Informed Trading Reactions to New Private Information:

Evidence from Nonpublic Merger Negotiations

Shane Heitzman*

Marshall School of Business – University of Southern California

Sandy Klasa**

Eller College of Management – University of Arizona

October 21, 2017

Abstract:

Theory provides competing predictions with regards to the question of whether informed investors

immediately trade on newly generated private information. We address this question using SEC-

mandated disclosures to identify the dates when new private information about target or acquiring

firm value is generated. We find that informed investors immediately trade on new private

information. Next, we investigate what factors drive the speed of these investors’ trading reactions

to newly generated private information. We show that cross-sectional variation in the speed of

their trading reactions can be explained by the number of privately informed investors, institutional

ownership, the expected profits from informed trading and associated risk of attracting the

attention of enforcement agencies, and the existence of public information about an acquisition

deal.

We thank Anup Agrawal, Linda Bamber, Diane Del Guercio, Luke Devault, Ro Gutierrez, Kathy Kahle,

Iva Kalcheva, Ron Kaniel, Eric Kelley, Chris Lamoureux, Jochen Lawrenz, Lubo Litov, Wayne Mikkelson,

Bill Schwert, Rick Sias, Jerry Warner, and workshop participants at MIT, the Universities of Arizona,

Georgia, Innsbruck, Oregon, Rochester, and Utah, and the Divison of Economics and Risk Analysis at the

Securities and Exchange Commission for helpful comments. We are also grateful to Denis Sosyura

(discussant) and participants at the 2012 University of Washington Summer Finance Conference. We

recognize the excellent research assistance provided by Tyler Brough, Michael Dambra, Douglas Fairhurst,

Maryam Fathollahi, Jordan Neyland, and Matthew Serfling. *Los Angeles, CA 90089; phone: 213.740.6531; email: shane.heitzman@marshall.usc.edu **Tucson, AZ 85721; phone: 520.621.8761; email: sklasa@eller.arizona.edu

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1. Introduction

It is well known that investors exploit opportunities to trade on private information. For

instance, research documenting price runups in acquisition targets prior to a deal announcement or

price movements ahead of earnings announcements provides indirect evidence of informed

trading. Further, prior work shows that connected institutions make profitable trades based on non-

public information about an acquisition (Bodnaruk, Massa, and Simonov (2009)), private loans

made to a firm (Massa and Rehman (2008), Ivashina and Sun (2011), and Massoud, Nandy,

Saunders, and Song (2011)), and that both institutions and individuals execute profitable trades

prior to earnings announcements (Campbell, Ramadorai, and Schwartz (2009) and Kaniel, Liu,

Saar, and Titman (2012)).

To infer private information-based trading, related work often begins with a corporate

disclosure, such as an announcement of quarterly earnings, a merger agreement, or a corporate

loan amendment. Price and trading patterns during a window before the announcement are used to

infer evidence of informed trading. While this approach is useful in identifying the presence of

informed trading, it becomes harder to assess when the informed investors trade relative to the

date that value-relevant private information about a firm is generated, in large part because the

timing of stock returns and trading activity cannot be directly linked to the underlying information

events. We address this empirical challenge in a setting that allows us to directly make this link.

To provide new insights on informed investors’ trading behavior, we tackle the following two

questions in this paper: Do informed investors appear to immediately trade on new private

information about firm value? If so, what are the relevant factors that drive the speed of their

reaction to new private information?

Theory offers conflicting predictions on whether informed investors will immediately trade

on new private information. For instance, it is predicted that privately informed investors will

camouflage their trades by spreading them out over time (Kyle (1985)) or waiting for periods of

high trading volume (Admati and Pfleiderer (1988)). Theory also shows that competition between

informed investors can increase their propensity to rapidly trade on new private information

2

(Holden and Subrahmanyam (1992)). On the other hand, Acharya and Johnson (2010) predict that

a larger number of privately informed investors can lead to no change in the level of private

information-based trading if insider trading enforcement intensifies when suspicious trading

activity is accompanied by greater trading volume. From a practical standpoint, one might expect

an investor to avoid trading immediately on new private information because events that lead to

the generation of this information can be disclosed ex post and could make it easier for enforcement

agencies to link the investor’s trades to her nonpublic information. Exposure to litigation risk is

further heightened by dramatic increases over the last decades in the enforcement budget of the

Securities and Exchange Commission (SEC) (Del Guercio, Odders-White, and Ready (2017)).

For a sample of 545 acquisitions completed between 1995 and 2006, we use SEC-mandated

disclosures of background information on the acquisition to identify the dates of various material

“events” that occur during private (nonpublic) negotiations between the target and potential

acquirers, that is, before the acquisition is publicly announced. These dates serve as proxies for

discrete points in time when new private information about firm value is generated and provide us

with a particularly attractive setting to test hypotheses pertaining to informed trading. Private

negotiation events include the initiation of merger talks, an acquirer making an offer for the target,

or a meeting of the target’s or acquirer’s board to discuss a proposed deal, among others. Over the

three months before the first public disclosure of a preliminary merger agreement, the average deal

has nearly seven unique trading days with at least one material nonpublic negotiation event.

Importantly, while the negotiating executives, directors, legal and financial advisors, and

financing providers have strong financial and reputational incentives to keep deal negotiations

secret, leakage is difficult to control. For instance, lower-level managers and staff, and their family

and friends could trade on direct or indirect knowledge of negotiations. Also, individuals

possessing privileged information about deal negotiations could pass along this information to

traders in exchange for compensation, possibly through their involvement in “expert networks”.1

1 Expert network firms retain current and past managers and other industry specialists as consultants to investors such

as mutual funds and hedge funds. While these firms have policies that bar their consultants from passing along

confidential information, it appears that many consultants have done so for compensation. For instance, see SEC

3

Overall, the pool of individuals with private information about deal negotiations—obtained illicitly

or not—can become large, thereby increasing the potential for informed trading activity

immediately after nonpublic negotiation events.2

With respect to our first question, we find compelling evidence that informed investors

trade immediately on new private information. For each nonpublic negotiation event, target firm

excess abnormal returns average 19 basis points the same day and 23 basis points the next,

resulting in two-day estimated excess returns of 42 basis points per event. These returns are

incremental both to the well-documented average positive abnormal target returns during the

period prior to the deal announcement and to market reactions to press rumors during the same

period. For the average deal, the price response is limited to these two days, becoming insignificant

until the next event.

We also find that abnormal trading volume, order imbalance, and medium-size trades spike

around private negotiation events. This is additional evidence that informed investors respond

quickly to the arrival of new private information. We further show that the price reaction to a

negotiation event is more pronounced when contemporaneous abnormal trading volume, order

imbalance, or the fraction of trades that are medium-sized is higher, consistent with the notion that

the price response to a negotiation event depends on the intensity of informed trading activity.

To further examine the economic importance of the informed trading activity we observe,

we investigate whether the negotiation parties can discern that investors immediately trade on

private information generated by their negotiations. We do this by examining the mapping between

stock price movements following negotiation events and the final negotiated deal price. Prior

research shows that for every dollar increase in the target’s stock price during the period prior to

vs. Longria et al., February 8, 2011, SDNY 11-CV-0753; Susan Pulliam, Michael Rothfield, Jenny Strasburg, and

Gregory Zuckerman, “U.S. in vast insider trading probe,” The Wall Street Journal, November 10, 2010; and Jenny

Strasburg and Kara Scannell, “SEC probes transactions in a hunt for inside trades,” The Wall Street Journal,

December 10, 2009. 2 Informed trading before a merger announcement includes both illegal insider trading on material nonpublic

information and legitimate trading based on superior information acquisition and processing. Moreover, the term

“insider” in insider trading as used in this paper refers to any individual that knowingly trades on material nonpublic

information. This could include, but is not limited to, corporate insiders as defined under Section 16 requirements

for reporting trades (e.g., officers, directors, and some blockholders).

4

the deal announcement, acquirers increase the final offer price by at least one dollar (Schwert

(1996)). Using firm-specific regressions to decompose stock returns during this period, we find

that price increases attributable to immediate trading following negotiation events lead to

significantly smaller revisions in the final offer price relative to other price movements over this

period. This result suggests that the impact of immediate trading on new private information from

merger negotiations on target stock prices is large enough so that deal negotiators recognize this

trading activity, and consequently discount price movements following negotiation events.

We next turn to our second question and examine what leads to variation across deals in

price reactions to newly-generated private information. To understand how the number of investors

with private information affects the sensitivity of target firm stock prices to negotiation events, we

first consider the effect of Regulation Fair Disclosure (Reg FD). Reg FD was adopted in 2000 to

curtail firms’ selective disclosure of nonpublic information to market professionals, such as fund

managers and analysts. Once these selective disclosures ceased, the number of informed investors

presumably fell (e.g., Cohen, Frazzini, and Malloy (2010) and Ke, Petroni, and Yu (2008)). Our

results show that target stock price reactions to private negotiations are significantly greater before

Reg FD, suggesting that a larger number of informed investors leads to quicker trading on newly

generated private information. This finding is also consistent with the empirical evidence in

Acharya and Johnson (2010) who show that a greater number of deal insiders is associated with

more unusual target stock returns over the five days before a deal announcement.

We also investigate the effect of institutions’ ownership in the target on the sensitivity of

target stock prices to negotiation events. Griffin, Shu, and Topaloglu (2012) argue that institutions

are reluctant to trade on information obtained through their connections because their future

business depends on their reputation. However, Massa and Rehman (2008), Bodnaruk, Massa, and

Simonov (2009), Ivashina and Sun (2011), and Massoud, Nandy, Saunders, and Song (2011) find

evidence that institutions do trade on information obtained through their connections. We find that

institutional ownership in the target is negatively associated with stock price reactions to

negotiation events. Thus, even if institutions trade on nonpublic information from merger

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negotiations, reputational concerns may constrain them from trading on such information right

after it is generated. Further, we document that this result is driven by deals that occur after Reg

FD’s adoption. An interpretation of this finding is that institutions continued to obtain at least some

private information about merger negotiations after Reg FD, perhaps through their connections to

deal insiders, however, the reputational risk from immediately trading on this information

increased. Alternatively, it may be that institutions, as direct targets of Reg FD’s limitations on

selective disclosure, were placed at a comparative disadvantage relative to individuals in obtaining

private information.

Next, we consider the effect of expected trading profits, a key determinant of incentives

for informed trading. In the acquisition context, expected trading profits are a function of two

inputs: the offer premium and the likelihood of deal completion. We proxy for the likelihood of

deal completion with the fitted values from the Cremers, Nair, and John (2009) model predicting

the ex-ante likelihood of receiving a takeover offer. This assumes that a higher likelihood of an

offer translates into a greater probability of deal completion contingent on receiving an offer. We

document that the sensitivity of target firm prices to negotiation events is significantly greater

when there is a higher ex-ante likelihood of the target firm receiving an offer.

The offer premium compares the initial offer price to the target’s price three months before

the deal announcement. As expected, average abnormal returns during the entire preannouncement

period are increasing in the offer premium. However, target stock price reactions to negotiation

events are declining in the offer premium. Thus, while informed investors appear to trade more

aggressively in high premium deals, they avoid doing so immediately after a nonpublic negotiation

event. In additional tests, we find four important related results. First, for the subsample of deals

in which some investors later become subject to prosecution for illegal insider trading, stock price

reactions to negotiation events are increasing in the offer premium. Second, in low premium deals

the price reaction to a negotiation event is significantly different from zero only on the event day,

whereas in high premium deals the price reaction is delayed, taking up to four days for the event

to have a significant impact on the target’s stock price. Third, among the subsample of high

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premiums deals, we find a small and significantly negative price reaction on the event day when

there is no subsequent prosecution for insider trading, but a positive and significant event day

reaction of approximately 80 basis points when there is subsequent prosecution. Fourth, we model

the likelihood of prosecution for illegal insider trading in the target firm and find that the offer

premium has a positive statistically and economically significant effect on this likelihood. Put

together, these results suggest that informed investors rationally avoid trading right after a

nonpublic negotiation event in high premium deals to hedge their exposure to enforcement actions.

Importantly, while these results do not speak directly to the Acharya and Johnson (2010) prediction

that prosecution risk mitigates the incentives for informed trading when there are many insiders,

they are consistent with the broader prediction that informed investors adjust their trading

strategies based on perceived determinants of prosecution risk.

We also examine the effect of the existence of public information about the acquisition

deal, in the form of press speculation, on informed investors’ incentives to quickly trade on new

private information. Press rumors are relatively rare, showing up in roughly one out of every six

deals in our sample. However, litigation risk from trading on private information can be mitigated

if the trader can argue that the trade was based on public information about a potential deal.

Furthermore, press speculation can accelerate the formal deal announcement, before an informed

investor has had the time to exploit her remaining private information. The preceding discussion

leads to the prediction that press speculation increases an informed investor’s incentives to

immediately trade on new private information. Supporting this prediction, we find that target

firms’ stock prices respond significantly more to negotiation events that take place on or after the

day of the first news rumor about a potential acquisition.

To reinforce our interpretation of the results as evidence of immediate informed trading

reactions to newly generated private information, we examine the sensitivity of bidder firm stock

prices to private negotiation events. Because, on average, bidder acquisition announcement returns

are small and negative, we expect that if bidder firms experience informed trading driven abnormal

returns following negotiation events, these returns will be negative. To the best of our knowledge,

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existing work does not provide direct evidence of informed trading in the stock of bidder firms

during the period leading to the deal announcement.3 For the 406 bidders with available stock

return data, abnormal returns over the two-day period following nonpublic negotiation events are

insignificant. However, abnormal returns are significantly negative following negotiation events

in deals announced before Reg FD, providing further evidence that more informed investors results

in more immediate trading on newly generated private information. We next extend our model by

conditioning the bidder’s stock price response to negotiation events on the contemporaneous

abnormal stock return of the target, a proxy for the value of the private information generated by

the event and the trading incentives of informed investors. We find that over our full sample period,

the bidder’s stock price response to negotiation events is significantly more negative on days

accompanied by larger positive target stock price movements. Overall, our results for bidder

abnormal stock returns following negotiation events are additional support for the notion that

informed investors immediately trade on new private information.

