American Economic Association
Event Studies in Economics and Finance
Author(s): A. Craig MacKinlay
Source: Journal of Economic Literature, Vol. 35, No. 1 (Mar., 1997), pp. 13-39
Published by: American Economic Association
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Journal of Economic Literature
Vol. XXXV (March 1997), pp. 13-39
Event Studies in Economics and
Finance
A. CRAIG MACKINLAY
The Wharton School, University of Pennsylvania
Thlanks to John Can ipbell, Briuce G-tr udly, Andrti-ewv Lo, and twvo anonymnotus referees for helpful
comments andcl discussion. Research suLpport from the Rodiney L. WVhite Center for Finiancial
Research is gratefiully acknzowvledged.
1. Introduction
ECONOMISTS are frequently asked to
measure the effects of an economic
event on the value of firms. On the sur-
face this seems like a difficult task, but a
measure can be constructed easily using
an event study. Using financial market
data, an event study measures the impact
of a specific event on the value of a firm.
The usefulness of such a study comes
from the fact that, given rationality in
the marketplace, the effects of an event
will be reflected immediately in security
prices. Thus a measure of the event's
economic impact can be constructed
using security prices observed over a
relatively short time period. In contrast,
direct productivity related measures may
require many months or even years of
observation.
The event study has many applica-
tions. In accounting and finance re-
search, event studies have been applied
to a variety of firm specific and economy
wide events. Some examples include
mergers and acquisitions, earnings an-
nouncements, issues of new debt or eq-
uity, and announcements of macro-
economic variables such as the trade
deficit.1 However, applications in other
fields are also abundant. For example,
event studies are used in the field of law
and economics to measure the impact on
the value of a firm of a change in the
regulatory environment (see G. William
Schwert 1981) and in legal liability cases
event studies are used to assess damages
(see Mark Mitchell and Jeffry Netter
1994). In the majority of applications,
the focus is the effect of an event on the
price of a particular class of securities of
the firm, most often common equity. In
this paper the methodology is discussed
in terms of applications that use common
equity. However, event studies can be
applied using debt securities with little
modification.
Event studies have a long history. Per-
haps the first published study is James
Dolley (1933). In this work, he examines
the price effects of stock splits, studying
nominal price changes at the time of the
split. Using a sample of 95 splits from
1921 to 1931, he finds that the price in-
I The first three examples will be discussed later
in the paper. Grant McQueen and Vance Roley
(1993) provide an illustration of the fourth using
macroeconomic news announcements.
13
14 o ournal of Economic Literature, Vol. XXXV (March 1997)
creased in 57 of the cases and the price
declined in only 26 instances. Over the
decades from the early 1930s until the
late 1960s the level of sophistication of
event studies increased. John H. Myers
and Archie Bakay (1948), C. Austin
Barker (1956, 1957, 1958), and John
Ashley (1962) are examples of studies
during this time period. The improve-
ments included removing general stock
market price movements and separating
out confounding events. In the late
1960s seminal studies by Ray Ball and
Philip Brown (1968) and Eugene Fama
et al. (1969) introduced the methodology
that is essentially the same as that which
is in use today. Ball and Brown consid-
ered the information content of earn-
ings, and Fama et al. studied the effects
of stock splits after removing the effects
of simultaneous dividend increases.
In the years since these pioneering
studies, a number of modifications have
been developed. These modifications re-
late to complications arising from viola-
tions of the statistical assumptions used
in the early work and relate to adjust-
ments in the design to accommodate
more specific hypotheses. Useful papers
which deal with the practical importance
of many of the complications and adjust-
ments are the work by Stephen Brown
and Jerold Warner published in 1980 and
1985. The 1980 paper considers imple-
mentation issues for data sampled at a
monthly interval and the 1985 paper
deals with issues for daily data.
