Information Flow and Liquidity Around Anticipated and Unanticipated Dividend Announcements John R. Graham Duke University [email protected]Jennifer Koski University of Washington [email protected]Uri Loewenstein University of Utah [email protected]June 26, 2003 We thank Deepika Bagchee, Lakshman Easwaran, Julia Litvinova, Jack Wolf, and Ge Zhang for excellent research assistance. We also thank Alon Brav, Gautam Kaul, Pete Kyle, Charles Lee, Mike Lemmon, Roni Michaely, Michael Roberts, Dan Rogers, Matt Spiegel, S. Viswanathan, Richard Willis, faculty and students at Indiana University, and seminar participants at the Pacific Northwest Finance conference, Cornell, and Duke University for helpful comments. Any errors are ours. Graham acknowledges financial support from an Alfred P. Sloan Research Fellowship. Correspondence: John Graham, Fuqua School of Business, Duke University, Durham NC 27708-0120. (919) 660-7857.
Transcript
Information Flow and Liquidity Around Anticipated and Unanticipated Dividend Announcements
We thank Deepika Bagchee, Lakshman Easwaran, Julia Litvinova, Jack Wolf, and Ge Zhang for excellent research assistance. We also thank Alon Brav, Gautam Kaul, Pete Kyle, Charles Lee, Mike Lemmon, Roni Michaely, Michael Roberts, Dan Rogers, Matt Spiegel, S. Viswanathan, Richard Willis, faculty and students at Indiana University, and seminar participants at the Pacific Northwest Finance conference, Cornell, and Duke University for helpful comments. Any errors are ours. Graham acknowledges financial support from an Alfred P. Sloan Research Fellowship. Correspondence: John Graham, Fuqua School of Business, Duke University, Durham NC 27708-0120. (919) 660-7857.
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Information Flow and Liquidity Around Anticipated and Unanticipated Dividend Announcements
Abstract: We compare changes in information flow and liquidity around anticipated and unanticipated dividend announcements. When the timing of the news announcement can be anticipated in advance, traditional market microstructure models predict that liquidity will deteriorate before the announcement and return to normal afterwards. Our empirical results generally confirm these predictions and show that the return to normal happens fairly rapidly. The time required to return to normal increases in the information content of the news announcement. In a separate sample of surprise dividend announcements (i.e., initiations, the timing of which is not publicly known in advance), our empirical analysis detects abnormal volume, but no change in liquidity prior to the news release. If informed trading occurs before these unanticipated events, it is apparently not detected by market makers. After the unanticipated dividend announcements, we find that liquidity is low and volume and volatility are high for a short period – but this uncertainty appears to be resolved relatively quickly. We also find that informational asymmetry and price impact decline following dividend initiations. By contrasting market microstructure for anticipated and unanticipated events, we find results that are generally consistent with microstructure theory that models news announcements as uncertainty-reducing events. We find less evidence that news announcements increase uncertainty by introducing new information that informed traders have an advantage interpreting. The main take-away from our analysis is that market reactions before and after information events differ depending on whether the timing of the event is known in advance, implying that researchers should consider whether event timing is known ex ante when studying news announcements.
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How is information processed in securities markets? How does information asymmetry
between market participants affect liquidity and trading activity? Does it matter whether
the timing of a news release is anticipated in advance? We attempt to provide answers to
these questions by examining market conditions around information events. In particular,
we study liquidity, volatility, informational asymmetry, and trading volume before and
after dividend announcements. The most novel aspect of our paper is that we directly
contrast market reactions to dividend announcements with very anticipated timing versus
reactions to dividend announcements with surprise timing. This allows us to analyze
timing effects separately from information effects related to content, whereas most
previous research studies a mixture of the two.
Financial theory makes predictions about market microstructure before and after
news releases, conditional on whether the timing of the event is anticipated.1 In the period
prior to an anticipated news announcement, theory tells us that discretionary liquidity
traders are hesitant to trade because they fear being exploited by informed traders
(Admati and Pfleiderer (1988)). Further, if there is an increased probability that an
informed agent will initiate a trade prior to an anticipated event, market makers will
reduce liquidity (increase spreads and decrease depth). In contrast, when an upcoming
event is unanticipated, its timing is not publicly known in advance and therefore there
should either be (i) no market reaction preceding the news release (if informed investors
either do not trade in advance or else if their pre-event trading is not detected), or (ii)
1 By “anticipated” we refer to events with timing that is predictable in advance. In contrast, an event is “unanticipated” if the timing of the news release is not possible to predict in advance using publicly available information. Note that news announcements can have surprise content, whether or not the timing of the event is predictable in advance.
announcements, and increases even further after the announcement. The results in these
papers are consistent with informational asymmetry increasing before the announcement
because of increased informed trading, and the Krinsky and Lee post-event results are
consistent with KV94’s implication that informed traders have an advantage interpreting
public earnings announcements.
Lee, Mucklow, and Ready (1993) also examine earnings announcements and find
that market makers widen spreads and reduce depth in anticipation of upcoming earnings
announcements, perhaps to offset the effect of informed traders dominating the market
just before the event.3 At the time of the announcement, volume and the bid-ask spread
are high, and depth is low; however, Lee et al. do not find abnormal ex post liquidity after
controlling for volume. Lee et al.’s results are not consistent with KV94 once volume
effects are controlled.
Relative to these papers, our empirical results (for anticipated dividend events) are
closest to Lee et al. (1993) because we also find that liquidity declines prior to the news
release but is not abnormally different after the event. We also examine volatility and
decompose the spread into its component parts and find no evidence of increased post-
event informational asymmetry. Moreover, unlike these three papers, we explicitly
compare events with anticipated timing to events with surprise timing, and attempt to
separate the effects of knowledge about timing from information about content.4
2 See Barclay and Dunbar (1996) in addition to the papers we discuss here. 3 Libby, Mathieu, and Robb (2002) model spread and depth choices as being made jointly and confirm these results for Canadian earnings announcements. 4 Venkatesh and Chiang (1986) state explicitly that periodic earnings and dividend announcements are anticipated by the market. Other papers question this view and argue that the exact timing of earnings announcements is not always fully anticipated (see Chambers and Penman (1984) for a discussion of early and late announcements). Lee et al. (1993) argue that the timing of earnings announcements is largely anticipated but acknowledge that this is not always the case. In our analysis, we explicitly verify whether the timing of an event is possible to anticipate in advance, and therefore our analysis should be relatively
Brooks, Patel, and Su (2003) explicitly examine unanticipated news
announcements, in particular, 21 catastrophic events (death of CEO, airplane crash, etc.)
during 1989-1992. 5 Like us, they find wide spreads and high volume and volatility after
the unanticipated announcement. However, Brooks et al. do not compare unanticipated
events to anticipated events, examine depth or adverse selection, nor attempt to separate
content from timing effects. We explicitly perform these tasks, which also allows us to
compare and contrast the theoretical implications from asymmetric information models.
Our analysis of anticipated events (where there is no timing uncertainty) indicates
that there is a content effect because spreads widen after informative events but not after
uninformative events; however, this effect is short-lived because it lasts only 60 minutes.
For unanticipated events, there is an effect that most likely occurs because the timing of
the announcement is a surprise. We find no evidence that the content of the unanticipated
announcement matters, indicating that the effect of timing surprise dominates content
surprise in our sample. Overall, our analysis indicates that what is expected to happen in
the market microstructure, and in fact what actually does happen, depend on whether the
timing of an announcement is anticipated in advance. Therefore, researchers should
attempt to separate anticipated from unanticipated events when analyzing corporate
events and news announcements.
clean in terms of isolating microstructure effects that result from knowledge about the timing of a news release. 5 Two other papers claim to examine unanticipated announcements. Chae (2002) examines microstructure variables around merger announcements but does not verify whether there is leakage prior to the announcement and therefore does not verify whether the events are unanticipated. Ederington and Lee (1996) examine large price changes in aggregate indices on days that they are not able to confirm there is a scheduled macroeconomic news announcement, and they conjecture that these price reactions are caused by unanticipated information. However, they make no attempt to verify whether unanticipated events in fact occur on these days.