Broadly, our study contributes to the literature on private information-based trading. By

focusing on the dates when private information is created rather than disclosed, our study provides

a novel and intuitive empirical approach to examine whether informed investors immediately trade

on new private information about firm value and what factors lead to variation in their trading

reactions. Our evidence indicates that informed investors promptly trade on newly generated

private information. Variation in this timeliness can be explained by the number of informed

investors, institutions’ equity ownership of a firm, the expected profits from trading on private

information and the associated risk of attracting the attention of enforcement agencies, and the

existence of some public information related to the information event.

The remainder of the paper is organized as follows. Section 2 discusses our sample and the

data on private merger negotiation events. Section 3 presents our main empirical results. Section

4 provides the results of additional analyses. Section 5 concludes.

3 For evidence of informed trading prior to the deal announcement in bidder firm equity options, see Chan, Ge, and

Lin (2015) and Lowry, Rossi, and Zhu (2017).

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2. Sample and Data

Our sample consists of 545 completed acquisitions announced between 1995 and 2006

identified from the Securities Data Corporation (SDC) Mergers and Acquisitions database with

background disclosures available for either the target or bidder. The sample is mostly drawn from

the deals analyzed by Heitzman (2011) who focuses on targets in the S&P 1500. To be included

in the sample, the acquirer must own no more than 50% of the target prior to the announcement

and the consideration must be in cash and/or shares of the acquirer. The target must have a stock

price of at least $1 and a market capitalization of at least $10 million measured three months before

the deal announcement. For tests involving bidder returns, we impose similar restrictions.

To calculate abnormal returns, we first estimate a market model regression over a one-year

period ending 127 trading days before the first public announcement of an offer, i.e.:

𝑅𝑖𝑡 = 𝛼𝑖 + 𝛽𝑖𝑅𝑚𝑡 + 𝑒𝑖𝑡, 𝑡 = −379, … , −127, (1)

where t is the trading day relative to the deal announcement day, Rit is the continuously

compounded return to target firm i, and Rmt is the continuously compounded return on the Center

for Research on Security Prices (CRSP) value-weighted market portfolio. Each target must have

at least 100 observations in the market model estimation to be included in the sample.4 Following

Schwert (1996), the stock price runup before the deal announcement is calculated by summing

abnormal returns over a window before the announcement which ends the day before the

announcement:

𝐴𝑅𝑖𝑡 = 𝑅𝑖𝑡 − �̂�𝑖 − �̂�𝑖𝑅𝑚𝑡 (2a)

𝑅𝑢𝑛𝑢𝑝𝑖 = ∑ 𝐴𝑅𝑖𝑡

−1

𝑡=−63

(2b)

In Table 1, we report descriptive statistics for the sample. The average runup measured over the

three-month runup period (the 63 trading days ending the day before the deal announcement) is

9.9%. In comparison, the average initial offer premium is 32.9%.

4 We note that 526 of the 545 target firms have trading on all 252 days. For the other 19 targets, the median firm has

only one day with zero trading volume.

9

A disclosure about the background of the transaction is required for tender offer (Form SC

14D-9), securities issuance (Form S-4), and proxy statement (Form DEFM14A) filings, and

provides key insight into the acquisition process that is not possible using only SDC data.5 Item

1005 of Regulation M-A, reproduced in Appendix B, describes firms’ current obligations to

disclose material contacts that occur prior to the announcement of the acquisition. In Appendix C,

we provide an example of such a disclosure. We note that prior to, during, and subsequent to our

sample period we are not aware of any meaningful changes to the disclosure requirements for

material contacts that take place before the deal announcement. As documented in Table 1, SDC

reports that 3% of deals have competing offers after the announcement. However, consistent with

the findings in Boone and Mulherin (2007), background disclosures indicate that 39% of the deals

have competing offers prior to the deal announcement. These disclosures also reveal that the

average length of time between the initiation of negotiations and the first formal public

announcement of a deal by the merging firms is 177 calendar days.

We exploit these disclosures to obtain the precise dates and nature of material

developments during the private negotiations that precede the deal’s public announcement.

Because these events occur before the first public disclosure of a merger agreement, they generate

private signals about firm value. Additionally, these events expand the distribution and increase

the precision of private information, further increasing the likelihood of informed trades.6 These

events include the initiation of merger talks, the retention of a financial advisor, the signing of

various contracts (including confidentiality, standstill, and exclusivity agreements), the initiation

5 Examples of empirical work that use this data source includes Sanders and Zdanowicz (1992), who provide evidence

that unusual target firm stock price movements prior to the public announcement of an acquisition deal tend to begin

once private merger talks are initiated. Boone and Mulherin (2007) use background merger disclosures to provide

more texture on the role of auctions versus negotiations in the takeover market. Also, Heitzman (2011) utilizes these

disclosures to identify the CEO’s role in merger negotiations. 6 Trading on material nonpublic information is illegal, but as a practical matter, insider trading cases are difficult to

prosecute. Moreover, insider trading rules require Section 16 insiders (i.e., officers and directors) to disclose their

trades in a timely manner, and short-swing profit rules limit the insider’s gain. As a result, Section 16 insiders do

not appear to trade directly on their inside knowledge of the deal (Agrawal and Jaffe (1995)). Most prosecuted

insider trading cases are not against these corporate insiders.

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of due diligence, acquisition-related board meetings, and private bids made to a target, among

others. They serve as our proxy for the arrival of new private information about firm value.

In Panel A of Table 2, we report the frequencies and timing of various negotiation events

over the three-month period prior to the deal announcement. We identify a total of 3,550 event-

days with precise dates disclosed, averaging between six and seven unique event-days per deal

during the three-month period before the deal announcement. Target board meetings are the most

frequent (1,855 observations), followed by bids made for the target (824), and acquirer board

meetings (551).7 For the 94% of targets with precise board meeting dates disclosed in the filings,

we observe board meetings on nearly four different trading days, and the average board meeting

is 18 trading days before the deal announcement. For the 70% of deals in which the precise date

of a private bid is disclosed, there are on average two different trading days with bids that typically

occur 17 trading days before the deal announcement. In Panel B, we provide evidence on the

clustering of negotiation events. For each event, we calculate the number of trading days since the

last event. This panel shows that 21.4% of negotiation events immediately follow another event,

while 13.9% of events occur two days after the most recent event, and 56.4% of events occur at

least four days after any prior event. This panel also documents that 8.1% of the events are the first

event over the entire negotiation period.

Press speculation is an important source of public information that can alter the incentives

for informed trading. To identify deals rumored by the press prior to the deal announcement, we

search Factiva over a six-month window before the announcement for news reports that include

the target’s name and the words ‘merger’, ‘takeover’, or ‘acquisition’ and their variants. We then

verify whether these news reports contain specific rumors that the target firm is in play or that

detail purported negotiations between the target and potential bidders. Panel A of Table 1 reports

that for 18% of our sample (97 deals) there is at least one instance of press speculation of a deal

7 We only consider events that have a precise date mentioned within the three-month window. Events with ambiguous

dates or those that occurred prior to the event window are not considered. If an event occurs on a non-trading day,

we treat the next trading day as the event day. Thus, a private bid offer received on Saturday and a board meeting

two days later on Monday are both treated as occurring on Monday (assuming Monday is not a holiday).

11

during a six-month window before the formal deal announcement. Focusing on the 85 deals with

rumors in the last three months before the deal announcement, Panel A of Table 2 shows that these

deals, on average, experience press speculation on two different trading days and that the average

rumor is published nearly one month (20 trading days) before the deal announcement.

Our ability to document links between private negotiation events and trading is subject to

some caveats. Informed traders have incentives to delay or spread out their trades following a

negotiation event to avoid revealing their information to the market and to minimize litigation risk.

Alternatively, if these traders establish their position in anticipation of an expected negotiation

event, they will be less likely to add to their position following the event. In both cases, informed

trading still occurs, but trades and price adjustments would not be concentrated around negotiation

events. There is also a risk that the disclosures we rely on to identify negotiation dates are noisy

or biased. Background disclosures are idiosyncratic, and managers may have private incentives to

obscure the timing and nature of negotiation events after the fact. If this is the case, the negotiation

events we identify from these disclosures could have little connection to informed trading activity.

3. Empirical Strategy and Results

3.1. Negotiations and informed trading: evidence from daily returns

Figure 1a depicts average abnormal returns over the five-day window surrounding

negotiation events. For this figure, we exclude negotiation events on adjacent trading days as well

as those with a press rumor the day of or adjacent to the event day. The non-event day returns

included for comparison include all trading days during the three months prior to the deal

announcement with no negotiation events or press rumors during the three-day window centered

on that day. The depicted pattern suggests that returns around negotiation events are larger on the

day of and following a negotiation event.

To tackle our first question—whether traders respond immediately to the arrival of new private

information—we estimate the following pooled cross-sectional model over the 63 trading days

prior to the first public announcement of the acquisition:

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where AR is the daily abnormal return for firm i on day t relative to the announcement date (t = 0).

Lagged abnormal returns control for autocorrelation in stock returns.8 Negotiation is an indicator

variable equal to one if a material negotiation event occurs on the day the return is measured (t) or

on the day before (t – 1). The lagged negotiation event coefficient (b2) reflects the impact of a

negotiation event on the next day’s return and is in part intended to capture the effects of

negotiation events that occur after the close of trading. Although only 85 of the 545 deals in our

sample have identifiable press rumors during the three-month period we analyze, about 22% of

those rumors take place on the same day as a negotiation event and 19% occur the day after (not

tabulated). Controlling for the effect of these rumors allows us to isolate the effect of private

information-based trading.9 Thus, in equation (3) we include the indicator variable Rumor which

is equal to one if there is press speculation of a possible deal on the day (t) or the day before (t –

1) the return is measured.10 We let c represent firm fixed effects.

The results are reported in the first column of Table 3. The estimate for b1 (coeff. = 0.186,

p < 0.001) implies that across all targets and events, the average abnormal return is 0.19% higher

on days when a nonpublic merger negotiation event takes place. The estimate for b2 (coeff. = 0.229,

p < 0.001) indicates that a negotiation event leads to a 0.23% higher abnormal return on the

following day as well. The average total effect and significance level is reported at the bottom of

the table and indicates an excess abnormal return of 0.416%, or 42 basis points, over the two days

8 As a robustness test, we re-estimate the models in Tables 3, 5, and 8 in which daily abnormal stock returns is the

dependent variable, including up to four lags of these returns as independent variables and obtain similar results.

Likewise, we obtain similar results if we do not control for the lag of daily abnormal returns. 9 Ahern and Sosyura (2014) report results that suggest during the period prior to the deal announcement, bidder and

target firms will sometimes disseminate non-merger related information about themselves to the press to generate

media coverage and short-term runups in their stock price. To the extent this media coverage increases target firms’

abnormal returns during the three months prior to the deal announcement, it biases downward our estimates of target

firm excess abnormal returns subsequent to merger negotiations. 10 Prior research shows that target firms with news rumors have substantially larger preannouncement runups (Jarrell

and Poulsen (1989)). However, rumors can themselves be the result of abnormal price and volume movements

(Pound and Zeckhauser (1990)).

𝐴𝑅𝑖𝑡 = 𝑎 + 𝑏0𝐴𝑅𝑖𝑡−1 + 𝑏1𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + 𝑏2𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡−1 + 𝑏3𝑅𝑢𝑚𝑜𝑟𝑖𝑡 +

𝑏4𝑅𝑢𝑚𝑜𝑟𝑖𝑡−1 + 𝑐𝑖 + 𝑒𝑖𝑡, (3)

13

starting on the event day. This effect is, by construction, incremental to the average positive

abnormal return during the runup period.11

Turning to the 85 deals with press rumors during the 63-day runup period, excess abnormal

returns average 4.13% (4.48% minus 0.35%) over the two days following the rumor (p < 0.001).

This is obviously large relative to the price reaction to private negotiation events reported above.

However, care should be taken when comparing the price reactions to public press rumors with

the price reactions to nonpublic negotiation events for the following reasons: First, press rumors

are revealed to and processed by all investors, informed and uninformed. For instance, a rumor

can trigger buying by merger arbitrageurs to provide liquidity and selling by institutions to

rebalance their portfolios. Second, press rumors can change informed investors’ incentives to

trade, and thus, trading on rumor days could be partly driven by informed traders who trade on

these days to minimize litigation risk (or sell to liquidate their positions) or who trade on these

days if they believe a press rumor could accelerate the announcement of a takeover deal. Third,

we only consider cases of press speculation in which the article references knowledge of actual

merger negotiations. Such instances of press speculation should generate larger market reactions.

Finally, for 460 (84.4%) of the deals in our sample there is no identifiable press rumor to influence

the results during the three-month window before the deal announcement.

The second model reported in Table 3 includes indicator variables for specific events. We

choose board meetings and bids based on their higher relative frequency and the retention of a

financial advisor based on Bodnaruk, Massa, and Simonov (2009), who report a link between deal

advisors and informed trading. Turning to two-day effects, board meetings and bids appear to be

the most informative for traders. Over the 63-day runup period, the estimated two-day excess

11 Because in Tables 3 and 8 we control for both a lag of the dependent variable and deal fixed effects this could result

in a correlation between the lagged dependent variable and the error term (Nickell (1981)). However, this correlation

is inversely related to the panel length, which in our case is the 63 trading days from day -63 to -1 relative to the

deal announcement. Once the panel length exceeds 30 this correlation is expected to become inconsequential.

Nevertheless, we examine whether our findings for the first model in Table 3 and those for Table 8 are robust to

using the Arellano and Bond (1991) and Blundell and Bond (1998) dynamic panel data estimators. We find our

results are very similar when we use these estimators.