In this paper, event study methods are
reviewed and summarized. The paper
begins with discussion of one possible
procedure for conducting an event study
in Section 2. Section 3 sets up a sample
event study which will be used to illus-
trate the methodology. Central to an
event study is the measurement of an ab-
normal stock return. Section 4 details
the first step-measuring the normal
performance-and Section 5 follows
with the necessary tools for calculating
an abnormal return, making statistical in-
ferences about these returns, and aggre-
gating over many event observations.
The null hypothesis that the event has no
impact on the distribution of returns is
maintained in Sections 4 and 5. Section 6
discusses modifying this null hypothesis
to focus only on the mean of the return
distribution. Section 7 presents analysis
of the power of an event study. Section 8
presents nonparametric approaches to
event studies which eliminate the need
for parametric structure. In some cases
theory provides hypotheses concerning
the relation between the magnitude of
the event abnormal return and firm char-
acteristics. Section 9 presents a cross-
sectional regression approach that is use-
ful to investigate such hypotheses.
Section 10 considers some further issues
relating event study design and the pa-
per closes with the concluding discussion
in Section 11.
2. Procedure for an Event Study
At the outset it is useful to briefly dis-
cuss the structure of an event study. This
will provide a basis for the discussion of
details later. While there is no unique
structure, there is a general flow of
analysis. This flow is discussed in this
section.
The initial task of conducting an event
study is to define the event of interest
and identify the period over which the
security prices of the firms involved in
this event will be examined-the event
window. For example, if one is looking at
the information content of an earnings
with daily data, the event will be the
earnings announcement and the event
window will include the one day of the
announcement. It is customary to define
the event window to be larger than the
specific period of interest. This permits
examination of periods surrounding the
MacKinlay: Event Studies in Economics and Finance 15
event. In practice, the period of interest
is often expanded to multiple days, in-
cluding at least the day of the an-
nouncement and the day after the an-
nouncement. This captures the price
effects of announcements which occur
after the stock market closes on the an-
nouncement day. The periods prior to
and after the event may also be of inter-
est. For example, in the earnings an-
nouncement case, the market may ac-
quire information about the earnings
prior to the actual announcement and
one can investigate this possibility by ex-
amining pre-event returns.
After identifying the event, it is neces-
sary to determine the selection criteria
for the inclusion of a given firm in the
study. The criteria may involve restric-
tions imposed by data availability such as
listing on the New York Stock Exchange
or the American Stock Exchange or may
involve restrictions such as membership
in a specific industry. At this stage it is
useful to summarize some sample char-
acteristics (e.g., firm market capitaliza-
tion, industry representation, distri-
bution of events through time) and note
any potential biases which may have
been introduced through the sample se-
lection.
Appraisal of the event's impact re-
quires a measure of the abnormal return.
The abnormal return is the actual ex post
return of the security over the event win-
dow minus the normal return of the firm
over the event window. The normal re-
turn is defined as the expected return
without conditioning on the event taking
place. For firm i and event date t the
abnormal return is
AR1t = R1, - E(RjrjXr) (1)
where AR,,, Ri,, and E(Ri,IXt) are the ab-
normal, actual, and normal returns re-
spectively for time period t. Xl is the
conditioning information for the normal
return model. There are two common
choices for modeling the normal re-
turn-the constant mean return model
where Xl is a constant, and the market
model where Xl is the market return.
The constant mean return model, as the
name implies, assumes that the mean
return of a given security is constant
through time. The market model as-
sumes a stable linear relation between
the market return and the security re-
turn.
Given the selection of a normal perfor-
mance model, the estimation window
needs to be defined. The most common
choice, when feasible, is using the period
prior to the event window for the estima-
tion window. For example, in an event
study using daily data and the market
model, the market model parameters
could be estimated over the 120 days
prior to the event. Generally the event
period itself is not included in the esti-
mation period to prevent the event from
influencing the normal performance
model parameter estimates.
