1983 - Bowman - Understanding and Conducting Event Studies - Journal of Business Finance & Accounting

UNDERSTANDING AND CONDUCTING EVENT STUDIES

Robert G. Bowman*

An important methodological approach to market based empirical research in finance and accounting is the event study. Also known by other names such as residual analysis and abnormal performance index tests, these studies involve the analysis of security price behavior around the time of an information announcement or event. The approach has been used to study a variety of events such as the announcements of annual accounting earnings, accounting principle changes, large block trades and corporate mergers.

A very broad interpretation should be placed on what constitutes an event. Research to date has primarily been in the context of an announcement as the event and usually an announcement emanating from a fm. However, announcements from outside of fm (e.g., from an accounting standard setting body) or more general “happenings” (e.g., an oil embargo) are includable as events. The broad class of research which can be labelled event studies can have numerous objectives and methodological adaptations. The diversity of the event study literature, both in terms of the range of topics covered and specific technique choices available, can be overwhelming. Yet, the structure of event studies is rather straightforward. The purpose of this paper is to provide a structure for the design of event studies, to differentiate them by type and to discuss some issues which are crucial to their understanding.

What have come to be known as event studies have their modem roots in studies by Elall and Brown (1968) and Fama, Fisher, Jensen and Roll (1969).’ Ball and Brown investigated the security price reaction to the unanticipated component of annual accounting earnings. They found that 85 to 90 percent of the information contained in the annual earnings report had been reflected in security prices before the announcement date. At the time this result was first published, many people, particularly in accounting, found it disconcerting, if not threatening. However, upon reflection, the result should not have been a surprise. The result implies an eminently reasonable information milieu where various sources, including interim earnings reports, are utilized in forming expectations of the annual earnings. The announcement of the annual accounting earnings report, after year end, then serves to revise the previous estimates.

*The author is Associate Rofessor 01 Accounting at the University of Oregon. Earlier versions of this paper were presented at workshops at Momsh University and the University of Queenskmd. The author acknowledges the he@jid corn- ments of the workshop participants, particularly Bob Ofker. In addition, the comments of L. Dann, G. Foster, R. King, T. O’KeefeandB. Spicer helped to shmpen the exposition and organization of the paper. (Paper received August 1982, revised December 1982)

561 Journal of Business Finance & Accounting 10,4(1983)

Ball and Brown provided a rigorous investigation of the information contained in the annual earnings announcement, and their results served to clarify our understanding of the role of accounting information.

Fama, Fisher, Jensen and Roll (FFJR) investigated a different type of event; stock splits.2 They analyzed the behavior of stock prices during the 60 months were interested in whether the stock market was “efficient” with respect to stock splits? They analyzed the bahavior of stock prices during the 60 months surrounding the months of the stock split. Their finding indicated that security prices adjust rapidly to the information implicit in a stock split. Although their data was coarse for addressing the question: they found no evidence to suggest that news of the split could be used to increase trading profits.

These two studies, looking at classic events of accounting and finance, and using similar methodologies, set the stage for an important and growing body of research. These seminal works also serve to illustrate two of the four basic types of event studies.

1. Information content (Ball and Brown) 2. Market efficiency (FFJR) 3. Model evaluation 4. Metric explanation

The fust two types differ in a very fundamental way which was illustrated in the two studies discussed. The information content of an event is studied by analysis of security price behavior up to and concurrent with the event4 Tests of market efficiency involve the analysis of security price behavior subsequent to the event.5 Studies of model evaluation and metric explanation are generally concurrent with an information content study. Additional discussion of these two will follow later in the paper.

The remainder of the paper is organized as follows: the next section sets a structure for the conduct of an event study and each of the five steps are discussed and alternative techniques identified. The specificity of the coverage is intended to provide an adequate basis for the understanding of event studies but is not sufficient to constitute a “cookbook” for the conduct of an event study. The second section considers studies of the model evaluation and metric explanation type, and the final section considers a number of issues of interpretation, evaluation and design which are crucial to the effective conduct and understanding of an event study. It seems important at this point to also specify what the paper does not undertake. The body of research which comprises extant event studies is very -large and continually expanding. There is no attempt here to review this literature, but at numerous points however, specific studies are cited as examples of particular applications of the basic methodology. In many cases other studies could have been chosen as illustrations of the various points, and thus, the citations made here can be viewed as a non-random sample from a larger population.

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Structure of an Event Study As will be seen, there is a broad range of techniques used in event studies.

In some cases the technique will be dictated by the context of the study. In other cases the researcher will be free to choose from an array of alternative techniques. But the class of tests we are primarily concerned with here have much in common. This section focuses upon information content and market efficiency tests. Model evaluation and metric explanation will be treated in the following section and shown to be outgrowths of the testing described here. In their simplest form, information content and market efficiency tests involve five steps. Following the identification of these steps, each one will be discussed in detail.

Identirjt the event of interest. For example, the two studies mentioned above looked at annual earnings announcements and stock splits. Model the security price reaction This generally involves an expectations model conditional upon the event. Estimate the excess returns. This step generally entails the calculation of residuals from some model of the process generating security returns. Organize and group the excess retltms. The residuals may be treated individually, but time series cumulations are the standard procedure. Analyze the results. When possible this will be done with statistical tests of significance designed for a stated (null) hypothesis.

Identi& the Event oflnterest The first step in the conduct of an event study is to identify the event of

interest. This is a crucial step in at least four respects. As with any project the researcher begins to study with an objective(s) in mind, generally a hypothesis to be tested. It is perhaps trivial, yet worth stating, that the choice of an event considerably restricts the possible hypotheses which can be meaningfully tested. This will be discussed in more detail below.

