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The relevance of Annual General Meetings in stock returns, trading volumes and
volatility: Evidence from Spain
Authors:
Josep García Blandón*
Mònica Martínez Blasco
Lucinio González Sabaté
Facultat d’Economia, IQS, Universitat Ramon Llull
*
Via Augusta, 390, 08017, Barcelona, Spain. Ph.: +34 932 672 000. Fax: + 34 932 056 266. E-mail:
josep.garcia@iqs.edu.
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The relevance of Annual General Meetings in stock returns, trading volumes and
volatility: Evidence from Spain
ABSTRACT
Although the investigation of the effects of corporate events on stock prices is a well
established line of research in accounting and finance, very little attention has been
devoted to one of the most important corporate events: the Annual General Meeting
(AGM). The effects of AGM on stock returns will largely depend on the relevance of
the information released to the market as well as on the level of efficiency of the
financial market. In this paper, we have investigated the effects of AGM on stock
returns, volatility and trading volumes, in the Spanish stock market. Our results indicate
that AGM do not have significant effects in any of the three indicators, either on AGM
days or nearby days. After the exam of the possible explanations, we conclude that no
relevant information seems to be released to the market during AGM.
JEL: G21.
Key-words: event studies; Annual General Meeting; stock returns; volatility; trading
volumes.
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1. INTRODUCTION
The reaction of stock prices to information releases during public announcements
constitutes a well established line of research in financial economics. Researchers,
nevertheless, do not agree either about the real magnitude of the reaction, or about the
implications for the Efficient Market Hypothesis (EMH). Regarding the last point,
abnormal returns around company events have been usually interpreted as evidence
against the EMH. Fama (1998) argues, however, that event studies methodology that
investigates the reaction of stock prices to specific company events can not be properly
used to test the EMH. The reason would be that, while this methodology assumes that
any lag in the response of prices to an event is short-lived, returns should be examined
over long time horizons in order to discuss about market efficiency. The author
concludes that, since the literature does not clearly identify overreaction or
underreaction of stock prices as the dominant phenomenon, the observed random split
between over and under-reactions would not question the EMH. In the same line,
Bhattacharya et al. (2000) discus about the difficulties to interpret the lack of reaction of
stock prices to company events, in terms of the EMH. Four possible, and sometimes
contradictory, situations were compatible with this behavior, by combining the concepts
of market efficiency and the relevance of information releases: 1) the market is
inefficient, and thus prices do not react to the arrival of relevant information; 2)
companies do not make value-relevant corporate announcements; 3) the stock market is
efficient and the news are value-relevant, but this information has been already
completely anticipated by the market; and 4) insider trading prohibitions do not either
exist or are not enforced and thus the superior information of insider traders has been
incorporated to stock prices through their trades. The authors point it out about the
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importance of examining the behavior of stock prices during pre-announcement periods
in order to assess the likelihood of each one of the former situations.
The effects of corporate events in the behavior of stock prices is a well establish line of
research in accounting and finance. Numerous studies have investigated the reaction of
stock prices to a great variety of corporate events, being earning announcements the
most popular one. Some examples are the seminal paper of Beaver (1968), Aharony and
Swary (1980), Ball and Kothary (1991), Abarbanell and Bernard (1992), and more
recently Landsman and Maydew (2002) and Landsmand et al. (2002). Dividend
announcements are another strongly investigated company event. A short list of papers
dealing with the reaction of stock prices to company dividend announcements must
include Watts (1973), Denis et al. (1994) and Michaely et al. (1995). Other examples of
company events that have received important attention in the literature would be: stock
splits (e.g. Lamoureux and Poon, 1987 and Ikemberry et al., 1996) corporate news (e.g.
Battacharya et al., 2001; Chan, 2003; Frazzini, 2006 and Kothary et al., 2008) and
executive compensation plans (Tehranian and Waegelein, 1985 and Gaver et al., 1992).