With the parameter estimates for the
normal performance model, the abnor-
mal returns can be calculated. Next
comes the design of the testing frame-
work for the abnormal returns. Impor-
tant considerations are defining the null
hypothesis and determining the tech-
niques for aggregating the individual
firm abnormal returns.
The presentation of the empirical re-
sults follows the formulation of the
econometric design. In addition to pre-
senting the basic empirical results, the
presentation of diagnostics can be fruit-
ful. Occasionally, especially in studies
with a limited number of event observa-
tions, the empirical results can be heav-
ily influenced by one or two firms.
Knowledge of this is important for gaug-
ing the importance of the results.
Ideally the empirical results will lead
to insights relating to understanding the
sources and causes of the effects (or lack
16 Journal of Economic Literature, Vol. XXXV (March 1997)
of effects) of the event under study. Ad-
ditional analysis may be included to dis-
tinguish between competing explana-
tions. Concluding coiimments complete
the study.
3. An Example of an Event Study
The Financial Accounting Standards
Board (FASB) and the Securities Ex-
change Commission strive to set report-
ing regulations so that financial state-
ments and related information releases
are informative about the value of the
firm. In setting standards, the informa-
tion content of the financial disclosures
is of interest. Event studies provide an
ideal tool for examiining the informnation
content of the disclosures.
In this section the description of an
example selected to illustrate event
study methodology is presented. One
particular type of disclosure-quarterly
earnings announcements-is considered.
The objective is to investigate the infor-
mation content of these announce-
ments. In other words, the goal is to see
if the release of accounting informnation
provides information to the marketplace.
If so there should be a correlation be-
tween the observed change of the mar-
ket value of the company and the infor-
mation.
The example will focus on the quar-
terly earnings announcements for the 30
firms in the Dow Jones Industrial Index
over the five-year period from January
1989 to December 1993. These an-
nouncements correspond to the quar-
terly earnings for the last quarter of 1988
through the third quarter of 1993. The
five years of data for 30 firms provide a
total sample of 600 announcemnents. For
each firm and quarter, three pieces of in-
formation are compiled: the date of the
announcement, the actual earnings, and
a measure of the expected earnings. The
source of the date of the announcement
is Datastream, and the source of the ac-
tual earnings is Compustat.
If earnings announcements convey in-
formation to investors, one would expect
the announcement impact on the mar-
ket's valuation of the firm's equity to de-
pend on the magnitude of the unex-
pected component of the announcement.
Thus a measure of the deviation of the
actual announced earnings from the mar-
ket's prior expectation is required. For
constructing such a measure, the mean
quarterly earnings forecast reported by
the Institutional Brokers Estimate Sys-
tem (I/B/E/S) is used to proxy for the
market's expectation of earnings. I/B/E/S
compiles forecasts from analysts for a
large number of companies and reports
summary statistics each month. The
mean forecast is taken from the last
month of the quarter. For example, the
mean third quarter forecast from Sep-
tember 1990 is used as the measure of
expected earnings for the third quarter
of 1990.
To facilitate the examination of the
impact of the earnings announcement on
the value of the firm's equity, it is essen-
tial to posit the relation between the in-
formation release and the change in
value of the equity. In this example the
task is straightforward. If the earnings
disclosures have information content,
higher than expected earnings should be
associated with increases in value of the
equity and lower than expected earnings
with decreases. To capture this associa-
tion, each announcement is assigned to
one of three categories: good news, no
news, or bad news. Each announcement
is categorized using the deviation of the
actual earnings from the expected earn-
ings. If the actual exceeds expected by
more than 2.5 percent the announce-
ment is designated as good news, and if
the actual is more than 2.5 percent less
than expected the announcement is des-
ignated as bad news. Those announce-
MacKinlay: Event Studies in Economics and Finance 17
ments where the actual earnings is in the
5 percent range centered about the ex-
pected earnings are designated as no
news. Of the 600 announcements, 189
are good news, 173 are no news, and the
remaining 238 are bad news.