14

abnormal return is 0.37% (p < 0.001) for target board meetings, 0.50% (p = 0.012) for acquirer

board meetings, and 0.30% (p = 0.055) for bids.12

Taken together, the evidence in Table 3 supports the conclusion that prices respond quickly

to the arrival of private information. While private information about merger negotiations is likely

incorporated in price throughout the runup period, the significant spikes in returns following

private negotiation events is consistent with informed traders immediately trading on newly

generated private information and is independent of any contemporaneous press rumor.

3.2. Evidence from additional measures of trading activity

To provide further evidence on the link between informed trading and deal negotiations,

we examine the impact of negotiation events on daily order imbalance, abnormal trading volume,

and trade size. To calculate order imbalance, we apply the Lee and Ready (1991) algorithm to

classify trades reported in the Trade and Quote (TAQ) database as buyer- or seller-initiated.13 We

then follow Chordia and Subrahmanyam (2004) and calculate daily order imbalance as the

difference between the number of buyer- and seller-initiated trades scaled by the total number of

trades that day. We estimate abnormal volume with daily abnormal share volume growth following

Schwert (1996).14 For trade size, we follow Barclay and Warner (1993) who provide evidence that

informed traders concentrate their trading activity in medium-size trades (big enough to make a

12We investigated if target price reactions are larger subsequent to physical board meetings as compared to

teleconference meetings because with physical meetings it is possible that more people would directly or indirectly

find out that a board meeting is taking place given that board members could be spotted travelling to a physical

board meeting or discuss their travel plans with family, friends, or colleagues. The fractions of target and bidder

firm acquisition-related board meetings that are physical board meetings are 79% and 84%, respectively. We find

that target price reactions do not differ between the two types of board meetings, which potentially suggests deal

insiders leak private information created during both physical and teleconference board meetings. 13 Lee and Ready (1991) use a five-second delay to match quotes to trades. Given our more recent sample period and

decreases in trade reporting lags, we follow Ellis, Michaely, and O’Hara (2000) and Chordia, Roll, and

Subrahmanyan (2011) and use the most recent quote in effect to sign trades. When a trade does not occur at either

the bid or ask, we classify the trade based on the trade price relative to the most recent trade price. See Bessembinder

(2003) for a discussion of trade classification methods. 14 Specifically, to calculate abnormal volume growth, we estimate the following regression over a one-year period

ending 127 days before the deal announcement:

𝑣𝑜𝑙𝑖𝑡 = 𝜔 + 𝜌𝑣𝑜𝑙𝑖𝑡−1 + 𝛾0𝑣𝑜𝑙𝑚𝑡 + 𝛾1𝑣𝑜𝑙𝑚𝑡−1 + 𝑣𝑖𝑡

where volit is the first difference of logged share volume for firm i on day t, and volmt is the first difference of logged

share volume for the market on day t. We then use the estimated coefficients to calculate abnormal volume growth

(vit) for firm i on day t of the runup.

15

profit and small enough to avoid detection from other traders or regulators) and define medium-

size as trades of 500 to 9,900 shares.15 Panel A of Table 4 reports the distributional statistics for

our three trading measures during the 63-trading day period prior to the announcement.16

If the positive returns we find around negotiation events are driven by private information-

based trading, we expect to see similar spikes in our three trading measures. In the first column of

Panel B in Table 4 we document a 1.8% (p < 0.001) increase in order imbalance on the day of a

negotiation event and a 1.4% (p = 0.002) increase the next day. The average daily abnormal volume

growth surrounding a negotiation event day is depicted in Figure 1b and suggests a spike in volume

on these days. In the second column of Panel B, we show that abnormal volume grows by 4.1% (p

< 0.001) on the event day and becomes insignificant (p = 0.108) the following day. In the third

column of Panel B, we find that the proportion of trades that are medium-size increases by 0.55%

(p = 0.003) and 0.32% (p = 0.09) the day of and following a negotiation event.17 Consistent with

our returns tests, we control for the release of press rumors. While order imbalance does not

respond to press rumors, volume and trade-size responses are generally many times larger than the

corresponding responses to nonpublic negotiation events. As with returns, the presence of large

trading responses to rumors for a subset of the sample does not dilute the importance or the

interpretation of the trading response to private negotiation events. Rather, the evidence in Panel

15 During the preannouncement period for a sample of tender offer targets, Barclay and Warner (1993) find that

medium-size trades account for 45.7% of the total number of trades, but are responsible for 92.8% of the cumulative

price change. 16 Trade size values are similar to those reported in Barclay and Warner (1993). However, we note that since 2000,

small-size trades are often executed through electronic clearing networks while medium-size trades are at times

executed through these networks. Because trades that are executed electronically are often broken up into smaller

trades this could lead to changes in the distribution of small-size and medium-size trades subsequent to the year

2000. We do observe such a shift. However, because in our tests we identify effects involving trade sizes using

within-firm variation, temporal variation in the distribution of trade size due to institutional factors should have

little effect on event-related changes in trade-size distributions for a given target firm. 17 As already discussed, in our models we include the lagged negotiation event variable. As untabulated robustness

tests, we re-estimated equation (3), in which the dependent variable is daily abnormal stock returns, including two

additional lags of the negotiation event variable and similarly controlling for another two lags of the rumor variable.

Likewise, we re-estimated the models in Panel B of Table 4 in which the dependent variable is order imbalance,

abnormal trading volume, or the proportion of trades that are medium-size trades including two additional lags of

the negotiation event variable and controlling for two more lags of the rumor variable. In no case do we find

significant coefficients on the higher order lags of the negotiation event variable.

16

B provides confirming evidence that the return responses documented in Table 3 are driven by

immediate trading on private information about the value of the target firm.

If stock price responses to negotiation events are attributable to immediate trading on

private information, then we expect the largest price movements to occur on days when non-price

signals of informed trading are strongest. We therefore extend equation (3) and allow the stock

price response to negotiation events to be a function of the contemporaneous signal from order

imbalance, abnormal trading volume, or trade size by interacting the negotiation event indicators

with informed trading proxies measured on the same day as the return. In each regression, we

control for the independent effects of volume, order imbalance and trade size on observed returns,

and include lagged returns and firm fixed effects.

In Panel C of Table 4, the coefficients on the interactions between the negotiation event

indicators and the trading signal (Negotiation × Informed trade proxy) reveal that abnormal stock

returns on and following a negotiation event day are increasing in the non-price measures of

informed trading. Based on the two-day effects reported at the bottom of Panel C, a 13 percentage

point increase in the fraction of buyer-initiated trades (roughly a one quartile shift from the median

order imbalance) is associated with a 14 basis point larger reaction over the two days following a

negotiation event (0.13 × 0.011). Likewise, an increase from the median to third quartile of

abnormal volume growth is associated with a 48 basis point larger price reaction.18 Finally, a

similar increase in the proportion of medium-size trades is associated with an 18 basis point larger

price reaction. Overall, the results in Table 4 provide corroborating evidence that informed

investors respond quickly to the arrival of private information and that the price response to a

negotiation event depends on the intensity of informed trading activity.

18The Table 4, Panel C results for abnormal trading volume provide some support for the prediction in Admati and

Pfleiderer (1988) that privately informed investors will camouflage their trades by waiting for periods of high

trading volume to trade on their information. However, the results in Panel B of this table suggest over the three

months leading to the deal announcement that abnormal trading volume over a particular trading day is itself partly

a function of whether a negotiation event takes place that day. Thus, while some investors who have knowledge

about new private information from merger negotiations may choose to immediately trade on this information if

trading volume in the target’s stock is high, it is likely that a considerable portion of this high trading volume is

itself due to other informed investors who are trading on the same information.

17

3.3 What drives the immediate stock price response to private negotiation events?

We now turn to the paper’s second research question: What factors drive investors to

immediately trade on new private information? To do this, we investigate whether the sensitivity

of stock returns to the arrival of private information about merger negotiations—that is, the

coefficients on the negotiation event indicators—is conditional on factors predicted to be

associated with this sensitivity. We therefore expand our pooled model in equation (3) to allow for

interactions between the negotiation event indicators and variables expected to affect the

sensitivity of target firm abnormal returns to nonpublic negotiation events:

𝐴𝑅𝑖𝑡 = 𝑎0 + 𝑏0𝐴𝑅𝑖𝑡−1 + 𝑏1𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + 𝑏2𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡−1 + ∑ ajθij

𝐽

𝑗=1

+ ∑ (b1jNegotiationit

𝐽

𝑗=1+ 𝑏2𝑗𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡−1)θij

+ 𝑏3𝑅𝑢𝑚𝑜𝑟𝑖𝑡 + 𝑏4𝑅𝑢𝑚𝑜𝑟𝑖𝑡−1 + 𝛿𝑌𝑒𝑎𝑟 + 𝑒𝑖𝑡

(4)

AR, Negotiation, and Rumor are defined as before. 𝛿𝑌𝑒𝑎𝑟 represent year fixed effects. 𝜃 is a vector

of J firm- and deal-specific variables predicted to affect the sensitivity of the target firm’s stock

price to nonpublic negotiation events. The 𝑎𝑗 represent the independent effects of these variables

(𝜃) on abnormal returns before the deal announcement while 𝑏1𝑗 and 𝑏2𝑗 represent their

incremental effect on the relation between negotiation events and stock returns. The sum of the

estimated interaction variable coefficients, 𝑏1𝑗 + 𝑏2𝑗, provides an estimate of the conditional effect

of variable θ on the two-day stock price response to a private negotiation event. To identify

empirical proxies, we consider four factors that are expected to be associated with target stock

price reactions to private negotiation events: the number of informed investors, institutional

investor ownership, expected trading profits, and the existence of public information about the

acquisition deal.

The number of informed investors. As discussed earlier, a larger number of informed investors

makes a delay in trading costly, resulting in more rapid trading on private information (Holden

and Subrahmanyam (1992)). However, if insider trading enforcement becomes stricter in the

18

number of privately informed investors, an increase in informed traders should not change

informed trading activity (Acharya and Johnson (2010)).

In constructing measures that capture the number of informed investors, we consider both

those that existed prior to the deal and those that arise because of the deal. The former group is

important because a trader’s ongoing investment in private information about a firm should

translate into an informational advantage should an acquisition offer be made for that firm. This

requires proxies for the extent of informed trading prior to the deal. Our first proxy for the existing

number of informed investors is the probability of informed trading (PIN), which estimates the

fraction of trades in a firm’s shares that arise from informed trades. It is estimated following the

approach of Easley, Hvidkjaer, and O’Hara (2002 and 2010) over the 12-month period ending

three months before the deal announcement.

Second, we consider the impact of Reg FD, enacted by the SEC on October 23, 2000 and

adopted in an effort to end selective disclosure to market professionals such as securities analysts,

hedge fund managers, and investment advisors. Prior to Reg FD, such market professionals

arguably benefited from greater access to private information from firms’ managers. After Reg

FD, information disclosed directly to market professionals must simultaneously be disclosed to

the public, effectively ceasing the flow of private information. If Reg FD was effective in

controlling selective disclosure, we expect more informed investors and thus stronger price

responses to negotiation events in the period before Reg FD.

Finally, to capture the number of informed investors with connections to the deal itself, we

construct a composite measure based on the following variables: the number of acquirer and target

firm financial and legal advisors, the number of bidders for the target firm from the background

disclosures, whether the target initiated the deal, whether the bidder is a public firm, and following

Acharya and Johnson (2010), the number of lead banks providing syndicated loans to the bidder.

Deals are ranked on these five attributes, and the ranks are combined to create a composite score

that ranges from zero to one.

19

Institutional investor ownership. Institutional investors are generally believed to be

sophisticated and informed investors whose trades drive prices to their fundamental value

(Boehmer and Kelley (2009)). The evidence in Campbell, Ramadorai, and Schwartz (2009)

implies that institutions trade ahead of information released in future earnings announcements,

while Bodnaruk, Massa, and Simonov (2009) find results suggesting that institutions trade ahead

of merger announcements on nonpublic information obtained through their connections to deal

insiders. If institutions are more likely to make informed trades than are other investors, and prices

reflect their information, then higher institutional ownership could be associated with more rapid

stock price reactions to nonpublic negotiation events. On the other hand, Griffin, Shu, and

Topaloglu (2012) argue that institutions are reluctant to trade on information obtained through

their connections because their future business depends on their reputation. Also, long-term

institutional investors, an important subset of institutional owners, appear less likely to make trades

based on private information for short-term gains (e.g., Hartzell and Starks (2003), Chen, Harford,

and Li (2007), Yan and Zhang (2009)). Further, the evidence in Kaniel, Liu, Saar and Titman

(2012) suggests that individuals also engage in a large amount of private information-based trading

activity. Thus, one can also predict that the level of institutional ownership has no association or a

negative association with target firm price reactions to negotiation events. We measure

institutional ownership as the fraction of equity owned by institutions at the end of the most recent

calendar quarter before the deal announcement.

Expected trading profits. We assume that incentives to rapidly trade on new private information

are increasing in the expected profits from this activity. In the context of trading on private

information about a potential acquisition, the expected profits from informed trading are a function

of the expected offer premium and the probability of a successful deal. The initial offer premium,

calculated as ln(Poffer/Pt - 63), is our proxy for the expected offer premium. We proxy for the

likelihood of deal success with two variables. First, we use the fitted values from the model in

Cremers, Nair, and John (2009) that predicts a firm’s ex-ante likelihood of receiving a takeover

offer and assume that when this likelihood is higher there is a greater ex-ante likelihood of deal

20

completion contingent on receiving an offer. Second, given the evidence in Officer (2003) and

Bates and Lemmon (2003), we expect that the likelihood of deal success is higher if the target

agrees to a termination fee clause. While informed investors stand to gain more from immediately

trading on private information when the expected profits from doing so are higher, their exposure

to litigation risk is also an increasing function of the profits made on the trades—especially if the

trades can be linked to the information event. Thus, we make no directional predictions regarding

the impact of expected profits from informed trading on investors’ incentives to immediately trade

on newly generated private information.

The existence of public information about the acquisition deal. Public information about the

acquisition deal, in the form of press rumors, can also affect informed investors’ incentives to

quickly trade on new private information. On one hand, rumors are a form of public information.

