Upload
others
View
1
Download
0
Embed Size (px)
Citation preview
Informed Options Trading prior to M&A Announcements:
Insider Trading?∗
Patrick Augustin† Menachem Brenner‡ Marti G. Subrahmanyam§
McGill University, Desautels New York University, Stern New York University, Stern
December 11, 2013
Abstract
We investigate the possibility of insider trading in equity options around major informational
events such as corporate mergers and acquisitions (M&A) for a sample of 1,859 US events, fo-
cusing on both the target and the acquirer. Our goal is to quantify the likelihood of informed
trading and information leakage based on trading volume and implied volatility attributes. We
further focus our attention on options strategies which, in anticipation of public news announce-
ments and the presence of private information, should result in abnormal returns to insiders.
We find a highly positive abnormal trading volume and excess implied volatility in anticipation
of M&A announcements, with stronger effects for OTM call options on the target company. We
further document a decrease in the term structure of implied volatility and an average rise in
percentage bid-ask spreads of approximately 20 percentage points prior to the announcements.
Keywords: Asymmetric Information, Insider Trading, Mergers and Acquisitions, Market Mi-
crostructure, Equity Options
JEL Classification: G13, G14, G34, G38
∗We thank participants in the OptionMetrics Research Conference, New York, and a seminar at NYU Stern forcomments on a prior draft.†McGill University - Desautels Faculty of Management, 1001 Sherbrooke St. West, Montreal, Quebec H3A 1G5,
Canada. Email: [email protected].‡New York University - Leonard N. Stern School of Business, 44 West 4th St., NY 10012-1126 New York, USA.
Email: [email protected].§New York University - Leonard N. Stern School of Business, 44 West 4th St., NY 10012-1126 New York, USA.
Email: [email protected].
1 Introduction
The recent announcement of the leveraged buyout of H.J. Heinz by an investor group made up
of Warren Buffett’s Berkshire Hathaway Inc. and Brazilian private-equity firm 3G Capital has
sparked concerns about the unusual option activity prior to the deal announcement. Was this
abnormal volume in the options of Heinz an indication of trading based on insider information?
Apparently, the Securities and Exchange Commission (SEC) thought so, alleging that a brokerage
account in Switzerland was used for illegal insider trading. Another noteworthy case from an
earlier period is the merger of Bank One with JP Morgan (JPM) Chase in July of 2004, where one
investor was alleged to have bought deep out-of-the-money (DOTM) calls just (hours) before the
announcement. While these cases received considerable publicity, they are by no means isolated
cases of such activity. Indeed, while the SEC has taken action in several cases where the evidence
was overwhelming, one can assume that there are many more cases that go undetected, or where the
evidence is not as clear-cut, in a legal/regulatory sense.1 Academic research on the role of informed
trading in equity options around major news events, and in particular, the announcements of
mergers and acquisitions (M&A), has been scanty.2 We aim to fill this gap in the research presented
in this paper.
The objective of our study is to investigate the pervasiveness of trading on inside information
in the context of M&A activity. We conduct a forensic analysis of volume and implied volatility
over the 30 days preceding a formal announcement. In addition, we examine alternative strategies
that may yield abnormal returns to informed traders. The focus is on options strategies, although
some of these may also involve trading in the underlying stocks. We examine companies on both
sides in an M&A transaction: the target and the acquirer. More specifically, we examine option
trading volume (and prices) prior to M&A announcements in the U.S. from January 1 1996 through
December 31 2012. We attempt to quantify the likelihood that a sudden and significant spike in
put or call trading volume prior to a major informational event is based on insider trading activity,
rather than being random. We also analyze the microstructure effects in the equity options market
arising from the arrival of informed trading prior to the M&A announcement dates, as a consequence
1See, for example, Options Activity Questioned Again in the Wall Street Journal, 18 February 2013.2The related issue of insider trading activity prior to earnings announcements and other important corporate
announcement has also received very little attention.
1
of leakage of information due to the trading activity of insiders or some other means.
We find evidence of statistically significant average abnormal trading volume in equity options
written on the target firm over the 30 days preceding M&A announcements. Approximately 25%
of all cases have positive abnormal volume that is significant at the 5% level. The proportion
of cases with positive abnormal volume is relatively higher for call options (25%) than for put
options (15%). Stratifying the results by depth-in-moneyness, we find that there is significantly
higher abnormal trading volume (both in levels and frequencies) in OTM call options compared
to ATM and ITM calls. In addition, the ratio of average abnormal trading volume in OTM call
options relative to OTM put options, about 2.06, is significantly higher than the ratio of average
abnormal trading volume in ITM call options relative to ITM put options, which is lower at around
1.54. This is preliminary evidence that insiders may not only engage in OTM call transactions,
but potentially also sell ITM puts. In contrast, we find that there is no statistically significant
average cumulative abnormal trading volume for equity options written on the acquirer firms over
the 30 pre-announcement days, while it is positive and significantly different from zero for targets
(≈ 15,267 contracts).
In addition to evidence of abnormal trading volume in anticipation of M&A announcements, we
provide statistical evidence that the two-dimensional volume-moneyness distribution significantly
shifts, to options with higher strike prices, over 30 days as the announcement day approaches.
We further provide statistical tests of positive (no) excess implied volatility for target (acquirer)
firms in the pre-event window. Thus, informed trading has an impact on equity option prices and
we show that this leads to an attenuation of the term structure of implied volatility for target,
but not for acquirer firms. Similarly, the relative higher abnormal volume in OTM call options
for targets translates on average into a steepening of the implied volatility spread prior to the
announcement day. We also find that the percentage bid-ask spread on target equity options rises
from an average of 35% to 55% over the 30 days preceding the announcement. This effect is limited
to DOTM and OTM, as well as short- to medium-dated options. However, the increase in bid-ask
spread disappears if we adjust it by the options’ measure of leverage. Similar evidence for acquirer
equity options is weak.
This paper provides a preliminary forensic analysis of trading volume and implied volatility for
2
equity options on both targets and acquirers involved in M&A announcements in the pre-event
window. It naturally suggests a classification scheme based on volume and price attributes that
may help regulators and prosecutors to detect illicit insider trading. Our evidence suggest that
informed trading is more pervasive than what one would expect based on the modest number of
prosecuted cases announced in the financial press.
The outline of the paper is as follows. In Section 2, we provide a review of the relevant literature.
We describe the data selection process and review the basic summary statistics in Section 3. The
main hypotheses and methodology are presented in Section 4. We analyze the results in the
subsections of Section 5. We end with a summary and preliminary conclusions in Section 6.
2 Literature Review
Our work relates more generally to the theoretical literature emphasizing the options market as a
preferred venue for informed traders, such as for example Easley et al. (1998).3 While the bulk
of the empirical research on options markets focuses on index options, there are fewer studies
using equity options (i.e., options on individual stocks), although they were trading for almost a
decade prior to the introduction of index options in the U.S.4 There are even fewer studies relating
to informed trading around major informational events, such as mergers and acquisitions, using
options strategies, and these are typically based on relatively small data sets.
Cao et al. (2005), for example, focus on the target firms in M&A transactions and study 78
U.S. merger or takeover firms between 1986 and 1994. They find evidence that the options market
displaces the stock market for information-based trading during periods immediately preceding
takeover announcements. More specifically, buyer-seller initiated call-volume imbalances, but not
stock imbalances, are associated with higher next-day stock returns. However, during periods of
normal trading activity, only buyer-seller initiated stock-volume imbalances exhibit predictability,
while option volume is uninformative. Option volume imbalances before M&A transactions are
concentrated in firms that eventually have successful takeovers and cannot be explained by target
3John et al. (2003) illustrate that the choice of informed trading in the derivative market depends on frictionssuch as margin requirements and wealth constraints.
4The main constraint in the earlier period was the availability of reliable data, which has changed dramaticallywith the advent of OptionMetrics as a reliable source for academic research in this area.
3
firm characteristics. Chan et al. (2013), on the other hand, focus on the acquiring firms, using a
sample of 5,099 events for 1,754 acquirers, over the period 1996 to 2010. The one-day pre-event
implied volatility spread and the implied volatility skew, two proxies for informed option trading,
are respectively positively and negatively associated with acquirer cumulative abnormal returns.5
The predictive power of both measures increases if the liquidity of options is high relative to that
of the underlying stocks.
Barraclough et al. (2012) exploit the joint information set of stock and option prices to disen-
tangle true synergies from news in merger and acquisition transaction announcements. A sample
of 167 takeover announcements from April 1996 through December 2008 suggests that the increase
in trading volume from the pre-announcement period to the announcement day is most dramatic
for call options with an increase of 212.3% for bidder call options, and an increase of 1,619.8% for
target call options.
Acharya and Johnson (2010) study a sample of leveraged buyout deals from 2000-2006 and
document that the presence of more insiders lead to greater levels of insider activity, in the sense
that a larger number of equity participants in the syndicate is associated with suspicious stock and
options activity. Podolski et al. (2013) also provide some indirect evidence that the option-to-stock
volume ratio increases in the pre-takeover period, and increases relatively more for small deals that
are less likely to be detected. Finally, Nicolau (2010) studies the behavior of implied volatility
around merger announcements and interprets positive abnormal changes in implied volatility prior
to the announcement as evidence of information leakage.
Several other papers are peripherally related to the specific issue studied in this paper. Spyrou
et al. (2011) provide evidence of informed trading and the role of options markets in information
revelation around M&A announcements from the UK equity and options market. Keown and
Pinkerton (1981) studies excess stock returns earned by insider trading in the presence of merger
announcements, but does not investigate equity option activity. Meulbroek (1992) studies a sample
of illegal insider trading cases detected and prosecuted by the SEC from 1980 to 1989 and likewise
does not focus on option trading. Roll et al. (2010), on the other hand, study the relationship
5The implied volatility spread is calculated as the average difference between implied volatilities of same secu-rity/strike/maturity calls and puts). The implied volatility skew is calculated as the difference between impliedvolatilities of OTM puts and ATM calls.
4
between the option-to-stock trading volume and post-earnings announcement returns. Bester et al.
(2011) and Subramanian (2004) develop theoretical option pricing models for the target in the case
of cash and stock-for-stock mergers respectively. Dubinsky and Johannes (2006) develop estimators
of the anticipated uncertainty of fundamental information released on earnings announcement dates
using equity option prices. Easley et al. (1998), Pan and Poteshman (2006), Xing et al. (2010),
Cremers and Weinbaum (2010), Johnson and So (2011), Driessen et al. (2012), Jin et al. (2012) and
Hu (2013) more generally relate various information-based measures derived from option trading
volume and prices to the predictability of stock returns. Finally, while Bollen and Whaley (2004)
and Garleanu et al. (2009) show how price pressure affects option prices, Cao and Ou-Yang (2009)
theoretically studies how differences in beliefs generate trades in options relative to stocks.
While the above studies tend to focus on either the target or the acquirer, we study the trading
patterns of equity options of both the target and acquirer, using data on both trading volume and
prices. More specifically, we focus on the behavior of the entire volume distribution and option-
implied volatility across the depth-in-the-money dimension, prior to takeover announcements. Im-
portantly, while some papers in the previous literature have investigated the informational content
of option trading volumes for post-announcement stock returns, none of them has focused on the
role of options strategies in illegal insider trading. Moreover, in contrast to the above studies, which
focus on various aspects of the M&A announcements using options data, our study focuses on the
extent to which informed trading, some of it illegal, can be detected by analyzing various option
strategies, using both puts and calls, of the target company and the acquirer. The likelihood of
informed trading in these cases will be quantified in our analysis. Our study is also more compre-
hensive in scope than the above prior studies, based on a much larger sample, with more rigorous
statistical tests.
3 Data Selection and Summary Statistics
The data for our study are obtained from three primary sources, Thomson Reuters SDC Platinum
Database, Center for Research in Securities Prices (CRSP) and OptionMetrics. The start date of our
sample period is dictated by the availability of option information in OptionMetrics, which initiated
5
its reporting on 1 January 1996. Our final sample consists of 1,859 corporate transactions, for which
we could identify matching stock and option information for the target. These deals are undertaken
by 1,279 unique acquirers on 1,669 unique targets.6 For a sub-sample of 792 transactions, option
information is available for both the target and the acquirer. The starting point of our sample
selection is the full domestic Mergers and Acquisitions sample for US targets from the Securities
Data Company Platinum database over the time period January 1996 through December 2012.
We restrict the sample to deals with the intent of effecting change of control, i.e., to be included
in our sample, the acquirer needs to own less than 50% of the target’s stock before the transaction,
and is seeking to own more than 50% post transaction. Hence, we include only mergers, acquisitions
and acquisitions of majority interest in our sample, thereby excluding all deals that are acquisi-
tions of partial interest/minority stake purchases, acquisitions of remaining interest, acquisitions of
assets, acquisitions of certain assets, recapitalizations, buybacks/repurchases/self-tender offers and
exchange offers. In addition, we exclude deals for which the status is pending or unknown, i.e., we
only include completed, tentative or withdrawn deals. Next, we require available information on
the deal value and eliminate all deals with a transaction value below 1 million USD. Finally, we
match the information from SDC Platinum with price and volume information for the target in
both CRSP and OptionMetrics. We require a minimum of 90 days of valid stock and option price
and volume information on the target prior to and including the announcement date. We further
retain short-dated options expiring before the announcement date, as long as they are at-the-money.
All matches between SDC and CRSP/OptionMetrics are manually checked for consistency based
on the company name.7
Panel A in Table 1 reports the basic characteristics for the full sample, in which we require
option information availability only for the target. Pure cash offers make up 48.6% of the sample,
followed by hybrid financing offers with 22.3% and share offers with 21.7%. 82.9% of all transactions
are completed, and mergers are mostly cross-industry with 89.2% of all deals being undertaken with
a company not in the same industry based on the 4-digit SIC code. 90.2% of all deals are considered
6Thus, 190 of the targets were involved in an unsuccessful merger or acquisition that was ultimately withdrawn.However, we include these cases in our sample, since the withdrawal occurred after the takeover announcement.
7Overall, we have up to a maximum of one year of stock and option price information before and after theannouncement date. The cut-off of one year is arbitrary, but follows from the trade-off of the following two objectives:having of a sufficiently long time series before the announcement day to conduct the event study analysis, and keepingthe size of the dataset manageable to minimize computational complexity.
6
to be friendly and only 3.4% are hostile, while 11.6% of all transactions are challenged. For a small
sub-sample of 6.5%, contracts contain a collar structure, 76.5% of all deals contain a termination fee,
and in only 3.5% of the transactions did the bidder already have a toehold in the target company.
Panel B shows that the average deal size is 3.8 billion USD, with cash-only deals being, on average,
small (2.2 billion USD), in comparison to stock-only transactions (5.4 billion USD). The average
one-day offer premium, defined as the offer price to the target’s closing stock price 1 day before the
announcement date, is 31%. Statistics for the sub-sample for which we have option information on
both the target and acquirer are qualitatively similar.