In addition to these strongly investigated events, we can also find examples of other,
somehow, more extraordinary ones, as for example, the reaction of stock prices to
auditor switches (Hong, 1992) or to sudden executive death (Johnson et al., 1985). All
the mentioned events have in common the release of potentially relevant information to
the market. It is, therefore, quite surprising, that one of the most important company
event, as it is the Annual General Meeting (AGM) has received almost no attention in
the literature. During these meetings the top executives of the company address not only
shareholders but the whole financial community. There are certain decisions that can
only be approved on the AGM, as for example the election of the Board of Directors,
and important managerial announcements, usually concerning the managers views about
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the company prospects, are usually made during these meetings. We have only found
two previous investigations on the issue1: Brickley (1985) and Olibe (2002). The former
addresses the effects of AGM on returns, without considering trading volumes or
volatility, while the latter proceeds in the opposite way, investigating trading volumes
and volatility, but without considering stock returns. Brickley (1985) conducted his
research for a random sample of U.S. firms, reporting significantly positive abnormal
returns around shareholder meeting dates. Nevertheless, the author complains that the
lack of comparable investigations makes it difficult to interpret his results in the
framework of previous research. More recently, Olibe (2002) investigates the effects of
AGM in U.K. based companies, listed in the U.S. market. The author reports
particularly high levels of volatility in stock returns on AGM days. Nevertheless, the
effect of AGM on trading volumes is minimal, suggesting that U.S. investors do not
generally find AGM informative.
Regarding the theoretical foundations of the expected relationship between AGM and
stock prices, we can adopt the standard framework used in the literature to analyse the
reaction of stock prices to any particular corporate event implying the release of
potentially relevant information to the market. In particular, we can extend the
explanation proposed by Kalay and Loewenstein (1985) to the reported abnormally high
returns on dividend announcement dates. The authors interpreted this finding in terms of
the increase in expected return and risk associated to predictable events that would
likely generate new information. In such cases, the risk per unit of time on common
stock would not remain constant over time but increase on the day of the event.
Similarly as dividend announcements, AGM dates are also known in advance by market
participants, and both situations imply the release of company information to the
market.
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Another theoretical approach to analyze the importance of annual general meetings in
stock returns relies on information asymmetries. It could be argued that, due to the fact
that AGM constitutes a media event, company executives would prefer to avoid
communicating bad news during AGM, since it could have a stronger impact on the
firm’s market value than if this bad news were communicated on other days. Following
this line, Kothary et al. (2008) suggest that managers can time the release of bad and
good company news. The rationality of this behavior would rely on the agency theory,
and particularly on the existence of information asymmetry between managers and
investors. One example of the management of information is provided by Frankel et al
(1995) reporting that managers tend to make public good news about the company prior
to the issue of new stock. Similarly, Yermack (1997) observes that managers tend to
accelerate bad news or withhold good news prior to the grant of stock options, to lower
the price of the stock and consequently the strike price of the option. More recently,
Graham et al. (2005) conclude that financial executives managed financial reporting
practices in order to influence the price of the stock. The authors conducted a survey
about the factors driving reported earnings and disclosure decisions. In particular, they
point out managers’ strong incentives to withhold bad news, with the hope that the
situation reverse in the nearby future and thus they will never have to release this bad
news. Kothary et al. (2008) explicitly mentions the recent case by the European
Aeronautic Defence and Space Company (EADS) involving the new Airbus A-380, as
an example of this situation2.
Another example of the behavior of managers regarding the release of company news is
the tendency to release good news while the stock markets are open and bad news after
the closing of the markets (Patell and Wolfson, 1982). Supporting this view, the socalled Monday effect, generally defined as returns being abnormally low on Mondays,
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is usually explained by the tendency to release bad news during the weekend, when the
markets are close. Although no paper, to our knowledge, has investigated the
withholding of bad news prior or during AGM, when the company receives an unusual
attention by the media, such a behavior could be also explained in similar terms.