In this section we discuss several theoretical asymmetric information models and
highlight their predictions for anticipated versus unanticipated announcements, both
before and after news releases. The theoretical predictions about market liquidity (i.e.,
spreads and depth), trading volume, and price volatility are summarized in Table 1.
1.1 Modeling Information Events as Decreasing Informational Asymmetry
1.1.1 Endowed Information Advantage for Informed Traders
Asymmetric information models traditionally assume that a news announcement
decreases informational asymmetry. In most of these traditional models there are two
classes of traders: uninformed liquidity traders and informed traders (e.g., Glosten and
Milgrom (1985)). Informed traders may have private information about the timing of the
upcoming announcement and may also know something about its content. Uninformed
traders may know the timing of anticipated announcements but nothing else. Informed
traders have private information that allows them to profit in their trades with liquidity
traders and market makers. Consequently, uninformed investors are hesitant to trade in
situations in which it is likely that informed traders have valuable private information,
and market makers reduce liquidity (i.e., widen the spread and/or reduce depth) because
the probability of trading against an informed trader is high (Easley and O’Hara (1992)).
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Therefore, if informed traders are likely to trade on valuable private information
in the period leading up to dividend announcements, we expect market liquidity to be low
preceding the event. However, low liquidity might only occur when the timing of the
dividend announcement is known in advance (i.e., anticipated). When the timing of the
dividend announcement is unknown (i.e., unanticipated), uninformed investors will, by
definition, trade normally before the event. In contrast, informed investors might exploit
their superior information before the event, unless they are barred from doing so for legal
reasons. If the informed do not trade before unanticipated announcements, or if they
mask their trades sufficiently that the market maker does not detect their trades, market
liquidity will appear normal although volume might be higher. In contrast, if the
informed trade on their valuable information and the market maker is aware of and reacts
to this trading activity, market liquidity will deteriorate before unanticipated events.
The traditional models assume that the announcement reduces informational
asymmetry between the informed and uninformed. Thus, spreads should return to normal
soon after the new information is processed by the market. In standard asymmetric
information models, at the time of the event, price volatility increases with the amount of
new information impounded into market prices, representing the revision in investors’
beliefs. However, as soon as the new information is impounded, volatility should decline
because informational asymmetry has declined. Finally, volume might be low prior to the
news event because discretionary liquidity traders stay away; however, this effect can be
more than offset by informed investors trading aggressively before the event to take
advantage of their private information (Easley and O’Hara (1992)). After the
announcement, volume should return to normal as soon as the new information is fully
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processed by the market, and pent-up demand or portfolio rebalancing is complete. These
predictions are summarized in the top two rows of Table 1.
1.1.2 Endogenous Acquisition of Information by Informed Traders
Most traditional asymmetric models assume that informed traders are endowed
with superior information. In contrast, Kim and Verrecchia (1991) present a model in
which investors actively gather (or produce) private information prior to a news release,
with the intent of profitably trading on this information. The pre-event information
gathering leads to increased informational asymmetry between the informed and
uninformed. In this section we discuss how this different modeling of information
advantage sometimes affects predictions about liquidity, volume, and volatility. One
reason that we discuss Kim and Verrechia (1991) is that it is the only paper that explicitly
makes predictions about the size of reactions to unanticipated events relative to
anticipated event reactions.
Volume predictions in Kim and Verrecchia’s (1991) model are more complex
than in most asymmetric information models. The gathering of information increases
informational asymmetry, which in isolation increases volume.6 However, the increased
private information production also reduces the impact of the public information when it
is released, thereby weakening price changes, which has a dampening effect on volume in
this model. The overall effect on volume at the time of anticipated events is determined
by whichever of these effects dominates. Relative to anticipated events, the volume in
otherwise identical unanticipated events can be higher if informational asymmetry is
higher, or if (possibly) smaller private information gathering prior to unanticipated events
6 In Kim and Verrecchia (1991), volume equals the product of information asymmetry and the absolute value of the price change.
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leads to larger price changes at the time of the announcement. The combination of these
two effects will determine whether volume is higher for unanticipated announcements,
relative to anticipated events.
Though Kim and Verrecchia (1991) do not explicitly model liquidity, if informed
traders do not ex ante know the exact timing of unanticipated events, we conjecture that
ex post liquidity effects should be small in unanticipated events (relative to anticipated
events) because there is less opportunity for pre-event information gathering prior to
unanticipated events, and hence less information asymmetry. Similarly, for volatility,
prior to unanticipated events, reduced pre-event information gathering would lead to
larger price reactions and volatility after the news announcement, relative to anticipated
events.
1.2 Modeling Information Events as Increasing Informational Asymmetry
The decreasing asymmetric information models predict that liquidity, volume and
volatility will return to normal shortly after an announcement. Kim and Verrecchia
(1994) argue that informed market participants have an advantage over the uninformed in
terms of interpreting public news announcements. This implies that informational
asymmetry should be high, and liquidity accordingly low, after a news event. Note that
this should be the case for both anticipated and unanticipated announcements. If
unanticipated events are similar to anticipated events except with less-precise prior
information, KV94 predict that post-event liquidity will be relatively lower after
unanticipated events because the interpretation advantage of the informed is greater (p.
53). Though not formally modeled, KV94 discuss the possibility that liquidity can be
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lower before anticipated events to the extent that there is informational leakage (and
hence an interpretation advantage to the informed) prior to the event, and the market
maker is aware of the leakage. Extending their reasoning, this same possibility should
hold for unanticipated events.
According to KV94, price volatility will increase as information is impounded
into market prices. Relative to anticipated events, the increase in price volatility should
be higher for unanticipated events because the precision of prior information is lower (p.
57). In contrast to the other models, volatility should remain high even after the time of
the news release, as long as the informed maintain their interpretation advantage. Finally,
volume predictions are ambiguous in this model. Volume might be low after a dividend
announcement if discretionary liquidity traders stay out of the market. In contrast, volume
could increase if the informed trade aggressively to take advantage of their superior
information interpretation skills. These predictions are summarized in the bottom two
rows of Table 1.
2. Data Sample and Summary Statistics
We gather information for two samples: 1) firms that make an unanticipated
announcement that they will begin paying dividends, and 2) firms that make extremely
predictable dividend announcements. By unanticipated, we mean that the timing of the
dividend announcement is not predictable in advance, and by predictable we mean that
the dividend is announced at approximately the same time each quarter. We also gather
data on a third sample of “matched firms” that make predictable dividend payments but
are otherwise similar to the unanticipated firms in terms of size and other characteristics.
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To keep the focus on the primary two samples, we mention results related to the matched
sample only when they are informative, and we also relegate discussion related to sample
formation of the matched sample to the appendix.7 However, we do present some
summary information about the matched sample in this section.
The dividend announcements we examine occur between 1990 and 1998. For the
unanticipated sample, we use CRSP to identify NYSE firms that make an initial dividend
announcement. By initial, we mean an announcement of a first-ever dividend, or the first
dividend payment in at least 36 months. We identify the exact time of these initial
announcements with word searches using LEXIS/NEXIS, Dow Jones News Retrieval,
and the Wall Street Journal Index. We choose the earliest time from any of these sources
as the time of the initial dividend announcement. So that we can observe the market
reaction, we require that the dividend announcement is made during trading hours. We
also gather information on whether sample firms release other news in the week
surrounding the dividend announcement day (day –2 to day +2). Results for a clean
subsample that excludes firms with other news events are nearly identical to those
reported herein.