In Figure 1, we plot the average option trading volume in calls and puts for both the target
and acquirer, 60 days before, and after the announcement date. The run-up in volumes is a first
indication of information leakage prior to the public news announcements. However, these simple
averages mask significant cross-sectional differences across firms and options in abnormal trading
volumes. A more detailed analysis is provided in Section 5, the empirical section that follows the
discussion of our hypotheses.
4 Research Questions and Hypotheses
We attempt to quantify the likelihood of insider trading by investigating various trading strategies
that, in the presence of private information, should result in abnormal trading volumes and returns
in equity options in anticipation of public M&A announcements.8 We investigate several hypotheses
to test for such activity.9 The underlying assumption for all these hypotheses is that insiders are
capital constrained.
4.1 Insider Activity without Leakage of Information
• H1: There is evidence of positive abnormal trading volume in equity options, written on the
target firms, prior to M&A announcements.
If informed trading is present, without leakage of information, informed traders should benefit
8In this version, we do not present results for hypothesis H3. Additional evidence supporting the other hypothesesas well as robustness checks will be presented in the next version of the paper.
9We write these hypotheses as statements of what we expect to find in the data, rather than as null hypothesesthat would be rejected.
7
relatively more from strategies that use options due to the leverage they obtain, if they are
capital constrained. A takeover announcement tends to be generally associated with a stock
price increase for the target (see Schwert 1996). An insider engaging in illegal trading is,
therefore, likely to engage in directional trading strategies to maximize his profits on his
private information, given his capital constraints. In that case, we would expect to see
significant positive abnormal trading volume in options for the target firms, in anticipation
of major corporate takeover announcements.
• H2: There is a higher ratio of the abnormal trading volume in (a) OTM call options compared
to ATM and ITM call options, and (b) ITM put options compared to ATM and OTM put
options, written on the target firms, prior to M&A announcements.
In the presence of informed trading, an option strategy involving the purchase of OTM call
options should generate significantly higher abnormal returns, as a consequence of the higher
leverage. Hence, we expect a relatively larger increase in abnormal trading volume for OTM
calls relative to ATM and ITM calls, in the presence of such activity.10 Moreover, an in-
sider taking advantage of his privileged knowledge of the direction of the target’s stock price
evolution is also likely to increase the trading volume through the sale of ITM puts.
• H3: In anticipation of major news events, there should be a volume increase in long-gamma
trading strategies for the acquirer firm, prior to M&A announcements.
Unlike the target firm, where the stock price almost always goes up after the announcement,
there is generally more uncertainty associated with the post-announcement direction of the
stock price of the acquiring firm. For the acquirer, we therefore anticipate an increase in
trading volume of option pairs which have high gamma, such as ATM straddle strategies for
example.
4.2 Insider Activity with Leakage of Information
• H4: There is positive (no) excess implied volatility for equity options, written on the target
(acquiring) firms, prior to M&A announcements.
10This case corresponds to the case study of JPM-Chase merging with Bank One, which exhibits such a pattern.
8
Information about anticipated takeover targets may leak to financial markets and give rise to
informed trading. Jarrell and Poulsen (1989) argue that one such possible source of legitimate
information available to market participants is 13D filings when investors acquire more than
5% of the target firms stock. Thus, anticipating a price increase for target but not for
acquiring companies, we would expect to observe positive excess implied volatility on average
for takeover targets, and weak or no excess implied volatility for acquirers.
• H5: : The percentage bid-ask spread for options written on target firms should widen, prior
to M&A announcements.
Similar to H4, there should be no pattern in the bid-ask spread in the options on the target
firms as the announcement date approaches, absent insider activity with leakage of informa-
tion. An increase in the percentage bid-ask spread conditional on positive abnormal trading
volume would be evidence that information about a potential merger has leaked to the market
makers.
• H6: The convexity of the option smile, for target firms, should increase for call options and
decrease for put options, prior to M&A announcements.
According to H2, we expect abnormal trading volume in OTM call options and ITM put
options. Higher demand for OTM call options and ITM put options from the insider could
increase the relative expensiveness of OTM Call options (ITM puts) and hence, increase the
convexity of the option smile, in the presence of insider trading when the trading causes a
leakage of information to the market. Both real insider trading and higher trading activity
due to leaked information are associated with positive abnormal trading volume. Conditional
on positive abnormal trading volume, leakage should be identified through an increase in
both volume and prices, i.e., the convexity of the option smile should increase. However, if
we identify abnormal trading volume without a statistically significant shift in the slope of
the call option smile, then the increasing trading activity is more likely based on illegal insider
information, which has not yet leaked to the market.
• H7: The term structure of implied volatility should decrease for options on the target firms
before takeover announcements, and remain flat for options on the acquiring firms.
9
Informed traders obtain the highest leverage by investing into short-dated OTM call options.
Demand pressure on short-dated options should lead to a relative price increase in options
with a short time to expiration compared to long-dated options. Thus, the term structure of
implied volatility should decrease for call options written on target firms and remain flat for
call options written on acquirer firms.
5 Analysis
We investigate the above hypotheses along two dimensions: volume and price. We begin by looking
into the behavior of volume prior to the M&A announcement dates.
5.1 Abnormal Volume
In order to address hypotheses H1 to H2, we conduct a forensic analysis of trading volume in
equity options during the 30 days preceding takeover announcements. We first summarize the
trading volume in our sample. We next look at specific trades that are most susceptible of insider
trading, the strongly unusual trading sample, and compare it to a matched random sample. We
further test for the presence of positive abnormal volume in call and put options across moneyness
categories, using the event study methodology. Next, we formally test, using an approximation to
the bivariate Kolmogorov-Smirnov test, whether the entire volume-moneyness distribution shifts in
anticipation of takeover news releases. Finally we show some evidence on the behavior of conditional
trading volume.
5.1.1 Statistics on Equity Option Trading Volume
We start by reporting basic summary statistics on option trading volume, stratified by time to
expiration and depth-in-money in Table 2.11 We classify three groups of time to expiration: less
or equal than 30 days, bigger than 30 but less or equal than 60 days and more than 60 days.
In addition, we assign five groups for depth-in-moneyness, where depth-in-moneyness is defined
as S/K, the ratio of the stock price S over the strike price K. Deep out-of-the-money (DOTM)
11Data on equity options volume have the particular feature of multiple zero-volume observations. These datapoints are omitted for the purpose of the basic summary statistics.
10
corresponds to S/K ∈ [0, 0.80] for calls ([1.20,∞] for puts), out-of-the-money (OTM) corresponds
to S/K ∈ ]0.80, 0.95] for calls ([1.05, 1.20[ for puts), at-the-money (ATM) corresponds to S/K ∈
]0.95, 1.05[ for calls (]0.95, 1.05[ for puts), in-the-money (ITM) corresponds to S/K ∈ [1.05, 1.20[
for calls (]0.80, 0.95] for puts), and deep in-the-money (DITM) corresponds to S/K ∈ [1.20,∞[ for
calls ([0, 0.80] for puts). Panels A to C report statistics on all options in the sample, while Panels
D to F and G to I report the numbers separately for calls and puts.
A first observation is that, regardless of depth-in-money, the level of trading volume, as indicated
by the mean volume statistics, is significantly higher for short and medium-dated options compared
to long-dated options. For example, the average number of traded contracts in OTM options, for
the target (acquirer), is 370 and 285 (497 and 384) contracts for maturities of respectively less than
30 and 60 days, while it is 130 (193) contracts for options with more than 60 days to maturity.
This difference is more pronounced for call options than for put options. Second, the average
trading volume is higher for options on the acquirer firms (547 contracts) than on targets (283
contracts). Third, the distribution of volume as a function of depth-in-moneyness exhibits a hump-
shaped pattern for acquirers, irrespective of whether the options are short- or long dated. Hence,
trading volume tends to be highest ATM and decreases as S/K moves further into or OTM. In the
entire universe, for instance, the average volume is 1,084 contracts ATM, 497 and 398 contracts
respectively OTM and ITM, and 127 and 214 contracts respectively for DOTM and DITM options.
On the other hand, for targets, the highest average trading volume tends to be associated with
OTM options. Finally, we observe that the standard deviation of trading volume decreases with
time to expiration.
5.1.2 Strongly Unusual Trading Volume and Matched Random Sample
Our goal is to distinguish insider trading from random speculative bets. We are looking for un-
usual trading patterns that are considerably different from patterns exhibited by randomly selected
samples. Evidence of non-random trading would point to the existence of illicit option trades,
i.e., trades based on insider information. Thus, we start with cases that potentially are the most
likely ones to reflect insider trading. We define as strongly unusual trading (SUT) observations
11
corresponding to the following four criteria for individual options:12 (1) the daily best recorded
bid is zero. This corresponds normally to DOTM options where the market maker, through his
zero bid, signals his unwillingness to buy but is willing to sell at a non-zero ask price. (2) The
option should expire on or after the announcement day, but is the first one to expire thereafter
(the so called front month option). Obviously, an insider would buy options that expire after the
announcement. In order to get the biggest bang for the buck, he would try to buy the cheapest ones
that are most likely to end up in the money. Short-dated OTM options tend to be cheaper and
provide the greatest leverage. (3) The option must have strictly positive trading volume. As many
individual equity options, especially OTM, have zero trading volume (all options have quotes in
a market making system) we focus on those that have positive volume assuming that the insiders
pick those OTM options that are the most likely to be exercised. (4) Finally, the transaction must
take place within 30 days of the event-date, defined as the 0 date (i.e., from event date -29 to 0).
An informed trader faces the tradeoff of leveraging on his private information prior to the event,
while likely not trading too close to the event, which may entail a higher risk of getting caught.13
Table 3 presents the sample statistics for the SUT sample. From the entire dataset, we identify
2,042 (1,847) option-day observations for the target (acquirer), passing the SUT selection criteria.14
The share of calls is slightly more than half, with a total of 1,106 (954) observations for the target
(acquirer). The average trading volume is 124 (110) contracts for targets (acquirers) and the
average trading volume for calls and puts is respectively 137 and 108 (126 and 94) for the target
(acquirer).15 Median trading volume is somewhat more stable, with a value of 20 contracts for
options written on the target, and around 15 contracts for options written on the acquirer. We
observe that, in the sample most susceptible of insider trading, the volume of the target firm, at
each percentile of the distribution, is significantly above that of the acquirer. The basic summary
statistics in Table 2 showed exactly the opposite.
We compare the statistics from the SUT sample to those from a randomly selected sample. The
12An observation corresponds to an option-day pair, i.e., the end-of-day volume for a given option on the target oracquirer.
13An additional aspect that we do not explicitly consider is the number of insiders involved and their connectionswith each other. Such information may reveal whether the information was shared by many players and potentiallyleaked to them.
14Note that the full sample has approximately 12,000,000 observations. For each event, the event time spans fromroughly 1 year before to 1 year after the announcement date.
15The average is taken across all observations satisfying the SUT selection criteria.
12
sampling procedure is as follows: For each of the 1,859 (792) events with traded options on the
target (acquirer), we randomly select a pseudo-event date. We treat the pseudo-event date as a
hypothetical announcement date, and then apply the SUT selection criteria, i.e., we keep option-
day observations with a zero bid price, non-zero trading volume, that are within 30 days of the
pseudo-event date and which have an expiry date after the pseudo-event date.
The SUT sample statistics are compared to the Random Sample Trading (RST) statistics in
Panel B of Table 3.16 The number of observations, deals and options are somewhat higher in the
RST sample than in the SUT sample. The ratios of number of observations, deals and options in
the RST sample to those in the SUT sample range approximately between 1.4 and 1.8. However,
the average and median trading volume in the SUT sample is more than double that of the RST
sample. The maximum observed trading volumes are significantly higher in the SUT sample than in
the RST sample. However, the distributional statistics illustrate that this effect arises not because
of outliers. In the RST sample, around the 50th percentile of the distribution and upwards, volumes
are consistently less than half the trading volumes observed in the SUT sample at comparable cut-
offs of the volume distribution. Another interesting feature is that the distance between the median
and the mean is roughly constant around 100 traded contracts in the SUT sample. Finally, while a
similar pattern emerges for options written on the acquirer, the effect is overall weaker. In particular
statistics for put options are statistically similar across both samples. The significant difference in
average and median trading volumes between the SUT and RST samples for target firms is a first
indication of a shift in the volume distribution in anticipation of M&A announcements. We point
out that the difference between the two samples is likely to be conservative. For each event, we
have a maximum of 1 year of data before and after the event, rather than the whole universe of
traded options. Under normal circumstances, we should be using for each event all observations
going as far back as January 1996 until today. In such a situation, the difference is likely to be
even stronger.
To summarize, the entire distribution in trading volume differs significantly between the SUT
and RST samples for target firms, but not for acquirer firms. In particular, we observe that an
16As we have confined our study to a limited period and due to the fact that the variance may be large, we havedouble checked our results using 100 random samples of 1,859 (792) pseudo-events for the target (acquirer) in orderto minimize the standard error of our estimates. The results from this robustness check were very similar.
13
average trading volume above 100 contracts, with the mean to median distance of 100 contracts,
can be considered to be strongly unusual and non-random, when the transactions occurs at a zero-
bid within 30 days of the announcement date on options expiring after the announcement. This
first pass test is preliminary evidence in favor of the hypothesis H1 by showing that there is a
non-random increase in trading volume on targets, but not on acquirers, prior to a public M&A
announcement.
5.1.3 Shifts in Option Trading Volume Density
The previous section has illustrated that the 30 days prior to M&A announcement dates exhibit
strongly unusual trading patterns, in particular for targets. The question is whether there is a
monotonic shift in the option trading volume distribution as the announcement date approaches.
We formally test for a shift of the bivariate volume-moneyness distribution across time in anticipa-
tion of the announcement dates.
Figure 2 visually illustrates the shift in volume distribution for calls and puts written on the
target as we approach the announcement date. Each individual line reflects a local polynomial
function fitted to the volume-moneyness pairs. It is striking to see how the volume distribution,
for target call options, shifts to the tails and puts more weight on DITM and DOTM categories as
we approach the announcement date. In addition the volume level keeps increasing, in particular
in the event window [−4,−1]. The last event window [0, 0] incorporates the announcement effect,
whereby the overall average trading level is lifted upwards, and the distribution shifts to ITM call
options and OTM puts. The lower two panels illustrate the same graphs for the acquirer firms.
While it is possible to see a small shift in the volume distribution across time, the magnitudes
of the shifts are small. It is mainly the announcement effect that is clearly distinguishable for
acquirers. Another way to visualize the change in the distribution is given in Figure 3, although
this graph doesn’t reflect the bivariate dimension of the distribution. The dashed blue line and the
solid green line in each plot represent the 90th and 95th percentile of the distribution, whereas the
dotted red lines reflect the interquartile range. The percentage increase in the percentiles of the
volume distribution is clearly much more pronounced for the targets than for the acquirers. For
example, the interquartile range for target call options increases from a level below 50 contracts
14
to approximately 2000 contracts on the announcement day. For acquirer call options, on the other
hand, the interquartile range increase from approximately 200 to about 1,200 contacts on the
announcement day.