Frequently, one investigates the impact of a single event. An example would be an event study of a pronouncement by the FASB where the event occurs at a single point in calendar time. However, many studies are investigations of a type of event, such as earnings announcements or stock splits which occur for different companies at different calendar times. Bringing these events to- gether into a single sample requires the introduction of the concept of event time. The calendar date of the specific event becomes time zero in event time. Then all time periods are described in event time relative to the zero time when the event occurred.

The difference between a study of a (literal) single event and a type of event is very pronounced when one is attempting to control for systematic experimental error. In a single event, all observations in the sample have been exposed to a common set of exogenous, contemporaneous influences (e.g.,

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the change in the market index). In a study of a type of event there is a distribution of calendar times and thus a distribution of exogenous, noncontem- poraneow influences in operation. The distribution of influences will tend to be offsetting and to reduce, perhaps dramatically, the problem of systematic experimental error. In general, a study of an event type will be more robust than a study of a single event.

An important point in identifying an event of interest concerns the ability to ascertain the timing of the event. Brown and Wamer (1980), in their important simulation research of event study methodologies, found that the power of the tests was very sensitive to the precision with which an event date could be identified. For example, if one’s interest is the security price reaction to a change in dividend rate one would want to specify the event as the time the news of the impending dividend change became available to the market. This would certainly not be the date the changed dividend is paid. In most cases it would be expected that the event would occur on the date when the public announcement was made of the change. However, i t is possible that the decision to change the dividend rate was made at an earlier date, and the news became available to the market prior to the formal announcement.

To the extent that the timing of the event under study was misidentified, one can expect to seriously reduce one’s ability to observe any security price movement. In a recently published study, Dodd (1980) identifies a major problem with previous studies of the relationship between stock price and mergers as an improper identification of the relevant event. “Accurate estimation of the market response to mergers requires use of the date of fust public announcement of the proposal @.107).” Previous studies had focused on the effective dates of the mergers rather than the dates of first public announcement. It is certainly possible that the event of interest occurs even earlier than the first public announcement of the merger, perhaps through a leakage of information from insiders. Dodd’s positive results indicate that this is not likely to be a serious problem. Notice, however, that had Dodd found negative results (i.e., no observable security price movement) one explanation would be that the event was improperly identified.

Another important consideration in the choosing and defming of an event is the problem presented by confounding events. For example, if dividend announcements are accompanied by earnings announcements, the latter will be a potentially confounding event in an event study of the former. Watts (1973) studied dividend announcements using an event study approach. His methodology involved a control for the possibly confounding effect of earnings announcements. Confounding events will frequently exist in event studies. Their presence can have a significant impact on the results of empirical tests. Confounding events which are firm specific are particularly troublesome (eg., the earnings-dividend case discussed above). The success of many event studies will depend upon how effectively the researcher controls for the impact of confounding events.

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Identifying the event to be studied should generally fit within the scheme of previous research and serve to expand one’s understanding and/or resolve conflicts. The research surrounding a firm’s earnings provides a good illustra- tion. The early studies in accounting focused upon the announcement of a fm’s earnings as the event; first, reported annual earnings (Ball and Brown, 1968; and Beaver, 1968), then quarterly earning may, 1971;andBrown and Kennelly, 1972), then projected earnings (Foster, 1973; and Patell, 1976), then compon- ents of earnings (Foster, 1975b; and Gonedes, 1975), then specialized industries (Foster, 1975a; and McKnight, 1981). This reflects a natural expansion of the accumulating knowledge as well as increasing specificity in the research. How- ever, it is interesting that in one regard we are still at square one. The security price behavior subsequent to the public announcement of firms’ earnings has consistently been found anomalous to market efficiency. The evidence in- dicates an ability to earn excess returns based upon the infomiation in the announcement over a postannouncement period. This evidence is reviewed and possible explanations are discussed by Ball (1978).

If a researcher 1s interested in the information content of earnings announcements it might be fruitful to choose a previously unexplored event (e.g., sub- earnings of the segments of conglomerates in interim reports). However, if market efficiency testing is the objective, the researcher is perhaps better advised to focus upon clarifying and resolving the conflict presented by anomalous results at the most basic levels!

Model the Securiv Rice Reuction In the simplest cases the estimation of security price reaction to an event is

not a problem. In some cases the direction of the effect of the event is expected to be the same for all fums being studied. One might hypothesize, for example, that firms which announce a decrease in dividends wil l suffer negative security price reactions (Spangler, 1973). In other cases one may not be willing to predict the direction of the security price reaction, but expect all of the f m s being studied to be affected in the same direction, whichever that direction might be. Thus, it might be hypothesized that bidder firms in a merger wil l all be affected in the same direction by the announcement of the merger roposal, but allow the data to indicate the direction of the effect @odd, 1980).

In many other studies, including most studies of earnings announcements, one expects the direction of the security price reaction to the event to differ across fm and to be conditional upon information in or relevant to the event. Thus, a model is developed to artition the frrms into expected positive and

.p

negative security price reactions. P Analytically one can hypothesize:


where

eit = q = expectations model

yit =

measure of excess returns for firm i in time period t

information from 17 for firm i and time t.

For an example of the application of this hypothesis consider the Ball and Brown (1968) study. They used two different expectations models (an index model and a random walk model) to estimate annual accounting earnings. The information (or signal) from the model was the unanticipated earnings (i.e., the actual earnings minus the expected earnings). Positive unanticipated earnings were assumed to be correlated with positive excess returns. Ball and Brown then partitioned the firms in their sample into two groups:

Put more simply, they hypothesized that the signs of yit and eit would be the same .’