According with the previous discussion, both the Kalay and Loewenstain’s argument as
well as the management of the release of company news could explain positive
abnormal returns on AGM dates.
In this paper, we investigate the behavior of stock returns, trading volumes and returns
volatility around AGM dates in the Spanish stock market. We have used the classical
Brown and Warner (1985) methodology for event studies. As it has been already
mentioned, we have found only two previous comparable investigations on the issue,
one carried out with a random sample of U.S. companies for the period 1978-82, and
the other with U.K. companies traded on the NYSE and AMEX from 1994 to 1998.
With so little previous research on the issue, additional empirical evidence should be
welcomed in order to draw sounder conclusions about the impact of shareholders
meetings on stock prices and the possible explanations. In addition, since the
transmission of information contended in company events to stock prices will largely
depend on the microstructure of financial markets, evidence reported for one particular
market should not be immediately translated to another. Besides, Brickley (1985)
focuses only on the behavior of returns without analysing volumes or volatility, while
Olibe (2002) do the opposite. Unlike both papers, we have investigated not only returns,
but also returns volatility and trading volumes. This more comprehensive approach will
allow a better understanding of the causes behind the behavior of stock returns during
AGM. The non-significance, for instance, of abnormal returns during AGM dates will
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have different implications if are followed by an increase in volatility and/or trading
volumes.
The remaining of the paper is as follows: next section discusses the methodology and
dataset we propose to investigate the relevance of AGM on stock returns, volatility and
trading volumes in the Spanish stock market. Finally in sections 3 and 4 we discuss our
results and present the main conclusions.
2. METHODOLOGY AND SAMPLE SELECTION
In this section we present the methodology we propose to analyze the behavior of stock
returns, returns volatility and trading volumes around AGM dates, as well as the sample
and dataset used in our investigation.
2.1. Methodology
We have followed the classical Brown and Warner (1985) event studies methodology.
Accordingly, abnormal returns (AR) have been computed as the difference between
actual and normal returns, while normal returns are defined as expected returns without
conditioning on the event occurrence.
Thus, abnormal return for stock i on day t is expressed as,
ARit = Rit – E(Rit|Xt)
(1)
Where ARit is the abnormal return of stock i on day t, Rit the actual return, adjusted by
dividends and stock splits, calculated in the usual way as ln((Pt+Dt)/Pt-1), where Pt and
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Dt are the closing price and the dividend paid on day t respectively, and E(Rit/Xt) the
expected return for day t. Finally, Xt is the conditioning information set for the expected
return on day t. Expected or normal returns have been computed through the market
model. The event window and estimation period are given by the intervals [-5, +5] and
[-90, -20], respectively, with day 0 the AGM day. Although papers on event studies
tend to use wider event windows (eg Olibe (2002) uses the interval [-10, +10]), the
effects, when they exists, are systematically detected nearby the event day. In addition,
the aim of event studies methodology is clearly short-term. Therefore, we have
investigated the effects of AGM in a five days period around AGM dates, while normal
daily returns have been computed through an estimation of the market model for the
seventy one days period ending twenty days before AGM. Market returns have been
computed through the Indice IBEX-35, the most relevant index in the Spanish stock
market, formed by the thirty five most liquid companies quoted in the Spanish Stock
Exchange. Normal daily returns for each day within the event window have been
estimated through ordinary least squares.
After estimating daily average abnormal returns (AAR) for each stock i, the average
abnormal return on day t, has been calculated as:
AARt 
1
N
N
 AR
i 1
(2)
it
Cumulative average abnormal return (CAAR) has been computed by adding AAR for
different intervals through the event window, as showed by expression (3).
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b
CAARb   AARt
(3)
t a
Our first null hypothesis states that average abnormal returns will be zero on AGM
dates. We have performed two statistical tests, the parametric t-test and the
nonparametric rank test, in order to decide about the rejection of the null hypothesis for
each day within the event window.
The t-test is the standard procedure to test the null hypothesis in event studies.