To summarize, there is a significant shift in both the mean and median trading volume in
anticipation of U.S. M&A transactions for target firms. This shift is more pronounced for DOTM
and OTM call options, compared to ITM and DITM options. On the other hand, the increase in
the level of trading volume for acquirers is more moderate and doesn’t seem to be much different
for ITM options relative to OTM calls and puts. This confirms our hypothesis H2 that there is
a higher abnormal trading volume in deep OTM call options, compared to ATM and ITM call
options, but that abnormal trading volume, if any, should be equal across moneyness categories for
acquirers. In what follows, we apply a formal statistical test for the shift in volume distribution.
In order to test whether the bivariate volume-moneyness distribution shifts across time prior
to announcement dates, we use a two-sample bivariate Kolmogorov-Smirnov test. The two-sample
Kolmogorov-Smirnov (KS) test is a non-parametric test for the equality of two continuous distri-
bution functions. Essentially, the KS statistic quantifies the distance between the two empirical
cumulative distribution functions. While the test statistic is straightforward to compute in the
univariate setting with distribution-free properties, the computation in the multivariate setting can
become burdensome, in particular, when the sample size is large. The reason for this is because
in the univariate setting, the empirical cumulative distribution function diverges only at its ob-
served points, while it diverges at an infinite number of points in the multivariate setting. To see
this, remember that in a multivariate setting, there is more than one definition of a cumulative
distribution function. In particular, in the bivariate setting, the four regions of interest are
H(1) (x, y) = P [X ≤ x, Y ≤ y] , H(1) (x, y) = P [X ≤ x, Y ≥ y] (1)
H(1) (x, y) = P [X ≥ x, Y ≤ y] , H(1) (x, y) = P [X ≥ x, Y ≥ y] , (2)
and we need to evaluate the empirical cumulative distribution function in all possible regions. To
reduce computational complexity, we rely on the Fasano and Franceschini generalization of the
two-sample bivariate KS test. Define the two sample sizes (x1j , y
1j
): 1 ≤ j ≤ n and
(x2j , y
2j
):
15
1 ≤ j ≤ m, with their corresponding empirical cumulative distribution functions H(k)n and H
(k)m for
regions k = 1, 2, 3, 4. The Fasano-Franceschini (Fasano and Franceschini (1987)) FF-test statistic
is then defined as
Z′n,m = maxT ′(1)n,m, T
′(2)n,m, T
′(3)n,m, T
′(4)n,m, (3)
where
T′(k)n,m = sup(x,y)∈R2
√nm
n+m
∣∣∣H(k)n (x, y)−H(k)
m (x, y)∣∣∣ . (4)
Although the analytic distribution of the test statistic is unknown, its p-values can be estimated
using an approximation, based on Press et al. (1992), to Fasano and Franceschini’s (FF) Monte
Carlo simulations.
Our prior is that the FF-statistic, which reflects the distance between the two bivariate empirical
distribution functions (EDF), should monotonically increase for target firms as we get closer to the
announcement date.17 In contrast, there should be no evidence for such an increase, or at worst
weaker evidence, for acquirer firms. Essentially, the difference in EDFs should be larger between
event windows [−29,−25] and [−24,−20], than between [−29,−25] [−19,−15]), etc. In addition,
the FF-statistics should increase relatively more for short-dated options, which are closer to the
announcement date. These predictions are clearly confirmed by the results in Tables 4 and 5. The
FF test reveals statistically significant differences in the bivariate volume-moneyness distributions,
as we move closer to the announcement date. We compare the distributions in event window blocks
of 5 days. A glance at the table reveals that the test is statistically significant, at the 1% level, for
almost all comparisons. In addition, the magnitude of the statistic is monotonically increasing as
we move from the left to the right, and as we move from the bottom to the top.
Panel A and B in Table 4 report results for calls and puts respectively for the target. For
example, the first row shows that the bivariate distribution significantly shifts from event window
[−29,−25] to [−24,−20], with an FF statistic of 0.0279. The test statistic increases to 0.1592, if we
17One can think of the FF-statistic as a KS-statistic in the multivariate setting. The FF-statistic is computationallyless intensive in the multivariate case, but is consistent and does not compromise power in large sample sizes. SeeSimon L. Greenberg, Bivariate Goodness-of-fit tests based on Kolmogorov-Smirnov Type Statistics.
16
compare event windows [−29,−25] and [−4,−1], and to 0.4070, for the event windows [−29,−25]
and [0, 0]. Overall, the largest test statistics seem to be associated with comparisons between the
announcement date ([0, 0]) and the event window immediately preceding it ([−4,−1]). However,
for short-dated options with time to expiration less than 30 days, the statistic for the difference
in distributions for the shift from event window [−29,−25] to [−4,−1], excluding the announce-
ment effect, is with a value of 0.3388 (0.34) for call (put) options higher than the announcement
effect going from the event window [−4,−1] to the announcement date. Changes in the bivariate
distributions are statistically significant at the 1% level for almost all event windows.
Results for the acquirer are reported in Table 5 and confirm our prior. The shifts in the
distributions, as indicated by the FF test statistics, are generally significantly smaller than for the
target. For instance, comparing the period [−29,−25] to [−4,−1] for call options in the entire
sample, the test-statistic indicates a value of 0.0499 for the acquirer and 0.1592 for the target. The
average ratio across comparisons is about 2. Even the shifts on the announcement dates are less
acute than for the target companies. Finally, statistical significance is much weaker for adjacent
event windows, and is mostly restricted to short-dated options just prior to the announcement date.
5.1.4 Abnormal Trading Volume - Event Study
Hypothesis H1 asserts that there is positive abnormal trading volume in call equity options written
on the target prior to a public M&A announcement. We test this by running a classical event
study. For each of the 1,859 deals in the sample, we source the aggregated total option volume
on the target’s stock, as well as the total aggregated volume traded in calls and puts. Option
information for the acquirer is limited to a subsample of 792 deals. To compute abnormal trading
volume, we use as a benchmark a constant-mean-trading-volume model, as well as two different
volume-based versions of the market models. We define the market trading volume as the median
(mean) total call and put trading volume across all options in the OptionMetrics database. As we
are interested in the abnormal trading volume in anticipation of the event, we use as the estimation
window the period starting 90 days before the announcement date until 30 days before the event.
The event window in our case stretches from 30 days before until one day prior to the event. To
account for the possibility of clustered event dates, we correct all standard errors for cross-sectional
17
dependence.
The results are reported in Table 6. The average cumulative abnormal trading volume is posi-
tive and statistically significant for the target across all model specifications. The magnitude of the
average cumulative abnormal volume over the 30 pre-event days is also economically meaningful
with an estimate of 11,969 contracts for call options using the median market model. For put
options on the target, average cumulative abnormal volume is also positive and highly statistically
significant, but the average cumulative abnormal volume over the 30 pre-event days at 3,471 con-
tracts is economically much smaller. The evolution of average abnormal and cumulative abnormal
trading volume for targets is illustrated in the upper two panels in Figure 4. It is apparent that the
average cumulative abnormal trading volume in put options is quantitatively less important than
the average cumulative abnormal trading volume in call options, which is primarily driving the
results for the overall sample. The daily average abnormal volume for call options is positive and
steadily increasing to a level of approximately 1,500 contracts the day before the announcement.
Individually, the number of deals with positive abnormal trading volume at the 5% significance
level ranges from 472 to 492 for calls, and from 271 to 319 for puts, corresponding to respectively
25% and 15% of the entire sample.18 These results confirm the null hypothesis H1, that there is
positive abnormal trading volume in call and put equity options written on the target prior to a
public M&A announcement.
Focusing on the acquirer in Table 6, the average cumulative abnormal trading volume over the
30 pre-event days is statistically indistinguishable from zero and fluctuates between positive and
negative values depending on the model specification. Also, the number of deals for which there is
positive and statistically significant abnormal volume on the acquirer is lower than for the targets.
Depending on the model specification, there about 54 to 56 such cases for call options and 45 to 51
for put options. This corresponds to about 7% and 6% of all deals for calls and puts respectively.
The bottom two panels in Figure 4 illustrate the evolution of the average abnormal and cumulative
abnormal trading volume for acquirers. In line with our prior, there is no clear evidence of positive
abnormal trading volume for acquirers, whether for calls or puts. Daily average abnormal volume
18Unreported results indicate that, at the 1% significance level, the number of deals with positive abnormal tradingvolume for the entire sample range from 278 to 292 for calls, and from 138 to 195 for puts, corresponding to afrequency of respectively 16% and 8%.
18
resembles more a random walk rather than any clear pattern.
In addition to the aggregated results, we stratify our sample by moneyness and conduct an
event study for each category. We find that there is significantly higher abnormal trading volume
for targets in OTM and ITM call options, compared to ATM and ITM calls, both in terms of
volume levels and frequencies. Using the median market model, for instance, the average cumulative
abnormal volume is 3,797 (1,860) contracts for OTM calls (puts) and 1,702 (1,110) contracts for
ITM calls (puts), while it is 1,059 (188) for ATM calls (puts). These values correspond to 383
(300, 448) deals or 21% (16%, 24%) for OTM (ATM, ITM) calls and 387 (254, 316) deals or 21%
(14%, 17%), for OTM (ATM, ITM) puts, respectively. In addition, while we find that the average
cumulative abnormal volume is positive and statistically significant for both OTM and ITM calls
and puts, it is only statistically significant at the 5% level for ATM call options, but not for put
options. Panel B reports the results from paired t-tests for differences in means of the cumulative
average abnormal volumes across different depths. Consistent with our hypothesis H2, these results
emphasize that there is a higher ratio of abnormal trading volumes for OTM call options compared
to ATM and ITM call options. The difference in means, using the median market model, for
OTM calls relative to ATM and ITM calls is 2,738 and 2,096 respectively, which is positive and
statistically different from zero. On the other hand, the difference in means between ATM and ITM
calls is slightly negative (-643), but not statistically different from zero. Even though we do confirm
that the ratio of the average cumulative abnormal volume for ITM put options is higher than for
ATM put options, we also find that there is a higher expected abnormal volume for OTM compared
to ITM put options, even though the difference of 750 contracts is small in magnitude, given that it
is a cumulative measure over 30 days. This is some preliminary evidence that insiders may not only
engage in OTM call transactions, but potentially also sell ITM puts. The corresponding statistics
for the acquirer stratified by moneyness do not show any clear pattern.
To summarize, the event study further supports hypotheses H1 and H2. In other words, there
is ample evidence of positive abnormal volume in equity options for the targets, but not for the
acquirers in M&A transactions, prior to the announcement date. In addition, we document that,
for the targets, there is a significantly larger amount of abnormal trading volume in OTM call
options compared to ATM and ITM call options. However, the evidence that insiders may also
19
engage in writing ITM put options is weak. The results of the event study are supported by formal
statistical evidence that the two-dimensional volume-moneyness distribution significantly shifts,
over both time and moneyness, over the 30 days preceding the announcement day. Hence, the level
of the volume distribution increases with a higher frequency of trades occurring in both OTM and
ITM options. Moreover, this shift in the bivariate distribution is not random, as we illustrate by
comparing the volume distribution from a suspicious trading sample to that of a randomly matched
sample.
5.1.5 Non-parametric Volume at Risk.
To supplement our forensic analysis on the behavior of options volume before takeover announce-
ments, we analyze trading volume patterns conditional on no trading volume for one to five earlier
days. This analysis may be insightful, given the significant amount of zero volume observations that
characterize the data for equity options. To do so, we calculate, non-parametrically, the probability
of spikes in trading volume, conditional on zero trading volume between one and five days prior to
the spike. We define as the conditionalVolume-at-risk (VolaR), at a confidence level α ∈ (0, 1), the
smallest trading volume ν, such that the probability of the volume V exceeding ν is less than or
equal to (1− α). Formally, V olaR is given by:
V olaRiα = infν ∈ R : P (V > ν) ≤ 1− α,i∑
j=1
Vt−j = 0. (5)
The statistics in Panel A of Table 9 show the VolaR statistics for the 30 days preceding the
announcement dates. We see that for target companies, if there is a day with zero option trading
volume on a given company, the probability that the volume exceeds 100 (200; 1,000) contracts
during the next day is less than 10% (5%, 1%). When we look at the options written on the
acquiring firm, the numbers look very similar. The probability of observing more than 96 (191;
1,000) traded contracts on a company when there was no option trading volume on its stock during
the preceding day, is less than 10% (5%, 1%). Naturally, the V olaRiα levels decrease for the more
restrictive cases with several preceding days with zero trading volume (i > 1). However, the
values are close to the benchmark case (i = 1) of zero trading volume for one preceding day only.
20
In the case with five earlier days with no recorded option trading, the probabilities of exceeding
respectively more than 65, 126 or 710 traded contracts are less than 10%, 5% and 1%. Statistics
for the acquirer sample are again very similar, with V olaR5α values of 70, 143 and 700 contracts at
the confidence levels 90, 95 and 99.
We compare these critical levels of trading volumes to the sample statistics from the SUT se-
lection, which correspond to the same 30 days before the announcement dates. We have argued
that the unconditional probability of exceeding more than 100 traded contracts on any given com-
pany, when there was no trading volume on the preceding day, is less than 10%. This volume of
100 contracts seems small in comparison to the average trading volume in call options, written
on the target, from the SUT sample. The average trading volume of option trades characterized
as strongly unusual, which occurred during the 30 days preceding a M&A announcement, is 124
contracts, with the volume for call options being slightly higher (137 contracts). Thus, the VolaR
statistics further emphasize the strongly unusual trading patterns in anticipation of these publicly
unexpected events.
In addition, we compare the VolaR statistics to those of a randomly matched sample. Similarly
to the earlier exercise, we randomly choose 1,859 (792) pseudo-event dates for the target (acquirer)
companies, and then investigate the VolaR statistics over the 30 days preceding the pseudo-event
dates. Given that trading picks up during the pre-announcement period, one would expect to see
fewer days with zero trading volume for individual equity options, and as a consequence, the VolaR
statistics should be lower in sample. Panel B in Figure 9 compares our prior. The VolaR levels
are higher in the randomly matched sample. For instance, the 1% VolaR level for target (acquirer)
companies is 1,525 (1,492) option contracts in the random sample compared to 1,000 (986) in the
real sample.
The forensic analysis of the trading volume behavior in equity options confirms our priors stated
in hypotheses H1 and H2. The next step is to investigate hypotheses H4 to H7 by focusing on the
information embedded in equity options prices, based on their implied volatilities.
21
5.2 Implied Volatiliy
The price behavior of options is embedded in the summary statistic of implied volatility. As a
complement to the volume results, we conduct a forensic analysis on implied volatility over the 30
days preceding the announcement date. We first conduct an event study to test for the presence of
positive excess implied volatility relative to a market benchmark. Second, we study the behavior
of the convexity of the option smile, in anticipation of news releases, followed by investigations of
the bid-ask spread. Finally, we address the hypothesis related to the term structure of implied
volatility.