In a different vein, Collins and Dent (1979) studied the effect of deliberations regarding the accounting for oil and gas exploration costs. They dichotomized their sample based upon the accounting method used by the firm (full cost or successful efforts). Then they hypothesized differing security price reactions for the two groups of firms which were identified by their model. Thus, their model of security price reaction depended upon the accounting principles preexisting in firms given that accounting policy makers are taking action (in this case the actions are deliberations) affecting the accounting principles.

On occasion, the unfolding of event study research will focus upon the proper choice of an expectations model. The research into the choice of firms to change their inventory accounting from FIFO to LIFO is a case in point. Sunder (1973) examined the security price reactions surrounding this voluntary change of accounting event. His model included only the action taken by the firm (to change their accounting method). Ricks (1982) expanded the information used in his model to include an assessment of unanticipated earnings. The results and implications from Ricks’ expanded model are strikingly different. As the FIFO/LIFO change area appears to yield anomalous results, based upon Ricks’ research, it seems only a matter of time until futher work is done in this area. A logical area to focus upon is additional improvements in the models used to form expectations on security price reactions. For example, an improved model might utilize quarterly earnings information.

A critical element in all of the procedures discussed above is the efficacy of the model used to predict (or interpret) security price reaction. In particular, a study which finds no price reactions is vulnerable to the criticism that the

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negative results are attributable to deficiencies in the model rather than the absence of a security price reaction. This issue will be discussed further in the section on joint test considerations.

Noreen and Sepe (1981) recently developed a methodology which does not require this prediction of reactions by individual firms. Rather, their methodology depends upon identifying a sequence of events (a pair is sufficient) where the events are expected to have opposite effects on a given firm. Thus, if the first effect is positive (negative), the second effect is expected to be negative (positive). Ex ante specification of the sign of the effect is not required. The crucial step is the identification of events which will unambiguously lead to the reversal in security price reaction.' Here again, when a study yields negative results it is difficult to defend the charge that the events were not properly chosen. The Noreen and Sepe methodology has been used to study the information content of deliberations and actions of authoritative bodies (i.e., the FASB and the SEC). The methodology has not been used in a direct test of market efficiency. How- ever, in at least some restrictive settings, the reversal procedure could be used for such tests. Although this methodology provides a valuable addition to the available tests, it seems to be limited to those cases where a series of related events are expected to induce reversals in the behavior of security prices.

Estimate the Excess Returns The third step in the conduct of an event study is to choose a method of

estimating the excess returns for the firms or portfolios under study. There are many estimation methods available to the researcher. Those which are commonly considered can be classified as: unadjusted or mean adjusted returns, risk adjusted returns and risk controlled portfolio returns.' '

The first category of estimating returns was available and commonly employed in research before the availability of risk adjustment procedures which followed from the development of the Capital Asset Pricing Model by Sharpe (1964), Lintner (1965a and 1965b) and Mossin (1966). The unadjusted procedure simply defines the realized return as the excess return, thus assuming zero expected return. The mean adjusted procedure defines the expected return as the mean of past security returns (defined over an arbitrary period). Both of these methods seem crude compared to the elaborate and intricate methods which have developed during the past decade. However, Brown and Warner (1980) found that the mean adjusted returns measure was very robust and under many conditions performed as well or better than the more sophisticated methods.' This is somewhat perplexing as there are solid theoretical reasons to support the more sophisticated methods. The desirability of risk adjustments in the determination of excess returns is so well established as to seem self- evident. Further investigation in this area will be helpful. For now, a researcher has strong support (i.e., the Brown and Warner study) for using the compu- tationally simple mean adjusted return.


The second group of estimation techniques covers the wide range of risk adjustment methodologies, most of which have been developed from the Capital Asset Pricing Model (CUM). The most common is the market model where the systematic risk parameter (beta) is equal to the slope coefficient in a time series regression of individual firm returns on the return on a market index. Other models which have been or could be used for the risk adjustment include the CAPM, the Black (1972) two parameter asset pricing model, the Fama and MacBeth (1973) procedure, and the various methods recently developed to control for serial correlation and nonsynchronous trading such as by Scholes and Williams (1977) and Dimson (1979). The more recently developed arbitrage pricing model (Ross, 1976), which provides an alternative to the mean- variance approach, could also be used. In addition, there are many empirical situations, where it is desirable to expand the asset pricing model to control for other factors, most commonly industry wide effects.

The risk adjustment model is used to formulate the estimated return on a security. The excess return is then the difference between the realized return and the expected return. An example wil l illustrate the procedure. One of the most popular models is the market model:

N N N

Rit = 4 + h b t + eit

where

Rit

Knt

4 and 4 eit = disturbance term (residual).

N

= return on security i in period t

= return on the market portfolio in period t

= constants for security i N

The parameters of the model are estimated using ordinary least squares regression and then used to calculate the residuals

which are assumed to have the properties,

Since the expected value of the residuals is zero, any non-zero value of the residuals is termed the excess return. The general procedure is to compute individual security excess returns, and then to group the returns according to

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information derived from the second of the five steps which was discussed previously; estimating the security price reaction.

Another approach has been used by Gonedes (1975) and others. Under this procedure, referred to here as the risk controlled portfolio approach, the grouping of f m s according to the estimated security price reaction procedes the calculation of excess returns.” For each portfolio of firms developed by the grouping, weights are determined for each fum so as to ensure that the weighted portfolio will have a beta equal to Then, individual firms’ returns are calculated, weighted and aggregated into portfolios. The excess return for the portfolio is the difference between this portfolio return and the market return.

