimated
coefficient from model (2) of Panel A, we find that a one standard deviation (26.07) increase in
institutional ownership is associated with a 9.65% increase in firm value.
Hypothesis 3 predicts a positive relation between a firm’s share price level and its value. Providing
strong support for this prediction and our theory, we find that the value of the firm is significantly (at the
0.01 level) and positively related to the share price using all specifications (share price or split-to price
and in both panels).
When we examine the control variables we find that, consistent with existing literature, growth
opportunities (as measured by R&D expenses and asset growth) and S&P 500 membership are
positively related to the value of the firm, while size is negatively related to firm value (see, for
example, Lang and Stulz, 1994).
[Insert Table III about here]
C. Share Price Levels
Our last set of predictions concerns the determinants of cross-sectional differences in the share
price levels of firms as summarized in Hypotheses 4 and 5. These predictions are tested in Tables IV
and V. Table IV reports our empirical findings using share price levels and Table V repeats the analysis
using split-to prices. Our findings provide significant support for both these hypotheses. Regardless of
which specification we use, share price levels and split-to prices are positively and significantly (at the
0.01 level) related to institutional ownership and firm information quality as measured by the S&P rank.
It is especially important to note that this relation is robust to our control for stock market liquidity as
measured by share turnover. These results are also robust to controls for size, analyst forecast precision,
listing exchange, growth rates and returns volatility.
24Examining the coefficient estimate of institutional ownership in model (3) of Table V, for example,
we find that a one standard deviation (26.07) increase in institutional ownership leads to an increase in
the split-to price of the firm by around 14.86 percent. At the median firm this implies an increase in the
split-to price from around $22 per share to around $25 per share. The economic effect of S&P rank on
share price levels is also notable. For example, the estimated model (3) of Table V implies that a one
unit increase in S&P rank (e.g., from C to B) leads to around a 7.01 percent increase in the split-to price
of the firm.
[Insert Tables IV and V about here]
Consistent with our prediction in Hypothesis 5, we also find a significant (at the 0.01 level)
negative interaction effect between the precision of broker information and the firm’s information
quality as measured by the S&P quality rank. This result is consistent with the hypothesis that the
precision of broker information and precision of information about the firm act as substitutes in
determining the share price level of the firm.
Overall, the findings in this section provide strong support for our theory. Firm value is positively
related to firm specific information precision, broker information precision, and institutional ownership
while share price levels are positively related to institutional ownership and firm specific information
precision. Furthermore, share prices of firms with higher information precision are less sensitive to
broker information precision and vice versa.
IV. Conclusions
We develop a model to examine cross sectional differences in institutional ownership and share
prices across firms. In contrast to the prior literature, we explicitly incorporate the role of institutional
investors in monitoring and information gathering and show how institutional investors can substitute
for costly information generation by analysts. We show that firms select share prices by trading off the
benefits of institutional ownership against the cost of information generation by market intermediaries.
Firms that anticipate small net benefits from institutional ownership set lower share prices to increase
25the brokerage revenue associated with trading their shares and thereby induce more information
generation by market intermediaries. In contrast, firms that anticipate large net benefits from
institutional ownership set higher share prices to decrease the cost to investors of owning their shares. In
equilibrium, higher priced firms have a higher institutional ownership and higher valuations than lower
priced firms. Additionally, our findings also imply that the share price level will signal a firm’s quality.
In addition to establishing a theoretical basis for the empirically observed positive relation between
share prices and institutional ownership, we show that this relation exists independently of differences
in size and market liquidity, which are widely believed to be the drivers for institutions to hold higher
priced stocks. Firms with higher institutional ownership will choose higher split-to prices when they
split their shares. We show that analyst following and institutional ownership are inversely related and
therefore, that high-priced firms with high institutional ownership and high value have a lower analyst
following when size differences are controlled for. Overall, our findings provide several new insights
and point to a fruitful new line of research from both a theoretical and an empirical standpoint.
