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Efficient Market Tests of the Informational Content of Dividend Announcements: Critiqueand ExtensionAuthor(s): Clarence C. Y. KwanSource: The Journal of Financial and Quantitative Analysis, Vol. 16, No. 2 (Jun., 1981), pp.193-206Published by: Cambridge University Press on behalf of the University of Washington School ofBusiness AdministrationStable URL: http://www.jstor.org/stable/2330646 .

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JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS Volume XVI, No. 2, June 1981

EFFICIENT MARKET TESTS OF THE INFORMATIONAL CONTENT

OF DIVIDEND ANNOUNCEMENTS: CRITIQUE AND EXTENSION

Clarence C. Y. Kwan*

I. Introduction

In recent years a major controversy has formed in the finance literature

regarding the empirical evidence of the informational content of dividends.

Despite considerable support for the position of dividend nontriviality by

various studies, the work by Watts [13] represents a formidable challenge.

Because of the close proximity of the firm's earnings and dividend announcement

dates, the major issue of the dispute has centered on the identification and

control of contemporaneous earnings information. In an attempt to settle this

controversy, the present study evaluates and extends Watts' methodology.

Three methodological issues are considered here. First, the standard Lint?

ner [8] and Fama-Babiak [4] annual dividend models (which were used to identify

the firm's potential dividend information in Watts' study) are refined to incor-

porate quarterly earnings and dividend data. Second, the potential problem of

misclassification of information due to the inherent noise of empirical models,

which may have contributed substantially to Watts' results, is treated by a

filtering process based on the concept of prediction intervals. Third, this

McMaster University, Ontario, Canada. This paper is based on the author's Ph.D. thesis (1979) at the University of Toronto. The author wishes to thank members of his thesis committee, M. J. Gordon (Chairman), P. J. Halpern, and W. R. Waters, for guidance and his examiners, J. E. Pesando and R. Westerfield, for suggestions. The author also wishes to thank R. R. Pettit, J. L. Callen, L. D. Johnson, and a referee of this Journal for valuable comments, and W. H. Lo for computational and programming assistance.

The empirical studies observing nontrivial dividend information include those reported by Pettit [10], Griffin [6], Charest [3], and Aharony and Swary [1]. Watts' [13] observation of trivial dividend information was challenged by Laub [7] and Pettit [11]. Laub's argument was rebutted by Watts [14] as a semantic issue. Watts [15] also emphasized various methodological deficiencies in Pettit's studies. The position of dividend triviality was also found by Gonedes [5] in his joint tests of the informational content of dividends, earn? ings, and extraordinary income numbers. Due to the similarities in his classification scheme for dividend information with that of the Watts study, there is no need to separately evaluate the Gonedes methodology.

193



study emphasizes an aspect ignored in Watts' methodology, namely, the isolation

of dividend information from the firm's other publicly available sources of in?

formation, including earnings information.

Even if these methodological issues are resolved, there remains one over-

riding concern about Watts' methodology. Recently, Aharony and Swary [1] classi?

fied dividend information naively according to the sign of a dividend change and

observed using daily stock return data that, during a period free of the firm's

earnings announcement, the market on average reacted favorably (unfavorably) to

its announcement of a dividend increase (decrease). This raises the issue as

to whether using empirical models of the Lintner and Fama-Babiak forms to iden?

tify potential dividend information is an unnecessary complication. The present

study addresses this issue by exploring the implications of different classifi?

cation schemes.

The above methodological considerations are presented in further details

in Section II. The sample used in this study is described in Section III. Em?

pirical results are discussed in Section IV. Finally, Section V concludes

this investigation.

II. A Revised Methodology Based on the Watts Approach

A. Problems of Identifying Quarterly Dividend Information Using Annual Models

Although the Lintner and Fama-Babiak models in their original forms are

generally considered adequate for explaining annual dividend changes, they are

unsuitable for the purpose of identifying information from quarterly dividend

announcements. Regardless of when the information became publicly available

during the firm's fiscal year, a test based on annual models could only treat

the information as if it were associated solely with the dividend declared dur?

ing the quarter of the annual earnings announcement. Unless the dividend change

in question was declared during that particular quarter, the examination of mar- 2

ket reactions in Watts' tests would have been made during the wrong time period.