The portfolio forming approach has an a priori appeal and has been used in a number of studies. However, the results of Brown and Warner (1980) indicate that the procedure performs poorly relative to the previously discussed methods. In light of their result it will be difficult to defend further use of the method in most situations.

Organize and Group the Excess Return After excess returns have been calculated, the researcher must organize

and group the excess returns preparatory to analysis of the results. This step normally entails the separation of f m s into portfolios according to the expected security price reaction determined in step two.’ ’ This may be an appropriate point for analysis in some research. In an efficient market and with proper asset pricing model specification, the expected value of the excess returns is always zero. Therefore, one may wish to analyze the portfolio excess returns for individual (event) time periods, particularly those on or immediately about the time of the event of interest, to determine whether they differ systemati- cally from zero. However, in most studies some time series aggregation is also desired.

The two principle methods of aggregation were developed in the first two studies of this type which made risk adjustments to the security returns. Fama, Fisher, Jensen and Roll (1969) used a procedure which they termed the Cumu- lative Average Residual (CAR). This is an arithmetic procedure where:

T I N CARt = Z ;; Z eit t= 1

eit = N = number of firms in the portfolio

T = number of time periods being aggregated.

excess return for firm i in period t


Notice the trading strategy implied by this approach. Equal dollar amounts are assumed to be invested in each of the N securities at time t=O. Then, at the end of each period the portfolio is rebalanced so that the total wealth is again equally distributed across the securities. This rebalancing would be accomplished by reducing the (end of period) investment in the securities with high excess returns and increasing the investment in securities with low (or negative) excess returns.

The second procedure was developed by Ball and Brown (1968) and involved a multiplicative formulation. They calculated an Abnormal Performance Index (MI) as:

This metric implies a trading strategy of investing 1/N of wealth in each security at t=O and holding the investment until time T. The portfolio is not rebalanced at any point. Returns are being compounded at the discrete time in- tervals under this by-and-hold strategy.

One perspective to use in evaluating these two alternative measures is to consider the time series properties of the excess returns. In an efficient market the following will hold:

Thus, in an efficient market with no transactions costs, the two implied trading strategies would be equivalent. With transaction costs, an investor would prefer the API trading strategy. However, the thrust of an event study is generally to determine whether the conditions shown above hold. If the market were in- efficient with respect to the event, the investor would expect E(eit) # 0 and cov (eit, eit+k) > 0 for some k > 0. The rebalancing implied by the CAR would be an advantageous strategy only when cov (eit, eit+k*) < 0 for some k* > 0 (Le., when there is negative correlation in the residuals) and zero for all other k in the range k* > k > 0. The API trading strategy would be superior in all other cases.

Obviously an investor who expected positive serial correlation in the excess returns could devise a trading strategy which would be preferred over that implied by either CAR or API by rebalancing his portfolio in favor of the securities which have had higher excess returns. However, from an empirical research perspective any such strategy would be ad hoe. There is no theory available to guide one in coordinating one’s investment shifts with one’s expectations concerning correlations. Furthermore, any advantage over CAR or API would generally be in terms of magnitude of the measure, not direction, if an effect is detected.

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There have been three additional formulations which refine the procedures described above and warrant mention. Beaver and Dukes (1972) modified the API measure by making the compounding of excess returns continuous. Al- though this modification is a logically appealing one, it is unlikely to be of any consequence unless the time period used is months and excess returns are cumulated over a long period. Pettit (1972) also used the compounding procedure but included the periodic rebalancing of the portfolios. Patell (1976) developed normalized cumulative prediction error. The measure is analogous to the CAR but is transformed to accommodate a parametric testing procedure.

Regardless of the more sophisticated and intuitively more appealing methods, the CAR methodology has become firmly established as the commonly used approach. Further, Brown and Warner (1980; footnote 28) reported that “the properties of the confidence bands traced out by such alternative metrics were similar to those discussed for the CAR’s.” Analyze the Results

The final step in an event study is to analyze and (perhaps) interpret the results. In some situations it may be sufficient or even necessary to confine this step to the use of descriptive statistics. For example, FFJR (1969) conducted no statistical tests in their pioneering study. The data (CAR’s) are presented in tables and graphically given an interpretation relative to their hypothesis, but the hypothesis is not tested in an inferential statistics sense. However, their conclusions would be hard to fault as the sample size was large and the effects shown graphically are so pronounced.

Ball and Brown (1968), and a number of subsequent studies, conducted a chi-square test on the 2x2 contingency table of sign of the unanticipated earnings against sign of the residual. The nonparametric test, in the context of a large sample size, avoids the necessity of making any strong assumptions on the sampling distribution of the residuals. However, the test is not very powerful and makes only nominal level use of the data.

The use of nonparametric statistical testing has expanded to include many different procedures including the binomial test, the KolmogorovSmirnov one-sample test, the sign test, the Wilcoxon matched-pairs signed-ranks test and the Mann-Whitney U test.’ The choice of a test is, of course, dependent upon the setting of the research. Nonparametric tests are particularly desirable in the event study context as the assumptions necessary for parametric testing may not be met. Generally, even when the researcher believes the parametric test assumptions are satisfied, the use of a nonparametric test as a complement will enhance the perceived validity of the statistical inferences. However, the researcher should not fall into the trap of assuming that no distributional assumptions are made by nonparametric tests. For example, some tests such as the Mann-Whitney U test, require that the two samples be independent. The Wil- coxon matched-pairs signed-ranks test requires that the distribution be sym- metrical about the mean. Brown and Warner (1980) found evidence in their simulation study that the right skewness of (monthly) residuals documented

Understanding and Conducting Event Studies 5 71

by FFJR appears to cause a marked distortion in the results given by the Wil- coxon test. The point is to verify the reasonableness of all statistical test procedures used, nonparametric as well as parametric.