Accordingly, our null hypothesis has been tested through the t-test at the standard 5%
and 1% significance levels. Brown and Warner (1985) discuss the implications of the
statistical properties of daily stock returns for the event studies methodology, in
particular departures from normality and the non-constantness of returns variance across
days. The problem of the lack of normality of daily returns is particularly serious for
small samples, since the Central Limit Theorem demonstrates that if excess returns are
independent and identically distributed drawings from infinite variance distributions,
the distribution of the simple mean excess return converges to normality as the number
of securities increases. The implications of non-constant returns variance is also
emphasized by Corrado (1989), who argues that the increase in the variance of day 0
returns distribution constitutes a major weakness in the performance of the t-test, since a
variance change will significantly increase the probability of type I errors (to reject the
null hypothesis when this hypothesis is true). This fact will cause significance levels in
event studies to be overstated when an increase in day 0 returns variance occurs.
Corrado proposes the nonparametric rank test (better known since then as Corrado test),
to overcome this shortcoming. Similarly as Brown and Warner (1985), he found that
doubling the day 0 returns variance causes severe misspecification for the t-test
statistics, since it more than doubles the probability of a type I error. On the contrary,
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the rank test remains relatively unaffected by these misspecification problems. In
addition, Corrado (1989) also concludes that the rank test is better specified under the
null hypothesis and more powerful under the alternative hypothesis. Accordingly,
regardless our relatively large number of events we have performed both, the t-test and
the Corrado rank test.
We have also investigated the effects of AGM on returns volatility. The positive
relationship between the release of information and price volatility is well established in
the literature (see, for example, Engle and Ng, 1993). In addition, by examining returns
volatility we will be able to better explain the behavior of stock returns around AGM
dates. To support this view, suppose, for example, that one half of the companies in the
sample reports relevant positive information during AGM, while the other half reports
relevant negative information. If financial markets are not strongly efficient, and
therefore this information has not been incorporated to prices yet, we would probably
report significantly positive/negative abnormal return for the first/second set of
companies. However, when we calculate average abnormal returns, positive abnormal
returns would be neutralized with negative ones, and the final result could be that
average abnormal returns on AGM dates were not statistically significant. In such a
case, we could wrongly conclude that during AGM no relevant information is released
to the financial market. Nevertheless, if we examine not only returns but also returns
volatility, we would observe that while abnormal returns are not significant, this is not
the case when they are computed in absolute values. Thus, through the exam of returns
volatility we could not conclude that no relevant information is released during AGM.
We have investigated the effects of AGM in returns volatility with the same approach
we used to investigate its effects on stock returns. The only difference has been that
since abnormal returns are now computed in absolute values, they can not be directly
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used for any statistical test, because the null hypothesis that a sum of absolute values is
zero will be certainly rejected. Thus, in order to perform the statistical tests it is
necessary to correct first absolute returns by the mean value.
Our second null hypothesis is that average absolute abnormal returns (AAAR) will not
be different from zero on AGM dates. As we did for testing the statistical significance
of abnormal returns, this hypothesis has been tested through both, the parametric t-test
and the non-parametric Corrado rank test.
Following Kyle (1985), high trading volumes around a company event would be
associated with the release of new information to the market. As it was the case with
abnormal returns in absolute values, the examination of trading volumes would allow a
better understanding of the behaviour of stock returns. Trading volumes have been
examined within a similar framework as the one we used with abnormal returns. Thus,
the abnormal trading volume (AV) of a given stock has been estimated as the
relationship between its actual traded volume and its expected or normal one, both in
euro values. In this case, the estimation period is not the same we used for the exam of
returns and returns volatility, but includes two thirty one days intervals [-50, -20] and
[+30, +60], one before and the other after AGM dates. The reason behind this change,
regarding the interval we used to estimate returns, is to avoid potential problems that
could derive from the existence of underlying trends in trading volumes that could
misrepresent the results. Thus, we define the abnormal trading volume, for stock i on
day t, AVit as:
AVit 
Vit
t  50
(4)
t  60
1
(  Vit   Vit ) *
62
t  20
t  30
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Where, Vit is the traded volume in euros of stock i on day t. As we did with returns, once
abnormal daily volumes have been computed for each firm i, the average abnormal
volume (AAV) on day t is calculated for the whole sample as:
AAVt 
1
N
N
 AV
i 1
(5)
it
Finally, cumulative average abnormal volume (CAAV) has been obtained by adding the
average daily abnormal volume for different intervals through the event window period.