To obtain a more appropriate identification of the information associated

with a quarterly dividend announcement, a minor adjustment to the Lintner and

Fama-Babiak annual models needs to be made. What is required is to relax the

definition of the fiscal year by treating any four consecutive quarters as one

2 Brown and Warner [2] have recently examined various methodological issues in measuring the impact of firm-specific events on stock returns. They observe that, if the hypothesis test is performed for month 0 which is not always the true event month, the abnormal stock returns are less likely to be detected. Thus, they emphasize that it is important to have accurately determined event dates in measuring abnormal stock returns.

194



year in these models. Then, for each of the four quarters of the firm's fiscal

year, the models will have the following forms:

(1) AeL +- = D~ 4- " D~ +._-, = a_ + a-D_. 4- -, + ?Ê? ^ + Z (Lintner) q,t q,t q,t-l o 1 q,t-l 2 q,t

T q,t

q = 1, 2, 3, 4

(2) AD E D - D . _ = a_D + a0E ^_ + ? E ^ _ + Z (Fama- q,t q,t <l't-l 1 q,t-l 2 q,t 3 q,t-l q,t Babiak)

q = 1, 2, 3, 4.

Here, the moving annual earnings E ^ are the sum of the firm's four consecu- q*t

tive quarterly earnings ending at quarter q of year t. The moving annual divi?

dend D is the sum of the firm's four consecutive quarterly announced dividends

(including regular and extra dividends) contemporaneous with E . In these q,t

models Z , and Z ^ are the error terms. q,t q,t

Although only the time series for quarter q is actually required to esti?

mate AD in each model, all four quarters can be pooled for estimating the

common parameters. The dividend announcement is classified into an information

group according to the deviation of the actual AD , from the fitted value q,t

AD? . Market reactions can then be examined around the time of the announce-

ment in question.

B. Noise in Regression

A direct classification of information groups based on the sign of the

observed dividend change from the fitted value, which is the essence of Watts'

technique, may bias the results even when the empirical model is adequate for

explaining quarterly dividend changes. As is true with any regression model,

a nonzero deviation of the actual amount from the fitted value can also be due

to the inherent randomness of the model. In an approach analogous to that imple-

mented by Pettit [11], this paper improves the ability of the empirical model

to identify dividend information by defining D in (1) or (2) as four times q, t

the regular dividend declared in the quarter of the earnings announcement for

E . This annualized quarterly dividend can be justified on the grounds that, q, t since quarterly dividend changes are infrequent, it provides a fairly accurate

description of the annual dividend which investors can expect to receive from

the firm after a dividend change announcement. Such a treatment avoids the

potential problem of obfuscating the true dividend signal when D is defined

195



as the moving annual dividend. Since the annualized quarterly dividend considers

only regular dividends, however, its applicability is limited to situations where

the firm does not declare extra dividends or the extras are only a small irre-

gular component of the firm's dividend payments.

Regardless of how the dividend variable is measured, an important remedial

treatment for the noise problem is to construct a filter using prediction inter- 3

vals. For example, let the filter be a 95 percent prediction interval for D q,t

according to a particular model. If the actual D falls within this interval, q, t

there is a 95 percent probability that either the dividend announcement conveys

no information or the model is unable to identify the potential signal from the

announcement because of the noise problem. If, however, the announced D falls

outside this interval, there is only a 2.5 percent probability that the announce?

ment is incorrectly classified. Therefore, the prediction interval can serve

as a filter to remove potentially misclassified cases for the two information

groups categorized according to the sign of D - D q* t q,t

C. Isolation of Dividend Information

The goal of isolating dividend information is empirically attainable under

the plausible assumption that all relevant corporate news is reported daily in

the Wall Street Journal (WSJ) and summarized in the Wall Street Journal Index

(WSJI). Using the WSJI as the reference, one Can identify those dividend an-

nouncements which are separable from other sources of information. More speci-

fically, the announcements are constrained to follow the sequential pattern

below.