Despite possible problems in meeting the necessary assumptions, the lure of parametric testing is great. The early studies to use parametric measures were Ball (1972), Kaplan and Roll (1972) and May (1971). A comprehensive discussion of the problems of parametric testing in event studies is beyond the scope of t h i s paper. However, two specific problems are prominent and warrant mention. The parametric tests require that the variable(s) being tested (generally the residuals) be independently and identically distributed. AU three of the studies cited discuss the problems with these assumptions and also employ nonparametric testing.' '

The excess returns are generally not identically distributed across securities in a sample. The accepted procedure for th is circumstance is to standardize the individual excess returns. Excess returns are adjusted (divided) by an estimate of the standard deviation of the excess returns. The appropriate choice of an estimate of the standard deviation of the excess returns will be discussed below.

The second problem in conducting parametric tests is that the excess returns are not independently distributed. The excess returns of securities exhibit cross-sectional correlation. Furthermore, the nature of event studies is such that this cross-sectional dependence is frequently exacerbated.' ' This dependence induces an upward bias in the test statistic. The approach commonly employed follows the procedure used by Jaffe (1974) and Mandelker (1974). The estimated standard deviation used on the denominator of the test statistic is calculated over a pre-test period as an average for all securities in the sample or a specified subset of the sample (i.e., a portfolio). Thus, it entails aggregating the data both cross-sectionally and over time. In the general case, where the numerator of the test statistic is a standardized excess return, the denominator wiU be the estimated standard deviation of the standardized excess return.

In summary, two steps are involved to obtain (approximately) independently and identically distributed data for use in developing a test statistic distributed Student-t. The numerator is the excess return standardized by a security estimate of standard deviation. The denominator is a global measure of the standard deviation of the standardized excess returns.

There is one possible shortcoming of this procedure. Excess returns during the testing period may be expected to have a higher dispersion, because of the influence of the event, than would be observed in other periods. Thus, the use of excess returns in a pre-test period to estimate the standard deviations may understate the dispersion measure. Such an understatement would then be expected to cause an upward bias in the resulting test statistic.

Watts (1978) used an alternative approach which is appealing, although its application will be limited to those types of events which occur fairly frequently as with the quarterly earnings announcement which he studied. Watts

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used a dispersion measure which is portfolio specific and event-time specific. For example, to estimate the standard error of the estimate of portfolio returns in week w, where w is defined relative to the week of the event, he uses the standard deviation of the returns for the portfolio for the weeks w of prior quarters. The validity of th is measure requires the assumption that the returns are stationary across the quarters, but in many cases this will not be onerous. Watts used the risk controlled portfolio methodology. However, the procedure he used to estimate the standard deviation could be used with other methods of estimating excess returns.

In spite of the strong distributional assumptions required for parametric testing, and the evidence indicating the assumptions are violated in the data, there is hope. Brown and Warner (1980) found that the t-tests on data transformed to approximate independently and identically distributed returns yielded test statistics which conformed well to the theoretical distribution of test statistics. In fact, the t-test was consistently a better approximation of the theoretical distribution than was either the sign or the Wilcoxon test. This should not be interpreted as a license for blind use of parametric techniques but is certainly encouraging and supportive of judicious applications of the more powerful tests.

Additional Event Study Types The introduction identified four types of event studies. The description of

the steps of a study in the preceding section is directly applicable to information content and market efficiency tests. The two types of event studies considered below grow out of the structure developed in the previous section.

Model Evaluation The second step in an event study, as described above, is to model the secur-

ity price reaction which is expected to be associated with the event. In the discussion above, the importance of the expectations model was stressed. When a partitioning of the sample is necessary to capture differential price effects, the magnitude of the excess return metric will be directly affected by the efficacy of the expectations model.’ This relationship allows one to evaluate alternative models of investors’ expectations. In the normal process of an information content study one wishes to select an expectations model which is an accurate representation of investors’ expectations because such a model will yield a stronger information content study. However, in empirical testing the expectations are always modeled with imperfect information. One does not have precise knowledge of the model actually used by investors. Therefore, the validity or reasonableness of a model is always open to question and evaluation.

Ball and Brown (1968) used two different models of expectations on accounting earnings and calculated MI’S for each model. Their motivation was to obtain the best representation of information content (i.e., the largest MI effect) rather than a desire to reach conclusions regarding the two models used.


This is not surprising as this was the original information content study. St&, they did notice some difference between the models, and their discussion considered possible explanations for what they observed with respect to the models.

Beaver and Dukes (1972) appears to be the first study conducted for the specific purpose of model evaluation. They were interested in inter-period tax allocation and whether earnings measured with tax allocation are more consistent with security price behaviour than earnings measured without tax allocation. The central thrust of their study was to model the expected security price reaction under alternative definitions of earnings. For each model the information content (i.e., API) was computed. The expectations model which yielded the highest API was then assumed to be the model most consistent with the setting of security prices.

The information content of an event here is not the focus of the study. Rather, it becomes a ranking measure which is used for the focus of the event study. The focus is on the expectations models. The information content (or more precisely the excess return metric) is used to draw inferences regarding the models used to derive the information content. To relate this type of event study to the five step structure developed in the previous section involves only two adjustments. Firstly, multiple models are developed for step two to reflect alternative hypotheses related to security prices. Secondly, the fmal step of analysis involves using the results obtained by step four to evaluate the alternative expectations models.