b
CAAVb   ( AAVt  1)
(6)
t a
Given the way abnormal volumes have been defined by expression (4), values
above/below one indicate positive/negative abnormal volumes. That makes necessary
that before summing daily volumes in order to calculate CAAV, AV need to be adjusted
by 1. Otherwise, if AAVit is below one, indicating a low abnormal volume, when we
calculate CAAVt its value will increase regarding CAAVt-1. Without any adjustment we
could wrongly interpret that trading volume has increased on day t, since its cumulative
value will have increased. After the correction included in expression (6) the problem
disappears, since the cumulative volume on day t increases/decreases when the
abnormal volume on this day is positive/negative.
Our third null hypothesis states that the average abnormal trading volume will be zero
on AGM dates. As we did with returns and returns volatility, t-test and Corrado rank
test have been performed to decide about the rejection of this hypothesis.
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Although we have not made any hypothesis about cumulative returns and volumes, the
analysis of these variables could show the existence of trends before and/or after AGM
that would be ignored if the analysis is limited to abnormal returns and trading volumes.
2.2. Sample selection
To accomplish the objectives of the paper, we have examined returns, returns volatility
and trading volumes around AGM dates in the Spanish Stock Market, from January
2002 to June 2009. Our sample is formed by the constituents of the IBEX-35 market
index on June 2009.
Daily data about stock prices and trading volumes have been obtained from the
Thompson-Reuters Xtra 300 database. Information about AGM dates has been handcollected from the Madrid Stock Exchange web page and from the corporate web pages
of the companies when this information was not available in our primary source. We
have finally worked with 226 AGM. In 36 of these events AGM were celebrated during
weekends. In those cases, the next trading day has been chosen as the day of the event.
The large number of events taken into account constitutes an advantage in terms of
robustness of the results.
3. RESULTS
Table 1 (panel A) shows AAR with its corresponding t-values and Corrado statistics for
the five days period around AGM dates. The parametric and non-parametric tests
provide similar results, indicating that AAR on shareholders meeting days are not
different from zero. The day immediately before AGM, according with the Corrado
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rank test, the null hypothesis of non-significant returns is rejected at a 5% level. With
this exception, the hypothesis that excess returns for each day within the event window
are equal to zero can not be rejected through any of the two performed tests, at the usual
5% significance level. Therefore, the null hypothesis that abnormal returns are not
different from zero on AGM dates can not be rejected. Our result contradicts the only
previous investigation we have found assessing the impact of AGM on stock returns.
Nevertheless, Brickley (1985) reports positive and significant returns on AGM dates,
based only on the t-test, although according with the non-parametric Wilcoxon signed
rank test, the null hypothesis of non-significant returns on AGM dates could not be
rejected.
Insert table 1 here
As it has been commented in the methodology section, the analysis of returns will
benefit from the exam of returns volatility around AGM dates. When abnormal returns
are taken in absolute values the potential problem of the neutralization of positive and
negative returns disappears. Table 1 (panel B) provides AAAR, jointly with t-values
and Corrado rank test statistics, for each day of the event window. We do not observe
higher levels of volatility in stock returns on AGM dates, compared with other days.