We require nonmissing CRSP data for volume, dividends, stock prices, and
returns. We also require nonmissing ISSM (1990-92) or TAQ (post-1992) data for depth,
spread, and number of trades. Many stocks that initiate dividends are small or
infrequently traded. Therefore, analogous to Krinsky and Lee (1996), we require a stock
7 There are two issues that reduce the value of the matched sample in our analysis. First, the matching criteria result in our only being able to find matches for about 80% of the unanticipated events, and we do not want to delete one-fifth of the unanticipated sample to maintain comparability with the matched sample. More importantly, the predictability of the matched sample is only modest (i.e., many firms in the matched sample have only a short history of dividend payments with predictable timing). Therefore, for the most part, we emphasize the very anticipated sample of firms, which have a long history of very predictable dividend announcements.
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price of at least $3 and a minimum of at least 20 quotes on the event day. These
requirements lead to a sample of 68 unanticipated events. While it is possible that
investors may have assigned a nonzero probability that these firms would initiate
dividends at some point, the exact day of these announcements would not have been
predictable in advance, so we consider these events to be unanticipated.
The very anticipated sample consists of stocks that make dividend announcements
that are very predictable in terms of their timing. For the very anticipated firms, we
require that the dividend announcement occur according to a predictable schedule for at
least 22 out of 24 consecutive quarters during the six years leading up to and including
the event date in our sample. We also require that in the quarter of the event in our
sample (e.g., the fourth quarter if our event occurs in December), there be a string of at
least six consecutive years with very predictable dividends in that quarter (e.g., the
dividend be paid on the first Wednesday of December for at least six years in a row). In
every case for the very anticipated sample, the announcement occurred on the exact date
predicted by past announcement dates.8 The principal advantage of this sample is that the
events are extremely anticipated, and therefore represent a stark contrast with the
unanticipated events. To be included in this sample, a firm must trade on the NYSE and
have nonmissing values for the key variables of interest. There are 92 firms in the very
anticipated sample.
Table 2 presents summary statistics for all the samples. The unanticipated and
matched sample firms are roughly the same size but much smaller than the firms in the
very anticipated sample. Quoted spreads equal approximately $0.21 for both
8 For additional details on the prediction rule of dividend announcement dates see Kalay and Loewenstein (1986, page 376).
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unanticipated and very anticipated firms, on average, and both are only two-thirds the
$0.31 spread for matched firms.9 As a percentage of stock price, very anticipated spreads
are 0.59%, unanticipated spreads are 1.16%, and matched spreads are 1.33%, which are
in line with the spreads of 1.1% observed in Krinsky and Lee (1996) and 1.3% in Lee,
Mucklow, and Ready (1993).
Depth (defined as the sum of depth at the bid and ask) is smallest for matched
firms (approximately 5,000 shares), while depth averages about 12,000 for unanticipated
and 14,480 for extremely anticipated stocks. The matched firms also trade least
frequently (averaging 6.2 trades per half hour during the benchmark period), while stocks
in the other two samples average 7.4 and 11.0 trades, respectively. Overall, the matched
firms are illiquid relative to the stocks in the very anticipated sample, and the analysis
below often finds noisier and less significant results for these firms. To minimize these
issues, in some of the analysis below, we examine the more liquid subset of stocks that
have at least 50 quotes on the event day. When used, this restriction results in 43
unanticipated, 26 matched, and 66 very anticipated firms.
Interestingly, the mean event reaction to announcements of very anticipated
dividends is negative, though insignificantly different from zero. This is consistent with
these events being anticipated by the market in both content and timing, on average. In
contrast, the event reaction to the dividend initiations is significantly positive for the
unanticipated announcements and, by construction, also significantly positive on average
for the matched sample.
Figure 1 presents the time of day for the dividend announcements. The events are
fairly evenly spread throughout the day for each sample. For example, for the
9 Liquidity measures are the average of all quotes during the half hour.
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unanticipated events, the number of announcements per half hour ranges from two to
eight over the 13 half-hour trading intervals. Dividend announcements do not appear to
be concentrated during one particular part of the trading day.
Figure 2 shows the time-of-day patterns of volume, depth, and spreads. Similar to
other papers (e.g., Lee et al. (1993)), for all three of our samples volume is relatively high
at the open and close, and lower during the mid-day hours. Similarly, the spread is widest
at the open and close, and lowest at mid-day. Depth is lowest at the open and highest at
mid-day, and not significantly different from normal at the close. Given that the news
announcements in our samples occur at various times during the day, the intra-day
patterns shown in Figure 2 highlight that it is important to benchmark to liquidity and
volume using data for the same half-hour of the day as the announcement time when
determining whether event-day behavior is abnormal. Therefore, for many of our
statistics, we report the percentage deviation from the corresponding mean during the
benchmark period. The benchmark periods are the same half-hour time intervals during
days –15 through –3 and +3 through +15 relative to the announcement day 0.
Figures 3 and 4 graph the variables of interest for the 26 half-hours preceding and
the 26 half hours after the event.10 Panel A shows that for very anticipated events, mean
volume (Fig. 3) and spreads (Fig. 4) increase before the announcement. Volume remains
quite high immediately after the announcement, while spreads return to normal. There is
no particular pattern for depth either before or after the event. In Panel B, results for the
matched sample are noisier than for the very anticipated sample, though depth does fall
just prior to the event. In contrast, depths and spreads for unanticipated announcements
10 For these figures only, we define half-hours based on clock time, similar to Lee, Mucklow and Ready (1993), rather than half-hours split exactly on the event time (like we use in the rest of the paper).
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are fairly normal in the pre-event period, as they should be if the events are not
anticipated and there is no information leakage or if insider trading preceding the event is
not detected (Figure 4, Panel C). Volume is somewhat higher than normal prior to the
event (Figure 3, Panel C). The picture changes dramatically, however, once the
announcement is made. Spreads widen, depths fall, and volume spikes in the half hours
following the announcement. Overall, there appears to be a large reaction to
unanticipated dividend announcements.
3. Methodology and Empirical Results
In this section, we present our empirical analysis of market liquidity and
information flow around anticipated and unanticipated dividend announcements. We
begin by presenting univariate comparisons of spreads, depth, number of trades, and
volume. Later, we perform multivariate analyses to control for the effect of volume on
the other variables. At the end of the section, we decompose the bid-ask spread to
determine the degree of adverse selection, and also examine price volatility, before and
after dividend announcements. The differences that we observe between anticipated and
unanticipated events allow us to discern between the theories discussed in Section 1.
One unique feature of our analysis is that we split the pre-and post-event periods
exactly on the event time. Most previous research uses clock half-hours to identify
periods, such as the half-hour including the event, the first half-hour after the clock-half
hour containing the event, etc. One disadvantage of the clock half-hour is approach is that
if market reactions are short-lived and are expected to differ in the pre- and post-event
periods, the “event half-hour” will average across both periods, making sharp inference
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somewhat difficult. This could be important in our analysis because we expect the pre-
and post periods to differ, especially for unanticipated events. However, we also perform
our analysis based on clock half-hours and get similar results (available on request).
Finally, note that in our analysis, and in similar research endeavors, grouping
observations in half-hour increments based on a realized announcement time should be
particularly helpful when the exact half-hour of the event is known to market participants
in advance. If the exact time is not known in advance (e.g., the day is known in advance
but not the half-hour), then we would not expect a dramatic peaking of activity just
before the event; in such a case, pre-event activity reflects market expectations of events
that the market expects imminently averaged with expectations about other events whose
degree of timing predictability varies. If the half-hour of the upcoming event were not
known in advance (at least to informed traders), this would work against our ability to
find significant pre-announcement results, even in our very anticipated sample.