Metric Explanation Another type of event study is similar to the model evaluation type. A

metric explanation event study focuses upon identifying variables which explain the excess return metric observed in an information content or market efficiency test. In terms of the five step structure of the previous section, steps one, three and four provide the basis for beginning to explain the excess return metric. The second step of modeling the excess returns is omitted. The fifth step is where the metric explanation is performed. The excess return metric is analyzed, not by conducting tests of statistical significance, but rather by attempting to identify factors which are associated with the metric. A recent issue of the Journal of Accounting and Economics consisted of three studies of this type. The study discussed below was chosen for two reasons: it is a more straightforward metric explanation study, and it found significant results.

Collins, Rozeff and Dhahwal (1981) studied the proposed elimination of full cost accounting in the oil and gas industry (the event was the release of the FASB Exposure Draft). The excess return for each individual firm (security) was calculated for the period surrounding the event. Collins, et al., developed four theories to explain the impact of the event upon the security prices of the firms affected by the proposed mandatory accounting change. The theories were then operationalized into variables. The analysis of the study consisted of

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a multiple regression model with the excess return metric as the dependent variable and the independent variables representing the four theories. The validity of each theory was then evaluated based on the statistical significance of the independent variables designed to reflect the theory?O

The metric explanation approach is a fairly recent development which has many applications. The approach is simple and a valuable addition to theevent study methodologies. Its advantages and disadvantages are discussed in the next section.

Comparison The mechanical procedures used in the conduct of the two types of event

studies described in this section are quite different; and, in fact, the two are distinctly different, yet they also have much in common. Perhaps the easiest way to characterize the two types is to note that model evaluation entails an ex ante specification of models of expected security price reaction. Metric explanation is an ex post attempt to explain (i.e., model) the observed return metrics.

In principle, the two approaches are alternatives to each other. They are capable of addressing the same broad issues. For example, it is not difficult to imagine using the Collins, Rozeff and Dhaliwal (1981) independent variables to build models of expected security price reaction (step two) to use in partitioning the sample. Analysis would then proceed as in a model evaluation event study. The metric explanation approach has two advantages. Firstly, analysis is on a continuous scale as the partitioning into discrete groups is not required, and secondly, the analysis determines the model (as in multiple regression); relationships do not have to be specified ex ante. Even so, there are still certain types of events where the model evaluation approach will continue to be more appealing. Accounting earnings announcements is a very large and important class of events which may remain more amenable to the model evaluation analysis.

The metric explanation approach has one serious disadvantage. There is a definite danger that the method will be used as a data fitting technique; a procedure which involves ex post explanation of an observed outcome commonly suffers from this weakness. A researcher may be tempted to “test” a large number of variables without any more basis than that the use of the independent variables may increase the explanatory power of the model. This is an enticing abuse of the methodology. The researcher should have a sound theoretical basis for the selection of the independent variables; however, this is not always possible. There have been many cases where a well reasoned choice of variables has resulted in important empirical findings; Beaver, Kettler and Scholes (1970) provides a good example. Thus, both researcher and reader will frequently have to settle for a priori justification for independent variables.


Nonetheless, although the procedure is performed ex post, the appropriate independent variable should be identifiable ex ante. Only then could the metric explanation approach be considered an alternative to the model evaluation approach.

Additional Issues Interpretation of the Excess Return Metric

Some of the early event studies made statements which indicated a cor- respondence between the excess return metric and the private value of information. This was generally done by interpreting the metric as the outcome of an investment strategy over the securities in the sample. For example, a metric such as a CAR would be construed to be the value (i.e., excess return opportunity) of the information being studied to a private investor. The investment strategy was typically a hedged portfolio position involving long and short positions in the securities and, in some cases the market portfolio. Marshall (1975) developed a simple example which demonstrated that the excess return metric cannot always be relied upon to measure the private value of informatim(PV1). However, the ability to interpret the excess return metric as a measure of PVI is attractive because its interpretation is then given an intuitive as well as an economic setting.

Recently, Ohlson (1979) has investigated the sufficient conditions for est- ablishing a relationship between the PVI and the excess return metric. He notes that since the excess return metric is an average over the securities being studied the PVI must also be an average. An optimal investment strategy, as previously mentioned above, may involve investing different amounts in different securities. Therefore, the excess return metrics such as the API or CAR will not, in general, represent globally optimal investment strategies. Ohlson goes on to note the importance of controlling for commonality factors (e.g., market- wide effects) in the expectations models used to partition securities. Failure to abstract from commonalities in the partitioning signals may cause severe prob- l e m in the interpretation of the excess return metric. Furthermore, he shows that positive correlation between the partitioning signals and security returns does not mean that the excess return metric is meaningful. There is no necessary relationship between returns and excess returns.

Ohlson’s results give some specific guidelines for event studies if any PVI inferences are desired. The methodology must abstract from commonality factors with respect to both security prices and event signals. The use of excess returns serves to abstract from common influences affecting returns, although some cross-sectional interdependencies may persist. Steps necessary to control for common influences across firms from the signal will V ~ I Y with the research topic. The index model used by Ball and Brown (1968) to abstract from market- wide earnings effects is a good example of one such procedure. When commonalities are controlled, a positive expected excess return is both necessary and sufficient t o indicate a WI.