5.2.1 Excess Implied Volatility - Event Study
As informed traders benefit from their private information only to the extent that the outcome of
the announcement on stock prices is certain, we expect to find positive excess implied volatility for
equity options on target, but not acquirer companies. We use the interpolated volatility surface
in OptionMetrics for this exercise. To analyze the behavior of ATM implied volatility, we use
the 50 delta (or 0.50) options, and we reference the 80 and 20 delta options for ITM and OTM
options respectively. We test two different model specifications for our results: a simple constant
mean volatility model and a market model, where we use the S&P 500 VIX index as the market’s
benchmark for implied volatility. The estimation window runs from 90 to 31 days before the
announcement date, while our event window relates to the 30 days before the event, excluding the
announcement itself. All standard errors are clustered by time to account for the clustering of
events.
Panel A in Table 7 documents that excess implied volatility is quite pervasive in our sample. At
the 5% significance level, using the market model, there are about 812 cases (44% of the 1,859 deals)
with positive excess implied volatility for call options and about 798 cases (43% of the 1,859 deals)
with positive excess implied volatility for put options. The frequencies are similar for OTM implied
volatilities and slightly lower for ITM implied volatilities, where positive excess implied volatility
is documented for 39% (calls) and 41% (puts) of all cases. In addition, cumulative excess implied
volatilities are consistently positive and statistically significant for the target companies. The
average cumulative excess implied volatility over the 30 pre-announcement days is approximately
22
50% for calls and 40% for puts.
In contrast to the results for targets, average cumulative excess implied volatilities fluctuate
around zero and are statistically indistinguishable from zero. However, on a case by case basis,
39% (33%, 38%) of the deals exhibit positive and statistically significant positive excess implied
volatility for ATM (ITM, OTM) call options, while 38% (36%, 32%) of the deals exhibit positive
and statistically significant positive excess implied volatility for ATM (ITM, OTM) put options.
To summarize, the event study confirms our hypothesis H4, which asserts that there should be
on average positive cumulative excess implied volatility for the target companies, but not for the
acquirer firms. These results are graphically presented in Figure 5 for ATM implied volatilities.
The daily average excess ATM implied volatility starts increasing about 18 days before the an-
nouncement date and then steadily rises. In contrast, for acquirers, the daily average ATM excess
implied volatility fluctuates around zero and behaves much more like a random walk.
5.2.2 Information Dispersion and Bid-Ask Spreads
To address hypothesis H5, we study the evolution of the bid-ask spread in anticipation of the M&A
announcement. The prediction of the null hypothesis H5 is that the percentage bid-ask spread in
option premia should widen prior to the announcement. Strong evidence in favor of this hypothesis
would indicate that the information about a potential merger has leaked to the market makers and
that unusual trading activity is more likely to be associated with illicit trading.
Figure 6 plots the evolution of the average percentage bid-ask spread from 90 days before the
announcement date to 90 days after the event. Figure 6a shows that the average percentage bid-
ask spread on target options rises from about 35% to 55% and then jumps up to approximately
80% because of the announcement effect. Interestingly, this rise in bid-ask spreads is restricted to
DOTM and OTM options.19 In Figure 6b, the equivalent graph for the subsample for which we have
information on options on the acquirer is plotted. While the level of the average percentage bid-ask
spread is also increasing as we approach the announcement date, the increase is economically less
significant than for the options on the target, as it rises from approximately 13%, 90 days before
the announcement date, to 14%, and then stabilizes at event time zero. However, when we adjust
19These graphs are not reported because of space constraints but are available upon request.
23
the bid-ask spread to the leverage (using the options leverage measure omega, Ω = ∆Sc ), we find
no increase in the bid-ask spread before the announcement (see Figures 6c and 6d).
To summarize, although we find that the percentage bid-ask spread in targets’ option premia
increases prior to the announcement, which could be argued to be evidence that information about
the future transaction leaked to the market, the leverage-adjusted measure does not support this
conclusion.
5.2.3 Volatility Smile and the Term Structure of Implied Volatility
Hypotheses H6 predicts that the convexity of the option smile, for target firms, should increase
for call options and decrease for put options, prior to M&A announcements. We investigate this
question by plotting in Table 7 various measures relating to the convexity of the option smile.
Graphs 7a and 7c illustrate two documented measures of the implied volatility skewness. The first
measure, on the left scale, is measured as the difference between OTM call and put implied volatility,
standardized by ATM implied volatility. The second measure, on the right axis, is measured as
the difference between OTM implied put and ATM implied call volatility. To our surprise, both
measures seem to remain flat prior to the announcement date, whether for target or for acquiring
companies.
Finally, hypothesis H7 states that the term structure of implied volatility should decrease for
options on the target firms before takeover announcements, and remain flat for options on the
acquiring firms. The justification for this hypothesis is that informed traders obtain the highest
leverage by investing into short-dated OTM call options. Hence, demand pressure on short-dated
options should lead to a relative price increase in options with a short time to expiration compared to
long-dated options. Thus, a confirmation of our hypothesis would be supportive of the fact that, on
average, activity in the options market before major takeover announcements is partially influenced
by investors with inside information. Figure 7b documents that the average term structure of
implied volatility, calculated by the difference between the implied volatility of 3-month and 1-
month options, is decreasing from -1.8% by about 2.5 percentage points to approximately -4.3%
over the 30 days before the announcement date. This result obtains for both call and put options.
However, the evidence is much weaker for acquirers and is restricted, if any, to call options. Figure
24
7d illustrates that the level of the implied volatility term structure decreases from about -0.95%
to -1.45% for call options, and from roughly -1.2% to -1.45% for put options. These decreases are
statistically insignificant.
In a nutshell, we find supportive evidence of the fact that the average implied volatility spread
significantly increase for target firms, but not for acquirer firms, prior to M&A announcements. In
addition, the term structure of implied volatility becomes more negative for targets and remains
roughly flat for acquirers, as we approach the announcement.
5.3 Joint evolution of abnormal volume and implied volatility
We document that abnormal volume and excess implied volatility is more pervasive ahead of merger
and acquisitions than what would be expected based on the number of insider trading litigations.
The ultimate goal of our forensic study is to provide regulators with a clear guidance on which
suspicious trades are worth pursuing. In the future, we therefore plan to double sort all cases
based on their abnormal volume and excess implied volatility levels. This should in theory lead to
a classification scheme to those cases that are most susceptible of insider trading. An additional
filter given by the SUT selection criteria should further sharpen the monitoring results. This
analysis will follow in our future research.
6 Conclusion
Research on trading in individual equity options has been scanty, and even more so, centered on
major informational events, such as M&As. In light of recent investigations into insider trading
based on unusual abnormal trading volume in anticipation of major corporate acquisitions, we
investigate the presence of informed options trading around such unexpected public announcements.
We focus on equity options written on both the target and the acquirer. Our goal is to quantify the
likelihood of informed trading by investigating various options trading strategies, which should a
priori lead to unusual abnormal trading volume and returns in the presence of private information.
An analysis of trading volume and implied volatility over the 30 days preceding formal takeover
announcements suggests that that informed trading, whether based on inside information or leakage,
25
is more pervasive than what would be expected based on the actual number of prosecuted cases.
We find that there is statistically significant abnormal trading volume in call options written on
the target, prior to M&A announcements, with particular pronounced effects for OTM calls. We
provide formal tests of shifts in the bivariate volume-moneyness distribution and illustrate that the
levels of unusual volume in options trading cannot be replicated in a randomly matched sample.
We further find strong support for positive excess implied volatility for the target companies, but
not for the acquirers. In addition, for targets, the term structure of implied volatility becomes
more negative and the implied volatility spread widens, not so for acquirers. The evidence from
the bid-ask spread is more ambivalent.
Future analysis, based on the attributes of abnormal volume and excess implied volatility, will
lead to a classification that should ultimately be reflective of those cases that are most likely of
insider trading. We thereby hope to provide some guidance to regulators on where to start their
investigations into illicit options trading. While some insider cases are obvious, detecting others is
like finding the proverbial needle in a hay stack.
References
Acharya, V. V. and Johnson, T. C. (2010). More insiders, more insider trading: Evidence fromprivate-equity buyouts, Journal of Financial Economics 98(3): 500 – 523.
Barraclough, K., Robinson, D. T., Smith, T. and Whaley, R. E. (2012). Using option prices to inferoverpayments and synergies in m&a transactions, Review of Financial Studies .
Bester, A., Martinez, V. H. and Rosu, I. (2011). Option pricing and the probability of success ofcash mergers, Working Paper .
Bollen, N. P. B. and Whaley, R. E. (2004). Does net buying pressure affect the shape of impliedvolatility functions?, The Journal of Finance 59(2): 711–753.
Cao, C., Chen, Z. and Griffin, J. M. (2005). Informational content of option volume prior totakeovers, The Journal of Business 78(3): pp. 1073–1109.
Cao, H. H. and Ou-Yang, H. (2009). Differences of opinion of public information and speculativetrading in stocks and options, Review of Financial Studies 22(1): 299–335.
Chan, K., Ge, L. and Lin, T.-C. (2013). Informational content of option trading on acquirerannouncement return, forthcoming in the Journal of Financial and Quantitative Analysis .
Cremers, M. and Weinbaum, D. (2010). Deviations from put-call parity and stock return pre-dictability, The Journal of Financial and Quantitative Analysis 45(2): pp. 335–367.
26
Driessen, J., Tse-Chun, L. and Xiaolong, L. (2012). Why do option prices predict stock returns?,Working Paper .
Dubinsky, A. and Johannes, M. (2006). Fundamental uncertainty, earning announcements andequity options, Working Paper Columbia University .
Easley, D., O’Hara, M. and Srinivas, P. S. (1998). Option volume and stock prices: Evidence onwhere informed traders trade, The Journal of Finance 53(2): pp. 431–465.
Fasano, G. and Franceschini, A. (1987). A multidimensional version of the kolmorogov-smirnovtest., Monthly Notices of the Royal Astronomical Society 225: 155–170.
Garleanu, N., Pedersen, L. H. and Poteshman, A. M. (2009). Demand-based option pricing, Reviewof Financial Studies 22(10): 4259–4299.
Hu, J. (2013). Does option trading convey stock price information?, Journal of Financial Eco-nomics, forthcoming .
Jarrell, G. A. and Poulsen, A. B. (1989). Stock trading before the announcement of tender offers:Insider trading or market anticipation?, Journal of Law, Economics, & Organization 5(2): pp.225–248.
Jin, W., Livnat, J. and Zhang, Y. (2012). Option prices leading equity prices: Do option tradershave an information advantage?., Journal of Accounting Research 50(2): 401 – 432.
John, K., Koticha, A., Narayanan, R. and Subrahmanyam, M. G. (2003). Margin rules, informedtrading in derivatives and price dynamics, Working Paper New York University, Stern School ofBusiness .
Johnson, T. L. and So, E. C. (2011). The option to stock volume ratio and future returns, forth-coming in the Journal of Financial Economics .
Keown, A. J. and Pinkerton, J. M. (1981). Merger announcements and insider trading activity: Anempirical investigation, The Journal of Finance 36(4): pp. 855–869.
Meulbroek, L. K. (1992). An empirical analysis of illegal insider trading, The Journal of Finance47(5): pp. 1661–1699.
Nicolau, A. A. (2010). An examination of the behavior of implied volatility around merger an-nouncements, Bachelor of Science Thesis, New York University, Leonard N. Stern School ofBusiness .
Pan, J. and Poteshman, A. M. (2006). The information in option volume for future stock prices,The Review of Financial Studies 19(3): pp. 871–908.
Podolski, E. J., Truong, C. and Veeraraghavan, M. (2013). Informed options trading prior totakeovers does the regulatory environment matter?, Journal of International Financial Markets,Institutions & Money .
Roll, R., Schwartz, E. and Subrahmanyam, A. (2010). O/s: The relative trading activity in optionsand stock, Journal of Financial Economics 96(1): 1 – 17.
27
Schwert, G. (1996). Markup pricing in mergers and acquisitions, Journal of Financial Economics41(2): 153 – 192.
Spyrou, S., Tsekrekos, A. and Siougle, G. (2011). Informed trading around merger and acquisitionannouncements: Evidence from the uk equity and options markets, Journal of Futures Markets31(8): 703–726.
Subramanian, A. (2004). Option pricing on stocks in mergers and acquisitions, The Journal ofFinance 59(2): pp. 795–829.
Xing, Y., Zhang, X. and Zhao, R. (2010). What does the individual option volatility smirk tell usabout future equity returns?., Journal of Financial & Quantitative Analysis 45(3): 641 – 662.
28
Tab
le1:
Des
crip
tive
and
Fin
anci
alov
ervie
wof
M&
AS
amp
le-
Fu
llS
amp
le
Panel
Ain
this
table
pro
vid
esan
over
vie
wof
the
M&
Adea
lch
ara
cter
isti
csfo
rall
US
dom
esti
cm
erger
sand
acq
uis
itio
ns
inth
eT
hom
son
Reu
ters
SD
CP
lati
num
data
base
over
the
tim
ep
erio
dJanuary
1996
thro
ugh
31
Dec
emb
er2012,
wher
ea
matc
hw
as
found
for
the
targ
etin
the
CR
SP
mast
erfile
and
inO
pti
onM
etri
cs
base
don
the
6-d
igit
CU
SIP
.T
he
sam
ple
excl
udes
dea
lsw
ith
an
unknow
nor
pen
din
gdea
lst
atu
s,in
cludes
only
dea
lsw
ith
available
dea
lin
form
ati
on,
wher
e
the
dea
lva
lue
isab
ove
1m
illion
USD
and
wher
ean
effec
tive
change
of
contr
ol
was
inte
nded
.In
addit
ion,
we
requir
eva
lid
pri
ceand
volu
me
info
rmati
on
in
both
CR
SP
and
Opti
onM
etri
csfo
rth
eta
rget
for
at
least
90
day
spri
or
toand
on
the
announce
men
tday
.P
anel
Billu
stra
tes
the
financi
al
stati
stic
sof
the
dea
ls.
P1d
(P1w
,P
4w
)re
fers
toth
eP
rem
ium
1D
ay(1
wee
k,
4w
eeks)
pri
or
toA
nnounce
men
tD
ate
inp
erce
nta
ge
term
s.E
xpla
nati
ons:
The
dea
lva
lue
is
the
tota
lva
lue
of
consi
der
ati
on
paid
by
the
Acq
uir
er,
excl
udin
gfe
esand
exp
ense
s.T
he
dollar
valu
ein
cludes
the
am
ount
paid
for
all
com
mon
stock
,co
mm
on
stock
equiv
ale
nts
,pre
ferr
edst
ock
,deb
t,opti
ons,
ass
ets,
warr
ants
,and
stake
purc
hase
sm
ade
wit
hin
six
month
sof
the
announce
men
tdate
of
the
transa
ctio
n.
Lia
bilit
ies
ass
um
edare
incl
uded
inth
eva
lue
ifth
eyare
publicl
ydis
close
d.
Pre
ferr
edst
ock
isonly
incl
uded
ifit
isb
eing
acq
uir
edas
part
of
a100%
acq
uis
itio
n.