Because public information can substitute for private information, the more public information

about the deal embedded in the target’s stock price, the smaller the incentives for investing in and

trading on private information. On the other hand, public information about the deal could shield

informed traders from exposure to litigation risk by providing a justification that their trades were

actually made on the basis of public information. Also, if a press rumor triggers the acceleration

of a formal public announcement of a deal, an investor with private information will have stronger

incentives to trade quickly before losing her information advantage (McNichols and Trueman

(1994)). To test these predictions, we include an indicator equal to one if a press rumor has been

released at any point up to and including the return measurement date.19

In Panel A of Table 5, we report the results. The coefficient estimates and p-values on the

on the interaction terms are reported in the top section of the panel, with the main effects in the

bottom section. The reported interaction coefficients are the two-day effects estimated as the sum

19 Of course, the rumor itself could be endogenous to an informed investor’s incentives to sell shares (e.g., Hirshleifer,

Subrahmanyam, and Titman (1994), Van Bommel (2003) and Brunnermeier (2005)) or reduce litigation risk. In

untabulated tests, we model the likelihood of press speculation during the runup period as a function of several

explanatory variables used to understand the determinants of price reactions following negotiation events and also

if the bidder has a toehold in the target (e.g., Betton, Eckbo, and Thorburn (2009)). We find that rumors are more

likely if the target is larger, there are more deal insiders, or the bidder has a toehold in the target.

21

of the individual coefficients (𝑏1𝑗 + 𝑏2𝑗) and test whether the two-day negotiation event excess

abnormal return is conditional on the variable of interest.

As before, our regressions control for contemporaneous press rumors.20 We also control

for the target firm’s analyst coverage and interact it with the negotiation event indicators. Analyst

coverage is measured as the number of analysts covering the target firm at the end of the most

recent fiscal quarter prior to the deal announcement. To the extent analysts have access to private

information (Cohen, Frazzini, and Malloy (2010)), a larger number of analysts could be associated

with more informed investors. However, higher analyst coverage is also associated with a

reduction in adverse select costs (Brennan and Subrahmanyam (1995) and Anand and

Subrahmanyam (2008)), which would imply a lower level of private information-based trading.

Because variables such as institutional ownership are correlated with firm size, we also

control for the target’s market value of equity three months prior to the deal announcement and

interact this variable with the negotiation event indicators. Finally, we control for the method of

payment with an indicator variable for all cash deals and interact it with the negotiation event

indicators. We do so because the payment method could be associated with deal characteristics

that could impact informed investors’ incentives to rapidly trade on new private information. For

instance, whether a deal is executed as a tender offer may positively impact the likelihood of deal

success (e.g., Bates and Lemmon (2003)) and the method of payment used in tender offers is

usually cash. This could lead to a greater sensitivity of target stock prices to negotiation events in

cash deals. On the other hand, bidders may choose to use stock as the method of payment when

there is significant information asymmetry about the value of the target because the contingent-

pricing characteristics of stock payment help to mitigate information asymmetry about the target

(e.g., Hansen (1987) and Officer, Poulsen, and Stegemoller (2009)). As a result, when stock is

used as the method of payment, information asymmetry about the target’s value may be higher,

which could result in a greater sensitivity of target stock prices to negotiation events in stock deals.

20 We do not interact the contemporaneous press rumor indicators with the variables of interest here. Nevertheless, we

do so in robustness tests and find the inferences from the Table 5 results are qualitatively unchanged.

22

The first column of Table 5, Panel A reports the results for the full sample. The coefficient

on PIN is positive, but insignificant (p = 0.114). However, stock returns are significantly more

sensitive to negotiation events before Reg FD became effective. The coefficient of 0.427 (p =

0.029) implies that target stock prices react an average of 43 basis more to each negotiation event

prior to Reg FD. This finding supports the interpretation that Reg FD curtailed selective disclosure,

thus reducing the number of informed investors and hence the incentives for immediate trading on

private information. On the other hand, higher institutional ownership is associated with a lower

sensitivity of returns to negotiation events (p = 0.033). Increasing the fraction of institutional

ownership from the 25th to the 75th percentile is associated with a 24 basis point reduction in the

stock price reaction to a negotiation event ((74.74% - 35.21%) × -0.006). This finding suggests

that even if institutions trade on private information from merger negotiations they avoid trading

immediately after these events, perhaps due to reputational concerns.

The coefficient on the composite deal insider score variable is positive, but insignificant (p

= 0.602). In untabulated tests, we decompose this variable into its separate components, and find

that the coefficients on the individual variables are insignificant as well. Interestingly, Acharya

and Johnson (2010) report that a greater number of deal insiders, proxied by the number of equity

participants in the deal, is associated with more unusual target stock returns during the five days

before the deal announcement. Two factors may explain the seemingly different findings in their

study and ours. First, we examine trading immediately after private negotiations and it is possible

that deal insiders are averse to trading right after such events when their litigation risk would be

particularly high. Second, Acharya and Johnson (2010) examine a sample of private equity buyouts

and in these deals the number of equity participants is typically quite high. On the other hand, 84%

of the acquisitions in our sample are made by publicly traded firms. Thus, differences in sample

could also lead to differences in results across their study and ours.

Shifting our attention to the role of expected trading profits, we find that the initial offer

premium is negatively associated with stock price reactions to negotiation events. The coefficient

of -0.004 (p = 0.072) implies that an interquartile jump in the initial offer premium (about 27%) is

23

associated with an 11 basis point smaller price response to a negotiation event. This finding

contrasts with the average positive effect of the initial offer premium on returns during the pre-

announcement period as reported in the bottom of Panel A. One explanation for this

counterintuitive result is that while informed investors are more likely to purchase target firm

shares prior to the deal announcement in deals with a high expected premium, the same deals

increase their exposure to litigation risk and hence cause them to avoid immediately trading on

new private information. In Section 3.4 we report the results of additional tests that support this

interpretation. Turning to the effect of the likelihood of deal success, we find that increasing the

target’s ex ante likelihood of receiving an offer by 1% is associated with a 10 basis point stronger

price reaction to negotiation events (0.01 × 0.097, p = 0.015). This is consistent with informed

investors having larger incentives to immediately trade on new private information generated

during merger negotiations when the likelihood of deal completion is greater. However, we do not

find that the existence of a target termination fee clause is associated with these price reactions.

Finally, we document that a prior rumor increases the stock price reaction to a negotiation

event by about 69 basis points (p = 0.003). This finding suggests that incentives to immediately

trade on nonpublic information are larger when there is more public information about a potential

acquisition deal. This could be the case if litigation risk from trading on new private information

falls when there is press speculation about a potential acquisition or if such speculation induces

investors to more quickly trade on their remaining private information. Turning to our control

variables, analyst coverage and target size are not associated with the sensitivity of target prices to

negotiation events, while cash payment appears to be associated with larger price responses to

negotiation events.

3.4. The effect of regulation and enforcement

Reg FD appears to have had a significant effect on the average price reaction to private

negotiation events, a result potentially driven by its impact on the flow of private information to

market professionals and hence the number of informed investors. To further explore this, we split

24

our sample based on whether a deal was announced before or after the effective date of Reg FD.

The results are reported in the second and third columns of Table 5, Panel A. We focus on the

coefficients on PIN, institutional ownership, and the offer premium. The first two variables are

most directly related to the intent of the regulation, while the effect of the offer premium on

litigation risk from immediately trading on newly generated private information may be intensified

in the post-Reg FD period.

We find that before Reg FD, PIN has a positive and significant association with stock price

reactions to negotiation events (p = 0.044). If PIN reliably proxies for a larger number of trade

orders that arise from informed trading and consequently more informed investors, then it may be

that our tests using PIN have greater power prior to Reg FD when there was more potential for

variation in the number of informed investors. As such, the evidence that in the pre-Reg FD period

PIN is positively associated with the sensitivity of target prices to negotiation events is further

support for the notion that a larger number of informed investors leads to more rapid trading on

new private information.

Table V also shows that the negative effect of institutional ownership on price reactions to

negotiation events is driven by deals in the post-Reg FD period (coeff. = -0.009, p = 0.014). One

interpretation of this result is that even if institutions continue to obtain information about

nonpublic merger negotiations after Reg FD, possibly through their connections to deal insiders,

the reputational consequences from trading on this information immediately are intensified.

Alternatively, it could be that because institutions were directly targeted by Reg FD’s limitations

on selective disclosure, they faced a comparative information disadvantage relative to individuals

following its adoption. Finally, we find that the negative impact of the initial offer premium on the

sensitivity of target stock prices to negotiation events only exists after Reg FD (coeff. = -0.009,

p = 0.002). One interpretation of this result is that some market professionals continue to obtain

private information after Reg FD, but become averse to immediately trading on it in deals

involving high offer premiums.

25

In Table 5, Panel B we split our sample based on whether the SEC or the Department of

Justice (DOJ) later alleged illegal insider trading took place in the target’s equity. To do so, we

search SEC EDGAR and Factiva to identify deals later associated with prosecution. We find that

12.3% of the deals in our sample (67 deals) were identified by prosecutors in civil or criminal

insider trading complaints. If this subsample provides evidence on the behaviors of traders that

“slip up” in their trading decisions and this leads to prosecution, then comparing the patterns of

trading reactions to negotiation events in deals with ex post prosecution to those without can help

shed light on why investors, who naturally prefer a high offer premium, are actually averse to

immediately trading on their information about it.

The average stock price response to negotiation events is roughly the same between deals

later associated with insider trading prosecution and those that are not (results not tabulated).

However, there are significant differences in what drives price reactions to negotiation events

across the two groups of deals. Panel B of Table 5 shows that the initial offer premium has a

significant positive association with target stock price responses to negotiation events in deals later

associated with prosecution (p = 0.038). The coefficient magnitude of 0.018 suggests that a modest

increase in premium of ten percentage points translates into an 0.18% larger price reaction to each

event. In contrast, the offer premium continues to have a negative and significant association with

stock price responses to negotiation events in deals without ex-post prosecution (coeff. = -0.005,

p = 0.026). This evidence supports the notion that the expected litigation risk from trading

immediately on new private information in high premium deals is substantial enough to cause

informed investors to delay their trades. However, in such deals, some investors still appear to

quickly trade on new private information, and it is those deals that are more likely to end up with

ex-post prosecution for insider trading.

Panel B of Table 5 also shows that the full sample results of a negative effect of institutional

ownership and a positive effect of the period prior to Reg FD on the sensitivity of target prices to

negotiation events only exists in the subsample of deals without ex post prosecution for insider

trading. The former result is consistent with the interpretation that institutions avoid trading

26

immediately on new private information to reduce the likelihood of attracting the attention of

enforcement agencies. Similarly, the latter result may indicate that the class of traders affected by

Reg FD, market professionals, are sensitive to litigation risk from informed trading.

Tables 6 and 7 provide the results of analyses that shed additional light on the negative

association between the immediate price reaction to a negotiation event and the initial offer

premium observed in the full sample. In Table 6 we report the daily target firm excess abnormal

returns subsequent to a negotiation event over the four days beginning on the event day. Extending

the window after the negotiation event allows us to observe differences in the timing of informed

trading. We partition the sample into three groups based on the initial offer premium: the bottom

three deciles (low premium), the middle four deciles, and the top three deciles (high premium).

For low premium deals, target firm abnormal returns are significantly different from zero only on

the contemporaneous negotiation event day. In sharp contrast, target abnormal returns in high

premium deals are insignificant on the event day, but become significantly positive on the

following day and up to four days out. Compared to, an average four-day cumulative abnormal

return of 38 basis points for low premium deals, it approaches 81 basis points for high premium

deals. Thus, while informed investors appear to react more aggressively in high premium deals, as

expected given the potential trading profits, their trading reactions are delayed and only apparent

by extending the event window. This trading delay is likely attributable to an aversion to litigation

risk, which we show more directly next.

In Panels B and C of Table 6, we compare the results between deals that do not later become

associated with prosecution for illegal insider trading with those that do. For the set of deals that

are not later associated with prosecution, we continue to find that for low premium deals abnormal

returns are only different from zero on the event day. For this set of deals, we also continue to find

that for high premium deals target firm abnormal stock returns are not significant and positive on

the event day. Interestingly, for these deals we find a small significant negative abnormal stock

return, on average, on the event day. Among the deals that are later associated with prosecution,

we do not find significant abnormal returns over the four days beginning on the event day when

27

the premium is not high. However, when the premium is high and there is subsequent prosecution,

we find significantly positive abnormal returns of 79 basis points on the event day and four-day

cumulative abnormal returns of 218 basis points. The striking difference between the results for

the deals associated and not associated with subsequent prosecution supports the intuition that

litigation risk from trading immediately on newly generated private information when the premium

is high is significant enough to cause informed investors to delay making trades on this

information, on average. In making this interpretation, we rely on the existence of traders who

make mistakes by immediately trading on their inside information when the potential profits from

doing so are high.

In Table 7 we estimate a logit model of the likelihood of prosecution for illegal insider

trading in the target firm based on target and deal characteristics, focusing on the role of the initial

offer premium. The first model reported includes only the initial offer premium as an explanatory

variable, while the second model adds the independent variables included in the Table 5 models.

The results show that the offer premium has a significant positive effect on the ex post likelihood

of prosecution. In addition, the results document that target firm size and whether the deal is paid

for with cash, have significant positive effects on this likelihood. Using the marginal effects

estimates in the second model, we calculate that an increase from the 25th to the 75th percentile of

the initial offer premium increases the likelihood of prosecution by 5.1%. Relative to the

unconditional likelihood of prosecution of 12.3%, this represents a 41.5% increase in the

likelihood that someone will be prosecuted for illegal insider trading in the target firm’s stock. The

effect is stronger if we focus on higher premium deals. Using marginal effect estimates calculated

at the 70th percentile of the offer premium instead of at the mean offer premium, we determine that

a move from the 50th to the 90th percentile of the offer premium leads to a 10.8% increase in the

probability of prosecution, or in an 87.8% increase relative to the unconditional likelihood.

