Ž .
Mech 1997 observe that stock splits are declared much more frequently before issue announcements than afterward.
After controlling for anticipation and cross-sectional differences in asymmetric information, we find no evidence that split declarations, dividend announcements,
or earnings releases decrease the information asymmetry around equity issue announcements. Our work contributes to the literature in four additional ways.
First, we present a model and evidence concerning determinants of firms’ equity-
Ž .
Ž .
issue decisions. Choe et al. 1993 , Jung et al. 1996 , and Opler and Titman Ž
. 1995 present evidence on firms’ choice between debt and equity issues. Pagano
Ž .
et al. 1998 analyze factors that affect Italian firms’ decision to go public.
However, none of the previous empirical studies has examined the issue and no-issue choice of public firms. Second, we use a much larger and more recent
Ž .
sample than previous studies. Korajczyk et al. 1991 examine whether earnings releases reduce asymmetric information before equity issues for the period of
Ž .
1978–1983. Loderer and Mauer 1992 investigate whether pre-issue dividend announcements reduce the price drops at the issue announcements for the period
of 1973–1984. Our sample covers seasoned equity issues between 1980 and 1994. Third, we consider jointly the effects of dividend declarations, earnings releases,
and stock split declarations on issue announcement returns, after controlling for
Ž .
cross-sectional differences in uncertainty. Korajczyk et al. 1991 and Loderer and Ž
. Mauer 1992 only examine the effect of a single announcement, such as earnings
releases or dividend declarations, on issue announcements. The effects of other information releases are therefore not controlled in their analyses. Finally, we also
address a technical difficulty that has received scant attention in the conditional event-study literature. Conditional event-study procedures frequently estimate the
ex-ante probability of an event from a logit or probit regression, using announce- ment data. The probability of an event, however, differs from the probability that
it will be announced on any given day. This introduces a subtle sampling bias that can be corrected by introducing a parameter for the managers’ decision horizon.
The remainder of the paper is organized as follows. Section 2 defines our measure of asymmetric information and describes our conditional event-study
procedure. Section 3 presents the testable hypotheses. Section 4 describes the data and sample. Section 5 applies our conditional event-study procedure to study the
effects of pre-issue information releases on equity issue announcements. The paper concludes with Section 6.
2. Asymmetric information, anticipation, and conditional event studies
2.1. Measuring the importance of asymmetric information Asymmetric information pertains to the difference in information possessed by
managers and the public. In particular, we assume that managers possess private
information about their companies in addition to all relevant public information. From a practical point of view, the incremental value of the manager’s private
information equals the difference between the valuation of the firm with and without this information. From the perspective of the investing public, the
importance of the private information is the magnitude of the effect that the information could have on the valuation of the firm. This can be measured by the
variance of the difference in the valuation of the firm with and without the managers’ private information.
Any release of information by managers reduces asymmetric information in the sense that it makes private information public. It is conceivable, however, that new
information could actually increase the perceived importance of whatever private information managers still retain. In one sense asymmetric information falls, but
the importance of asymmetric information from the perspective of shareholders may actually increase. This might be the case, for example, when a firm with very
stable earnings that announces a very risky new venture. If investors do not anticipate the new venture, the announcement will increase their assessment of
uncertainty and the importance of managers’ private information. For the balance of this paper, we use asymmetric information to mean the perceived importance of
managers’ private information. This can be measured as the variance of the difference between the managers’ and the market’s valuation of the firm’s equity.
Although it is theoretically possible for this variance to increase with some types of announcements, we assume that most common events, such as earnings releases
and dividend announcements, decrease the impact of asymmetric information.
2.2. OÕerÕiew of the conditional eÕent-study procedure To test whether pre-issue information releases can reduce the asymmetric
Ž .
information at issue announcements, we modify Acharya 1988
conditional event-study procedure to allow the variance of the managers’ private information
to vary across firms. Let Õ denote managers’ public and private information about the effect of an
i
equity issue on the value of the claim of existing shareholders of firm i. As in Ž
. Ž
. Ž
. Myers and Majluf 1984 , Ambarish et al. 1987 , and Korajczyk et al. 1992 , we
assume managers maximize the interests of existing shareholders. Variable Õ is
i
simply the sum of the public’s prior expectation of Õ and managers’ private
i
information. This can be expressed Õ
s u q h , 1
Ž .
i i
i
where u is the public’s prior expectation of Õ and h reflects managers’ private
i i
i
information. Managers choose the action if and only if Õ G 0, i.e., when h G yu .
i i
i
Ž .