3.1 Univariate Comparisons
3.1.1 Spreads, Depth, Volume, and Number of Trades
In Table 3 we examine spreads, depth, and volume. Because Figures 3 and 4
indicate that most trading activity occurs in the several hours surrounding the event, the
table is limited to the six half hours before and after the event. Statistical significance in
the tables is determined by bootstrapped standard errors [Lee, Mucklow and Ready
(1993, p. 363)]. Table 3 presents results for each of the overall samples and also for the
more liquid subsamples (i.e., subsamples of stocks that have at least 50 quotes on the
event day). As expected, due to the smaller sample size and modest history of predictable
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dividend timing, results for the matched sample are not generally statistically significant.
Therefore, we omit matched sample results from Table 3 and subsequent tables.
Anticipated Events
Panels A and B of Table 3 indicate that spreads are abnormally high (based on a
significance level of 10%) in four of the six half-hours preceding the very anticipated
announcements, including the three half-hours immediately preceding the event. Depth is
abnormally low in the half-hour just before the event, though the p-value is only 0.18 in
Panel A. Finding higher spreads and lower depth is consistent with market makers using
the tools at their disposal to offset the effects of informed trading just before an
anticipated announcement. Volume is also higher in four of the five half-hour trading
periods preceding the event.11 Results for the matched sample (not tabulated) are similar
but weaker statistically.
In contrast to the decreased liquidity before the event, post-event behavior is very
different. For very anticipated events, spreads and depths return to normal immediately
after the announcement. Volume stays significantly high after the very anticipated event
and positive but insignificant for the matched firms.
We further explore market activity in Table 4, which summarizes evidence related
to the number of trades in each half hour. Cready and Hurtt (2002) indicate that inference
based on the number of trades is more powerful than that based on volume or abnormal
returns in terms of detecting investor reaction to information events. For very anticipated
announcements, Panel A of Table 4 indicates that the total number of trades is
11 We confirm these results using sign tests (not reported in a table). Median volume is positive and significant, and median depth is significantly negative for the very anticipated sample in half-hour –1 but not in any other half hour preceding the event.
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statistically significantly above normal in half hour –1. This is also the case for big trades
and buys.12
After the announcement, very anticipated events experience a significant increase
in the number of trades, with the total number of trades being significantly above normal
at a 5% level in four of the six half-hours. Overall, the behavior of the number of trades is
consistent with the volume results presented in Table 3, and indicates an increase in
trading activity leading up to the event and remaining high after.
For anticipated events, our results are generally consistent with Lee, Mucklow,
and Ready’s (1993) conclusion that market makers reduce liquidity before anticipated
events due to adverse selection resulting from informed trading. However, in our sample
spreads appear to be a more important tool in adjusting pre-event liquidity, while Lee et
al. find that depth and spreads are adjusted jointly. Our results are also generally
consistent with Lee et al. (1993) in terms of finding abnormal volume both before and
after the event. Both our results and those in Lee et al. are consistent with there being less
informational asymmetry implicit in trades after the announcement takes place, which we
investigate further in Section 3.2.
Our liquidity results are consistent with the hypothesis that the announcement
reveals information and the market responds quickly; liquidity is worse before the event
but returns to normal shortly after the announcement, perhaps because adverse selection
declines. Our liquidity result is not consistent with Kim and Verrecchia’s (1994)
prediction that information asymmetry increases after the news release, unless the
12 All trades that occur above the prevailing bid-ask midpoint are classified as purchases, and all trades that occur below the midpoint are classified as sales [see Hasbrouck (1991), Huang and Stoll (1994), and Koski and Michaely (2000), among others]. Note that the sum of buys and sells does not necessarily equal the
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advantage that the informed have in interpreting information is very short-lived. The
increased volume before the event implies that informed trading increases at this time.
The increased ex post volume is consistent with the possibility in KV94 that informed
trading increases after an information event. It may alternatively reflect pent-up demand,
or portfolio rebalancing, as the market responds to the information in the announcement.
Unanticipated events
Prior to unanticipated news announcements, spreads and depth do not exhibit
significant abnormal behavior (Table 3, panels D and E), which is what we would expect
if the events are not anticipated by the market maker. There is evidence of abnormal
volume in the four half-hours leading up to the unanticipated dividend-initiating
announcement. This could indicate “stealth” informed trading before the event.13 This
increase in volume is confirmed by a modest increase in the number of trades (especially
large trades) prior to the announcement (Table 4). Apparently the market maker does not
attribute the increased trading activity to informed trading because the spread is not
increased in response. This is consistent with Chakavarty and McConnell (1997), who
find that spreads and depth did not change in response to insider trading by Ivan Boesky.
As expected, the post-event reaction to unanticipated events is much larger than
the pre-event reactions, and also is larger than the post-event activity following
anticipated announcements. After the unanticipated announcement, market makers widen
total number of trades because we exclude trades that occur at the midpoint. Quotes are delayed 5 seconds relative to transactions to adjust for reporting differences (Lee and Ready (1991)). 13We do not find an abnormal amount of insider trading, as declared in Form 4 filings, prior to the unanticipated events. If insider trading is occurring, it is not being reported to the SEC. We also investigate whether there is a profitable trading rule associated with this increase in pre-event volume – but do not find evidence of profits larger than transactions costs from buying at the ask before and selling at the bid after the announcement.
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spreads to almost 10% above normal in the hour after the event (panels D and E of Table
3). For events associated with at least 50 quotes (Panel E), there is evidence that depth
decreases in the five half-hours after the event – but only significantly so in half-hour +3.
Though not reported in the table, median depth is significantly below zero in each of
these half-hour periods. Overall, these results are consistent with market makers being
surprised by the dividend-initiating announcement and reducing liquidity in response.
Importantly, the results also indicate that it takes longer for the market to adjust to new
information and return to normal after an unanticipated announcement than it does to
respond to an anticipated announcement.
Volume is also significantly higher after the unanticipated announcement. In the
half-hour immediately following the announcement, volume for the full sample is 134%
above normal volume during the benchmark period (p-value of 0.0001). Results are even
stronger for the liquid sample in Panel E. Abnormal volume is also large and positive in
half-hours +2 (p-value=0.063) and +3 (p-value 0.11) for the liquid sample.
The number of trades also increases significantly after the event. The average
total number of trades is 87.5% above normal in half-hour +1 (p-value=0.0001) and
42.6% above normal in half-hour +2 (p-value=0.01) (see Panel B of Table 4). The most
notable increase in the number of trades is for large buys in half-hours +1 and +2. We
also report the number of trades for the subset of observations that are associated with
positive event reactions (44 observations in Panel C). We do this in case the reaction is
asymmetric for positive and negative returns. In panel C of Table 4, the number of trades
for positive announcement reactions, especially large buys, increases significantly in half-
hours +1 and +2. In contrast, there is much less evidence of an increased number of
23
trades preceding the event in Panels B or C. Overall, the volume and number of trades
indicate increased market activity following unanticipated dividend announcements. This
likely occurs because information processing takes time to work its way through the
market, or because of portfolio rebalancing.14 For example, the increased activity after
unanticipated events may indicate that a stock is viewed as being fundamentally different
after it initiates a dividend, which leads to large rebalancing of ownership by informed
and uninformed investors alike.