This result is observed independently of the statistic test used to analyze the significance
of absolute returns. The only significant value we report, according with the t-test but
not with Corrado rank test, is observed on day -3 and has a negative sign, indicating an
abnormally low level of volatility three days before AGM. It should be noted, however,
that four of the five days before AGM show lower levels of return volatility, although
only for day -3 is this lower volatility statistically significant. Therefore, the null
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hypothesis that absolute abnormal returns are not different from zero on AGM dates can
not be rejected. Similarly, previous results by Olibe (2002) support lower levels of
volatility before AGM, as in our case however, only statistically significant for day -3.
Nevertheless, unlike our results, the author reports high levels of volatility on AGM
days.
The behavior of trading volumes around AGM dates is reported in table 1 (panel C),
jointly with t-values and Corrado rank test statistics for each day within the event
window. Similarly, as reported in panels A and B, results are robust to the statistical test
performed. Shareholder meetings do not seem to have any effect in stocks trading
volumes. Within the event window, only day +2 shows abnormally high trading
volumes, statistically significant at a 5% level with both, the t-test and Corrado rank
test. Consequently, the null hypothesis that abnormal volumes will not be different from
zero on AGM dates can not be rejected. Consistently with the observed increase in
volatility during AGM, Olibe (2002) shows an increase in trading volumes on these
days. Nevertheless, the author only performs the parametric t-test, and the significance
of trading volumes is not robust to the way volumes are defined.
The effects of AGM on stock returns will be determined by the relevance of the
information released and by the speed of adjustment of prices to this information.
However, we should not assume that both issues are independent of the type of
company. On the one hand, blue chip companies can show stronger incentives to be
involved with information management practices around AGM, since their meetings
constitute a media event in more extend than non-blue chips companies’. On the other
hand, the analysis of how information is incorporated into prices needs to take into
account non-synchronous trading. Non-synchronous trading is considered to be one of
the most important causes of the reported positive levels of autocorrelation in stock
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index returns. The reason is that the prices of the least liquid components of the index
can not completely reflect all the available information. In our case, non-synchronous
trading could contaminate or results, if markets prices of the least liquid stocks of the
sample do not fully incorporate all the available information. Since our sample is
formed by the 35 most liquid companies listed in the Spanish Stock Exchange, we do
not expect non-synchronous trading to be a serious problem. Nevertheless, the level of
liquidity of blue chip and non-blue chip stocks included in the index is clearly different.
In order to avoid these potential problems, our analysis has also been carried out for the
sub-sample of blue chip companies, formed by: Santander, Telefónica, BBVA, Repsol,
Endesa and Iberdrola. This sub-sample includes 46 events. Similarly as table 1 for the
whole sample, table 2 reports the effects of AGM on returns, volatility and trading
volumes for the sub-sample of blue chips. The results are largely unchanged. Blue chip
stocks show the same behavior of returns, volatility and trading volumes around AGM
dates as the whole sample.
Insert table 2 here
The simultaneous investigation of returns, volatility and trading volumes allows a better
interpretation of the findings. According with the discussion carried out in the
introductory section, there are four possible explanations behind the non-existence of
significant returns around AGM dates: 1) markets are efficient and thus all the
information made public in the AGM had already been incorporated in the price of the
stock; 2) markets are inefficient, and so they do not quickly incorporate the new
information to prices; 3) there is some level of insider trading in the market, and thus,
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information had been already incorporated in prices through relatively well informed
investors and 4) no relevant information is released during shareholder annual meetings.
The analysis performed in this paper will provide some light about the likelihoodness of
each of the former explanations. If markets are efficient and thus all information is
already incorporated to prices, we should observe abnormally high levels of return
volatility before AGM dates, but not on AGM days. The reason of this expected
behavior would be that if the information to be communicated in the AGM mostly
consisted in good/bad news, we should observe relatively big positive/negative
abnormal returns before AGM dates. When we calculate AAR for the whole sample,
positive abnormal returns will be balanced with negative ones. Nevertheless, if we
compute average absolute abnormal returns this problem would not occur, and therefore
we would observe significant returns in absolute values before AGM dates. Under the
insider trading hypothesis, we should expect significant abnormal returns, trading
volumes before AGM dates. If markets are inefficient, the same reasoning we applied to
justify explanation number one can be used, but relatively high levels of trading
volumes and returns volatility should be observed after AGM dates, once the new
information was incorporated to stock prices. Finally, under explanation number four,
that is, if no relevant information is provided during shareholder’s meetings, we should
not observed abnormal returns, trading volumes, or volatilities, before, during and after
AGM dates.