Ifa
port
ion
of
the
consi
der
ati
on
paid
by
the
Acq
uir
eris
com
mon
stock
,th
est
ock
isva
lued
usi
ng
the
closi
ng
pri
ceon
the
last
full
tradin
gday
pri
or
toth
e
announce
men
tof
the
term
sof
the
stock
swap.
Ifth
eex
change
rati
oof
share
soff
ered
changes
,th
est
ock
isva
lued
base
don
its
closi
ng
pri
ceon
the
last
full
tradin
gdate
pri
or
toth
edate
of
the
exch
ange
rati
och
ange.
For
public
targ
et100%
acq
uis
itio
ns,
the
num
ber
of
share
sat
date
of
announce
men
t(C
AC
T)
is
use
d.
The
pre
miu
mpaid
isdefi
ned
as
the
Pre
miu
mof
off
erpri
ceto
targ
etcl
osi
ng
stock
pri
ce1
day
(1w
eek,
4w
eeks)
pri
or
toth
eori
gin
al
announce
men
t
date
,ex
pre
ssed
as
ap
erce
nta
ge.
Sourc
e:T
hom
son
Reu
ters
SD
CP
lati
num
.
Panel
A:
Deal
Info
rmati
on
Off
er
Str
uctu
reC
ash
Only
Hybri
dO
ther
Share
sU
nknow
nT
ota
l
Desc
ripti
on
No.
%of
Tot.
No.
%of
Tot.
No.
%of
Tot.
No.
%of
Tot.
No.
%of
Tot.
No.
%of
Tot.
Nbr.
of
Dea
ls903
48.6
%415
22.3
%80
4.3
%403
21.7
%58
3.1
%1,8
59
100.0
%C
om
ple
ted
Dea
ls746
40.1
%357
19.2
%67
3.6
%339
18.2
%33
1.8
%1,5
42
82.9
%H
ost
ile
Dea
ls35
1.9
%14
0.8
%3
0.2
%7
0.4
%4
0.2
%63
3.4
%Sam
eIn
dust
ryD
eals
53
2.9
%83
4.5
%6
0.3
%54
2.9
%5
0.3
%201
10.8
%C
hallen
ged
Dea
ls111
6.0
%55
3.0
%7
0.4
%32
1.7
%11
0.6
%216
11.6
%C
om
pet
ing
Bid
der
83
4.5
%32
1.7
%3
0.2
%20
1.1
%4
0.2
%142
7.6
%C
ollar
Dea
l4
0.2
%54
2.9
%3
0.2
%52
2.8
%7
0.4
%120
6.5
%T
erm
inati
on
Fee
698
37.5
%352
18.9
%51
2.7
%292
15.7
%29
1.6
%1,4
22
76.5
%B
idder
has
ato
ehold
42
2.3
%11
0.6
%2
0.1
%7
0.4
%3
0.2
%65
3.5
%
Panel
B:
Deal
Fin
ancia
ls
Off
er
Str
uctu
reC
ash
Only
Hybri
dO
ther
Share
sU
nknow
nT
ota
l
Desc
ripti
on
Mea
nSd
Mea
nSd
Mea
nSd
Mea
nSd
Mea
nSd
Mea
nSd
DV
al
(mil)
$2,2
42.0
$4,1
47.2
$5,8
80.9
$10,0
71.5
$5,0
74.2
$10,3
87.7
$5,4
29.8
$15,1
58.5
$1,6
35.7
$2,5
03.7
$3,8
48.4
$9,4
01.3
P1d
33.6
%31.7
%28.5
%27.5
%25.1
%40.5
%28.3
%39.5
%33.3
%29.6
%31.0
%33.1
%P
1w
36.6
%31.0
%32.4
%29.1
%29.5
%42.5
%33.6
%61.5
%33.4
%29.8
%34.7
%39.8
%P
4w
41.1
%35.6
%35.0
%32.4
%31.2
%46.1
%36.7
%45.3
%38.0
%33.6
%38.3
%37.7
%
29
Tab
le2:
Su
mm
ary
Sta
tist
ics
-O
pti
onT
rad
ing
Vol
um
e(W
ith
out
Zer
oV
olu
me
Ob
serv
atio
ns)
This
table
pre
sents
basi
csu
mm
ary
stati
stic
son
opti
on
tradin
gvolu
me,
excl
udin
gze
ro-v
olu
me
obse
rvati
ons,
stra
tified
by
tim
eto
expir
ati
on
and
dep
th-i
n-
money
nes
s.W
ecl
ass
ify
thre
egro
ups
of
tim
eto
expir
ati
on:
less
or
equalth
an
30
day
s,big
ger
than
30
but
less
or
equalth
an
60
day
sand
more
than
60
day
s.W
e
ass
ign
5gro
ups
for
dep
th-i
n-m
oney
nes
s,w
her
edep
th-i
n-m
oney
nes
sis
defi
ned
asS/K
,th
era
tio
of
the
stock
pri
ceS
over
the
stri
ke
pri
ceK
.D
eep
out-
of-
the-
money
(DO
TM
)co
rres
ponds
toS/K∈
[0,0.8
0]
for
calls
([1.2
0,∞
]fo
rputs
),out-
of-
the-
money
(OT
M)
corr
esp
onds
toS/K∈
]0.8
0,0.9
5]
for
calls
([1.0
5,1.2
0[
for
puts
),at-
the-
money
(AT
M)
corr
esp
onds
toS/K∈
]0.9
5,1.0
5[
for
calls
(]0.9
5,1.0
5[
for
puts
),in
-the-
money
(IT
M)
corr
esp
onds
toS/K∈
[1.0
5,1.2
0[
for
calls
(]0.8
0,0.9
5]
for
puts
),and
dee
pin
-the-
money
(DIT
M)
corr
esp
onds
toS/K∈
[1.2
0,∞
[fo
rca
lls
([0,0.8
0]
for
puts
).Sourc
e:O
pti
onM
etri
cs
Target
(N
=2,2
14,2
60)
Acquirer
(N
=3,5
82,3
94)
DITM
Mean
SD
Min
Med
p75
p90
Max
Panel
A:
All
opti
ons,
TT
E=
[0,3
0]
DO
TM
(3%
)246
1,9
73
120
76
300
94,1
77
OT
M(5
%)
370
1,9
90
141
164
596
88,0
86
AT
M(7
9%
)273
1,2
91
140
152
531
231,2
04
ITM
(5%
)356
6,2
14
120
80
333
539,4
82
DIT
M(5
%)
275
3,2
64
110
40
171
200,0
00
Tota
l(1
00%
)283
2,1
35
135
138
500
539,4
82
DITM
Mean
SD
Min
Med
p75
p90
Max
Panel
A:
All
opti
ons,
TT
E=
[0,3
0]
DO
TM
(10%
)127
594
120
71
231
27,3
77
OT
M(2
2%
)497
1,4
97
179
355
1,2
07
55,1
67
AT
M(2
6%
)1,0
84
3,0
38
1204
927
2,7
44
198,1
46
ITM
(23%
)398
5,2
09
142
175
624
679,6
20
DIT
M(1
6%
)214
3,2
86
116
54
191
300,8
41
Tota
l(1
00%
)547
3,3
61
152
279
1,1
46
679,6
20
Panel
B:
All
opti
ons,
TT
E=
]30,6
0]
DO
TM
(6%
)163
863
120
63
229
29,0
45
OT
M(9
%)
285
1,2
01
132
128
500
55,2
22
AT
M(7
1%
)184
855
125
95
328
71,8
22
ITM
(6%
)190
3,2
44
120
65
254
475,5
13
DIT
M(6
%)
208
5,2
88
110
37
137
523,0
53
Tota
l(1
00%
)194
1,7
87
123
90
316
523,0
53
Panel
B:
All
opti
ons,
TT
E=
]30,6
0]
DO
TM
(14%
)141
838
120
76
245
95,0
00
OT
M(2
7%
)384
1,3
88
169
269
830
94,5
52
AT
M(2
5%
)551
1,6
66
1101
425
1,2
99
90,4
97
ITM
(20%
)236
3,4
88
130
108
367
458,0
19
DIT
M(1
2%
)334
12,5
43
111
40
133
1,6
09,0
02
Tota
l(1
00%
)354
4,8
41
141
183
659
1,6
09,0
02
Panel
C:
All
opti
ons,
TT
E=
]60,...]
DO
TM
(25%
)117
1,0
35
115
45
143
339,7
51
OT
M(2
4%
)130
847
115
50
175
101,8
85
AT
M(2
0%
)131
845
115
50
180
116,4
16
ITM
(14%
)99
923
110
35
111
142,6
47
DIT
M(1
5%
)83
1,1
05
110
27
89
137,8
04
Tota
l(1
00%
)115
949
113
42
142
339,7
51
Panel
C:
All
opti
ons,
TT
E=
]60,...]
DO
TM
(24%
)112
774
120
59
176
137,4
30
OT
M(2
5%
)193
1,0
72
126
100
328
246,5
07
AT
M(1
8%
)208
927
128
108
382
88,1
31
ITM
(15%
)106
678
117
53
164
125,0
27
DIT
M(1
5%
)80
1,7
74
110
30
86
582,5
00
Tota
l(1
00%
)145
1,0
82
120
67
224
582,5
00
Panel
D:
Call
opti
ons,
TT
E=
[0,3
0]
DO
TM
(2%
)285
1,9
14
120
78
334
78,9
37
OT
M(4
%)
438
2,2
66
149
194
711
83,6
37
AT
M(7
8%
)302
1,4
61
144
171
592
231,2
04
ITM
(7%
)446
7,3
63
122
97
431
539,4
82
DIT
M(7
%)
220
3,1
61
110
40
152
200,0
00
Tota
l(1
00%
)311
2,5
64
137
150
545
539,4
82
Panel
D:
Call
opti
ons,
TT
E=
[0,3
0]
DO
TM
(6%
)96
434
113
49
185
18,5
53
OT
M(2
1%
)523
1,5
72
175
361
1,2
81
55,1
67
AT
M(2
5%
)1,2
85
3,5
98
1244
1,1
06
3,2
39
198,1
46
ITM
(24%
)499
6,5
95
150
215
750
679,6
20
DIT
M(2
3%
)192
3,3
79
117
58
192
300,8
41
Tota
l(1
00%
)603
4,1
43
150
283
1,2
33
679,6
20
Panel
E:
Call
opti
ons,
TT
E=
]30,6
0]
DO
TM
(4%
)168
790
120
70
250
25,0
00
OT
M(8
%)
313
1,2
92
137
144
533
36,9
55
AT
M(7
0%
)202
923
127
100
363
55,2
08
ITM
(7%
)213
3,8
28
120
73
293
475,5
13
DIT
M(8
%)
213
5,9
67
110
34
116
523,0
53
Tota
l(1
00%
)212
2,1
97
125
96
342
523,0
53
Panel
E:
Call
opti
ons,
TT
E=
]30,6
0]
DO
TM
(9%
)123
907
120
70
225
95,0
00
OT
M(2
7%
)425
1,4
71
172
296
935
53,0
60
AT
M(2
4%
)657
1,9
34
1123
528
1,5
93
90,4
97
ITM
(21%
)297
4,4
80
133
128
432
458,0
19
DIT
M(1
7%
)349
14,2
51
111
39
119
1,6
09,0
02
Tota
l(1
00%
)412
6,3
86
142
200
741
1,6
09,0
02
Panel
F:
Call
opti
ons,
TT
E=
]60,...]
DO
TM
(23%
)108
1,1
49
115
46
140
339,7
51
OT
M(2
6%
)124
829
115
50
169
101,8
85
AT
M(2
0%
)137
931
115
50
182
116,4
16
ITM
(13%
)108
1,0
83
110
34
111
142,6
47
DIT
M(1
6%
)82
1,2
49
110
25
79
137,8
04
Tota
l(1
00%
)114
1,0
40
112
41
139
339,7
51
Panel
F:
Call
opti
ons,
TT
E=
]60,...]
DO
TM
(19%
)111
744
120
63
187
137,4
30
OT
M(2
7%
)199
1,1
67
128
106
347
246,5
07
AT
M(1
8%
)214
954
130
115
398
88,1
31
ITM
(15%
)110
753
117
56
171
125,0
27
DIT
M(2
0%
)75
1,9
76
110
28
80
582,5
00
Tota
l(1
00%
)147
1,2
31
120
70
230
582,5
00
Panel
G:
Put
opti
ons,
TT
E=
[0,3
0]
DO
TM
(4%
)220
2,0
10
120
75
275
94,1
77
OT
M(6
%)
306
1,6
89
139
141
508
88,0
86
AT
M(8
1%
)234
1,0
03
135
130
455
58,8
19
ITM
(4%
)139
976
115
50
189
42,7
08
DIT
M(2
%)
485
3,6
27
111
43
250
100,0
10
Tota
l(1
00%
)242
1,2
75
130
120
431
100,0
10
Panel
G:
Put
opti
ons,
TT
E=
[0,3
0]
DO
TM
(14%
)145
672
124
90
260
27,3
77
OT
M(2
5%
)468
1,4
10
183
349
1,1
28
40,4
32
AT
M(2
9%
)855
2,2
10
1166
750
2,1
85
77,8
74
ITM
(21%
)249
1,6
70
132
130
465
184,5
84
DIT
M(8
%)
294
2,9
15
113
47
188
105,0
04
Tota
l(1
00%
)471
1,8
46
154
274
1,0
50
184,5
84
Panel
H:
Put
opti
ons,
TT
E=
]30,6
0]
DO
TM
(9%
)159
915
120
60
210
29,0
45
OT
M(1
0%
)253
1,0
84
130
110
449
55,2
22
AT
M(7
1%
)155
739
122
80
280
71,8
22
ITM
(5%
)136
836
115
50
197
41,1
77
DIT
M(3
%)
192
1,2
64
112
50
232
54,0
04
Tota
l(1
00%
)166
830
121
80
284
71,8
22
Panel
H:
Put
opti
ons,
TT
E=
]30,6
0]
DO
TM
(21%
)152
795
122
81
250
45,1
95
OT
M(2
7%
)332
1,2
77
165
244
716
94,5
52
AT
M(2
6%
)424
1,2
63
181
315
1,0
10
32,2
39
ITM
(18%
)145
700
124
83
271
45,4
70
DIT
M(6
%)
281
1,9
89
113
52
203
80,4
01
Tota
l(1
00%
)280
1,1
68
140
165
570
94,5
52
Panel
I:P
ut
opti
ons,
TT
E=
]60,...]
DO
TM
(29%
)129
855
115
45
150
61,1
23
OT
M(2
2%
)141
880
115
50
193
83,0
66
AT
M(2
1%
)120
680
115
50
175
56,0
00
ITM
(14%
)84
580
111
38
110
40,9
06
DIT
M(1
2%
)87
669
110
33
100
70,0
14
Tota
l(1
00%
)118
769
114
44
150
83,0
66
Panel
I:P
ut
opti
ons,
TT
E=
]60,...]