576 Bowman

The excess return metric has also been interpeted as a measure of association between the event signals and excess returns. This is done in at least two ways. In an information content study, a statistically significant metric is considered evidence that the signal is associated with (related to) the excess returns observed. In a model evaluation study, the model which yields the higher metric is considered to be more highly associated with the excess returns. Marshall’s (1975) example demonstrated that this association need not hold. However, it is possible to show that if PVI can be quantified it will provide a measure of association between signals and returns. It follows then that the conditions proposed by Ohlson (1979) to relate PW and the excess return metric will also allow an interpretation of the metric as a measure of association.

Joint Test Considerations As with most empirical financial studies, event studies involve joint tests.

That is, the results obtained are necessarily conditioned by more than a single effect. This can best be illustrated by considering how one might interpret an empirical result where the excess return metric was not significantly different from zero. Assume the event is defined as the release of the annual report t o shareholders. The hypothesis is formulated that a certain item of information contained in the annual report is not publicly available prior to the release of the report and is capable of altering security prices. Assume a test is conducted, following the steps previously outlined, and no significant excess return metrics subsequent to the event date are found. How is one to interpret these results? They can be taken as supportive of the position that the securities market is efficient with respect to the specific information contained in the annual report; but, a joint test has been conducted, and alternative explanations of the results are possible.

The complete structure of the joint test is complex, and all aspects will not be discussed here. However, three aspects are immediately apparent when the steps involved in the conduct of the test are reviewed. Firstly, the results are dependent upon accurate identification of the release date of the report (i.e., timing). As previously discussed, even small errors in the determination of event timing may result in no observable effect. Secondly, the expectations model of security price reactions may be faulty, and thirdly, the methods of calculating and/or aggregating excess returns may be improper. Any of these three methodological steps can cause the excess return metric to exhibit no appreciable effect, even if the hypothesis being tested is correct.

Researchers are generally the most concerned about the implications of joint testing when a study finds no significant (i.e., negative) results. However, the joint testing conditions are present even when positive results are found. For example, Ball (1978) considers twenty tests which found systematic excess returns after the announcement of earnings. He gives three possible explanations for these anomalies: the securities market is not efficient, omitted variables or specification errors affect the measuring of excess returns, or systematic


experimental errors exist. The latter two possibilities are the result of joint conditions.

Can joint test conditions be eliminated? The answer is, No, at least not entirely. However, one can be aware of the potential and use methodological resources and ingenuity to minimize the potential ambiguity their presence may create.

Tests of the Efficient Market Hypothesis The discussion has previously referred to the use of event studies as market

efficiency tests. Numerous studies have interpreted their results as pertaining to the market efficiency issue. Further, Ball (1978) surveys and discusses a set of tests which may be interpreted as indicating market inefficiencies. The link between event studies and testing of the efficient market hypothesis, however, is weak at best, and perhaps indicative rather than direct. The preceding discussion of joint test considerations obviously mitigates against direct tests. In a quite different vein, some would even argue that it is not possible to test the efficient market hypothesis (EMH) because it has never been satisfactorily stated in an empirically testable form.” In addition, there are substantive theoretical issues to be considered

Ohlson (1978) considered some theoretical problems. “The major conclusion is that AF’I (or residual) analysis is not satisfactory for testing market efficiency and, therefore, it seems premature to conclude that available evidence is consistent with efficiency (p.175).” The crux of the problem which Ohlson identifies is the necessity of relying upon the Capital Asset Pricing Model as the equilibrium model of security prices. Roll (1977) has argued on theoretical grounds that the CAF’M is an untestable hypothesis. Empirical evidence has also accumulated which questions its descriptive validity. Thus, the CAF’M is suspect on both theoretical and empirical grounds.

The conclusion must be that we are not yet in a position to conduct research which will unequivocally test the EMH. This is cause for concern, and research efforts to clarify the relationship between event studies and the EMH are highly desirable. However, the unsettled nature of the relationship should not be a deterrent to further event studies. Much has been learned from the research to date, and the empirical research has played a clear roll in stimulating the theoretical work. The benefits of event study research can be expected to continue.

Summary The empirical literature in accounting and finance over the past fifteen years

is notable on at least two counts. The sheer quantity of security price based research is very impressive, and the event study approach is the dominant methodology in that research. This paper provides an overview of event study research as a methodology.

578 Bowman

The introduction identified four types of event studies: information content, market efficiency tests, model evaluation and metric explanation. The next section focused on the first two types of event studies and described and discussed the five steps involved in conducting the research. The second section presented the model evaluation and metric explanation types of studies. The two were discussed within the context of the five steps previously outlined, and their similarities and differences noted. The third section covered some additional issues which are relevant to event study research. These issues are the interpretation of the excess return metric, joint test considerations and the testing of market efficiency.

The general class of tests which can be called event studies will continue to make important empirical contributions to our understanding of information and security prices. Also, the specific techniques employed can be expected to improve and expand. It will speak well of progress in this area if this paper is soon in need of an update.