The results for the unanticipated new releases indicate that there is very little
reaction before the announcement, but a large reaction after. These results differ from
those for the anticipated sample in which there is a greater reaction before the
announcement and an almost immediate return to normal after. Results for the anticipated
sample are consistent with traditional asymmetric information models, in which the
greatest adverse selection problem exists before the announcement. The pre-event versus
post-event results for the unanticipated sample are potentially more consistent with Kim
and Verrecchia’s (1994) suggestion that information asymmetry peaks after an
announcement because market participants differ in their ability to interpret the
information. Moreover, relative to the reaction for anticipated announcements, the larger
change in volume and liquidity following unanticipated announcements is also consistent
with KV94 in that it could occur because of a relatively greater advantage to informed
trading. Finally, note that the reduced liquidity observed after unanticipated events,
relative to liquidity after anticipated events, is not consistent with Kim and Verrecchia
(1991).
14 Unlike anticipated events, the excess volume following unanticipated events can not be attributed to pent-up trading by discretionary liquidity traders.
24
Content versus timing
Contrasting anticipated versus unanticipated news announcements indicates that there are
important differences based on whether the timing of the upcoming event is known in
advance, which we refer to as timing effects. We also examine how announcement
content affects market reactions.
Panels C and F of Table 3 contain univariate statistics for events that lead to event
reactions that are at least 0.8% in magnitude, the idea being that big event returns are
associated with news that contains a relatively large amount of content.15 After
anticipated events that have relatively large content, volume and spreads are high for up
to five half-hours after the announcement (see Panel C). Therefore, at least for these
anticipated events, we see that informative announcements result in reduced market
liquidity. Contrasting with the results in Panels A and B allows us to isolate the effect of
content and conclude that larger content leads to larger liquidity effects. Similar but
generally weaker results are obtained for the matched sample when content is large.
Interestingly, the depth is statistically significantly negative for the matched sample in the
first half hour following the event (not reported in table). Note that these post-event
reactions are consistent with KV94.16
For unanticipated events, the announcement reaction is potentially a mixture of
timing surprise and announcement content (panels D and E). In panel F we examine only
15 0.8% is the approximate cutoff for the largest quartile of event returns in the very anticipated sample. Note that we obtain nearly identical results if we instead we examine events that have a return of at least 0.8% in absolute magnitude. 16 As a robustness check on the content analysis, we examine a subsample of very anticipated dividend announcements that precede a dividend increase. (In the full very anticipated sample, nearly three out of four announcements do not lead to an increase in dividends.) For the 29 dividend-increasing
25
the events with the most overall effect on the market (i.e., an event reaction of at least
0.8%). Given that the timing surprise of all unanticipated events should be about the
same, the larger announcement effect indicates that these unanticipated events have the
most content. Panel F shows that larger content in unanticipated announcements does not
lead to greater liquidity effects. Therefore, at least for our sample, the timing surprise
dominates the content surprise for unanticipated news releases.
3.1.2 Multivariate Analyses of Spreads, Depth, and Volume
We perform regressions of depth and spread on volume and dummy variables that
capture the mean effect for day –2, day –1, the event half-hour, day +1, and day +2 (see
Table 5). Like Lee, Mucklow, and Ready (1993), we do this because it is well known that
there is a general relation between liquidity and volume, while the liquidity predictions in
Table 1 are generated holding volume constant. For unanticipated events, we find that
spreads are abnormally high in the event half hour and on event day +1, even after
controlling for volume (Table 5, panel B). We also find that depth is abnormally low on
day +1 following unanticipated announcements and marginally negatively significant on
the event day. This is consistent with market makers reducing market liquidity after
surprise events. In contrast, there is very little evidence of spreads or depth being altered
either before or after anticipated events, after controlling for volume effects. Overall, our
analysis underscores the strength of the reaction to unanticipated events and highlights
the effects that knowledge about timing have on information processing.
announcements, spreads (depths) are significantly high (low) in three of the six half-hours following the announcement but volume is only abnormally high in the half hour immediately after the announcement.
26
3.2 Spread Decomposition and Price Impact
We begin this section by examining whether the wide spreads and small depth
preceding anticipated announcements, and the same effects following unanticipated
announcements, are associated with adverse selection. Krinsky and Lee (1996) note that
total spread can widen for reasons other than adverse selection (i.e., inventory and order
processing costs); therefore, one should not draw conclusions about adverse selection and
informed trading based solely on the total spread.
Following Krinsky and Lee (1996), we use the methodology of Stoll (1989) to
decompose the bid-ask spread into adverse selection, inventory, and order processing
components. The analysis in Table 3 shows that spreads widen prior to anticipated
events, which could be attributable to market makers widening spreads because they
believe that the probability of trading with an informed investor increases just prior to an
anticipated information event. The results in Table 6 do not support this conjecture.
Table 6 indicates that adverse selection is an important component of the bid-ask
spread in the pre-event benchmark period for anticipated events. (The benchmark period
is days –3 and –4 relative to the event day.) For very anticipated events adverse selection
constitutes 86% of the spread in the benchmark period, in comparison to 16% for
inventory holding costs and no costs for order processing.17 Adverse selection falls in the
pre-event period for very anticipated events to 63% of the spread and increases to 70%
during the post-announcement period; the change of the adverse selection component
17 Given the negative value for order-processing costs, we do not interpret these cost percentages literally but instead focus on the difference in the costs before and after the event. Note that if we constrain the parameters so that order-processing costs are non-negative [see Affleck-Graves, Hedge and Miller (1994, footnote 5) and Krinsky and Lee (1996, footnote 15)], the order processing component increases to zero and the adverse selection component falls by a like amount (e.g., 2.1% in the benchmark period) and statistical significance of the changes is reduced somewhat.
27
between the pre-announcement and post-announcement period is not statistically
significant. The absolute level of adverse selection is, however, relatively high both
before and after anticipated events, constituting between 63% and 86% of the total bid-
ask spread during all periods. Our numbers are consistent with those in Krinsky and Lee
(1996), who find adverse selection percentages ranging from 60% in the pre-event period
to 76% in the post-event period.
The spread decomposition is particularly interesting for unanticipated events.
Adverse selection effectively constitutes all of the bid-ask spread during the benchmark
and pre-announcement periods (Panel B of Table 6). The portion of the bid-ask spread
attributable to adverse selection falls from 102% just prior to only 42% of the spread after
the unanticipated news announcement (which is significant with a 2-sided p-value of
0.022 using the bootstrap approach from Affleck-Graves, Hedge, and Miller (1994)).
This reduction in adverse selection is not consistent with KV94’s implication that the
advantage of the informed is higher following news releases.
This large reduction in adverse selection after unanticipated dividend initiations
can be quantified more precisely. Table 6 reports that the cost per dollar of stock price
attributable to adverse selection changes from about 1.28 cents to 0.48 cents after the
announcement. This is a large economic reduction in adverse selection costs. Note also
that our 0.48 cents estimate of ex post adverse selection costs is lower than the 0.83 cents
documented by Krinsky and Lee (1996) after earnings announcements.
We also examine the price impact of trades before and after dividend
announcements, as an alternative way to investigate adverse selection. We interpret large
price impact as evidence of large adverse selection. The approach we use to measure
28
price impact is derived in Huang and Stoll (1996), Bessembinder and Kaufman (1997),
and Venkataraman (2001). The difference between the effective spread (i.e., the total cost
that traders pay) and the realized spread (i.e., the amount that market makers receive) is
the "loss" due to adverse information. Like Bessembinder and Kaufman, we call this
adverse information piece the price impact.
For anticipated events, the price impact is fairly flat moving from the benchmark
to the pre-event period and likewise does not change much in the post-event period (see
Table 7). Therefore, there is not much evidence of changes in adverse selection around
anticipated events in our sample. In contrast, for unanticipated events, the post-event
price impact is smaller than the price impact in both the benchmark and pre-event
periods. This reduction in price impact is consistent with a reduction in adverse selection
following unanticipated news releases, which is not consistent with KV94, but is
consistent with the traditional asymmetric information models.