Nx E_*_D *_N2

other earnings dividend other news announcement announcement news

time

Here * denotes that at least five trading days have passed during which no

other news items about the firm are reported in the WSJI. The period suitable

for examining daily excess stock returns lies between points E and N in this

See, for example, Theil [12, pp. 134-135]

196



4 diagram, with point D labeled as day 0. The requirement that the firm's earn?

ings announcement precedes its dividend announcement ensures public availability of its earnings data for estimating AD ^ via (1) or (2).

q, t Following Charest [3], the excess return or the residual u., for stock i

on day t can be taken as the deviation of its daily return RfJ from the market Dt

return R .. The conclusion as to whether dividend information is nontrivial mt can be reached by examining the behavior of the cumulative average residual,

CAR, around the announcement day for each information group described in sub-

section B above.

D. Classification Schemes of Dividend Information: Naive Approach versus Lintner and Fama-Babiak Models

The naive classification scheme adopted by Aharony and Swary implies that

all dividend changes are unexpected, i.e., the expected dividends are the past

dividends, and that large (small) dividend changes are associated with large

(small) deviations from market expectations. The empirical adequacy of such a

scheme depends on whether the market reacts strongly to announcements of large

dividend changes and weakly to small changes. Yet, Pettit [10] observed that

the market reacted favorably to dividend increases in the range of 10 to 25

4 There is a lag of one trading day between the dividend announcement day

(as recorded in Moody's Dividend Records) and the day when this dividend news appears in the WSJ. The latter date is chosen as the reference point, i.e., day -1 is the public announcement day. Note also that, because the length of the period used in the residual analysis for each announcement is not fixed, the number of stocks for the computation of the average residual becomes fewer as the day under consideration is further from the dividend announcement day in either direction.

The statistical significance of the average residual on each day t may be tested under the assumption that the individual excess return u. in an in?

formation group is a random draw from a normal distribution with unknown mean and variance. If there is informational content in the dividend announcements in this population, and if the market is efficient, a nonzero average residual can be expected. The t-statistic may be used to test the hypothesis that the population mean is zero against an alternative hypothesis that it is nonzero. Statistically significant nonzero average residuals around the dividend announce ment day for various information groups should provide adequate evidence for the position of dividend nontriviality. The t-test was recently used by Lloyd- Davis and Canes [9] in their study of market reactions to financial analysts' buy/sell recommendations for stocks, and by Aharony and Swary in their tests of dividend information. It must be realized that the t-test is not valid if the assumption of statistically independent observations is violated. In the pres? ent study, since almost all dividend announcements in the sample were reported on different calendar days, the excess returns u for a given day t could

reasonably be assumed to be statistically independent.

197



percent and unfavorably to all dividend decreases, but did not react at all to

dividend increases of less than 10 percent or over 25 percent. If Pettit's evi?

dence based on small samples is valid, market expectations of dividends as de?

scribed by the naive scheme are distorted considerably. Then, there is a need

for an empirical model that can portray market expectations more accurately.

Insofar as the model does adequately serve its intended purpose, the degree of

market reaction ought to be strongly correlated with the deviation of the an?

nounced dividend from the anticipated level. Unfortunately, due to the noise

problem, models (1) and (2) are unable to pinpoint market expectations. A pre?

diction interval can be constructed instead. The severity of the noise problem

should be reduced considerably when the dividend variable in these models is

defined as the annualized quarterly dividend. Ignoring any remaining noise

problems, the relationship between the degree of market reaction and the predic?

tion error can be examined via the CAR's for the subgroups constructed according

to the size of (D ^ - D ,)/D . The latter is the deviation of the actual q,t q,t q,t

dividend from its predicted value as a fraction of the actual dividend. This

approach should be able to assess the usefulness of these empirical models in

identifying dividend information.