Overall, the Table 7 results offer further support for the conclusion that informed investors have

incentives to avoid immediately trading on their new private information in high premium deals

to reduce their litigation risk.

28

4. Additional Analyses

4.1. Evidence from bidder returns

As an additional test of whether informed investors immediately trade on newly generated

private information, we investigate whether these investors rapidly trade in the stock of bidder

firms subsequent to private merger negotiations. We have the necessary data to do so for 408 of

the 545 deals in our sample.21 A large body of work shows that bidders experience significant

negative announcement returns at the deal announcement. As reported in Panel A of Table 8, the

average three-day abnormal return for the bidder surrounding the deal announcement is -2.89% in

our sample. Thus, we expect trading on information about private negotiation events should be

associated with negative abnormal returns for bidders, on average.

In Panel B of Table 8 we report the results of a model similar to the first model in Table 3

except that we regress the bidder’s abnormal stock returns on lagged abnormal stock returns and

the negotiation event and rumor indicator variables. The average two-day excess abnormal stock

return for bidders following a negotiation event is -0.10% and insignificant (p = 0.11). Panel B

also shows that prior to Reg FD two-day excess abnormal returns for bidders following

negotiations average -0.16% (p = 0.06). However, subsequent to Reg FD these returns become

insignificant. This is additional evidence that a larger number of informed investors results in more

immediate trading on newly generated private information.22

The tests on bidder returns are likely to have lower power than those on target returns

because the magnitude of abnormal returns at the deal announcement is markedly lower for bidders

than it is for targets. To increase power, we extend the model used in Panel B by conditioning the

bidder’s stock price response to negotiation events on the contemporaneous stock return of the

target, which proxies for the value of the private information generated by the event and the

21 Out of the 545 acquisition deals in our sample, 458 deals have bidders that are publicly traded according to SDC.

However, we drop 34 of these deals because the bidders are foreign firms and not included on CRSP. Also, we are

unable to calculate abnormal stock returns for 16 domestic bidders due to insufficient stock return data on CRSP. 22 We also compared bidder stock price reactions to negotiation events between acquisitions that were paid only with

cash and acquisitions that were paid at least in part with the bidder’s equity. We find that for both of these groups

of deals that bidder stock price reactions to negotiation events are not significantly different from zero.

29

incentives of investors to trade on it. Specifically, we extend the model by interacting the

negotiation event variables with the contemporaneous percentage abnormal return in the target.

Our findings, reported in Panel C of Table 8, show the following. First, the two-day results

for the main effects of the negotiation event indicators, provide additional evidence of investors

selling bidders’ stock immediately after a negotiation event (coeff. = -0.11, p = 0.08). Most

importantly, the two-day results for the interaction variables suggest that more valuable private

information generated by a merger negotiation event is associated with more selling of the bidder’s

stock immediately after the event (coeff. = -0.06, p < 0.01). When we partition on Reg FD, we

document that the results for the interaction variables are driven by both the periods before and

after Reg FD. However, the findings for the main effects of the negotiation event indicators are

driven by deals that take place before Reg FD.

4.2. Can deal negotiators discern immediate trading subsequent to private negotiations?

To further assess the economic importance of the trading reactions to private merger

negotiations, we examine whether bidder and target negotiators are able to discern that investors

immediately trade on newly generated private information from their negotiations. To do this, we

consider the mapping between stock price movements following negotiation events and the final

negotiated deal price. Jarrell and Poulsen (1989) argue that if stock price movements during

negotiations reflect information that the acquirer and target already have, there should be no impact

on the final negotiated premium. Under this substitution hypothesis, higher stock price runups

before the deal announcement lead to lower returns after the announcement, but the total premium

is the same and acquirers are no worse off. If stock price runups reflect a change in the stand-alone

value of the target or the presence of a competing bidder, the acquirer may be forced to increase

the acquisition offer price to secure the deal. Further, runups driven by informed trading can also

affect the negotiated premium if the acquirer and target do not agree on the impact of informed

trading on the target’s stock price (Schwert (1996)). Under this markup pricing hypothesis, the

final acquisition price is effectively a markup on the target’s stock price before the deal

30

announcement. Hence, a $1 increase in price during the period prior to this announcement leads to

a $1 increase in the final acquisition price. Schwert’s evidence is consistent with this hypothesis.23

The predictions under the substitution and markup pricing hypotheses are derived from

assumptions about the source of the change in a target’s stock price during negotiations. A stock

price increase that is unambiguously driven by informed trading should have a smaller effect on

the negotiated premium than an increase driven by a change in the target’s stand-alone value or

the arrival of a competing bidder. For each target firm, we estimate the component of the runup

attributable to immediate trading on private negotiation events—the negotiation CAR—using a

firm-specific estimate of excess abnormal returns following each negotiation event (negotiation

returns) multiplied by the number of negotiation event-days during the three-month period before

the deal announcement. To obtain negotiation returns, we first estimate the following regression

for each firm using daily abnormal returns over the 63 trading days before the deal announcement:

𝐴𝑅𝑖𝑡 = 𝑎𝑖 + 𝑏i0𝐴𝑅𝑖𝑡−1 + 𝑏𝑖1𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + 𝑏𝑖2𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡−1

+ 𝑏𝑖3𝑅𝑢𝑚𝑜𝑟𝑖𝑡 + 𝑏𝑖4𝑅𝑢𝑚𝑜𝑟𝑖𝑡−1 + 𝑒𝑖𝑡

(5)

The coefficients bi1 and bi2 capture target firm i’s negotiation returns, defined as the excess

abnormal stock returns associated with a nonpublic negotiation event (bi3 and bi4 are not estimated

for firms without a press rumor). The negotiation CAR is thus:

𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛 𝐶𝐴𝑅𝑖 = (�̂�𝑖1 + �̂�𝑖2) × 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑛𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛 𝑒𝑣𝑒𝑛𝑡𝑠𝑖 (6)

This approach is in the spirit of Meulbroek (1992), who estimates firm-specific runups attributable

to informed trading from the returns on days when trades are made by individuals later prosecuted

for insider trading. In Panel A of Table 9, we report the distribution of the estimated negotiation

CAR for all events. We also estimate a press rumor CAR defined as the excess abnormal return

attributable to press rumors. For deals with no press rumors, the rumor CAR is set to zero.

In the first column of Panel B in Table 9, we provide the results of a benchmark regression

of the final deal premium on the total 63-day runup. We report standard errors in this table to allow

23 For a more recent perspective on markup pricing, see Betton, Eckbo and Thompson (2014).

31

the reader to more easily make inferences about whether the coefficient on the runup variable

differs from alternative benchmarks of zero and one. The coefficient of 1.126 on the runup variable

suggests that a $1 price increase before the announcement is associated with a $1.13 higher final

acquisition price, a result very similar to the finding reported in Schwert (1996). In the second

column, we decompose the total runup into the negotiation CAR and the net runup, defined as the

total runup less the negotiation CAR. The coefficient on the negotiation CAR is 0.852 compared

to a coefficient on the net runup of 1.137. In other words, every $1 of immediate price reaction to

a private negotiation event is associated with an $0.85 increase in the final acquisition price;

although significantly greater than zero, it is a significant discount to the $1.14 increase in the final

deal premium that arises for every $1 of price movement on non-event days.

To further investigate this issue, in the third column we examine how the results vary across

specific events. We find that price reactions to private bids for the target have the smallest effect

on premium. That is, a $1 increase in the price response to a private bid increases the final premium

by only $0.53, significantly less than the $1.14 impact of non-event price increases on the final

premium. In the final column in Table 9, we include the rumor CAR on the right hand side and

redefine net runup as the total runup less the sum of the negotiation CAR and the rumor CAR. To

the extent rumors reflect information already known by deal negotiators, we should find similar

discounting of price reactions to rumors in the acquisition price. The regression coefficients on

negotiation CAR and rumor CAR are 0.84 and 0.76, and the difference between them is

insignificant. Overall, given that bidder and target firm negotiators know the dates of key negotiation

events, the evidence in Table 9 suggests that these negotiators can indeed discern that some investors

immediately trade on new private information generated by their negotiations. As a result, negotiators

discount price movements that take place immediately after private negotiation events.24

24 Schwert (1996) measures the runup over the 42 trading days before the deal announcement. We measure it over the

63 trading days prior to this date because the goal of the Table 9 analyses is to investigate the effect of negotiation

CARs on the final acquisition premium and a significant number of negotiation events occur during month -3

relative to the deal announcement. However, we examined over the 42-day window before the deal announcement

the impact of negotiation CARs and rumor CARs on the final deal premium and find the results to be very similar

to those tabulated in Table 9.

32

5. Conclusion

We use SEC-mandated background disclosures on acquisitions to identify the precise dates

when new private information about firm value is generated, enabling us to investigate whether

informed investors immediately trade on it. We document average target firm excess abnormal

returns of 42 basis points over the two days following nonpublic negotiation events. Additional

evidence from measures for informed trading are uniformly consistent with the interpretation that

informed investors quickly react to newly generated private information.

Our analyses point to systematic factors that can explain the speed of informed investors’

trading reactions to newly generated private information. Price reactions are stronger before Reg

FD, when there were more selective disclosures to market professionals and presumably a larger

number of investors with access to private information about firm value. Price reactions are

decreasing in institutional ownership, consistent with institutions having reputational concerns

about immediately trading on newly generated private information. With respect to the effect of

expected profits from informed trading, we find that although price responses are increasing in the

likelihood of deal completion, these responses are decreasing in the initial offer premium. Further

analyses suggest that informed traders delay trading on their private information in high premium

deals to reduce their litigation risk. Lastly, we find stronger price reactions to negotiation events if

there exists public information about a possible takeover deal in the form of a press rumor,

suggesting that public information about the deal reduces traders’ litigation risk or induces them

to quickly trade on their remaining private information.

Acquirer stock prices respond negatively to negotiation events, especially prior to Reg FD

and when the private information generated by a negotiation event is particularly relevant for

investors, as reflected by the contemporaneous stock return of the target. Lastly, we investigate

whether the effect of immediate trading on new private information from merger negotiations on

target firm stock prices is large enough so that deal negotiators recognize this trading activity.

Implying that this is the case, we find that when deciding on the final acquisition price, negotiators

ignore an important fraction of stock price movements following nonpublic negotiations.

33

Overall, our findings are broadly consistent with the notion that informed investors

immediately trade on new private information. Our evidence on the determinants of these trading

reactions to new nonpublic information provides novel insights on private information-based

trading. Further, our approach provides an avenue for future research to link the arrival of new

private information with the daily trading activities of specific investor groups.

34

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Table 1: Sample statistics The sample consists of 545 completed acquisitions announced between 1995 and 2006 identified from the SDC

Mergers and Acquisitions database. Deal statistics are reported in Panel A. Target characteristics are reported in

Panel B. Variables are defined in Appendix A.

Panel A: Deal characteristics

Mean Q1 Median Q3

Runup [CAR(-63,-1)] (%) 9.88 -4.06 7.95 21.14 C1

Initial offer premium (%) 32.87 18.77 31.62 45.99 C126

Target announcement return (%) 15.48 5.11 13.90 23.99

Final deal premium [CAR(-63,126)] (%) 28.98 7.95 27.15 47.82

Target-initiated (0,1) 0.34 0.00 0.00 1.00 T

Bidder is publicly traded (0,1) 0.84 1.00 1.00 1.00 A

Number of deal advisors 2.61 2.00 2.00 3.00 TOV

Number of lead banks 1.28 0.00 1.00 2.00

Competing bidder post-announcement (0,1) 0.03 0.00 0.00 0.00

Competing bidder pre-announcement (0,1) 0.39 0.00 0.00 1.00 AM

Press rumor (last six months (0,1)) 0.18 0.00 0.00 0.00 NMz

All cash deal (0,1) 0.42 0.00 0.00 1.00 AL

Target termination fee clause (0,1) 0.80 1.00 1.00 1.00

Time in private negotiation (days) 177 70 132 233 N

Before Reg-FD (0,1) 0.60 0.00 1.00 1.00

Insider trading prosecution (0,1) 0.12 0.00 0.00 0.00 SE

Panel B: Target characteristics

Mean Q1 Median Q3

Market value of equity ($billion) 3.07 0.42 0.91 2.37

Book assets ($billion) 8.54 0.39 1.13 3.77

Market-to-book assets 1.72 1.11 1.42 1.93

Market leverage 0.17 0.04 0.15 0.26

Probability of acquisition 0.08 0.06 0.09 0.10

Return on assets (%) 3.80 1.22 3.88 7.21

PIN 0.17 0.12 0.16 0.20

Institutional ownership (%) 51.92 35.21 56.96 74.74

Analyst coverage (0,1) 0.89 1.00 1.00 1.00

Analyst coverage 7.49 3.00 6.00 11.00

39

Table 2: Nonpublic negotiation activity in the three months before the deal announcement The sample consists of 545 completed acquisitions announced between 1995 and 2006 identified from the SDC

Mergers and Acquisitions database. In Panel A, we report the frequency of various negotiation events with

precise dates disclosed during the three months before the deal announcement. We report the average number of

events per deal with at least one of those events disclosed. The average number of days until deal announcement

is the number of trading days between a specific negotiation event (or press rumor) and the first formal

announcement of a deal. In Panel B, we examine the relative timing of negotiation events. For each event, we

report the number of trading days since the most recently disclosed event. Variables are defined in Appendix A.