If the public’s expectation u is unbiased, then the mean of h is zero. We
i i
assume that h is independently and normally distributed with zero mean and
i
2
Ž .
variance v . Our model differs from Acharya 1988 in that the variance of
i
managers’ private information, v
2
, varies across observations. Note that v
2
is our
i i
measure of the importance of the asymmetric information for firm i, as described in Section 2.1. We assume that the variance of private information, v
2
, can be
i
expressed as
3
v
2
s c
X
y , 2
Ž .
i i
where c is a vector of parameters and y is a vector of publicly observable
i
variables that are associated with asymmetric information. These variables include firm characteristics that are associated with valuation uncertainty, such as firm
size, and variables that reflect the timing of news, such as earnings releases, that have been hypothesized to reduce asymmetric information. If we could observe
v
2
directly, then we could test hypotheses about asymmetric information by
i
Ž .
2
regressing Eq. 2 . Because it is unobservable, we infer v
from the issue
i
announcement abnormal returns. Ž
. As in Acharya 1988 , we assume that the expected abnormal return at an issue
announcement is proportional to the managers’ private information revealed in the event, i.e.
f u rv
Ž .
i i
E AR N issue s p E h N h G yu s pv
. 3
Ž .
Ž .
Ž .
i i
i i
i
F u rv
Ž .
i i
Ž . Ž .
In Eq. 3 , E P is the expectation operator; p is the constant of proportionality; Ž .
Ž . f P and F P represent the standard normal density and distribution functions
Ž .
respectively. It can be shown that if p - 0, then EE AR rEv - 0 and
i i
Ž .
Ž .
Ž .
E E AR
rE u 0 Greene, 1997 . Consistent with our intuition, this result
i i
suggests that the price drop at an issue announcement increases with the uncer- tainty of managers’ private information and decreases with the anticipation of the
issue. The announcement day abnormal return equals the expected abnormal return
Ž . Ž .
plus an error, ´ . After combining Eqs. 2 and 3 , we have
i
f u rv f z
Ž .
Ž .
i i
i X
AR s pv
q ´ s p c y q ´ ,
4
Ž .
i i
i i
i
F u rv F z
Ž .
Ž .
i i
i
Ž .
Ž .
where z s u rv . As suggested by Acharya 1988 and Prabhala 1997 , we
i i
i
Ž .
estimate parameter vector c in Eq. 4 using a two-stage procedure. In the first
Ž . Ž .
stage, we obtain an estimate of f z rF z . In the second stage, we substitute
i i
3
Results are qualitatively the same when we assume, instead, that v s c
X
y .
i i
Ž . Ž .
f z rF z with the estimate from the first stage and run the nonlinear regres-
i i
Ž . sion of Eq. 4 to obtain an estimate of the vector of asymmetric information
coefficients, c. We explain the estimation details below.
2.2.1. First-stage estimation Ž .
Ž . Ž .
To compute f z rF z , we first estimate F z
and then calculate
i i
i
Ž . Ž .
Ž . f z rF z
from this estimate. Note that F z is the ex-ante probability that
i i
i
managers intend to issue stock. This is because the probability that managers decide to issue stock, p , is the probability that h
G yu . If h is normally
i i
i i
distributed with mean zero and variance v
2
, p can be expressed
i i
p s Prob h G yu s F u rv s F z .
5
Ž .
Ž .
Ž . Ž .
i i
i i
i i
We use a probit regression to estimate p . This probit regression is also used to
i
determine factors that help investors to anticipate equity issues. Notice that p , the
i
probability that managers have decided to take an action, differs from the probability that managers will announce the decision in any given period. The
stock market reaction to an announcement depends on p , the market’s assessment
i
of the probability that the event is forthcoming, rather than on the probability that the announcement falls on a particular day. For example, an equity issue an-
nouncement will have little reaction if an equity issue is strongly anticipated, even if the market did not expect the announcement to be made on that particular day.
This distinction is important because it has implications for how we will estimate p from announcement data later.
i
To estimate p , we need to correct for two sampling biases. The first is the
i
unequal sampling bias that arises when we sample different proportions of days with and without the issue announcement. This sort of adjustment is often made in
probit and logit analysis. The second sampling bias occurs because the probit regression estimates the probability that an announcement will be made on any
given day, while we are interested in p , the probability that an event is
i
forthcoming. We discuss these problems and our corrections in the Section 2.2.2. Our estimate of p , after correcting for the sampling biases, is denoted p
c
.
ˆ
i i
2.2.2. Second-stage estimation
y1
Ž
c
. The second stage regression is obtained by substituting F
p for z in Eq.
ˆ
i i
Ž . 4
y1 c
f F p
Ž .