DO
TM
(31%
)114
802
119
53
163
100,1
03
OT
M(2
3%
)181
871
124
88
296
78,4
92
AT
M(1
9%
)200
885
125
100
355
71,5
16
ITM
(16%
)101
555
116
50
152
39,4
20
DIT
M(9
%)
94
735
111
35
102
63,0
51
Tota
l(1
00%
)142
796
120
64
214
100,1
03
30
Tab
le3:
Str
on
gly
Unu
sual
Tra
din
g(S
UT
)S
amp
lean
dM
atch
edR
and
omS
amp
le
Panel
Apre
sents
sam
ple
stati
stic
sfo
rth
eStr
ongly
Unusu
al
Tra
din
g(S
UT
)sa
mple
,re
flec
ting
four
sele
ctio
ncr
iter
ia:
(1)
the
bes
tbid
pri
ceof
the
day
isze
ro,
(2)
non-z
ero
volu
me,
(3)
opti
on
expir
ati
on
aft
erth
eannounce
men
tdate
and
(4)
transa
ctio
nw
ithin
30
day
spri
or
toth
eannounce
men
tdate
.P
anel
Bpre
sents
com
para
tive
stati
stic
sfo
ra
random
lyse
lect
edsa
mple
from
the
enti
redata
,w
her
efo
rea
chev
ent
we
choose
apse
udo
even
tdate
and
then
apply
the
sam
e
sele
ctio
ncr
iter
iaas
for
the
SU
Tsa
mple
.Sourc
e:O
pti
onM
etri
cs
Panel
A:SUT
Selectionwithth
ehistorica
l1,859(792)ev
entdatesforth
etarget
(acq
uirer)
TargetObs
#Dea
ls#
Options
Mea
nVol
Med
Vol
Min
Vol
1st
pctile
5th
pctile
25th
pctile
75th
pctile
95th
pctile
99th
pctile
MaxVol
All
2,042
437
1,243
123.78
20
11
16
62
479
2,076
13,478
Calls
1,106
299
570
137.23
20
11
15
65
543
2,517
6,161
Puts
936
316
673
107.9
20
11
17.5
60
390
1,494
13,478
Acquirer
Obs
#Dea
ls#
Options
Mea
nVol
Med
Vol
Min
Vol
1st
pctile
5th
pctile
25th
pctile
75th
pctile
95th
pctile
99th
pctile
MaxVol
All
1,847
204
776
110.36
14
11
15
50
394
1,829
16,837
Calls
954
145
364
126.03
12
11
14
53
437
1,899
16,837
Puts
893
155
412
93.61
16
11
25
50
370
1,829
5,004
Panel
B:1random
sample
of1,859(792)pseudoev
entdatesforth
etarget
(acq
uirer)
TargetObs
#Dea
ls#
Options
Mea
nVol
Med
Vol
Min
Vol
1st
pctile
5th
pctile
25th
pctile
75th
pctile
95th
pctile
99th
pctile
MaxVol
All
3,412
574
1,901
57
10
11
15
32
200
813
5,000
Calls
1,813
351
941
64
11
11
15
40
232
893
5,000
Puts
1,599
387
960
49
10
11
15
30
182
759
3,000
Acquirer
Obs
#Dea
ls#
Options
Mea
nVol
Med
Vol
Min
Vol
1st
pctile
5th
pctile
25th
pctile
75th
pctile
95th
pctile
99th
pctile
MaxVol
All
2,878
337
1,253
73
12
11
15
40
294
1,102
5,938
Calls
1,781
226
576
60
10
11
14
36
224
1,000
4,287
Puts
1,097
228
677
94
15
11
17
50
401
1,540
5,938
31
Table 4: Bivariate Kolmogorov-Smirnov Tests - Target
The table reports the test statistics from a generalization of the bivariate two-sample Kolmogorov Smirnov test based
on Fasano and Franceschini (Fasano and Franceschini (1987)) . The null hypothesis of the test is that two bi-variate
samples come from the same empirical distribution function. ∗∗∗, ∗∗ and ∗ denote statistical significance at the 1%,
5% and 10% level respectively.
Panel A: CallsFull Sample
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0279∗∗∗ 0.0482∗∗∗ 0.0616∗∗∗ 0.1007∗∗∗ 0.1592∗∗∗ 0.4070∗∗∗
[−24,−20] . . 0.0228∗∗∗ 0.0368∗∗∗ 0.0744∗∗∗ 0.1334∗∗∗ 0.3911∗∗∗
[−19,−15] . . . 0.0173∗∗ 0.0556∗∗∗ 0.1134∗∗∗ 0.3694∗∗∗
[−14,−10] . . . . 0.0410∗∗∗ 0.0988∗∗∗ 0.3581∗∗∗
[−9,−5] . . . . . 0.0606∗∗∗ 0.3256∗∗∗
[−4,−1] . . . . . . 0.2798∗∗∗
[0, 0] . . . . . . .TTE = [0,30]
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0348 0.1255∗∗∗ 0.2157∗∗∗ 0.2750∗∗∗ 0.3388∗∗∗ 0.6102∗∗∗
[−24,−20] . . 0.1212∗∗∗ 0.2121∗∗∗ 0.2645∗∗∗ 0.3340∗∗∗ 0.6093∗∗∗
[−19,−15] . . . 0.0979∗∗∗ 0.1667∗∗∗ 0.2377∗∗∗ 0.5105∗∗∗
[−14,−10] . . . . 0.0979∗∗∗ 0.1700∗∗∗ 0.4408∗∗∗
[−9,−5] . . . . . 0.0867∗∗∗ 0.3607∗∗∗
[−4,−1] . . . . . . 0.2854∗∗∗
[0, 0] . . . . . . .TTE = ]30,60]
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0605∗∗∗ 0.0859∗∗∗ 0.0905∗∗∗ 0.1341∗∗∗ 0.1843∗∗∗ 0.4324∗∗∗
[−24,−20] . . 0.0390∗∗ 0.0453∗∗∗ 0.0874∗∗∗ 0.1421∗∗∗ 0.3925∗∗∗
[−19,−15] . . . 0.0246 0.0628∗∗∗ 0.1111∗∗∗ 0.3746∗∗∗
[−14,−10] . . . . 0.0554∗∗∗ 0.1050∗∗∗ 0.3605∗∗∗
[−9,−5] . . . . . 0.0611∗∗∗ 0.3232∗∗∗
[−4,−1] . . . . . . 0.2885∗∗∗
[0, 0] . . . . . . .TTE = [60,...]
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0227∗∗∗ 0.0323∗∗∗ 0.0364∗∗∗ 0.0675∗∗∗ 0.1195∗∗∗ 0.3897∗∗∗
[−24,−20] . . 0.0165∗ 0.0210∗∗∗ 0.0503∗∗∗ 0.1009∗∗∗ 0.3763∗∗∗
[−19,−15] . . . 0.0158∗ 0.0390∗∗∗ 0.0885∗∗∗ 0.3623∗∗∗
[−14,−10] . . . . 0.0350∗∗∗ 0.0853∗∗∗ 0.3599∗∗∗
[−9,−5] . . . . . 0.0549∗∗∗ 0.3324∗∗∗
[−4,−1] . . . . . . 0.2883∗∗∗
[0, 0] . . . . . . .Panel B: PutsFull Sample
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0331∗∗∗ 0.0414∗∗∗ 0.0382∗∗∗ 0.0607∗∗∗ 0.0820∗∗∗ 0.2760∗∗∗
[−24,−20] . . 0.0209∗∗ 0.0242∗∗∗ 0.0403∗∗∗ 0.0677∗∗∗ 0.2657∗∗∗
[−19,−15] . . . 0.0176∗ 0.0301∗∗∗ 0.0524∗∗∗ 0.2549∗∗∗
[−14,−10] . . . . 0.0295∗∗∗ 0.0561∗∗∗ 0.2564∗∗∗
[−9,−5] . . . . . 0.0389∗∗∗ 0.2351∗∗∗
[−4,−1] . . . . . . 0.2132∗∗∗
[0, 0] . . . . . . .TTE = [0,30]
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0318 0.1246∗∗∗ 0.1978∗∗∗ 0.2886∗∗∗ 0.3400∗∗∗ 0.5275∗∗∗
[−24,−20] . . 0.1280∗∗∗ 0.1978∗∗∗ 0.2893∗∗∗ 0.3407∗∗∗ 0.5266∗∗∗
[−19,−15] . . . 0.1003∗∗∗ 0.1752∗∗∗ 0.2280∗∗∗ 0.4149∗∗∗
[−14,−10] . . . . 0.0961∗∗∗ 0.1484∗∗∗ 0.3397∗∗∗
[−9,−5] . . . . . 0.0653∗∗∗ 0.2509∗∗∗
[−4,−1] . . . . . . 0.2104∗∗∗
[0, 0] . . . . . . .TTE = ]30,60]
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0670∗∗∗ 0.0975∗∗∗ 0.0907∗∗∗ 0.1228∗∗∗ 0.1355∗∗∗ 0.3370∗∗∗
[−24,−20] . . 0.0465∗∗ 0.0430∗ 0.0672∗∗∗ 0.0896∗∗∗ 0.3047∗∗∗
[−19,−15] . . . 0.0353 0.0484∗∗∗ 0.0747∗∗∗ 0.2895∗∗∗
[−14,−10] . . . . 0.0619∗∗∗ 0.0983∗∗∗ 0.3094∗∗∗
[−9,−5] . . . . . 0.0514∗∗ 0.2729∗∗∗
[−4,−1] . . . . . . 0.2361∗∗∗
[0, 0] . . . . . . .TTE = ]60,...]
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0293∗∗∗ 0.0309∗∗∗ 0.0264∗∗ 0.0371∗∗∗ 0.0657∗∗∗ 0.2706∗∗∗
[−24,−20] . . 0.0288∗∗∗ 0.0288∗∗∗ 0.0337∗∗∗ 0.0553∗∗∗ 0.2703∗∗∗
[−19,−15] . . . 0.0187 0.0184∗ 0.0487∗∗∗ 0.2525∗∗∗
[−14,−10] . . . . 0.0175 0.0454∗∗∗ 0.2534∗∗∗
[−9,−5] . . . . . 0.0361∗∗∗ 0.2429∗∗∗
[−4,−1] . . . . . . 0.2235∗∗∗
[0, 0] . . . . . . .
32
Table 5: Bivariate Kolmogorov-Smirnov Tests - Acquirer
The table reports the test statistics from a generalization of the bivariate two-sample Kolmogorov Smirnov test based
on Fasano and Franceschini (Fasano and Franceschini (1987)) . The null hypothesis of the test is that two bi-variate
samples come from the same empirical distribution function. ∗∗∗, ∗∗ and ∗ denote statistical significance at the 1%,
5% and 10% level respectively.
Panel A: CallsFull Sample
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0098 0.0220∗∗∗ 0.0248∗∗∗ 0.0287∗∗∗ 0.0499∗∗∗ 0.1074∗∗∗
[−24,−20] . . 0.0207∗∗∗ 0.0267∗∗∗ 0.0267∗∗∗ 0.0475∗∗∗ 0.1051∗∗∗
[−19,−15] . . . 0.0091 0.0183∗∗∗ 0.0320∗∗∗ 0.0880∗∗∗
[−14,−10] . . . . 0.0154∗∗∗ 0.0291∗∗∗ 0.0867∗∗∗
[−9,−5] . . . . . 0.0239∗∗∗ 0.0847∗∗∗
[−4,−1] . . . . . . 0.0695∗∗∗
[0, 0] . . . . . . .TTE = [0,30]
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0608 0.0866∗∗ 0.1154∗∗∗ 0.1259∗∗∗ 0.1374∗∗∗ 0.1732∗∗∗
[−24,−20] . . 0.0666 0.0877∗∗ 0.1040∗∗∗ 0.1100∗∗∗ 0.1625∗∗∗
[−19,−15] . . . 0.0443 0.0674∗∗∗ 0.0742∗∗∗ 0.1198∗∗∗
[−14,−10] . . . . 0.0370 0.0359 0.1077∗∗∗
[−9,−5] . . . . . 0.0222 0.1077∗∗∗
[−4,−1] . . . . . . 0.0982∗∗∗
[0, 0] . . . . . . .TTE = ]30,60]
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0357∗ 0.0537∗∗∗ 0.0535∗∗∗ 0.0686∗∗∗ 0.0666∗∗∗ 0.1159∗∗∗
[−24,−20] . . 0.0394∗∗ 0.0429∗∗∗ 0.0462∗∗∗ 0.0461∗∗∗ 0.1133∗∗∗
[−19,−15] . . . 0.0159 0.0289∗ 0.0244 0.0840∗∗∗
[−14,−10] . . . . 0.0253 0.0265 0.0918∗∗∗
[−9,−5] . . . . . 0.0345∗∗ 0.0917∗∗∗
[−4,−1] . . . . . . 0.0779∗∗∗
[0, 0] . . . . . . .TTE = [60,...]
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0100 0.0140∗∗ 0.0136∗∗ 0.0141∗∗∗ 0.0339∗∗∗ 0.0897∗∗∗
[−24,−20] . . 0.0142∗∗ 0.0133∗∗ 0.0138∗∗∗ 0.0340∗∗∗ 0.0890∗∗∗
[−19,−15] . . . 0.0103 0.0087∗∗∗ 0.0242∗∗∗ 0.0815∗∗∗
[−14,−10] . . . . 0.0121∗∗∗ 0.0283∗∗∗ 0.0839∗∗∗
[−9,−5] . . . . . 0.0247∗∗∗ 0.0841∗∗∗
[−4,−1] . . . . . . 0.0678∗∗∗
[0, 0] . . . . . . .Panel B: PutsFull Sample
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0165∗∗ 0.0205∗∗∗ 0.0232∗∗∗ 0.0356∗∗∗ 0.0483∗∗∗ 0.1174∗∗∗
[−24,−20] . . 0.0163∗∗ 0.0192∗∗∗ 0.0293∗∗∗ 0.0405∗∗∗ 0.1072∗∗∗
[−19,−15] . . . 0.0149∗∗ 0.0238∗∗∗ 0.0357∗∗∗ 0.1030∗∗∗
[−14,−10] . . . . 0.0172∗∗∗ 0.0305∗∗∗ 0.0975∗∗∗
[−9,−5] . . . . . 0.0218∗∗∗ 0.0860∗∗∗
[−4,−1] . . . . . . 0.0726∗∗∗
[0, 0] . . . . . . .TTE = [0,30]
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0668 0.0739 0.0712 0.0843∗∗∗ 0.1283∗∗∗ 0.2036∗∗∗
[−24,−20] . . 0.0774 0.0640 0.0713∗∗∗ 0.1159∗∗∗ 0.1861∗∗∗
[−19,−15] . . . 0.0393 0.0769∗∗∗ 0.1034∗∗∗ 0.1798∗∗∗
[−14,−10] . . . . 0.0616∗∗∗ 0.0917∗∗∗ 0.1547∗∗∗
[−9,−5] . . . . . 0.0624∗∗∗ 0.1390∗∗∗
[−4,−1] . . . . . . 0.0928∗∗∗
[0, 0] . . . . . . .TTE = ]30,60]
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0331 0.0382∗ 0.0450∗∗ 0.0577∗∗∗ 0.0663∗∗∗ 0.1235∗∗∗
[−24,−20] . . 0.0460∗∗∗ 0.0410∗∗ 0.0556∗∗∗ 0.0642∗∗∗ 0.1294∗∗∗
[−19,−15] . . . 0.0198 0.0381∗∗ 0.0379∗∗ 0.0972∗∗∗
[−14,−10] . . . . 0.0295 0.0350∗ 0.1038∗∗∗
[−9,−5] . . . . . 0.0318 0.0923∗∗∗
[−4,−1] . . . . . . 0.0828∗∗∗
[0, 0] . . . . . . .TTE = ]60,...]
Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0153∗ 0.0160∗∗ 0.0126 0.0182∗∗ 0.0268∗∗∗ 0.0919∗∗∗
[−24,−20] . . 0.0157∗∗ 0.0205∗∗∗ 0.0172∗∗ 0.0224∗∗∗ 0.0843∗∗∗
[−19,−15] . . . 0.0139 0.0128 0.0166∗ 0.0885∗∗∗
[−14,−10] . . . . 0.0155∗∗ 0.0243∗∗∗ 0.0914∗∗∗
[−9,−5] . . . . . 0.0137 0.0839∗∗∗
[−4,−1] . . . . . . 0.0774∗∗∗
[0, 0] . . . . . . .
33
Table 6: Positive Abnormal Trading Volume
Panel A reports the number and frequency of events with positive cumulative abnormal volume, as well as the t-
statistic on the average cumulative abnormal volume. Panel B reports the t-test statistics for the differences in the
average cumulative abnormal volumes across moneyness categories. We report heteroscedasticity-robust standard
errors. For the market model, the market option volume is defined as either the mean or the median of the total daily
trading volume across all options (respectively calls or puts) in the OptionMetrics database. The estimation window
starts 90 days before the announcement date and runs until 30 days before the event. The event window stretches
from 30 days before until one day prior to the event.
Panel A
Market Model (Median) Market Model (Mean) Constant Mean Model
Option Type All Calls Puts All Calls Puts All Calls Puts
All Options - TargetSign.t-stat 5% (#) 462 490 271 455 472 276 467 492 319Sign.t-stat 5% (freq.) 0.25 0.26 0.15 0.24 0.25 0.15 0.25 0.26 0.17E [CAV ] 15266.93 11969.28 3471.78 12955.74 10202.45 2688.79 14904.28 11546.02 3357.93t ¯CAV 5.19 5.69 3.70 4.33 4.70 2.72 5.12 5.51 3.59OTM Options - TargetSign.t-stat 5% (#) 405 383 387 394 383 397 462 572 591Sign.t-stat 5% (freq.) 0.22 0.21 0.21 0.21 0.21 0.21 0.25 0.31 0.32E [CAV ] 5650.09 3797.47 1859.50 5271.57 3581.55 1689.58 5477.21 3662.97 1814.23t ¯CAV 5.27 5.52 4.04 5.56 5.56 4.07 5.58 5.58 4.25ATM Options - TargetSign.t-stat 5% (#) 298 300 254 278 283 255 408 420 498Sign.t-stat 5% (freq.) 0.16 0.16 0.14 0.15 0.15 0.14 0.22 0.23 0.27E [CAV ] 1246.45 1059.16 188.04 1246.45 753.14 129.54 1307.18 1059.04 248.14t ¯CAV 1.85 2.34 0.79 1.14 1.45 0.49 1.92 2.27 1.00ITM Options - TargetSign.t-stat 5% (#) 358 448 316 354 434 317 424 596 619Sign.t-stat 5% (freq.) 0.19 0.24 0.17 0.19 0.23 0.17 0.23 0.32 0.33E [CAV ] 2804.58 1701.87 1109.71 2724.04 1644.19 1057.57 2791.03 1694.86 1096.17t ¯CAV 4.91 7.08 2.45 5.15 7 2.52 5.18 7.10 2.53All Options - AcquirerSign.t-stat 5% (#) 118 110 92 108 98 91 111 107 99Sign.t-stat 5% (freq.) 0.15 0.14 0.12 0.14 0.12 0.11 0.14 0.14 0.13E [CAV ] 934.35 310.10 796.90 -8447.54 -8806.43 -2606 -893.69 -1458.84 565.16t ¯CAV 0.08 0.05 0.17 -0.78 -0.99 -0.60 -0.08 -0.21 0.12OTM Options - AcquirerSign.t-stat 5% (#) 154 134 148 151 122 141 169 182 181Sign.t-stat 5% (freq.) 0.19 0.17 0.19 0.19 0.15 0.18 0.21 0.23 0.23E [CAV ] 11753.22 5945.83 5634.11 10629.36 5485.38 5134.54 11667.89 5981.59 5686.30t ¯CAV 5.75 4.96 5.13 5.65 4.65 4.84 6.32 5.20 5.58ATM Options - AcquirerSign.t-stat 5% (#) 247 235 163 230 225 154 254 240 210Sign.t-stat 5% (freq.) 0.31 0.30 0.21 0.29 0.28 0.19 0.32 0.30 0.27E [CAV ] 11969.14 7713.45 4231.43 11969.14 7122.31 4030.29 12082.69 7734.02 4348.66t ¯CAV 6.05 5.98 5.45 5.58 5.40 5.30 6.12 5.96 5.58ITM Options - AcquirerSign.t-stat 5% (#) 133 133 142 123 129 133 139 199 211Sign.t-stat 5% (freq.) 0.17 0.17 0.18 0.16 0.16 0.17 0.18 0.25 0.27E [CAV ] 6971.36 4975.12 2113.15 6137.50 4135.27 1562.08 6761.78 4827.16 1934.62t ¯CAV 3.12 2.79 1.56 2.77 2.24 1.15 3.04 2.73 1.43Panel B
Statistics Diff s.e. p-val Diff s.e. p-val Diff s.e. p-val
All Options - TargetOTM-ATM 4403.64 995.00 0.00 4414.89 1001.70 0.00 4170.03 965.00 0.00OTM-ITM 2845.51 679.97 0.00 2547.53 625.35 0.00 2686.17 644.32 0.00ATM-ITM -1558.13 768.04 0.04 -1867.35 870.18 0.03 -1483.86 803.99 0.07Call Options - TargetOTM-ATM 2738.31 640.40 0.00 2828.41 697.69 0.00 2603.93 655.36 0.00OTM-ITM 2095.60 609.21 0.00 1937.35 577.47 0.00 1968.11 587.85 0.00ATM-ITM -642.71 454.39 0.16 -891.06 514.97 0.08 -635.82 462.95 0.17Put Options - TargetOTM-ATM 1671.46 478.39 0.00 1560.04 443.08 0.00 1566.10 449.78 0.00OTM-ITM 749.79 300.46 0.01 632.01 313.97 0.04 718.06 310.18 0.02ATM-ITM -921.67 500.32 0.07 -928.03 499.72 0.06 -848.04 498.29 0.09All Options - AcquirerOTM-ATM -215.92 1607.93 0.89 -563.34 1491.05 0.71 -414.79 1512.74 0.78OTM-ITM 4781.86 2466.52 0.05 4491.87 2455.85 0.07 4906.11 2391.71 0.04ATM-ITM 4997.78 2424.68 0.04 5055.21 2517.13 0.04 5320.90 2447.53 0.03Call Options - AcquirerOTM-ATM -1767.63 1345.59 0.19 -1636.92 1321.14 0.22 -1752.44 1340.94 0.19OTM-ITM 970.71 2054.63 0.64 1350.12 2065.22 0.51 1154.43 2017.51 0.57ATM-ITM 2738.33 1813.65 0.13 2987.04 1916.52 0.12 2906.86 1816.19 0.11Put Options - AcquirerOTM-ATM 1402.68 881.56 0.11 1104.24 940.74 0.24 1337.64 806.92 0.10OTM-ITM 3520.95 1674.58 0.04 3572.46 1779.40 0.05 3751.68 1671.35 0.03ATM-ITM 2118.28 1383.63 0.13 2468.21 1434.58 0.09 2414.04 1394.93 0.08
34
Table 7: Positive Excess Implied Volatility
This table reports the results from a classical event study where we test whether there was statistically significant
positive excess implied volatility in anticipation of the M&A announcement. Two different models are used: excess
implied volatility relative to a constant-mean-volatility model, as well as a market model, where we use as the
market-implied volatility the CBOE S&P500 Volatility Index (VIX). The estimation window starts 90 days before
the announcement date and runs until 30 days before the event. The event window stretches from 30 days before until
one day prior to the event. Panel A reports the number and frequency of events with positive excess implied volatility,
as well as the t-statistic on the average cumulative excess implied volatility. Panel B reports the t-test statistics for
the differences in means of the average cumulative excess implied volatilities across moneyness categories. We report
heteroscedasticity-robust standard errors.
Panel A
Market Model (VIX) Constant Mean Model
Option Type Calls Puts Calls Puts
30-day ATM Implied Volatility (|δ| = 50) - TargetSign.t-stat 5% (#) 812 798 794 766Sign.t-stat 5% (freq.) 0.44 0.43 0.43 0.41E [CEIV ] 0.52 0.40 0.46 0.37t ¯CEIV 6.34 5.00 5.27 4.2930-day ITM Implied Volatility (|δ| = 80) - TargetSign.t-stat 5% (#) 733 756 712 762Sign.t-stat 5% (freq.) 0.39 0.41 0.38 0.41E [CEIV ] 0.51 0.42 0.46 0.41t ¯CEIV 5.66 4.81 4.72 4.3730-day OTM Implied Volatility (|δ| = 20) - TargetSign.t-stat 5% (#) 791 671 772 668Sign.t-stat 5% (freq.) 0.43 0.36 0.42 0.36E [CEIV ] 0.49 0.35 0.43 0.29t ¯CEIV 6.02 4.01 5.01 3.2630-day ATM Implied Volatility (|δ| = 50) - AcquirerSign.t-stat 5% (#) 305 302 265 264Sign.t-stat 5% (freq.) 0.39 0.38 0.33 0.33E [CEIV ] 0.02 0.00 -0.01 -0.04t ¯CEIV 0.24 0.02 -0.18 -0.5430-day ITM Implied Volatility (|δ| = 80) - AcquirerSign.t-stat 5% (#) 258 284 250 262Sign.t-stat 5% (freq.) 0.33 0.36 0.32 0.33E [CEIV ] 0.03 0.02 0.01 -0.02t ¯CEIV 0.35 0.25 0.10 -0.1930-day OTM Implied Volatility (|δ| = 20) - AcquirerSign.t-stat 5% (#) 297 257 265 223Sign.t-stat 5% (freq.) 0.38 0.32 0.33 0.28E [CEIV ] -0.02 0.02 -0.04 -0.07t ¯CEIV -0.33 0.27 -0.52 -0.83
Panel B
Market Model (VIX) Constant Mean Model
Statistics Diff s.e. p-val Diff s.e. p-val
Call Options - TargetITM − OTM 0.02 0.05 0.68 0.02 0.05 0.64Put Options - TargetITM − OTM 0.08 0.06 0.17 0.12 0.05 0.02Call Options vs. Put Options - TargetCITM − PITM 0.09 0.05 0.10 0.04 0.05 0.38CITM − POTM 0.16 0.07 0.02 0.16 0.06 0.01COTM − PITM 0.06 0.06 0.26 0.01 0.07 0.89COTM − POTM 0.14 0.05 0.01 0.14 0.05 0.00Call Options - AcquirerITM − OTM 0.05 0.04 0.23 0.05 0.04 0.19Put Options - AcquirerITM − OTM -0.00 0.06 0.97 0.05 0.04 0.22Call Options vs. Put Options - AcquirerCITM − PITM 0.01 0.04 0.87 0.03 0.03 0.43CITM − POTM 0.00 0.07 0.95 0.08 0.05 0.09COTM − PITM -0.04 0.05 0.36 -0.06 0.07 0.41COTM − POTM -0.05 0.06 0.44 0.03 0.03 0.41
35
Tab
le8:
Non
-par
amet
ric
Vol
um
e-at
-Ris
k
Panel
Are
port
sst
ati
stic
s(N
um
ber
of
obse
rvati
ons,
freq
uen
cy,
mea
n,
med
ian,
min
imum
,m
axim
um
)on
non-z
ero
tradin
gvolu
me
obse
rvati
ons
condit
ional
on
no
tradin
gvolu
me
duri
ng
the
one
to5
pre
cedin
gday
s.Sourc
e:O
pti
onM
etri
cs
Targ
et
Acqu
irer
Con
dit
ion
al
Volu
me
Sta
tist
ics
vari
ab
leN
Fre
q.
mean
p50
sdm
inm
ax
NFre
q.
mean
p50
sdm
inm
ax
Pan
elA
1:Vt>
0|V
t−1
=0
All
Op
tion
s90
0,01
30.
075
70.0
310
895.3
51
415,0
50
1,2
31,3
60
0.1
02
73.7
910
2,1
53
11,6
09,0
02
Cal
ls51
9,38
10.
0468
.58
10
1,0
92.3
01
415,0
50
654,8
90
0.1
08
67.1
010
2,8
96
11,6
09,0
02
Pu
ts38
0,63
20.
032
72.0
010
517.1
81
79,0
04
576,4
70
0.0
96
81.3
911
611
1100,1
03
Pan
elB
1:Vt>
0|V
t−1
=0,Vt−
2=
0A
llO
pti
ons
619,
190
0.05
162
.64
10
691.1
91
200,0
00
771,8
37
0.0
64
64.9
910
2,0
41
11,6
09,0
02
Cal
ls34
6,62
50.
028
61.0
610
819.5
61
200,0
00
395,0
71
0.0
65
60.2
910
2,8
08
11,6
09,0
02
Pu
ts27
2,56
50.
023
64.6
6710
480.7
31
54,0
04
376,7
66
0.0
63
69.9
210
517
1100,1
03
Pan
elC
1:Vt>
0|V
t−1
=0,Vt−
2=
0,Vt−
3=
0A
llO
pti
ons
474,
533
0.03
961
.66
10
743.9
31
200,0
00
556,7
29
0.0
46
61.8
810
2,3
47
11,6
09,0
02
Cal
ls26
0,01
90.
022
61.2
810
901.3
71
200,0
00
277,9
33
0.0
46
60.4
110
3,2
90
11,6
09,0
02
Pu
ts21
4,51
40.
018
62.1
310
489.3
21
54,0
04
278,7
96
0.0
46
63.3
510
456
160,0
01
Pan
elD
1:Vt>
0|V
t−1
=0,Vt−
2=
0,Vt−
3=
0,Vt−
4=
0A
llO
pti
ons
383,
559
0.03
262
.46
10
776.5
51
200,0
00
430,8
67
0.0
36
60.6
910
2,6
00
11,6
09,0
02
Cal
ls20
6580
0.03
462
.78
10
970.2
31
200,0
00
211,0
15
0.0
35
61.2
010
3,6
88
11,6
09,0
02
Pu
ts17
6979
0.02
959
.91
10
456.2
01
54,0
04
219,8
52
0.0
36
60.2
010
445
160,0
01
Pan
elE
1:Vt>
0|V
t−1
=0,Vt−
2=
0,Vt−
3=
0,Vt−
4=
0,Vt−
5=
0A
llO
pti
ons
320,
105
0.02
760
.70
10
790.2
91
200,0
00
347,2
48
0.0
29
59.3
510
2,8
17
11,6
09,0
02
Cal
ls16
9,90
70.