NOTES

Although it would not be considered “modem” in that it does not employ modem capital market theory, Ashley (1962) conducted an event study much earlier. He studied the stock price reaction to changes in earnings and dividends, focusing on the postannouncement period. The author thanks George Foster for calling this interesting paper to his attention. it was a precursor of what is discussed in this paper. The concept of an efficient market was evolving at the time of the FFJR study. They defined an efficient market as “. . . a market that adjusts rapidly to new information @.1).” This concept was stated more formally and given an empirically testable form by Fama (1970). However, it is nonetheless reasonable to consider the FFJR study a test of market efficiency. Definitional issues concerning market efficiency are still being de- bated but are beyond the scope of th is paper. Foster (1982) provides an excellent discussion of the subject and its complexities. FFJR used monthly data in their study. Their results do not preclude the possibility of adjustment lags existing in daily data. The precise interpretation of “concurrent” will vary with the research setting and the data available to the researcher. Theoretically the information in the event should be instantaneously impounded in security prices. Research by Dann, Mayers and Raab (1977) indicated the speed of adjustment to large block trades to be approximately one minute. However, in some research it may be appropriate to consider the announcement time period (a day, week or month) as being “concurrent.” Although these two testing approaches can be defined by partitioning a time line at the event as is done here, they are intenelated. In general, each test presumes the other. When an information content study is conducted the methodology requires the assumption of some degree of market efficiency. Similarly, a test of market efficiency with respect to an event presumes that the event has information content. The types of research being espoused here are what Kuhn (1970) calls “normal science” and “extraordinary research.” Normal science is research based upon the results of past research achievements. ‘‘Bringing a normal research problem to a conclusion is achieving the anticipated in a new way . . . @.36).” This contrasts with extraordinary research


where a fundamental anomaly is isolated more precisely and given structure. The objective of extraordinary research is to push the rules which have developed through normal sciencein anattempt to achieve a breakthrough which will lead to a whole new paradigm. Notice that sisllificance tests of the observed effect in the two cases described here would be diffexent. Where the direction of an effect is predicted a one-tailed test is appropriate. However, when the hypothesis does not predict a directional effect, one is conducting a two-tailed test. The case Shown here provides a dichotomous classification of the f m s in the sample; The simple may be partitioned more fmely. For example, Foster (1977) created five categories of f m s based upon the number of quarters in a year that had a positive change in e m . At this point the author is completely ignoring a host of issues, most of them highly technical, which bear upon the relationship of the expectations model, the calculated exces retum metric and its interpretation. The issues will be discussed somewhat at a later point in the paper. The interested reader is referred to Ohlson (1979) and Patell (1979) for more technical treatments.

lo A weakness of the Noreen and Sepe approach is that it is not amenable to joint tests of significance when the number of events being studied exceeds three or four. It is also not well suited for discriminating between competing hypotheses as to the cause of the price reaction observed. Basu (1981) has proposed a modification of the Noreen and Sepe procedure which may allow for broader tests of significance. Burgstahler and Noreen (1981) have developed a more robust test procedure that may be vaIuable in exploratory studies where there is little ex ante knowledge of the individual firm effects of related events. The classification scheme differs somewhat from that used by Brown and Warner (1980). They use the three classifications, mean adjusted returns, market adjuated returns and market and risk adjusted returns. They do not study unadjusted returns. Their category of market adjusted r e t w is equivalent to a risk adjusted return where the systematic risk of all %ns i s assumed to equal one. What ia here called risk controlled portfolio returns is termed control portfolios by Brown and Warner and included under their category market and risk adjusted returns. The Brown and Warner study used monthly returns, and it is not clear whether the power of the simple procedure would hold on weekly or daily data. The risk controlled portfolio approach should not be confused with the companion portfolio approach developed by Black and Scholes (1973). In the companion portfolio approach each % is compared to a Rpt to determine excess retum for security i in

e return on a portfoho for a period t where the portfolio is formed to crtlk k; model parameters as does security i Thus, the companion portfolio approach is a riak adjusted returns method. The approach generally used is to rank a l l securities on beta and then split them into two portfolios; a high beta portfolio and a low beta portfolio. The two portfolios are thm combined to form a single portfolio with 1. This is done by solving for the weights x and (1-x) where = 1 the return on the portfolio b expected to equal% return on the market. T h 2 e = R - ht. The primary motivation for using excess returns at the portfolio &el ra&er than the individual security level is to reduce the sampling error.

*' There are some situations where this forming of portfolios has either already been accomplished or where the methodology focuses upon the individual firm. The h k controlled portfolio approach to estimating excess returns, described above, is an example of the former. The latter case wil l be discussed in a later section.

'

% + (1-x) &, 4 = -1. Since

580 rrowman

l6 Examples of the use of these methods can be found in Beaver and Dukes (1973), Foster (1973), Kaplan and Roll (1972), Ball, Brown and Finn (1977) and Beaver and Dukes (1972) respectively.

’’ The remainder of this section attempts to explain the nature of the problems with parametric testing and the approaches being used to address the problems. The mathematics involved have been avoided, but a familiarity with statistical procedures is assumed. The reader who desires a more thorough treatment, including the mathematics involved, is referred to the articles referenced here, and to the appendix in Brown and Warner (1980). The reader who is unfamiliar with the statistical terms and techniques mentioned is unlikely to be interested in the following five paragraphs.

I’ For example, a much discussed dependence exists within industry classifications. Many event studies are conducted on samples with disproportionate industry concentrations. However, the problem is even more pervasive. For example, the bidder f m s in mergers may have above average cross-sectional correlation of their returns. Patell (1979) gives an extensive treatment of the relationship between studies of information content and studies of model evaluation. The former involves association between beliefs and security prices while the latter involves associations between beliefs and models. In particular, he develops the sufficient conditions under which a residual analysis technique can provide a valid test of information content or a ranking measure of competing models of investors’ expectations. In general, either information content or validity of an expectations model must be assumed to allow testing of the other.

‘O The Collins, Rozeff and Dhaliwal study was, of course, far more involved than what is represented here. The simplified characterization of the study given here is for purposes of understanding the methodology, not the specific application. The interested reader is referred to the original article, as well as the articles by Leftwich (1981) and Holthausen (1981). ’’ See Beaver (1981) for a recent discussion of this and other issues surrounding the concept of market efficiency.

’

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Documents

1983 - Bowman - Understanding and Conducting Event Studies - Journal of Business Finance & Accounting