3.3 Price Volatility
We also examine daily price volatility to further analyze the processing of
information around dividend announcements, and to better distinguish between the
theories presented in Table 1. If volatility is driven by informational dispersion across
heterogeneous agents, then the traditional microstructure models predict that there should
not be much difference in volatility behavior between anticipated and unanticipated
events. In contrast, unanticipated volatility should be high post-event in the two Kim and
Verrecchia models because (assuming that the informed do not ex ante know the exact
timing of an unanticipated event) there is less opportunity for information gathering prior
29
to unanticipated events (1991) or because the precision of prior information is lower prior
to unanticipated events (1994).
We measure daily volatility using RANGE, which is (HIGH-
LOW)/(HIGH+LOW)/2, where HIGH and LOW are the daily high and low bid-ask
midpoints, respectively.18 RANGE is averaged across all days in the relevant event
window for a single firm, then averaged across firms.
Volatility increases on the event day for both the unanticipated and very
anticipated samples (Table 8). Moreover, for both samples, volatility falls after the event
to levels that are below the event-day level. Based on standard t-statistics, the increase in
volatility from days –2/–1 to event day is statistically significant on both samples, and the
decrease from the event day to days +1/+2 is significant on the unanticipated sample.
Based on bootstrapped standard errors, there is statistical evidence that the range
volatility measure increases to peak on the event day and then declines after for
unanticipated events – but the evidence is somewhat weaker for the very anticipated
sample.19
In general, the volatility results are consistent with the implications from the
traditional asymmetric information models, in which informational asymmetry and
heterogeneity decline following news releases. In contrast, the decline in volatility after
the announcement is not consistent with KV94, in which informational asymmetry and
heterogeneity are hypothesized to remain high after the announcement. Relative to event
18 Akizadeh, Brandt and Diebold (2002) discuss range-based estimates of daily volatility. We also use the square of the daily return, computed using the closing bid-ask midpoint, as a measure of volatility and obtain qualitatively similar but statistically insignificant results. 19 Bootstrapped standard errors comparing two sample periods are computed adapting the technique in Affleck-Graves, Hedge and Miller (1994). In this case, the statistic of interest is the volatility estimate, rather than estimates of the components of the spread.
30
day and pre-event volatility, the reduction in volatility is greater for unanticipated events
than it is for anticipated events, which is not consistent with implications from KV94 or
Kim and Verrecchia (1991).
4. Conclusion
In this paper we contrast liquidity, volume, and price volatility between
anticipated and unanticipated dividend announcements. This allows us to understand the
effects of information processing in the context of models of asymmetric information.
Traditional models and prior empirical research generally predict that information events
represent the resolution of uncertainty, and therefore that adverse selection and reactions
to it should peak upon news release, and decline substantially after the event. In contrast,
Kim and Verrecchia (1994) argue that the release of information creates new uncertainty
and dispersion of information, and therefore that adverse selection and reactions to it will
increase after the event.
When we examine dividend announcements with timing that is very predictable in
advance, our evidence is generally consistent with asymmetric information models:
liquidity and volume increase before the event, and liquidity rapidly returns to normal
after the event. Moreover, there is no evidence of increased adverse selection or price
volatility after the news release. These results are not consistent with KV94. However,
when we examine anticipated events with notable content, which are the events for which
the informed might reasonably be expected to have an interpretation advantage, we find
some evidence that spreads remain wider for two and a half hours, which is consistent
with KV94.
31
When we examine unanticipated events, the results are consistent with
asymmetric information models in that adverse selection and price volatility decline after
the announcement. Interestingly, spreads widen, depth shrinks, and volume is high after
unanticipated news. This reaction is consistent with a transition period during which
information is impounded into prices and portfolios are rebalanced. After the transition
period, market conditions return to normal, which is generally consistent with the
traditional asymmetric information models.
The most novel aspect of our study involves investigating unanticipated events,
while most previous studies either examine anticipated events, or an unspecified mixture
of the two. Given the differences we detect between the two types of events, an important
implication from our paper is that future research should attempt to distinguish between
events that have anticipated versus unanticipated timing. Our analysis also highlights how
it is important to examine many different microstructure aspects of market behavior when
investigating information processing around news releases – because this allows one to
better distinguish between various theoretical predictions.
32
Appendix – Construction of Matched Sample
In addition to the unanticipated and very anticipated samples, we gather information for a third sample of firms. This sample consists of “matched firms” that make predictable dividend payments but are otherwise very similar to the unanticipated firms in terms of size and other characteristics. When forming the matched sample, so as to allow a sufficiently large pool of firms to match to the characteristics of the unanticipated firms, we only require moderate predictability of the dividend announcement. Specifically, for the matched sample, we require eight consecutive quarters with consistent timing of the dividend announcement, and in a few cases fewer than eight in a row. The modest predictability of the matched sample is the reason that we also form a “very anticipated” sample of firms that meet a more stringent predictability requirement (described in Section 2).
The matched sample firms make a dividend payment on the same day of week and week of quarter for at least two years, so that the timing of the dividend announcement is relatively predictable in advance. For each unanticipated observation, we identify all firms in the same 2-digit SIC industry that have market capitalization within 20% of the market capitalization of the given unanticipated firm. Among these candidates, we eliminate firms that vary the day of week of their quarterly dividend announcement from quarter to quarter in the eight quarters leading up to and including the announcement date in our sample. For example, we keep observations for which the first quarter announcement occurs, say, on the second Wednesday of March each year, but we eliminate observations if the announcement is Wednesday some years and Thursday other years. We only keep observations that meet the data requirements described in Section 2 (of having CRSP and TAQ data, etc.).
Among the candidate matching observations, we choose the one with the stock price reaction closest to the event reaction of the unanticipated firm (requiring that the event reaction is within +/- 50 basis points of the unanticipated reaction). We match by event reaction in an attempt to control for the surprise in the content of the announcement; therefore, for one thing, both samples have roughly the same proportion of positive and negative announcements. The matching and predictability requirements thus far described turn out to be relatively stringent, and in several cases, we are unable to find an adequate match to the unanticipated observation. If we cannot find a matched observation with an event reaction within 50 basis points, we repeat the matching process requiring a 1-digit SIC match and an event reaction within 100 basis points (but still requiring a market capitalization within +/- 20%). Finally, if we still can not find a match, we repeat the process eliminating the market capitalization requirement. In the end, we find matched observations for 55 of the unanticipated announcements.
33
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34
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Table 1 Theoretical Predictions about Liquidity, Volume, and Volatility Before and After Anticipated and Unanticipated News Announcements
Model Liquidity Volume Price Volatility Before announcement After announcement Before announcement After announcement After announcement Traditional Asymmetric Information Models: Anticipated Events
Reduced because the probability of trading against an informed trader increases
Back to normal, either quickly or gradually
Low if lack of liquidity traders dominates. High if informed trading to exploit info advantage dominates
High if uninformed traders have pent-up demand, or if info processing takes time and leads to trade, or if portfolio rebalancing
Increases at announce-ment because of changes in traders’ beliefs but then decreases because less information dispersion
Unanticipated Events Normal if trading by informed (if any) is not detected. Otherwise, if informed trade before the event and it is detected, liquidity is reduced
Normal if info processing immediate. Otherwise, low after event, gradually returning to normal
Normal if informed traders do not trade based on private information. If they do trade, volume might be higher
High if info processing takes time and leads to trade, or if portfolio rebalancing
No substantial difference from anticipated, as described one cell above
KV (1994): Anticipated Events
Reduced to offset possible leakage prior to event (conjectured in KV94 but not explicitly modeled)
Reduced because of information interpretation advantage of informed
Not emphasized in model
Low if lack of liquidity traders dominate. High if informed trading to exploit info advantage dominates
Announcement increases variance of price change, as info processed into price
Unanticipated Events Conjectured to be reduced if market maker detects possible info leakage and informed trading. Normal otherwise.