028
62.7
510
998.3
91
200,0
00
167,6
85
0.0
28
61.6
010
4,0
32
11,6
09,0
02
Pu
ts15
0,19
80.
024
58.3
710
451.0
81
54,0
04
179,5
63
0.0
30
57.2
510
406
160,0
01
36
Tab
le9:
Non
-par
amet
ric
Vol
um
e-at
-Ris
k
This
table
rep
ort
sth
etr
adin
gle
vel
sν
such
that
the
pro
babilit
yof
exce
edin
ga
tradin
gvolu
me
ofν
contr
act
s,co
ndit
ionalon
no
earl
ier
tradin
gvolu
me
duri
ng
1,
2,
and
up
to5
tradin
gday
s,is
less
than
or
equal
tox%
.P
anel
Aco
mpare
sre
sult
sfr
om
the
even
tw
indow
(30
day
sb
efore
the
announce
men
tdatet∈
[−29,−
1]
tost
ati
stic
sfr
om
ara
ndom
sam
ple
of
1859
pse
udo
even
tdate
sin
Panel
B.
The
stati
stic
sare
base
don
the
empir
ical
dis
trib
uti
on
funct
ions
of
volu
me,
defi
ned
as
the
num
ber
of
contr
act
str
aded
.)
Pan
el
A:
Event
Win
dowt∈
[−30,−
1]
Targ
et
Acqu
irer
Pan
elA
.1:P
(Vt≥ν|∑ n i
Vt−
i=
0;i
=1)≤x
%ν
1000
200
100
3010
52
11
x1
510
2550
75
90
95
99
ν986
182
92
30
10
42
11
x1
510
25
50
75
90
95
99
Pan
elA
.2:P
(Vt≥ν|∑ n i
Vt−
i=
0;i
=2)≤x
%ν
853
175
8530
10
52
11
x1
510
2550
75
90
95
99
ν802
150
79
26
10
41
11
x1
510
25
50
75
90
95
99
Pan
elA
.3:P
(Vt≥ν|∑ n i
Vt−
i=
0;i
=3)≤x
%ν
800
156
8026
10
52
11
x1
510
2550
75
90
95
99
ν770
143
73
25
10
41
11
x1
510
25
50
75
90
95
99
Pan
elA
.4:P
(Vt≥ν|∑ n i
Vt−
i=
0;i
=4)≤x
%ν
743
150
7525
10
52
11
x1
510
2550
75
90
95
99
ν750
131
69
24
10
41
11
x1
510
25
50
75
90
95
99
Pan
elA
.5:P
(Vt≥ν|∑ n i
Vt−
i=
0;i
=5)≤x
%ν
710
126
6522
10
41
11
x1
510
2550
75
90
95
99
ν700
143
70
25
10
52
11
x1
510
25
50
75
90
95
99
Pan
el
B:
Ran
dom
Sam
ple
Targ
et
Acqu
irer
Pan
elB
.1:P
(Vt≥ν|∑ n i
Vt−
i=
0;i
=1)≤x
%ν
1525
300
125
4012
52
11
x1
510
2550
75
90
95
99
ν1492
307
140
44
13
52
11
x1
510
25
50
75
90
95
99
Pan
elB
.2:P
(Vt≥ν|∑ n i
Vt−
i=
0;i
=2)≤x
%ν
1187
245
105
3410
52
11
x1
510
2550
75
90
95
99
ν1127
235
107
35
10
52
11
x1
510
25
50
75
90
95
99
Pan
elB
.3:P
(Vt≥ν|∑ n i
Vt−
i=
0;i
=3)≤x
%ν
1044
213
100
3010
52
11
x1
510
2550
75
90
95
99
ν1005
202
100
30
10
42
11
x1
510
25
50
75
90
95
99
Pan
elB
.4:P
(Vt≥ν|∑ n i
Vt−
i=
0;i
=4)≤x
%ν
1000
200
100
3010
52
11
x1
510
2550
75
90
95
99
ν990
196
91
30
10
42
11
x1
510
25
50
75
90
95
99
Pan
elB
.5:P
(Vt≥ν|∑ n i
Vt−
i=
0;i
=5)≤x
%ν
902
191
9030
105
21
1x
15
1025
5075
90
95
99
ν843
175
85
28
10
41
11
x1
510
25
50
75
90
95
99
37
Figure 1: Trading Volumes around Announcement Dates
These graphs plot the average option trading volume (left axis) and the average number of options used in the
calculation (right axis) 60 days before and after the announcement date. Graphs (1a) and (1b) plot the average call
trading volume for respectively the Acquirer and target. Graphs (1c) and (1d) plot the average put trading volume
for respectively the Acquirer and target. For each deal, the total trading volume on each day across calls and puts
is calculated. The bars represent the average daily total trading volume across all deals. Volume is defined as the
number of option contracts.
(a) (b)
(c) (d)
38
Figure 2: Volume vs Depth-in-Moneyness across Event Windows
These graphs illustrate local polynomial functions fitted to the volume-moneyness observations across 7 different
event windows and for the full sample excluding the event windows. Graphs (2a) and (2b) show the polynomial fits
for respectively call and put options on the target companies. Graphs (2c) and (2d) illustrate the polynomial fits for
respectively call and put options on the acquiring companies. Volume is defined as the number of option contracts.
Depth-in-moneyness is defined as the ratio of the stock price over the strike price. Volume is winsorized at the upper
99th percentile. Source: OptionMetrics
(a)
050
100
150
Vol
ume
(# C
ontr
acts
) -
Pol
ynom
ial F
it
0 .5 1 1.5 2S/K
Full Sample ex-EW [-29,-25] [-24,-20] [-19,-15][-14,-10] [-9,-5] [-4,-1] [0,0]
Call Options - Target
(b)
020
4060
8010
0V
olum
e (#
Con
trac
ts)
- P
olyn
omia
l Fit
0 .5 1 1.5 2S/K
Full Sample ex-EW [-29,-25] [-24,-20] [-19,-15][-14,-10] [-9,-5] [-4,-1] [0,0]
Put Options - Target
(c)
050
100
150
200
Vol
ume
(# C
ontr
acts
) -
Pol
ynom
ial F
it
0 .5 1 1.5 2S/K
Full Sample ex-EW [-29,-25] [-24,-20] [-19,-15][-14,-10] [-9,-5] [-4,-1] [0,0]
Call Options - Acquirer
(d)
050
100
150
Vol
ume
(# C
ontr
acts
) -
Pol
ynom
ial F
it
0 .5 1 1.5 2S/K
Full Sample ex-EW [-29,-25] [-24,-20] [-19,-15][-14,-10] [-9,-5] [-4,-1] [0,0]
Put Options - Acquirer
39
Figure 3: Trading Volume Distribution around Announcement Dates
These graphs plot distributional statistics of options trading volume, defined as the number of traded contracts, from
30 days before until 20 days after the announcement date. The left axis plots the 90th and the 95th percentile of the
volume distribution, while the right axis refers to the interquartile range. Graphs (3a) and (3b) refer to respectively
call and put volume for target companies. Graphs (3c) and (3d) refer to respectively call and put volume for acquiring
companies. Source: OptionMetrics
(a)
5010
015
020
0In
terq
uart
ile R
ange
050
010
0015
0020
00V
olum
e (#
Con
trac
ts)
-30 -20 -10 0 10 20Event Time
90th percentile 95th percentile Interquartile Range
Call Options Volume Distribution - Target
(b)
4060
8010
012
014
0In
terq
uart
ile R
ange
050
010
0015
0020
00V
olum
e (#
Con
trac
ts)
-30 -20 -10 0 10 20Event Time
90th percentile 95th percentile Interquartile Range
Put Options Volume Distribution - Target
(c)
8010
012
014
0In
terq
uart
ile R
ange
200
400
600
800
1000
1200
Vol
ume
(# C
ontr
acts
)
-30 -20 -10 0 10 20Event Time
90th percentile 95th percentile Interquartile Range
Call Options Volume Distribution - Acquirer
(d)
6080
100
120
Inte
rqua
rtile
Ran
ge
200
400
600
800
1000
1200
Vol
ume
(# C
ontr
acts
)
-30 -20 -10 0 10 20Event Time
90th percentile 95th percentile Interquartile Range
Put Options Volume Distribution - Acquirer
40
Figure 4: Abnormal Trading Volumes Before Announcement Dates
Graph 4c plots the average abnormal trading volume for respectively all equity options, call and put options over the
30 days preceding the announcement date. Graph 4d reflects the average cumulative abnormal trading volume over
the same event period.
(a)
050
010
0015
0020
00A
vera
ge A
bnor
mal
Vol
ume
(# C
ontr
acts
)
-30 -20 -10 0Event Time
All Call Put
Average Abnormal Volume
(b)
050
0010
000
1500
0A
vera
ge C
umul
ativ
e A
bnor
mal
Vol
ume
(# C
ontr
acts
)
-30 -20 -10 0Event Time
All Call Put
Average Cumulative Abnormal Volume
(c)
-100
0-5
000
500
1000
1500
Ave
rage
Abn
orm
al V
olum
e (#
Con
trac
ts)
-30 -20 -10 0Event Time
All Call Put
Average Abnormal Volume
(d)
-300
0-2
000
-100
00
1000
Cum
ulat
ive
Ave
rage
Abn
orm
al V
olum
e (#
Con
trac
ts)
-30 -20 -10 0Event Time
All Call Put
Average Cumulative Abnormal Volume
41
Figure 5: Excess Implied Volatility Before Announcement Dates
Graphs 5a (target) and 5c (acquirer) plot the average excess implied volatility relative to the VIX index for the
30-day ATM implied volatility from respectively call and put options, over the 30 days preceding the announcement
date. Graphs 5b (target) and 5b (acquirer) reflect the average cumulative excess implied volatility over the same
event period. Source: OptionMetrics.
(a)
.01
.02
.03
.04
.05
-30 -20 -10 0Event Time
Call Put
Average Excess Implied Volatility
(b)
0.1
.2.3
.4.5
-30 -20 -10 0Event Time
Call Put
Average Cumulative Excess Implied Volatility
(c)
-.00
4-.
002
0.0
02.0
04.0
06A
vera
ge E
xces
s Im
plie
d V
olat
ility
-30 -20 -10 0Event Time
Call Put
Average Excess Implied Volatility
(d)
-.00
50
.005
.01
.015
.02
Ave
rage
Cum
ulat
ive
Exc
ess
Impl
ied
Vol
atili
ty
-30 -20 -10 0Event Time
Call Put
Average Cumulative Excess Implied Volatility
42
Figure 6: Information Dispersion - Bid-Ask Spreads
These graphs illustrates the evolution of the percentage bid-ask spread from 90 days before the announcement date
to 90 days after the announcement date. The left column illustrates the results for target companies, while the
right graph illustrates results for the acquiring companies. Graphs (6a) and (6b) are simple average percentage
bid-ask spreads, Figures (6c) and (6d) reports the average omega-adjusted bid-ask spread. All bid and ask prices are
right-winsorized at the top 99.9th percrentile. Source: OptionMetrics.
(a)
.3.4
.5.6
.7.8
Per
cen
tag
e B
id-A
sk S
pre
ad
-80 -60 -40 -20 0 20 40 60 80Event Time
Percentage Bid-Ask Spread, Target
(b)
.125
.13
.135
.14
.145
.15
Per
cen
tag
e B
id-A
sk S
pre
ad
-80 -60 -40 -20 0 20 40 60 80Event Time
Percentage Bid-Ask Spread, Acquirer
(c)
.2.2
5.3
.35
.4.4
5
Om
ega-
Ad
just
ed B
id-A
sk S
pre
ad
-100 -50 0 50 100Event Time
Omega-Adjusted Bid-Ask Spread, Target
(d)
.2.2
5.3
.35
Om
ega-
Ad
just
ed B
id-A
sk S
pre
ad
-100 -50 0 50 100Event Time
Omega-Adjusted Bid-Ask Spread, Acquirer
43
Figure 7: Implied Volatility Smile and Term Structure
These graphs characterize the evolution of implied volatility around M&A announcement dates. Each node represents
the cross-sectional average within a time window defined on the x-axis. The top panel is for the target firms while the
bottom panel represents the data for the acquirier firms. Figures (7a) and (7c) plot the evolution of two additional
IV skewness measures for respectively the target and acquirer: the difference between the OTM IV for puts with a
delta of 25 and OTM IV for calls with a delta of 25, scaled by the average ATM IV with a delta of 50 (left axis); the
difference between OTM IV for puts with a delta of 20 and ATM IV for calls with a delta of 50 (right axis). Figures
(7b) and (??) depict the IV term structure for call options, defined as the difference between the ATM call options
(delta = 50) for respectively 91 and 30 days to maturity (left axis), and the IV term structure for put options, defined
as the difference between the ATM put options (delta = 50) for respectively 91 and 30 days to maturity (left axis).
Source: OptionMetrics.
(a).0
4.0
5.0
6.0
7.0
8.0
9IV
p-de
lta20
- IV
c-de
lta50
.01
.02
.03
.04
.05
.06
(IV
p-de
lta25
- IV
c-de
lta25
)/de
lta50
]...,-30] [-24,-20] [-14,-10] [-4,-1] [1,5] [11,15] [21,25] [31,...[Event Time
Carr-Wu (Mean) Cremers et al. (Mean)
IV-Skew (30-day options)
(b)
-.05
-.04
-.03
-.02
-.01
IVp-
delta
50/9
1 -
IVp-
delta
50/3
0
-.05
-.04
-.03
-.02
-.01
IVc-
delta
50/9
1 -
IVc-
delta
50/3
0
]...,-30] [-24,-20] [-14,-10] [-4,-1] [1,5] [11,15] [21,25] [31,...[Event Time
IV-Term Calls (Mean) IV-Term Puts (Mean)
IV-Term Structure (91day - 30day options)
(c)
.044
.046
.048
.05
.052
.054
IVp-
delta
20 -
IVc-
delta
50
.02
.022
.024
.026
.028
(IV
p-de
lta25
- IV
c-de
lta25
)/de
lta50
]...,-30] [-24,-20] [-14,-10] [-4,-1] [1,5] [11,15] [21,25] [31,...[Event Time
Carr-Wu (Mean) Cremers et al. (Mean)
IV-Skew (30-day options)
(d)
-.01
2-.
01-.
008
-.00
6-.
004
IVp-
delta
50/9
1 -
IVp-
delta
50/3
0
-.01
4-.
012
-.01
-.00
8-.
006
IVc-
delta
50/9
1 -
IVc-
delta
50/3
0
]...,-30] [-24,-20] [-14,-10] [-4,-1] [1,5] [11,15] [21,25] [31,...[Event Time
IV-Term Calls (Mean) IV-Term Puts (Mean)
IV-Term Structure (91day - 30day options)
44