Panel A: Frequency of disclosed negotiation events and press rumors

Total # of

event days

% of deals

with ≥ 1 event

Average # of

events per deal

|≥ 1 event

Average # of

trading days

until deal

announcement

Negotiation events:

Any event 3,550 100.0 6.5 20

Target board meeting 1,855 93.9 3.6 18

Bids (new or revised bids) 824 70.4 2.1 17

Acquirer board meeting 551 48.3 2.1 14

Bid revision 515 48.3 2.0 14

Confidentiality agreement 292 46.1 1.2 26

Due diligence begins 243 41.6 1.1 20

Initiation 173 26.4 1.2 33

Target retains fin. advisor 148 25.3 1.1 26

Standstill agreement 61 9.5 1.2 29

Acquirer retains fin. advisor 49 8.3 1.1 24

Exclusivity agreement 49 8.6 1.0 22

Termination fee clause (target) 42 7.7 1.0 5

Press rumor (last three months) 168 15.6 2.0 20

Panel B: The relative timing of negotiation events

Number of trading days between

the negotiation event and most

recent negotiation event Frequency % of sample Cumulative %

1 760 21.4 21.4

2 492 13.9 35.3

3 294 8.3 43.6

4 251 7.1 50.7

5 186 5.2 55.9

6 – 10 511 14.4 70.3

11 – 20 432 12.2 82.4

21 – 30 141 4.0 86.4

31+ 197 5.5 91.9

First negotiation event 286 8.1 100.0

3,550 100.0

40

Table 3: The sensitivity of target stock prices to private negotiation events This table reports the results of pooled regression models that explain target firm daily abnormal stock returns

during the 63-trading day period prior to the deal announcement for the 545 completed acquisitions included in

our sample announced between 1995 and 2006 identified from the SDC Mergers and Acquisitions database. The

model used to estimate the results in column (1) is:

𝐴𝑅𝑖𝑡 = 𝑎 + 𝑏0𝐴𝑅𝑖𝑡−1 + 𝑏1𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + 𝑏2𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡−1 + 𝑏3𝑅𝑢𝑚𝑜𝑟𝑖𝑡 + 𝑏4𝑅𝑢𝑚𝑜𝑟𝑖𝑡−1 + 𝑐𝑖 + 𝑒𝑖𝑡

where AR is the abnormal return for firm i, Negotiation equals one if any negotiation event occurs on day t,

Rumor equals one if there is at least one press mention of a possible deal on day t, and c represents firm fixed

effects. The model also includes one-day lags of Negotiation and Rumor. In column (2), we present the results

from expanding the basic model to include separate indicator variables for specific events. Coefficients are

reported with p-values in parentheses. Variables are defined in Appendix A.

(1) (2)

coeff. p-value coeff. p-value

ARt-1 -0.034 (<0.001) -0.034 (<0.001)

Negotiationt 0.186 (<0.001)

Negotiationt-1 0.229 (<0.001)

Target board meetingt 0.190 (0.011)

Target board meetingt-1 0.181 (0.023)

Acquirer board meetingt 0.263 (0.048)

Acquirer board meetingt-1 0.238 (0.112)

Bidt 0.059 (0.581)

Bidt-1 0.239 (0.036)

Target retains fin. advisort 0.033 (0.891)

Target retains fin. advisort-1 0.296 (0.237)

All other eventst 0.201 (0.071)

All other eventst-1 0.044 (0.689)

Rumort 4.478 (<0.001) 4.462 (<0.001)

Rumort-1 -0.350 (0.173) -0.369 (0.151)

Two-day totals:

Negotiation 0.416 (<0.001)

Target board meeting 0.371 (<0.001)

Acquirer board meeting 0.501 (0.012)

Bid 0.298 (0.055)

Target retains fin. advisor 0.330 (0.353)

All other 0.245 (0.116)

Rumor 4.128 (<0.001) 4.093 (<0.001)

N 34,335 34,335

Adjusted R2 0.011 0.012

41

Table 4: Nonpublic negotiation events and order imbalance, abnormal trading volume, and

trade size The sample consists of 545 completed acquisitions announced between 1995 and 2006 identified from the SDC

Mergers and Acquisitions database. Panel A reports descriptive statistics based on daily data for the 63-trading

day period prior to the deal announcement for order imbalance, abnormal volume, and trade size distributions.

Panel B reports the results of pooled cross-sectional regressions of order imbalance, abnormal volume, and the

percentage of the trades during a given day of a target firm’s stock that are medium-size trades on indicators for

contemporaneous and lagged negotiation events and press rumors during the 63-trading day period before the

deal announcement. Firm fixed effects are included in the models. The difference in the number of observations

across the three models reflects the fact that for some of the target firms we are unable to calculate the trading

activity measures over all of the 63 trading days prior to the deal announcement. Panel C reports the results of

pooled cross-sectional regressions in which target firm daily abnormal stock returns are regressed on indicators

for contemporaneous and lagged negotiation events and press rumors and interactions of these variables with

order imbalance, abnormal volume, and the percentage of the trades during a given day of a target firm’s stock

that are medium-size trades. Coefficients are reported with p-values in parentheses. Variables are defined in

Appendix A.

Panel A: Distributional statistics during the 63-trading day period before the deal announcement date

Mean Q1 Median Q3

Order imbalance (x100) 2.819 -10.127 4.127 17.023

Abnormal volume (%) 1.105 -38.087 -1.688 38.231

Trade size (% of trades)

Small (100 – 400 shares) 54.492 36.008 51.064 73.913

Medium (500 – 9,900 shares) 43.500 25.553 46.875 60.550

Large (10,000 + shares) 2.008 0.00 0.503 2.607

Panel B: Sensitivity of order imbalance, abnormal volume, and trade size to negotiation events

Dependent var. =

(1) (2) (3)

Order imbalancet Abnormal volumet (%) % medium-size tradest

Negotiationt 1.823 (<0.001) 4.100 (<0.001) 0.551 (0.003)

Negotiationt-1 1.360 (0.002) 2.098 (0.108) 0.324 (0.090)

Rumort 2.590 (0.155) 77.935 (<0.001) 5.230 (<0.001)

Rumort-1 -2.611 (0.177) 5.343 (0.362) 4.130 (<0.001)

Two-day totals:

Negotiation 3.183 (<0.001) 6.198 (<0.001) 0.875 (<0.001)

Rumor -0.021 (0.993) 83.278 (<0.001) 9.359 (<0.001)

Adjusted R2 / N 0.124 / 34,240 0.007 / 34,299 0.714 / 34,193

42

Table 4 (continued)

Panel C: Impact of order imbalance, abnormal volume and trade size on the sensitivity of stock returns

to negotiation events

Dependent variable = ARt (%)

(1) (2) (3)

Informed trade proxy =

Order imbalancet

(x 100)

Abnormal volumet

(%)

% medium-size

tradest

ARt-1 -0.042 (<0.001) -0.034 (<0.001) -0.036 (<0.001)

Informed trade proxy 0.037 (<0.001) 0.004 (<0.001) 0.014 (<0.001)

Negotiationt 0.097 (0.072) 0.130 (0.016) -0.114 (0.314)

Negotiationt-1 0.156 (0.006) 0.187 (<0.001) -0.002 (0.981)

Negotiationt × Informed trade proxy 0.005 (0.025) 0.005 (<0.001) 0.007 (0.005)

Negotiationt-1 × Informed trade proxy 0.006 (0.013) 0.007 (<0.001) 0.006 (0.024)

Rumort 4.511 (<0.001) 0.468 (0.121) -1.875 (0.007)

Rumort-1 -0.250 (0.314) -0.109 (0.357) 3.525 (<0.001)

Rumort × Informed trade proxy -0.021 (0.151) 0.044 (<0.001) 0.126 (<0.001)

Rumort-1 × Informed trade proxy 0.028 (0.039) -0.008 (0.002) -0.084 (<0.001)

Two-day totals:

Negotiation 0.253 (<0.001) 0.318 (<0.001) -0.117 (0.452)

Negotiation × Informed trade proxy 0.011 (<0.001) 0.012 (<0.001) 0.013 (<0.001)

Rumor 4.261 (<0.001) 0.359 (0.339) 1.650 (0.093)

Rumor × Informed trade proxy 0.007 (0.687) 0.037 (<0.001) 0.042 (0.024)

Adjusted R2 / N 0.079 / 34,240 0.039 / 34,299 0.017 / 34,193

43

Table 5: The determinants of the sensitivity of stock prices to nonpublic negotiation events This table provides the results from regressions of daily target firm abnormal stock returns on negotiation event indicators

and proxies for incentives to trade on private information over the 63-trading day period before the deal announcement

for a sample of 545 completed acquisitions announced between 1995 and 2006 identified from the SDC Mergers and

Acquisitions database. We estimate the following model:

𝐴𝑅𝑖𝑡 = 𝑎0 + 𝑏0𝐴𝑅𝑖𝑡−1 + 𝑏1𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + 𝑏2𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡−1 + ∑ ajθij

𝐽

𝑗=1

+ ∑ (b1jNegotiationit

𝐽

𝑗=1+ 𝑏2𝑗𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡−1)θij + 𝑏3𝑅𝑢𝑚𝑜𝑟𝑖𝑡 + 𝑏4𝑅𝑢𝑚𝑜𝑟𝑖𝑡−1 + 𝛿𝑌𝑒𝑎𝑟 + 𝑒𝑖𝑡

where AR is the abnormal return for firm i on day t, Negotiation equals one if any negotiation event occurs on day t,

Rumor equals one if there is at least one press mention of a possible deal on day t, and 𝜃𝑗 represent variables interacted

with the negotiation event indicators. Coefficients are reported with p-values in parentheses. Variables are defined in

Appendix A.

Panel A: All deals and deals announced before and after Reg FD

(1) (2) (3)

All Deals Before Reg FD After Reg FD

coeff. p-value coeff. p-value coeff. p-value

Interactions

PIN × Negotiation 1.421 (0.114) 3.331 (0.044) 0.566 (0.581)

Before Reg FD × Negotiation 0.427 (0.029)

Deal insider score × Negotiation 0.221 (0.602) -0.009 (0.989) 0.166 (0.777)

Institutional ownership × Negotiation -0.006 (0.033) -0.003 (0.507) -0.009 (0.014)

Initial offer premium × Negotiation -0.004 (0.072) 0.001 (0.750) -0.009 (0.002)

Prob(acquisition) × Negotiation 0.097 (0.015) 0.069 (0.247) 0.121 (0.026)

Target termination fee × Negotiation 0.299 (0.144) 0.423 (0.108) 0.096 (0.780)

Prior rumor × Negotiation 0.689 (0.003) 0.667 (0.057) 0.701 (0.017)

Analyst coverage × Negotiation -0.004 (0.793) -0.001 (0.976) -0.004 (0.811)

Target ln(MVE) × Negotiation 0.073 (0.375) 0.062 (0.626) 0.042 (0.696)

All cash deal × Negotiation 0.342 (0.030) 0.271 (0.261) 0.364 (0.078)

Main Effects

ARt-1 -0.025 (<0.001) -0.025 (<0.001) -0.027 (0.002)

Two-day Negotiationt (𝑏1 + 𝑏2) -1.932 (0.123) -1.757 (0.343) -0.902 (0.607)

Two-day Rumort (𝑏3 + 𝑏4) 3.729 (<0.001) 4.957 (<0.001) 2.698 (<0.001)

PIN -0.296 (0.156) -0.318 (0.338) -0.305 (0.244)

Before Reg FD -0.261 (0.047)

Deal insider score -0.025 (0.812) -0.079 (0.561) 0.058 (0.723)

Institutional ownership 0.000 (0.621) -0.000 (0.670) 0.001 (0.165)

Initial offer premium 0.007 (<0.001) 0.006 (<0.001) 0.008 (<0.001)

Prob(acquisition) -0.029 (0.011) -0.034 (0.012) -0.011 (0.605)

Target termination fee 0.010 (0.823) 0.029 (0.611) -0.048 (0.573)

Prior rumor 0.044 (0.506) 0.010 (0.253) 0.003 (0.976)

Analyst coverage 0.003 (0.439) 0.001 (0.804) 0.004 (0.443)

Target ln(MVE) -0.055 (0.012) -0.041 (0.171) -0.061 (0.060)

All cash deal -0.032 (0.422) 0.007 (0.593) -0.087 (0.151)

Adjusted R2 / N 0.018 34,335 0.023 20,601 0.016 13,734

44

Table 5 (continued)

Panel B: Results partitioned on subsequent insider trading prosecution (1) (2)

Prosecution No Prosecution

coeff. p-value coeff. p-value

Interactions

PIN × Negotiation 0.587 (0.834) 1.570 (0.102)

Before Reg FD × Negotiation -0.105 (0.861) 0.440 (0.036)

Deal insider score × Negotiation -1.893 (0.147) 0.377 (0.408)

Institutional ownership × Negotiation -0.001 (0.928) -0.006 (0.037)

Initial offer premium × Negotiation 0.018 (0.038) -0.005 (0.026)

Prob(acquisition) × Negotiation 0.110 (0.110) 0.098 (0.023)

Target termination fee × Negotiation 0.372 (0.514) 0.389 (0.080)

Prior rumor × Negotiation 0.833 (0.252) 0.600 (0.015)

Analyst coverage × Negotiation 0.009 (0.854) -0.002 (0.881)

Target ln(MVE) × Negotiation -0.118 (0.644) 0.091 (0.307)

All cash deal × Negotiation -0.682 (0.163) 0.450 (0.009)

Main Effects

ARt-1 -0.012 (0.446) -0.028 (<0.001)

Two-day Negotiationt (𝑏1 + 𝑏2) 1.267 (0.755) -2.367 (0.079)

Two-day Rumort (𝑏3 + 𝑏4) 3.113 (0.002) 3.714 (<0.001)

PIN -0.037 (0.964) -0.339 (0.126)

Before Reg FD -0.130 (0.753) -0.276 (0.049)

Deal insider score -0.051 (0.870) -0.001 (0.991)

Institutional ownership -0.001 (0.763) 0.000 (0.538)

Initial offer premium 0.004 (0.038) 0.007 (<0.001)

Prob(acquisition) -0.024 (0.408) -0.030 (0.015)

Target termination fee -0.112 (0.431) 0.012 (0.803)

Prior rumor -0.270 (0.245) -0.011 (0.879)

Analyst coverage 0.002 (0.901) 0.002 (0.561)

Target ln(MVE) -0.057 (0.398) -0.056 (0.019)

All cash deal 0.064 (0.634) -0.051 (0.245)

Adjusted R2 / N 0.046 4,221 0.017 30,114

45

Table 6: The initial offer premium and the sensitivity of target stock prices to private

negotiation events over an extended horizon This table reports the estimates of pooled regression models that explain target firm daily abnormal stock returns

during the 63-trading day period prior to the deal announcement for the 545 completed acquisitions included in

our sample announced between 1995 and 2006 identified from the SDC Mergers and Acquisitions database. The

model used to estimate the results regresses daily abnormal stock returns on indicators for whether a negotiation

event or a rumor occurs on day t, as well as one-, two-, and three-day lags of these two indicator variables, and

firm fixed effects. Regressions are estimated by decile of the initial offer premium (lowest three, middle four,

and highest three) and by whether a deal is later associated with prosecution for illegal insider trading. In Panels

B and C whether a deal is in the low, middle, or high initial offer premium group of deals is based on the

groupings constructed for the full sample. Coefficients are reported with significance levels of 10%, 5% and 1%

represented by *, **, and ***. Variables are defined in Appendix A.