Reduction more intense than for anticipated because advantage of informed higher (p. 53)
Not emphasized in model
Effects magnified versus anticipated if value of interpretation skill greater after unanticipated event
Higher than for anticipated event because precision of prior info lower, so announcement has more info (p. 57)
Table 1 summarizes the expected ex ante and ex post reactions to news announcements. The after announcement period starts immediately after the announcement, which includes the “announcement reaction” and the period after that immediate reaction. The predictions are presented separately by model, and also separately by anticipated versus unanticipated events. In the traditional asymmetric information models, informed traders posses superior information prior to a news event, but their advantage is partially or completely ameliorated by the news announcement. The reduction in informational advantage can be immediate or gradual. In Kim and Verrecchia (1994), the informed have an advantage interpreting the news announcement, so information asymmetry increases. Effects of this interpretation advantage can show up before the news announcement if there is leakage, or if the market maker prepares in advance for the effects of the announcement.
Table 2 Summary Descriptive Statistics during Benchmark Period
This table presents summary descriptive statistics for each sample during the benchmark period. The benchmark period includes days - 15 through -3 and +3 through +15. Half-hours are computed relative to the announcement time. Panel A reports results for the Very Anticipated sample. Panel B includes the Matched Sample, and Panel C contians the Unanticipated Sample. Volume reflects total volume per half-hour. Event Reaction is the two-day stock price reaction in excess of the value-weighted CRSP index return on that same day, averaged across firms in a given sample. All other statistics are averaged during each half-hour interval during the day, then averaged across all benchmark days for all companies. Statistic Mean
Standard Deviation Median
Upper Quartile
Lower Quartile
Panel A: VERY (N = 92) Stock Price ($) 41.99 20.35 35.86 54.96 27.86 Event Reaction (%) -0.04 2.05 -0.16 0.82 -1.37 Volume (00s) 186.38 283.20 126.84 217.39 45.81 Number of Trades 10.99 13.07 6.69 11.77 3.64 Number of Quotes 9.38 6.76 7.84 12.46 4.17 Quoted Spread ($) 0.21 0.04 0.20 0.23 0.18 Percentage Spread (%) 0.59 0.25 0.54 0.74 0.42 Quoted Depth (00s) 144.78 151.92 96.18 163.62 60.80 Quoted Depth as a % of Average Half-Hour Volume 239.54 395.31 88.16
193.92
49.17
Equity Market Cap. ($millions) 8,521.39 13,790.23 4,172.99 7,540.89 1,321.30 Panel B: MATCH (N=55) Stock Price ($) 30.83 15.56 30.42 41.47 18.46 Event Reaction (%) 1.10 3.05 0.62 2.56 -0.82 Volume (00s) 102.48 165.27 35.94 113.06 20.15 Number of Trades 6.23 9.67 2.92 6.54 2.06 Number of Quotes 10.09 16.72 3.30 9.35 2.15 Quoted Spread ($) 0.31 0.23 0.23 0.29 0.20 Percentage Spread (%) 1.33 1.19 0.91 1.61 0.51 Quoted Depth (00s) 49.07 40.20 35.72 63.35 20.00 Quoted Depth as a % of Average Half-Hour Volume 126.91 124.84 89.25
Table 3 Changes in Liquidity and Volume by Half-Hour Interval
Spread, depth and volume by half hour interval relative to the time of the announcement. Reported results are the percentage deviation from the corresponding mean during the benchmark period. Benchmark periods are the same half-hour time intervals during days -15 through -3 and +3 through +15 relative to the announcement day 0. Spread is the dollar bid-ask spread. Depth is the sum of ask and bid depth. Volume is total volume of shares traded during the half hour. Results are reported for very anticipated and unanticipated samples. Results are presented for all firms in the sample (Panels A and D), for the liquid subset of firms with at least 50 quotes on the event day (Panels B and E), and for events with large event reactions (panels C and F). P-values represent bootstrapped significance levels in one-tailed tests. N declines for half hours further from the event time because we only include data that occurred on the event day.
Panel A: Very Anticipated Dividend Announcements. All Firms Spread Depth Volume Half Hour
The number of trades is computed for each firm with at least one trade during the half hour, and then averaged across all such firms. BIG trades are trades of more than 400 shares. SMALL trades are trades less than or equal to 400 shares. BUYs are trades at prices above the prevailing midquote, and SELLs are trades below the prevailing midquote. Mean represents the mean percentage deviation from the corresponding mean during the benchmark period. Benchmark periods are the same half-hour time intervals during days -15 through -3 and +3 through +15 relative to the announcement day 0. p-val is the p-value of a 2-sided test that the percentage abnormal number of trades equals 0. Panels A and B report results for the very anticipated and unanticipated samples. Panel C analyzes unanticipated events that led to positive stock price reactions. BUY plus SELL does not have to equal Total number of trades because trades at the midpoint are not included in BUY or SELL.
Panel A: Very Anticipated Dividend Announcements. All Firms
Changes in Liquidity Around Announcements Controlling for Volume
This table reports cross-sectional means of coefficients of firm regressions of liquidity on volume and trading day dummy variables. Reported results include only the liquid firms in each sample, defined as firms with at least 50 quotes on the announcement day. The measures of liquidity are dollar spread, percentage spread and depth (defined as depth at the ask plus depth at the bid). Results are reported for the very anticipated sample (Panel A) and unanticipated sample (Panel B). P-values are reported in parentheses. Intercept Vol AR(1) Day -2 Day -1 Event Day 1 Day 2 Panel A: Very Anticipated
Spread ($)
-6.78 (0.00)
7.65 (0.00)
-0.04(0.00)
Spread (%)
-6.95 (0.00)
7.74 (0.00)
-0.05(0.00)
Depth -2.01 (0.13)
3.22 (0.02)
-0.15(0.00)
Spread ($)
-6.76 (0.00)
7.59 (0.00)
-0.02(0.01)
-0.50(0.69)
0.53(0.69)
0.84(0.74)
-0.73 (0.56)
1.35(0.37)
Spread (%)
-6.89 (0.00)
7.72 (0.00)
-0.03(0.00)
-0.95(0.48)
-0.06(0.96)
0.10(0.97)
-1.25 (0.32)
0.87(0.57)
Depth
-2.35 (0.07)
3.02 (0.03)
-0.11(0.00)
1.26(0.81)
5.45(0.28)
1.09(0.88)
8.60 (0.17)
1.47(0.78)
Panel B: Unanticipated
Spread ($)
-2.52 (0.00)
3.07 (0.00)
-0.08(0.00)
Spread (%)
-2.87 (0.00)
3.42 (0.00)
-0.08(0.00)
Depth -8.58 (0.00)
9.54 (0.00)
-0.17(0.00)
Spread ($)
-2.68 (0.00)
3.06 (0.00)
-0.06(0.00)
-0.08(0.97)
1.68(0.56)
5.74(0.17)
5.14 (0.01)
-2.68(0.23)
Spread (%)
-2.96 (0.00)
3.34 (0.00)
-0.06(0.00)
0.06(0.98)
2.33(0.48)
5.71(0.17)
4.51 (0.04)
-3.57(0.13)
Depth
-7.10 (0.00)
8.89 (0.00)
-0.13(0.00)
2.52(0.81)
-4.90(0.60)
-16.58(0.10)
-17.98 (0.01)
-4.17(0.59)
44
Table 6 Spread Decomposition
Estimates of the components of the bid-ask spread, using the Stoll (1989) method. The benchmark period is the 26 half-hour periods on Days -3 and -4. The PreANN period is half hours -26 through -1, and the PostANN period is half hours 0 through 25. Reported components are based on a comparison of transaction returns to bid returns; results are essentially identical when transaction returns are compared to ask returns. p-values are two-sided and based on the bootstrapping technique in Affleck-Graves, Hedge, and Miller (1994). Panel A reports results for the very anticipated events, and Panel B reports results for the unanticipated sample.
p-values: Bench PreANN PostANN Bench
vs. PreANN
PreAnn vs.