ˆ
i X
AR s p c y
q ´ . 6
Ž .
i i
i c
p
ˆ
i
Ž . We regress Eq. 6 using nonlinear least squares to get estimates of the asymmetric
information coefficients, c. Next, we take a closer look at the sampling problem in estimating p .
i
2.3. Probit estimation with sampling biases The probit regression requires observations in which managers intend to have
Ž .
the event Õ G 0 and observations in which managers intend not to have the event
i
Ž .
Õ -
0 . For the sake of brevity, we refer to these as the event and non-event
i
samples. Each observation represents a security-day, i.e. a particular security on a particular date. We cannot observe Õ directly, but we can assume that Õ
G 0 on
i i
days when the event is announced, so we use issue announcement dates to form the event sample. To get a non-event sample, we assume that Õ - 0 if the event
i
does not occur over the next 250 trading days. Let N equal the total number of security-days in the population. Of these, there are N observations with Õ
G 0,
E i
and N y N observations with Õ - 0. From the population, we sample S event
E i
E
announcements to get our event sample, and S observations without events over
NE
the next 250 trading days, to get our non-event sample. Using the joint sample of S
q S observations, we first estimate the probabil-
E NE
ity that an announcement in our sample occurs on a given day, without correcting for unequal sampling. We denote this uncorrected probability p . These estimated
ˆ
i
Ž .
probabilities as opposed to the probit coefficients are very easy to correct for unequal sampling. In particular, the corrected estimated probability that managers
of a firm intend to issue is simply
4
p
ˆ
i c
p s
, 7
Ž . ˆ
i
P
E
p q
1 y p
Ž .
ˆ ˆ
i i
P
NE
where P is the proportion of security-days with Õ
G 0 that are included in the
E i
sample, and P is the proportion of security-days with Õ - 0 represented in the
NE i
sample. In terms of the variables defined above, P equals S
rN , and P
E E
E NE
Ž .
equals S r N y N .
NE E
Recall that Õ is the managers’ assessment of the benefits of the event, and that
i
managers choose the event whenever Õ is greater than or equal to zero. Because
i
the event takes time to plan and to accomplish, Õ will be greater than zero for a
i
period of time before the event actually occurs. We observe the actual events, but not Õ , so we do not know N , the number of days in which Õ is greater than zero.
i E
i
To get around this problem, we introduce another parameter, H. We define H as the average number of days between the time that managers conclude that the
Ž . benefits of the event Õ
are positive and the date that the event actually occurs.
i
Intuitively, H reflects the average number of days in the horizon of the managers. For example, if an event occurs an average of 50 days after managers decide the
4
Ž . See Appendix A for the derivation of Eq. 7 .
benefits are positive, then H will equal 50. H also captures the effects of managers that change their minds about an event. Suppose that the managers of
100 firms decide the benefits of an event are positive. Fifty days later, 90 of the firms consummate the event and 10 change their minds. H will equal 100 = 50
r90, or 55.6 days.
If there are n unique events in the population, then H equals the total number
E
Ž .
Ž .
of security-days with Õ G 0
N , divided by the number of events n . The
i E
E
sampling proportions can be expressed in terms of H P
s S r n p
c
H ,
8
Ž .
Ž .
Ž .
ˆ
E E
E i
and P
s S r N y n H .
9
Ž .
Ž .
NE NE
E
Ž . Ž . Combining Eqs. 7 – 9 gives the corrected estimated probability of the event in
terms of H p
ˆ
i
H s
. 10
Ž .
S N
y n H
Ž .
E E
p q
1 y p
Ž .
ˆ ˆ
i i
S n H
NE E
Ž . With this substitution, Eq. 6 becomes
y1 c
f F p
H
Ž .
Ž .
ˆ
i X
AR
s p c y q ´ .
11
Ž .
i i
i c
p H
Ž .
ˆ
i
Ž .
In theory, H and c could both be estimated from Eq. 11 . In practice, this is
computationally expensive and produces questionable results. An attractive alter- native is to estimate the asymmetric information parameters, c, for several
plausible values of H. Recall that H is the average number of days from the managers’ decision to have an event to the event itself. This must be at least equal
to the average number of days between the issue announcements and the actual issue dates. We denote this T. If announcement days are uniformly distributed
between the private decisions of managers and the actual issue dates, then H will equal 2T. It is also possible for H to be somewhat longer than this. Because of
Ž .
these considerations, we recommend that Eq. 11 be estimated for H s T, 2T,
and 4T. To summarize, our conditional event-study procedure has three steps.
Ž .
1. Compute the predicted values p from the first-stage probit regression. This
ˆ
i
probit regression is also used to determine factors that affect firms’ equity-issue decision.
Ž .
2. Adjust these probabilities for unequal sampling using Eq. 10 . This gives corrected probabilities, p
c
.
ˆ
i
3. Estimate the parameter vector c, using a nonlinear weighted least squares