With the announcements categorized,
the next step is to specify the parameters
of the empirical design to analyze the eq-
uity return, i.e., the percent change in
value of the equity. It is necessary to
specify a length of observation interval,
an event window, and an estimation win-
dow. For this example the interval is set
to one day, thus daily stock returns are
used. A 41-day event window is em-
ployed, comprised of 20 pre-event days,
the event day, and 20 post-event days.
For each announcement the 250 trading
day period prior to the event window is
used as the estimation window. After
presenting the methodology of an event
study, this example will be drawn upon
to illustrate the execution of a study.
4. Models for Measuring Normal
Performance
A number of approaches are available
to calculate the normal return of a given
security. The approaches can be loosely
grouped into two categories-statistical
and economic. Models in the first cate-
gory follow from statistical assumptions
concerning the behavior of asset returns
and do not depend on any economic ar-
guments. In contrast, models in the sec-
ond category rely on assumptions con-
cerning investors' behavior and are not
based solely on statistical assumptions. It
should, however, be noted that to use
economic models in practice it is neces-
sary to add statistical assumptions. Thus
the potential advantage of economic
models is not the absence of statistical
assumptions, but the opportunity to cal-
culate more precise measures of the nor-
mal return using economic restrictions.
For the statistical models, the assump-
tion that asset returns are jointly multi-
variate normal and independently and
identically distributed through time is
imposed. This distributional assumption
is sufficient for the constant mean return
model and the market model to be cor-
rectly specified. While this assumption is
strong, in practice it generally does not
lead to problems because the assumption
is empirically reasonable and inferences
using the normal return models tend to
be robust to deviations from the assump-
tion. Also one can easily modify the sta-
tistical framework so that the analysis of
the abnormal returns is autocorrelation
and heteroskedasticity consistent by us-
ing a generalized method-of-moments
approach.
A. Constant Mean Return Model
Let [,u be the mean return for asset i.
Then the constant mean return model is
Rit= - i + Git (2) (2
E(4st) = 0 var (4i) = G7; .
where Rit is the period-t return on secu-
rity i and Cit is the time period t distur-
bance term for security i with an expec-
tation of zero and variance cy .
Although the constant mean return
model is perhaps the simplest inodel,
Brown and Warner (1980, 1985) find it
often yields results similar to those of
more sophisticated mnodels. This lack of
sensitivity to the model can be attributed
to the fact that the variance of the abnor-
mal return is frequently not reduced
much by choosing a more sophisticated
model. When using daily data the model
is typically applied to nominal returns.
With monthly data the model can be ap-
plied to real returns or excess returns
(the return in excess of the nominal risk
free return generally measured using the
U.S. Treasury Bill with one month to
inaturity) as well as nominal returns.
18 Journal of Economic Literature, Vol. XXXV (March 1997)
B. Market Model
The market model is a statistical
model which relates the return of any
given security to the return of the mar-
ket portfolio. The model's linear specifi-
cation follows from the assumed joint
normality of asset returns. For any secu-
rity i the market model is
Rit = (xi + PiR,,t + ?it (3)
E(eit = 0) var(eyt) = (T
where Rit and Riiit are the period-t re-
turns on security i and the market port-
folio, respectively, and Lit is the zero
mean disturbance term. oci, fPi, and G2
are the parameters of the market model.
In applications a broad based stock in-
dex is used for the market portfolio,
with the S&P 500 Index, the CRSP
Value Weighted Index, and the CRSP
Equal Weighted Index being popular
choices.
The market model represents a poten-
tial improvement over the constant mean
return model. By removing the portion
of the return that is related to variation
in the market's return, the variance of
the abnormal return is reduced. This in
turn can lead to increased ability to de-
tect event effects. The benefit from us-
ing the market model will depend upon
the R2 of the market model regression.
The higher the R2 the greater is the vari-
ance reduction of the abnormal return,
and the larger is the gain.