III. The Sample

In the present study, efficient market tests of dividend information were

performed using the revised methodology described in the previous section.

Firms included in the sample were restricted to those reported in Moody's Hand?

book of Common Stocks and listed on the New York Stock Exchange (NYSE). The

annoucements collected from the WSJI included regular dividend changes and extra

dividends declared during the period 1973-1977. For the purpose of estimating

the parameters in (1) and (2), each firm's quarterly earnings and dividend per

share data were collected from the Value Line Data Base and Moody's Dividend

Records for a period of 11 years up to the quarter under consideration. To

facilitate the residual analysis for each information group, daily returns were

calculated using stock price and dividend data from the Financial Research In?

stitute data base, and daily market returns were based on the NYSE Composite

Index.

According to the sample selection criteria of this study, 183 announcements

of regular dividend changes and extra dividends were collected. In the sample,

there were only 20 cases of regular dividend decreases including dividend omis-

sions. Empirical models (1) and (2), with the dividend variable measured by

the moving annual dividend, were initially used to identify potential informa?

tion from all these announcements. Upon replacing the dividend variable in

198



these models by the annualized quarterly dividend, the number of eligible cases

for the analysis was reduced from 183 to 147. These 147 cases consisted of

regular dividend changes declared by firms which, during the 11-year period prior

to the announcement under consideration, either had not declared any extra divi?

dend, or had not declared extra dividends consecutively for more than two years.

IV. Empirical Results

It was found in this study that the Lintner and Fama-Babiak models, (1)

and (2), performed almost equally well in identifying potential dividend infor?

mation. Regardless of which of the two models was used, the feature that mate-

rially affected the classification of information groups was how the dividend

variable was measured. Therefore, the results of the analysis presented here

are only those involving the Fama-Babiak model.

In the case where the dividend variable was measured by the moving annual

dividend, the 183 announcements fell into the following categories: (i) 95 in

the unfiltered positive group; (ii) 81 in the unfiltered negative group; and

(iii) 7 unclassified. Seven announcements were unclassified because the fitting

of the linear model in (2) using historical data failed to produce parameter

estimates with statistically significant correct signs. Insofar as either

-a or a was negative and statistically significant, the model interpreted by

the partial adjustment mechanism was misspecified. These seven cases were

eliminated from the residual analysis.

Despite the relatively few cases of dividend decreases in the sample, only

20 in total, the classification based on the sign of D - D actually placed g/t q,t

81 announcements in the negative group. The evidence of severe misclassifica?

tion was clearly reflected in the CAR for the unfiltered negative group as shown

in Table 1(a). There appeared to be no market reaction to the announcements in

this negative group on average. After filtering, however, the CAR for this group

declined sharply around the announcement day. Such empirical evidence indicates

that, although dividend information is nontrivial, the potential information

was often not discernible because of the inherent noise of the model involved.

The problem of misclassification should not be as severe for the unfiltered

positive group because most of the announcements in the sample were dividend

increases.

Due to the noise problem, the above model often failed to correctly iden?

tity the information from individual announcements. Nevertheless, with the help

of the filtering process for noise removal, it was still able to pick up signals

from some dividend announcements. Unfortunately, the filtering process left

behind only a small sample for the residual analysis. Of the 95 (81) announcements

199



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in the positive (negative) group, only 24 (9) of these had the announced

D ,'s outside the 95 percent prediction interval.

The ability of the empirical model to identify dividend information was

enhanced by using the annualized quarterly dividend as the dividend variable.

Using (2), the 147 announcements of quarterly regular dividend changes were

classified into three categories: (i) 110 in the unfiltered positive group;

(ii) 34 in the unfiltered negative group; and (iii) 3 unclassified. There was

a substantial reduction in the second category from 81 to 34. As shown in

Table 1(b), the CAR's for the unfiltered positive and negative groups were

clearly differentiated in the post-announcement period. The evidence for non-

trivial dividend information became more convincing as the filter was used to

reduce misclassified announcements. Although the filtering process did not cause

any material change of the CAR for the positive group, it led to a substantial 6

drop in the CAR for the negative group on the dividend announcement day.