Therefore, according with the former discussion, our results indicate that the most likely
explanation of the observed lack of effects of shareholder meetings in returns, trading
volumes and volatility is due to the lack of relevant information generated during these
meetings.
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As we have already commented, the analysis of cumulative returns and volumes may
shed additional light on the results. Graphs 1 and 2 respectively show the behavior of
cumulative returns and volumes across the event window. Interestingly, as shown by
graph 1, cumulative returns show a clearly upward trend before AGM dates and a
downward trend afterwards. Graph 2 shows the opposite behavior for abnormal
volumes, decreasing before AGM and increasing afterwards. Therefore, although from
table 1 (panel C) we concluded that daily abnormal volumes were not significantly
different from zero except in day +2, the analysis of cumulative volumes suggest that
investors postpone operations on stocks before shareholder meetings. We have checked
the robustness of this finding to other event windows ([-10, +10] and [-20, +20]) and the
result remains unchanged.
Insert graph 1 here
Nevertheless, the behavior of cumulative returns is more difficult to interpret. Table 1
(panel A) indicated that abnormal daily returns were not different from zero across the
event window (with the exception of day -1 according with Corrado rank test).
Regardless, if instead of focusing on single days we analyze longer periods, graph 1
could suggest that market participants seem to be too optimistic about the information
and company prospects to be released during the AGM. This optimism would generate
relatively high returns as the AGM approaches. Nevertheless, after the AGM
cumulative returns decrease because the actual information and company prospects
communicated during the meeting deceives expectations. This interpretation of the
findings could indicate a, somehow, optimistic bias by market participants regarding
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AGM, in a similar way as financial analyst issuing much more buying that selling stock
recommendations. However, this
Insert graph 2 here
4. CONCLUSIONS
The investigation of the effects of corporate events on stock prices is a well established
line of research in financial economics. Despite the important number of investigations
on the issue it is somehow surprising the low attention devoted to one of most important
periodic corporate event: the Annual General Meeting. We have only found two
previous investigations specifically addressing the issue. If AGM provides relevant
corporate information and supposing that financial markets are not strongly efficient we
should expect positive abnormal returns, trading volumes and returns volatility the day
of the meeting, given the relatively high level of risk associated to AGM dates
compared with ordinary days. In addition, if companies tend to manage the information
they provide to the market, we should observed a similar behavior, since they will not
be willing to issue negative information during the AGM. On the contrary, our results
indicate that AGM do not have significant effects in returns, trading volumes or
volatility. The fact that this situation is observed not only on AGM dates, but also in the
nearby days supports that no relevant information is released to the market during
AGM. In addition, we do not find any evidence of corporate information management.
The exam of cumulative returns and trading volumes complement the former picture.
While the cumulative returns increase before AGM and decrease afterwards, trading
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volumes show the opposite behavior. We conclude, therefore, that some transactions are
postponed due to the AGM, and that market participants show some level of optimistic
bias regarding the information to be released on the AGM. Nevertheless, this result
must be cautiously interpreted, and further research in this line should be particularly
welcome.
Our results are robust to the type of company considered. Neither information
management issues nor non-synchronous trading seem to have different effects for blue
chip companies. Returns, volatility and trading volumes of the blue chips sub-sample,
show the same behavior observed for the whole sample of stocks.
Although our results suggest that the lack of reaction of stock returns to AGM is more
likely due to the fact that no relevant information is released during these meetings, than
to the efficiency of financial markets, further research is needed, particularly focused on
the explanations of returns behavior around AGM dates.