Daily abnormal return relative to private

negotiation event date t Cumulative abnormal returns

N

Mean

prem. t t + 1 t + 2 t + 3

Two-

day

Three-

day

Four-

day

Panel A: All (545 deals)

Low 30% prem. 163 9.0% 0.37*** 0.10 -0.10 0.01 0.47*** 0.37** 0.38**

Mid. 40% prem. 219 37.8% 0.17** 0.15* 0.12 0.00 0.32*** 0.44*** 0.44***

High 30% prem. 163 83.3% -0.03 0.45*** 0.13 0.26** 0.42*** 0.55*** 0.81***

High – Low -0.40*** 0.35** 0.23 0.25 -0.05 0.18 0.43

Panel B: No Prosecution (478 deals)

Low 30% prem. 151 8.4% 0.41*** 0.14 -0.12 -0.02 0.55*** 0.43** 0.41**

Mid. 40% prem. 188 37.3% 0.22** 0.13 0.15 0.00 0.34** 0.49*** 0.50***

High 30% prem. 139 83.0% -0.18* 0.49*** 0.09 0.20* 0.31** 0.41** 0.61***

High – Low -0.59*** 0.35** 0.21 0.22 -0.24** -0.02 0.20

Panel C: Prosecution (67 deals)

Low 30% prem. 12 16.5% -0.10 -0.42 0.23 0.37 -0.53 -0.30 0.08

Mid. 40% prem. 31 41.0% -0.11 0.23 0.02 0.03 0.12 0.14 0.17

High 30% prem. 24 85.0% 0.79*** 0.21 0.59** 0.59** 1.00*** 1.59*** 2.18***

High – Low 0.89** 0.63 0.35 0.22 1.53** 1.89** 2.10**

46

Table 7: Logit model of the likelihood a deal is later associated with insider trading

prosecution This table provides the results from logit regressions of the determinants that an acquisition deal is later associated

with prosecution for illegal insider trading. The sample consists of 545 completed acquisitions announced between

1995 and 2006 identified from the SDC Mergers and Acquisitions database. The dependent variable equals one if

a deal is later associated with prosecution for illegal inside trading, and zero otherwise. Marginal effect estimates

calculated at the mean are reported with p-values in parentheses. Variables are defined in Appendix A.

(1) (2)

Marginal

effect p-value

Marginal

effect p-value

Initial offer premium 0.158 (0.007) 0.187 (0.003)

PIN 0.086 (0.548)

Before Reg FD 0.044 (0.242)

Deal insider score 0.022 (0.788)

Institutional ownership -0.006 (0.916)

Prob(acquisition) -0.591 (0.428)

Target termination fee -0.010 (0.773)

Prior rumor -0.001 (0.978)

Analyst coverage 0.000 (0.970)

Target ln(MVE) 0.031 (0.056)

All cash deal 0.073 (0.017)

Pseudo R2 / N 0.018 545 0.051 545

47

Table 8: Sensitivity of bidder firm stock returns to private negotiation events This table reports the results of pooled regression models that explain daily abnormal stock returns for the bidder

firm during the 63-trading day period prior to the deal announcement for 408 completed acquisitions by publicly

traded firms announced between 1995 and 2006, as identified from the SDC Mergers and Acquisitions database.

In Panel A, we report descriptive statistics on bidder abnormal stock returns at the deal announcement. The

model used to estimate the results in Panel B is:

𝐴𝑅𝑖𝑡𝐵𝑖𝑑 = 𝑎 + 𝑏0𝐴𝑅𝑖𝑡−1

𝐵𝑖𝑑 + 𝑏1𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + 𝑏2𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡−1 + 𝑏3𝑅𝑢𝑚𝑜𝑟𝑖𝑡 + 𝑏4𝑅𝑢𝑚𝑜𝑟𝑖𝑡−1 + 𝑐𝑖 + 𝑒𝑖𝑡

In Panel C, we interact the negotiation event indicators with the contemporaneous target firm return. The

model used to estimate the results in Panel C is:

𝐴𝑅𝑖𝑡𝐵𝑖𝑑 = 𝑎 + 𝑏0𝐴𝑅𝑖𝑡−1

𝐵𝑖𝑑 + 𝑏1𝐴𝑅𝑖𝑡𝑇𝑔𝑡

+ 𝑏2𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + 𝑏3𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡−1 + 𝑏4𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡

× 𝐴𝑅𝑖𝑡𝑇𝑔𝑡

+ 𝑏5𝑁𝑒𝑔𝑜𝑡𝑖𝑎𝑡𝑖𝑜𝑛𝑖𝑡−1 × 𝐴𝑅𝑖𝑡𝑇𝑔𝑡

+ 𝑏6𝑅𝑢𝑚𝑜𝑟𝑖𝑡 + 𝑏7𝑅𝑢𝑚𝑜𝑟𝑖𝑡−1 + 𝑐𝑖 + 𝑒𝑖𝑡

AR is the daily abnormal return for the bidder or target in deal i (target returns are percentage returns).

Negotiation equals one if a negotiation event occurs on day t, Rumor equals one if there is at least one press

rumor on day t. The two-day sum of the coefficients is reported, with p-values in parentheses. Variables are

defined in Appendix A.

Panel A: Descriptive statistics for bidder percentage announcement returns

All deals Pre-Reg FD Post-Reg FD

Mean (Median) -2.89 (-2.21) -2.51 (-1.84) -3.52 (-2.88)

Panel B: Sensitivity of bidder returns to private negotiation events

Dependent var. = ARBid All deals Pre-Reg FD Post-Reg FD

Two-day totals:

Negotiation -0.10 (0.11) -0.16 (0.06) -0.02 (0.79)

Rumor -0.70 (0.02) 0.06 (0.89) -1.49 (<0.01)

Adjusted R2 / N 0.01 / 25,586 0.01 / 16,055 0.01 / 9,531

Panel C: Sensitivity of bidder returns to private negotiation events conditional on target return

Dependent var. = ARBid All deals Pre-Reg FD Post-Reg FD

ARTgt 0.10 (<0.01) 0.09 (<0.01) 0.13 (<0.01)

Two-day totals:

Negotiation -0.11 (0.08) -0.19 (0.03) -0.00 (0.93)

Negotiation x ARTgt -0.06 (<0.01) -0.05 (0.03) -0.08 (<0.01)

Rumor -0.97 (<0.01) -0.15 (0.73) -1.87 (<0.01)

Adjusted R2 / N 0.03 / 25,586 0.02 / 16,055 0.04 / 9,531

48

Table 9: Can deal negotiators discern immediate trading after private negotiations? In this table we analyze the link between the final deal premium and runups for a sample of 545 completed

acquisitions announced between 1995 and 2006 identified from the SDC Mergers and Acquisitions database.

Panel A provides summary statistics on negotiation and rumor CARs. Panel B provides the results from

regressing the final deal premium on various decompositions of the runup. Year fixed effects are included in the

regression models. Standard errors are provided in italics. ***, **, and * signify that the coefficient is significantly

different from the coefficient on Runup (net) at the 1%, 5%, and 10% significance level using a two-tail test.

Variables are defined in Appendix A.

Panel A: The distribution of negotiation and rumor CARs

Mean Q1 Median Q3

Negotiation CAR (%) 2.33 -3.10 0.96 7.87

Rumor CAR (%) 1.36 0.00 0.00 0.00

Panel B: The pricing of negotiation returns

Dependent variable = Final deal premium

(1) (2) (3) (4)

coeff. s.e. coeff. s.e. coeff. s.e. coeff. s.e.

Runup (total) 1.126 0.048

Runup (net) 1.137 0.048 1.143 0.047 1.164 0.048

Negot. CAR 0.852*** 0.111 0.841*** 0.111

Negot. CAR (Targ. board mtg.) 0.858*** 0.114

Negot. CAR (Acq. board mtg.) 0.936 0.184

Negot. CAR (Targ. fin. advisor) 0.721 0.339

Negot. CAR (Bid) 0.530*** 0.181

Negot. CAR (Other) 0.913 0.174

Rumor CAR 0.756*** 0.155

N 545 545 545 545

Adjusted R2 0.531 0.540 0.544 0.543

49

Figure 1a

Figure 1b

Figure 1a (1b) reports the cross-sectional average abnormal returns (abnormal share volume)

surrounding a negotiation event on day 0. Event observations (dark shaded boxes) are those events

on day 0 with no negotiation events on adjacent trading days, and no press rumors for days -1

through +1. Non-event observations are those days with no negotiation events or press rumors on

days -1 through +1.

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

0.35%

0.40%

0.45%

-2 -1 0 1 2

Ab

no

rma

l re

turn

Day relative to negotiation event

Daily abnormal returns surrounding negotiation events

EVENT

RETURNS

NON-EVENT

RETURNS

-1%

0%

1%

2%

3%

4%

5%

-2 -1 0 1 2

Ab

no

rma

l sh

are

vo

lum

e

Day relative to negotiation event

Daily abnormal share volume surrounding

negotiation events

EVENT

VOLUME

NON-EVENT

VOLUME

50

Appendix A – Variable definitions

Abnormal stock returns are abnormal target or bidder firm daily stock returns. To calculate these returns, we

use data from the CRSP database. We first regress daily stock returns for the firm from days -379 to -127 relative

to the deal announcement date on the continuously compounded daily return of the CRSP value-weighted market

portfolio. Next, daily abnormal stock returns equal the firm’s actual stock return on a given day minus the firm’s

predicted stock return calculated using the firm-specific parameter estimates for the market-model described

above.

Abnormal volume is unexpected share volume growth, calculated as in Schwert (1996) using CRSP data.

All cash deal (0,1) is an indicator variable that equals one if it is reported in the SDC M&A database that the

acquisition was paid for only with cash, and equals zero otherwise.

Analyst coverage is the number of analysts that cover a target firm, as reported in the IBES database.

Analyst coverage (0,1) is an indicator variable that equals one if a target firm is covered by at least one analyst,

as reported in the IBES database, and equals zero otherwise.

Before Reg FD (0,1) is an indicator variable that equals one if a deal is announced prior to the effective date of

Regulation Fair Disclosure (October 23, 2000), and equals zero otherwise.

Bidder announcement return is the summation of bidder firm daily abnormal stock returns over days -1 to +1

relative to the deal announcement date.

Bidder is publicly traded (0,1) is an indicator variable that equals one if it is reported in the SDC M&A database

that a bidder is publicly traded, and equals zero otherwise.

Book assets is obtained prior to the acquisition using Compustat data on the target firm.

Competing bidder post-announcement (0,1) is an indicator variable that equals one if the SDC M&A database

reports that there was a competing bidder after the deal announcement, and equals zero otherwise.

Competing bidder pre-announcement (0,1) is an indicator variable that equals one if the background

disclosures indicate that prior to the deal announcement date there was a competing bidder, and equals zero

otherwise.

Deal announcement date is the first date of a formal public announcement of a merger agreement, as reported

in the SDC M&A database.

Deal insider score is a composite variable constructed as (1/5)*(Percentile rank of number of deal advisors +

Percentile rank of number of bidders + Percentile rank of number of acquirer lead banks + Target initiated (0,1)

+ Bidder is publicly traded (0,1)). The score ranges from 0 to 1.

Final deal premium is the summation of target firm daily abnormal stock returns from days -63 to +126 relative

to the deal announcement date. If a target firm is delisted from CRSP prior to day +126 relative to the deal

announcement date, then the final deal premium is calculated as the summation of target firm daily abnormal

stock returns from days -63 relative to the deal announcement date to the date when the target firm is delisted

from the CRSP database.

Initial offer premium is ln(Initial offer price as reported in the SDC M&A database / target share price 63 trading days prior to the deal announcement, obtained from CRSP).

51

Insider trading prosecution (0,1) is an indicator variable that equals one for deals in which investors who

traded on private information about the deal are later prosecuted for illegal insider trading by the SEC or the

Department of Justice, and equals zero otherwise. We search SEC EDGAR and Factiva to identify deals

associated with ex-post prosecution.

Institutional ownership is the percentage of the target firm’s equity owned by institutions at the end of the most

recent calendar quarter prior to the deal announcement date. This data is collected from the Thomson Reuters

institutional holdings database.

%Large-size trades is the percentage of the trades during a given day of a target firm’s stock that are large-size

trades (trades of 10,000 or more shares). This variable is calculated with data from the TAQ database.

Market leverage is book value of long-term debt plus debt in current liabilities / (Book value of assets – book

value of common equity + market value of equity). It is calculated prior to the acquisition using Compustat data

on the target firm.

Market-to-book assets is (Book value of assets – book value of common equity + market value of equity) /

book value of assets. It is calculated prior to the acquisition using Compustat data on the target firm.

Market value of equity is calculated 63 trading days prior to the deal announcement with CRSP data on the

target firm.

%Medium-size trades is the percentage of the trades during a given day of a target firm’s stock that are medium-

size trades (trades of between 500-9900 shares). This variable is calculated with data from the TAQ database.