When using the filter, the positive (negative) group size was reduced from

110 to 48 (34 to 16) announcements. In comparison with the model whose dividend

variable was represented by the moving annual dividend, the model here had a

considerably less severe noise problem. Since announcements of dividend de?

creases were less frequent than those of dividend increases in the sample, sizes

of the two groups as classified by any classification mechanism should be uneven.

Furthermore, the percentage of misclassified cases in the negative group was

likely to be higher than that of the positive group. Hence, the filter was par?

ticularly useful for the negative group. Market reactions to announcements in

the filtered negative group were considerably stronger than those for its posi?

tive counterpart. Because dividend decreases usually involved substantial unan?

ticipated changes, strong market reactions were reasonable even on an a priori

basis.

The residual analysis was also performed for the two information groups

classified naively according to -the sign of quarterly dividend changes. The

CAR's for these two groups were found to closely resemble those for the fil?

tered groups shown in Table 1(b). This finding need not imply, however, that

For all filtered and unfiltered information groups classified according to empirical models whose dividend variable was the annualized quarterly divi? dend, statistically significant nonzero average residuals (at the 0.01 or 0.05 level) of the correct signs were always observed around the dividend announce? ment day. This evidence strongly supported the position of dividend nontriviality. In contrast, due to the severe problem of misclassification, the in? formation groups classified according to models involving the moving annual dividend did not always have statistically significant nonzero average residuals around the announcement day. (See Table 1.)

202



empirical models are not useful for identifying potential information. Clearly,

insofar as the test of dividend information is concerned, the naive scheme can

serve the purpose of classifying information. Since this scheme treats all

dividend changes as totally unanticipated, however, it inevitably distorts the

description of market expectations. This claim was supported by results of the

residual analysis for three subgroups of dividend increases (i.e., less than 10

percent, between 10 and 25 percent, and over 25 percent) in this study. Consis?

tent with Pettit's finding, the degree of market reaction was found to be unre?

lated to the percentage change of the quarterly dividend.

To evaluate the ability of the empirical model in identifying dividend in?

formation, the residual analysis was performed for the two unfiltered positive

subgroups according to (D - D ^_)/D . < 10 percent. The analysis was re- q*t q/t q/t

stricted to cases where the dividend variable was measured by the annualized

quarterly dividend. Unfortunately, the size of the negative group was too small

to warrant any meaningful detailed classification. Results of the residual

analysis for the two positive subgroups are shown in Table 2. Not surprisingly,

the CAR for the subgroup of larger deviations (with 41 announcements) lies above

that of smaller deviations (with 69 announcements). This means that, the fur?

ther away is the announced D from its expected value, the more information q/t 7

is conveyed by the announcement. This evidence indicates that, despite the

noise problem, the model here is able to identify those announcements which have

a material impact on stock returns. Therefore, the reliance on an empirical

model to identify potential dividend information is useful in refining the re?

sults. The usefulness of a model must, however, be balanced against the poten?

tial misclassification of information. A model dependent test is ultimately a

joint test of the model and the hypothesis.

7 ... Note that, in each of these two subgroups, as well as in the positive

groups shown in Table 1, the CAR shows an upward trend in the post-announcement period, suggesting market inefficiency. In fact, the CAR in the subgroup of larger deviations also increases in the pre-announcement period. This may be related to the leakage of valuable information about the unanticipated dividend changes. Because of the leakage, the amounts declared publicly are in many cases not surprises to investors. Consequently, the average residuals imme? diately around the announcement day become statistically insignificant, The absence of any substantial movement of the CAR for the subgroup of smaller devi? ations in the pre-announcement period suggests that any leaked information prior to public announcements in this subgroup is not as valuable as that in the other subgroup. The interpretation of no leakage of information in this subgroup is consistent with the observation of statistically significant average residuals on and immediately following the announcement day, but not prior to the announce? ment day, for this subgroup. (See Table 2.)