26APPENDIX A
Proof of Proposition 1: The first order condition for institutional investors is:
⎡ γθ Ij ( 2 + m j θ Ij ) ⎤
μ j − Q j − cS j − ⎢ ⎥=0
⎢ 2 (1 + m θ )2 ( h + N h ) ⎥ (A1)
⎣ j Ij j j B ⎦
and the first order condition for retail investors is:
⎡ γθ Rj ⎤
μ j − Q j − cS j − ⎢ ⎥=0
⎢⎣ (1 + m j θIj )( h j + N j hB ) ⎥⎦
(A2)
where
Nj =
(θ Ij
+ θ Rj ) cS j
=
cS j
(A3)
f f
The market clearing condition for any firm j is:
θIj + θRj = 1
(A4)
Substituting (A3) and (A4) into (A1) and (A2), and solving (A1) and (A2) for Q j we get:
⎡ ⎤
⎢
⎢
Q j = μ j − cS j − ⎢
(
γ (1 − θ Rj ) 2 + m j (1 − θ Rj ) ) ⎥
⎥
⎥ (A5)
⎢ 2 1 + m (1 − θ ) ⎛⎜ h + ⎛ cS j ⎞ h ⎞⎟ ⎥
( )
2
⎢ j Rj
⎜ j ⎜ f ⎟ B ⎟⎥
⎣ ⎝ ⎝ ⎠ ⎠⎦
and
⎡ ⎤
⎢ ⎥
⎢ γθ Rj ⎥
Q j = μ j − cS j − ⎢ ⎥ (A6)
⎢ 1 + m (1 − θ ) ⎜⎛ h + ⎛ cS j ⎞ h ⎞⎟ ⎥
( )
⎢ j Rj
⎜ j ⎜ f ⎟ B ⎟⎥
⎣ ⎝ ⎝ ⎠ ⎠⎦
Equating (A5) and (A6) and then simplifying and solving the resulting expression yields the
following two solutions for θ Rj :
271 2 4 + mj (2 + mj ) − 4 − mj 1 2 4 + mj (2 + mj ) + 4 + mj
⎛ ⎞
θ Rj = ⎜ − , + ⎟ (A7)
⎜⎜ 2 6m j 2 6m j ⎟⎟
⎝ ⎠
The corresponding two solutions for θ Ij become:
1 2 4 + mj (2 + mj ) − 4 − mj 1 2 4 + mj (2 + mj ) + 4 + mj
⎛ ⎞
θ Ij = ⎜ + , − ⎟ (A8)
⎜⎜ 2 6m j 2 6m j ⎟⎟
⎝ ⎠
However, only the first solution in each case satisfies the second order conditions for institutional
and retail investors, which permits us to eliminate the second solution. Q.E.D.
Proof of Lemma 2: From (A6) we have that:
⎡ ⎤
⎢ ⎥ ⎡ ⎤
⎢ γθ Rj ⎥ γf θRj
Q j = μ j − cS j − ⎢ = μ j − cS j − ⎢ ⎥ (A9)
⎥
⎢ 1 + m (1 − θ ) ⎛⎜ h + ⎛ cS j ⎞ h ⎞⎟ ⎥
( )
⎢
⎣ (
( fhj + cS j hB ) 1 + m j (1 − θRj ) ) ⎥
⎦
⎢ j Rj
⎜ j ⎜ f ⎟ B ⎟⎥
⎣ ⎝ ⎝ ⎠ ⎠⎦
Substituting for θRj and simplifying, we get:
(1 + m ) 3 + (1 + m j ) − (1 + m j ) − 1
2 2
γf j
Q j = μ j − cS j −
( fh j
+ cS j hB ) mj (A10)
Q.E.D.
Proof of Proposition 2: The first order condition for Q j with respect to S j is:
⎛ ⎛ ⎞⎞
c ⎜ −m j ( fh j + cS j hB ) + f γhB ⎜ (1 + m j ) 3 + (1 + m j ) − (1 + m j ) − 1⎟ ⎟
2 2 2
⎝ ⎝ ⎠⎠
=0 (A11)
m j ( fh j + cS j hB )
2
Solving for S j yields two solutions,
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