Negotiation CAR is the product of the firm-specific average abnormal return associated with a negotiation event

and the number of negotiation events for the deal during the three-month window before the deal announcement

date. The firm-specific average abnormal return associated with a negotiation event is obtained from a regression

of target firm daily abnormal stock returns from day -1 to -63 relative to the deal announcement on lagged daily

stock returns and indicator variables for if a negotiation event or a media rumor took place that day or on the

prior day.

Negotiation events are nonpublic merger-related negotiation events. The dates of these events are collected

from merger background disclosures. The events include the initiation of merger talks, a merger-related acquirer

or target board meeting, a new or revised bid (acquisition offer) made to a target firm, the signing of a

confidentiality, standstill, or exclusivity agreement or a target termination fee clause, the hiring by an acquirer

or target firm of a financial advisor, or the beginning of due diligence on the target by the acquirer.

Net runup is the total runup less the negotiation CAR less the rumor CAR (if nonzero).

Number of bidders is the number of bidders who tried to acquire the target as determined from the background

disclosures.

Number of deal advisors is the total number of financial and legal bidder and target firm advisors as reported

in the SDC M&A database.

Number of lead banks is the number of different lead banks with syndicated loans outstanding to the bidder

between the deal announcement date and six months after this date as reported in the Dealscan database.

Order imbalance is the number of buyer-initiated trades less seller-initiated trades divided by the total number

of trades of a target firm’s shares over a given day following Chordia and Subrahmanyam (2004). Order imbalance is calculated using data from the TAQ database.

52

PIN is the probability of informed trading in the target firm’s stock. It is calculated using one year of data ending

three months before the deal announcement date following the approach in Easley, Hvidkjaer, and O’Hara (2002,

2010). PIN is calculated using data from the CRSP and TAQ databases.

Press rumor (0,1) is an indicator variable that equals one if a Factiva search during the six months prior to the

deal announcement date identifies press speculation that the target firm might be acquired, and equals zero

otherwise. In Table 2, Press rumor refers to press speculation about a potential acquisition deal during the three

months before the deal announcement.

Prior rumor (0,1) is an indicator variable that equals one if from Factiva it is determined that on or before a

given trading day there was a press rumor that the target firm might be acquired, and equals zero otherwise.

Probability of acquisition is the fitted value for a target firm from the Cremers, Nair, and John (2009) model

that predicts a firm’s ex-ante likelihood of receiving a takeover offer. Specifically, we use the regression model

from Panel A of Table 2 of that paper that does not include variables constructed with the G-index variable from

Gompers, Ishii, and Metrick (2003). The regression model is run over our 1995-2006 sample period and data

from the Compustat, Thomson Reuters institutional holdings, and SDC M&A databases is used to construct the

variables used in the regression model.

Return on assets is equal to net income divided by book assets. It is calculated prior to the acquisition using

Compustat data on the target firm.

Rumor CAR is the product of the firm-specific abnormal return associated with a media rumor and the number

of media rumors for the deal during the three-month window before the deal announcement date. The firm-

specific abnormal return associated with a media rumor is obtained from a regression of target firm daily

abnormal stock returns from day -1 to -63 relative to the deal announcement on lagged daily stock returns and

indicator variables for if a negotiation event or a media rumor took place that day or on the prior day.

Runup is the summation of target firm daily abnormal stock returns from days -63 to -1 relative to the deal

announcement date.

%Small-size trades is the percentage of the trades during a given day of a target firm’s stock that are small-size

trades (trades of between 100-400 shares). This variable is calculated with data from the TAQ database.

Target announcement return is the summation of target firm daily abnormal stock returns over days 0 to +1

relative to the deal announcement date.

Target initiated (0,1) is an indicator variable that equals one if the background disclosures indicate that the

target firm has put itself up for sale, and equals zero otherwise.

Target termination fee clause (0,1) is an indicator variable that equals one if it is reported in the SDC M&A

database that the target firm agreed to a termination fee clause, and equals zero otherwise.

Time in private negotiation (days) is determined from the background disclosures and is the number of days

between the first contact between the target and bidder firms and the deal announcement date.

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Appendix B

Item 1005 of Regulation M-A – Past Contacts, Transactions, Negotiations and Agreements

(a) Transactions. Briefly state the nature and approximate dollar amount of any transaction, other than those

described in paragraphs (b) or (c) of this section, that occurred during the past two years, between the filing

person (including any person specified in Instruction C of the schedule) and;

(1) The subject company or any of its affiliates that are not natural persons if the aggregate value of the

transactions is more than one percent of the subject company’s consolidated revenues for:

(i) The fiscal year when the transaction occurred; or

(ii) The past portion of the current fiscal year, if the transaction occurred in the current year; and

Instruction to Item 1005(a)(1) : The information required by this Item may be based on information

in the subject company’s most recent filing with the Commission, unless the filing person has

reason to believe the information is not accurate.

(2) Any executive officer, director or affiliate of the subject company that is a natural person if the

aggregate value of the transaction or series of similar transactions with that person exceeds $60,000.

(b) Significant corporate events. Describe any negotiations, transactions or material contacts during the past

two years between the filing person (including subsidiaries of the filing person and any person specified in

Instruction C of the schedule) and the subject company or its affiliates concerning any:

(1) Merger;

(2) Consolidation;

(3) Acquisition;

(4) Tender offer for or other acquisition of any class of the subject company’s securities;

(5) Election of the subject company’s directors; or

(6) Sale or other transfer of a material amount of assets of the subject company.

(c) Negotiations or contacts. Describe any negotiations or material contacts concerning the matters referred

to in paragraph (b) of this section during the past two years between:

(1) Any affiliates of the subject company; or

(2) The subject company or any of its affiliates and any person not affiliated with the subject company

who would have a direct interest in such matters.

Instruction to paragraphs (b) and (c) of Item 1005: Identify the person who initiated the contacts or

negotiations.

(d) Conflicts of interest. If material, describe any agreement, arrangement or understanding and any actual

or potential conflict of interest between the filing person or its affiliates and:

(1) The subject company, its executive officers, directors or affiliates; or

(2) The offeror, its executive officers, directors or affiliates.

Instruction to Item 1005(d): If the filing person is the subject company, no disclosure called for by this

paragraph is required in the document disseminated to security holders, so long as substantially the

same information was filed with the Commission previously and disclosed in a proxy statement, report

or other communication sent to security holders by the subject company in the past year. The document

disseminated to security holders, however, must refer specifically to the discussion in the proxy

statement, report or other communication that was sent to security holders previously. The information

also must be filed as an exhibit to the schedule.

(e) Agreements involving the subject company's securities. Describe any agreement, arrangement, or

understanding, whether or not legally enforceable, between the filing person (including any person specified

in Instruction C of the schedule) and any other person with respect to any securities of the subject company.

Name all persons that are a party to the agreements, arrangements, or understandings and describe all

material provisions.

54

Appendix C

Sample background disclosure from Swift Transportation’s announcement of a proposed

acquisition of M.S. Carriers on December 11, 2000 (as disclosed in form S-4 filed by Swift

on April 19, 2001)

Background of the Merger

The senior management of Swift and M.S. Carriers, particularly Mr. Moyes and Mr. Riley of Swift and

Mr. Starnes and Mr. Barrow of M.S. Carriers, have known each other for more than ten years. Over that

time the parties developed relationships of mutual respect and friendship.

In July 1999, Mr. Moyes wrote Mr. Starnes and proposed that the two companies open merger

discussions. Mr. Starnes replied that he was not interested. No further communications ensued until

July 2000.

In July 2000, several of Swift’s directors discussed the possibility of attempting to engage M.S. Carriers

in serious merger discussions. Swift had analyzed various internal and industry factors and identified M.S.

Carriers as a favorable merger candidate. The factors considered included compatibility and quality of

management, as well as the possibilities of purchasing economies of scale, expanded use of technology,

improved customer service, unified recruitment of drivers, an enhanced position in the Mexican freight

market, and more efficient asset utilization.

After reviewing publicly available financial information, on July 14, 2000, Mr. Moyes wrote

Mr. Starnes proposing to open merger negotiations. The letter discussed various industry trends, identified

several reasons for pursuing a merger, and analyzed the effect of various exchange ratios. Mr. Starnes

responded that he would be willing to discuss a merger. On July 20, 2000, M.S. Carriers contacted Merrill

Lynch to serve as its financial advisor with regard to the potential transaction. Over the next several days,

a representative of Merrill Lynch and Earl Scudder, a member of Swift’s board of directors and outside

legal counsel, held telephone discussions. They discussed a number of issues relating to the proposed

transactions. These issues included proposed exchange ratios, projected earnings of both companies for

2000 and 2001, potential cost-saving synergies, the value of investments by both companies in

Transplace.com and partially owned Mexican subsidiaries, the eligibility of each company to engage in a

pooling-of-interests, M.S. Carriers’ desire for a merger agreement with reciprocal representations and

warranties, the agreement of M.S. Carriers not to solicit any competing offer, and the prospect of

employment and noncompetition agreements with M.S. Carriers executive officers after the merger. The

results of these discussions were encouraging enough to schedule a meeting of the principals for each

company.

On August 1, 2000, Messrs. Moyes, Riley, and Scudder for Swift and Messrs. Starnes and Barrow and

a Merrill Lynch representative for M.S. Carriers, met in Memphis, Tennessee. At the meeting, the parties

discussed the issues previously discussed by Mr. Scudder and Merrill Lynch. They also spent considerable

time analyzing financial matters relating to each company, including accounting policies, equipment values

and financing, equipment trade policies, and projections for 2000 and 2001. They also discussed customer

overlap and cross-marketing opportunities.

During the month of August, the chief financial officers of Swift and M.S. Carriers exchanged key

operating statistics, financial forecasts, and the assumptions underlying the forecasts. Based upon the

forecasts and publicly available information, representatives of the two companies analyzed possible

exchange ratios and exchanged several proposals but were unable to agree on an exchange ratio.

On September 11, 2000, representatives of Swift and M.S. Carriers met in Dallas, Texas to discuss the

potential merger. On September 22, 2000, Swift’s board of directors met for a regularly scheduled meeting.

Mr. Riley informed the board of the ongoing discussions.

During September and October, the parties continued to evaluate the exchange ratio in light of updated

earnings prospects of both companies as reflected in published analysts’ estimates and internal forecasts.

55

Between September 8 and October 31, published analysts’ earnings estimates for M.S. Carriers were

reduced by approximately 25% for both 2000 and 2001. During the same period, published analysts’

earnings estimates for Swift were reduced by approximately 15% for both 2000 and 2001. The reductions

in analysts’ estimates were based primarily on high fuel prices and a slowing economy.

On October 30, 2000, representatives of Swift and M.S. Carriers met during the American Trucking

Association convention in San Diego, California. Swift proposed an exchange ratio of 1.7 Swift shares for

each share of M.S. Carriers stock. The parties discussed a plan for integrating the two companies’ operations

in a deliberate manner, the plan for M.S. Carriers to continue operating out of Memphis with existing

personnel, and employment and noncompetition arrangements for the M.S. Carriers executives. The parties

discussed a general timeline and the prospect of a closing in the spring of 2001. The parties did not reach

agreement on the exchange ratio.

On November 21, 2000, Messrs. Moyes and Riley of Swift and Messrs. Starnes and Barrow of M.S.

Carriers, along with the companies’ respective legal counsel, met in Phoenix, Arizona. During the meeting,

the principals agreed to an exchange ratio of 1.7 shares of Swift common stock for each share of M.S.

Carriers common stock, subject to confirmatory due diligence, board approval, and the negotiation of an

acceptable merger agreement. The parties directed their counsel to prepare a draft merger agreement and

related documents for consideration by the companies’ boards of directors. The parties also commenced

due diligence investigations of each other, which continued until December 11, 2000.

On November 22, 2000, Swift engaged Credit Suisse First Boston as its financial advisor in connection

with certain securities matters arising in connection with the acquisition of M.S. Carriers, including the

potential issuance of previously repurchased shares of Swift common stock prior to the merger. Credit

Suisse First Boston was not requested to, and did not, render an opinion with respect to the fairness of the

consideration to be paid by Swift in the merger.

On November 30, 2000, a special meeting of M.S. Carriers’ board of directors was held in Memphis,

Tennessee to consider the proposed merger with Swift. At this meeting, Mr. Starnes updated the board of

directors on the proposed terms of the merger agreement, the exchange ratio, the executive employment

contracts, the conversion of employee stock options, and other matters. The board unanimously approved

moving forward, subject to completion of due diligence, negotiation of an acceptable merger agreement

and related documents, and receipt of an opinion from Merrill Lynch with respect to the fairness of the

exchange ratio from a financial point of view to holders of M.S. Carriers common stock other than Swift

and each of its affiliates.

On December 1, 2000, Swift held a special meeting of its board of directors. Messrs. Moyes, Riley, and

Scudder reported on the material terms of the proposed transaction and recommended that the board of

directors approve a merger on substantially the terms discussed. The board unanimously approved the

merger and issuance of shares of Swift common stock at an exchange ratio of 1.7 to one. The board

authorized Jerry Moyes and Bill Riley to negotiate and sign a merger agreement on substantially the terms

proposed, subject to completion of due diligence and negotiation of an acceptable merger agreement and

related documents.

From December 1 to December 8, 2000, the parties negotiated outstanding issues in the merger

agreement and employment agreements and continued due diligence.

On December 8, 2000, M.S. Carriers held a special meeting of its board of directors to consider the

merger agreement. At this meeting, Merrill Lynch was asked to provide its opinion as to the fairness of the

exchange ratio from a financial point of view to holders of M.S. Carriers common stock, other than Swift

and each of its affiliates. For a detailed discussion of this opinion, see “Opinion of M.S. Carriers’ Financial

Advisor” below.

On December 11, 2000, M.S. Carriers’ board of directors met again to consider the transaction. The

board unanimously approved the merger agreement and authorized its Chief Executive Officer to sign and

deliver the merger agreement and proceed with the proposed transaction.

The merger agreement was executed on December 11, 2000, and the companies issued a joint press

release announcing the merger.