203



TABLE 2

CUMULATIVE AVERAGE RESIDUALS [FAMA-BABIAK MODEL:

EQUATION (2) WITH ANNUALIZED QUARTERLY DIVIDENDS]

Day

-15 -14 -13 -12 -11 -10 -9 -8 -7 -6

-5 -4 -3 -2 -1 0 1 2 3 4 5

6 7 8 9

10 11 12 13 14 15

16 17 18 19 20 21 22 23 24 25

r(#) - The average residual is significantly nonzero at the 0.05 (0.01) level.

204



V. Conclusion

In an attempt to settle the dispute in the finance literature on the empiri?

cal assessment of the informational content of dividends, three methodological

issues were considered in this paper: (i) the suitability of the standard Lint-

ner or Fama-Babiak annual model for identifying information from announcements

of quarterly dividend changes; (ii) the potential problem of misclassifying in?

formation due to the inherent noise of empirical models; and (iii) the isolation

of dividend information from the firm's other publicly available sources of in?

formation including earnings information. The empirical evidence in this study

was shown to be consistent with the widely held position of dividend nontriviality.

In view of the apparent success of the naive classification scheme of divi?

dend information (which treats all dividend changes as completely unanticipated),

this paper also considered the question as to whether using regression models

for the same purpose is an unnecessary complication. Upon examining the degree

of market reaction to announcements of dividend changes, this study observed

that, notwithstanding the inherent noise problem, some revised Lintner and Fama-

Babiak models are able to portray adequately market expectations of dividend

changes. Thus, with the appropriate caveats, these empirical models are useful

for identifying potential dividend information.

205



REFERENCES

[1] Aharony, J., and I. Swary. "Quarterly Dividend and Earnings Announcements and Stockholders' Returns: An Empirical Analysis." Journal of Finance, Vol. 35 (March 1980), pp. 1-12.

[2] Brown, S. J., and J. B. Warner. "Measuring Security Price Performance." Journal of Financial Economics, Vol. 8 (September 1980), pp. 205-258.

[3] Charest, G. "Dividend Information, Stock Returns and Market Efficiency - II." Journal of Financial Economics (June/September 1978), pp. 297-330.

[4] Fama, E. F., and H. Babiak. "Dividend Policy: An Empirical Analysis." Journal of the American Statistical Association, Vol. 63 (December 1968), pp. 1132-1161.

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[6] Griffin, P. A. "Competitive Information in the Stock Market: An Empirical Study of Earnings, Dividends, and Analysts' Forecasts." Journal of Finance* Vol. 31 (May 1976), pp. 631-650.

[7] Laub, P. M. "On the Informational Content of Dividends." Journal of Business, Vol. 49 (January 1976), pp. 73-80.

[8] Lintner, J. "Distribution of Incomes among Dividends, Retained Earnings and Taxes." American Economic Review, Vol. 46 (May 1956), pp. 97-113.

[9] Lloyd-Davies, P., and M. Canes. "Stock Prices and the Publication of Second-hand Information." Journal of Business, Vol. 51 (January 1978), pp. 43-56.

[10] Pettit, R. R. "Dividend Announcements, Security Performance, and Capital Market Efficiency." Journal of Finance, Vol. 27 (December 1972), pp. 993- 1007.

[11] . "The Impact of Dividend and Earnings Announcements: A Recon- ciliation." Journal of Business (January 1976), pp. 86-96.

[12] Theil, H. Principles of Econometrics. New York: John Wiley & Sons, Inc. (1971).

[13] Watts, R. "The Information Content of Dividends." Journal of Business, Vol. 46 (April 1973), pp. 191-211.

[14] . "Comments on 'on the Informational Content of Dividends.'" Journal of Business, Vol. 49 (January 1976), pp. 81-85.

[15] _. "Comment on 'the Impact of Dividend and Earnings Announce? ments: A Reconciliation.'" Journal of Business, Vol. 49 (January 1976), pp. 97-106.

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