PostANN
Bench vs.
PostANN Panel A: Very Anticipated
Mean Spread (%) 0.583 0.580 0.589 Components of % Spread Adverse Selection 0.860 0.627 0.701 0.056 0.516 0.172 Inventory Holding 0.161 0.303 0.274 0.148 0.801 0.285 Order Processing -0.021 0.070 0.026 0.534 0.798 0.738 Cost per Dollar of Price
Mean Spread (%) 1.238 1.254 1.144 Components of % Spread Adverse Selection 1.029 1.022 0.415 0.943 0.022 0.009 Inventory Holding 0.330 0.035 0.561 0.153 0.090 0.263 Order Processing -0.359 -0.056 0.023 0.192 0.675 0.203 Cost per Dollar of Price Adverse Selection 1.273 1.282 0.475 Inventory Holding 0.408 0.044 0.642 Order Processing -0.444 -0.070 0.026
45
Table 7 Price Impact
This table reports effective spreads, realized spreads and price impact for very anticipated events (Panel A) and unanticipated events (Panel B). Calculations are based on the technique in Bessembinder and Kaufman (1997, p. 290). Values are calculated for each trade as follows. Effective half spreads are defined as the absolute difference between a transaction price for a security and the prevailing midquote. Realized spreads are the absolute difference between a transaction price and the next trade price observed at least 5 minutes later. Price impact is the absolute difference between the next trade at least 5 minutes later and the prevailing midquote. All values are scaled by the prevailing midquote, and are expressed as percentages. The benchmark period is the 26 half-hour periods on Days –3 and -4. The Pre-Announcement Period is half hours -26 thought -1, and the Post-Announcement Period is half hours 0 through 25.
Measures of mean and median volatility before, during and after the announcement day. RANGE is computed as (HIGH - LOW)/(HIGH+LOW)/2, where HIGH and LOW are the daily high and low bid-ask midpoint, respectively. Results are presented for the Very Anticipated Announcements (Panel A) and Unanticipated Announcements (Panel B). The t-statistic is based on the volatility during a given period relative to the event day. The bootstrapped p-value is two-sided and is relative to event day volatility. RANGE Days
Mean(x10-2)
t-stat vs.event day
Bootstrap p-value
Panel A: Very Anticipated
-15 to –3 1.80 1.030 0.256 -2 to –1 1.67 1.939 0.045 Event Day 1.99 +1 to +2 1.78 1.307 0.216 +3 to +15 1.64 2.115 0.019 Panel B: Unanticipated -15 to –3 2.65 2.313 0.022 -2 to –1 2.83 1.678 0.115 Event Day 3.48 +1 to +2 2.65 2.247 0.028 +3 to +15 2.48 2.747 0.002
47
Figure 1: Timing of Announcements by Half-Hour Intervals
Panel A: Very Anticipated Events
02468
101214
9:30-10:00
10:01-10:30
10:31-11:00
11:01-11:30
11:31-12:00
12:01-12:30
12:31-13:00
13:01-13:30
13:31-14:00
14:01-14:30
14:31-15:00
15:01-15:30
15:31-16:00
Clock Half Hour Increments
Num
ber o
f Firm
s
Panel B: Matched Events
012345678
9:30-10:00
10:01-10:30
10:31-11:00
11:01-11:30
11:31-12:00
12:01-12:30
12:31-13:00
13:01-13:30
13:31-14:00
14:01-14:30
14:31-15:00
15:01-15:30
15:31-16:00
Clock Half Hour Increments
Num
ber o
f Firm
s
Panel C: Unanticipated Events
0123456789
9:30-10:00
10:01-10:30
10:31-11:00
11:01-11:30
11:31-12:00
12:01-12:30
12:31-13:00
13:01-13:30
13:31-14:00
14:01-14:30
14:31-15:00
15:01-15:30
15:31-16:00
Clock Half Hour Increments
Num
ber o
f Firm
s
48
Figure 2
Intraday Patterns in Liquidity
Panel A: Very Anticipated Announcements
-0.40-0.30-0.20-0.100.000.100.200.300.400.50
1 2 3 4 5 6 7 8 9 10 11 12 13
Half Hour Trading Interval
Perc
enta
ge D
evia
tion
from
Fu
ll D
ay M
ean
VolumeSpread (%)DepthSpread ($)
Panel B: Matched Announcements
-0.40-0.30-0.20-0.100.000.100.200.300.400.50
1 2 3 4 5 6 7 8 9 10 11 12 13
Half Hour Trading Interval
Perc
enta
ge D
evia
tion
from
Ful
l Day
Mea
n
VolumeSpread (%)DepthSpread ($)
Panel C: Unanticipated Announcements
-0.30-0.20-0.100.000.100.200.300.400.50
1 2 3 4 5 6 7 8 9 10 11 12 13
Half Hour Trading Interval
Perc
enta
ge D
evia
tion
from
Ful
l Day
Mea
n
VolumeSpread(%)DepthSpread ($)
49
Figure 3: Abnormal Volume by Half Hour Relative to Announcement
Panel A: Very Anticipated Announcements
-0.500
-0.250
0.000
0.250
0.500
0.750
1.000
1.250
1.500
-26 -22 -18 -14 -10 -6 -2 2 6 10 14 18 22 26
Clock Half Hour Increments, Relative to Time of Announcement
Perc
enta
ge D
evia
tion
from
B
ench
mar
k
Volume
Panel B: Matched Announcements
-0.500
-0.250
0.000
0.250
0.500
0.750
1.000
1.250
1.500
-26 -22 -18 -14 -10 -6 -2 2 6 10 14 18 22 26
Clock Half Hour Increments, Relative to Time of Announcement
Perc
enta
ge D
evia
tion
from
B
ench
mar
k
Volume
Panel C: Unanticipated Announcements
-0.500
-0.250
0.000
0.250
0.500
0.750
1.000
1.250
1.500
-26 -22 -18 -14 -10 -6 -2 2 6 10 14 18 22 26
Clock Half Hour Increments, Relative to Time of Announcement
Perc
enta
ge D
evia
tion
from
B
ench
mar
k
Volume
50
Figure 4: Abnormal Liquidity by Half Hour Relative to Announcement
Panel A: Very Anticipated Announcements
-0.300
-0.200
-0.100
0.000
0.100
0.200
0.300
0.400
-26 -22 -18 -14 -10 -6 -2 2 6 10 14 18 22 26
Clock Half Hour Increments, Relative to Time of Announcement
Perc
enta
ge D
evia
tion
from
B
ench
mar
k
DepthSpread
Panel B: Matched Announcements
-0.300
-0.200
-0.100
0.000
0.100
0.200
0.300
0.400
-26 -22 -18 -14 -10 -6 -2 2 6 10 14 18 22 26
Clock Half Hour Increments, Relative to Time of Announcement
Perc
enta
ge D
evia
tion
from
B
ench
mar
k
DepthSpread
Panel C: Unanticipated Announcements
-0.300
-0.200
-0.100
0.000
0.100
0.200
0.300
0.400
-26 -22 -18 -14 -10 -6 -2 2 6 10 14 18 22 26
Clock Half Hour Increments, Relative to Time of Announcement