NOTES
1. Although some of the mentioned events may have to be approved at the AGM, and
thus, it could be argued than these papers, indirectly investigate the effects of AGM on
stock returns, they fact is that they are focused on specific event and not on the AGM.
2. While in April 2006 the managers of Airbus already knew about production glitches
that would delay the delivery of the new Airbus A380, they withheld this news until
June 2006, when the public announcement of the news caused EADS to lose a quarter
of its value.
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Table 1. Returns, volatility and trading volumes around AGM dates
Panel A
Panel B
Return
Panel C
Volatility
Volume
Day
tAAR
-5 0.0008
t-statistic Corrado AAAR t-statistic Corrado AAV
statistic Corrado
0.890
0.840
0.0098
-0.673
-1.088
0.9781
-0.448
0.074
-4 0.0008
-0.858
-0.704
0.0106
0.312
0.849
0.9946
-0.110
0.188
-3 0.0013
1.352
1.784
0.0088
-2.088 * -1.524
0.9409
-1.208
-0.450
-2 0.0006
0.697
1.308
0.0100
-0.450
-0.835
0.9537
-0.946
0.226
-1 0.0015
1.592
1.984 * 0.0098
-0.730
-0.644
0.9944
-0.115
0.598
0 0.0001
0.075
-0.293
0.0106
0.422
0.905
1.0493
1.006
0.943
-0.504
-0.965
0.0094
-1.210
-1.088
1.0542
1.108
0.322
-
1 0.0005
2.138 * 2.113 *
2 0.0006
-0.683
-0.108
0.0109
0.737
0.775
1.1046
3 0.0008
-0.857
-1.263
0.0110
0.924
1.855
1.0455
0.929
1.143
4 0.0001
0.078
-0.848
0.0102
-0.208
-0.285
1.0231
0.471
0.565
-0.294
0.347
0.0096
-0.947
-0.719
1.0196
0.401
0.715
-
5 0.0003
*Statistically significant at a 5% level.
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Table 2. Returns, volatility and trading volumes around AGM dates (blue chips
sub-sample).
Panel A
Panel B
Return
Panel C
Volatility
Volume
Day
tAAR
t-statistic Corrado AAAR t-statistic Corrado AAV
statistic Corrado
-5 0.0008
0.527
0.652
0.0054
-1.603
-1.385
0.9407
-0.695
-0.075
-4 0.0029
1.859
1.724
0.0091
2.300
1.884
1.0955
1.119
0.568
-3 0.0016
1.009
1.067
0.0071
0.218
1.157
1.1265
1.483
1.285
-2 0.0018
1.152
1.193
0.0061
-0.885
0.005
0.9949
-0.060
0.399
-1 0.0009
0.564
1.067
0.0049
-2.183
-1.608
1.0480
0.562
0.811
-0.519
-0.728
0.0065
-0.409
-0.029
0.9986
-0.017
0.106
-0.175
-0.950
0.0052
-1.885
-1.283
0.9452
-0.643
-0.555
-0.383
-0.511
0.0073
0.361
0.804
1.0091
0.107
0.849
-0.129
-0.233
0.0061
-0.928
-0.707
0.9405
-0.698
-0.462
4 0.0013
-0.871
-1.042
0.0058
-1.225
-0.886
0.9876
-0.146
0.243
5 0.0000
0.008
-0.106
0.0063
-0.618
-1.167
0.9154
-0.992
-0.955
0 0.0008
1 0.0003
2 0.0006
3 0.0002
-
*Statistically significant at a 5% level.
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Graph 1. Cumulative abnormal returns across the event window
0.0040
0.0035
0.0030
0.0025
0.0020
0.0015
0.0010
0.0005
0.0000
-5
-4
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-3
-2
-1
0
1
2
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Graph 2. Cumulative abnormal volumes across the event window
0.2000
0.1500
0.1000
0.0500
0.0000
-0.0500
-5
-4
-3
-2
-1
0
1
2
-0.1000
-0.1500
-0.2000
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3
4
5