What’s Really in a Deal?

Evidence from Textual Analysis

Wenyao Hu

Lally School of Management

Rensselaer Polytechnic Institute

huw3@rpi.edu

Thomas Shohfi*

Lally School of Management

Rensselaer Polytechnic Institute

shohft@rpi.edu

Runzu Wang

Michael F. Price College of Business

The University of Oklahoma

wangrunzu@ou.edu

January 2019

* Contact author.

We thank the Donald Shohfi Financial Research Fund for support.

What’s Really in a Deal?

Evidence from Textual Analysis

January 2019

Abstract

Using a sample of 757 transcripts from 2011 to 2017, we examine information inside merger and

acquisition conference calls. Textual analysis shows significantly differences between these

transcripts and both contemporaneous corporate press releases and earnings conference calls. We

find participation of executive types in these calls is related to payment choice and consistent

with managerial incentives. Calls occur sooner following announcement of private target

acquisitions. We identify a negative relation between textual sentiment and market reaction

consistent with response to higher levels of information asymmetry. Greater quantitative

information, however, is positively related to the market reaction of M&A calls. We develop an

M&A motive dictionary to identify financial and strategic content within call transcripts.

Consistent with prior literature on merger motivation, deals with more financially (strategically)

orientated words have a higher (lower) market reaction. Overall, our results show that deal-

related textual analysis explains a highly significant and economically important component of

gains/losses to acquirers.

JEL Classifications: G14, G34

Keywords: Acquisitions; Conference Calls; Mergers; Press Releases; Textual Analysis

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1 Introduction

Over the past decades, voluntary disclosure has become an important topic of academic

research in both finance and accounting. Conference calls are a common medium for evaluating

voluntary disclosure and have been examined in various aspects: the factors influencing a firm's

decision to host a conference call (Tasker, 1998), the impact of conference calls on trading

(Frankel et al., 1999), and the effect of conference calls on analysts' forecasts (Bowen et al.,

2002). However, most of these previous studies focus on earnings conference calls which are

usually held at the end of each quarter. In this study, we focus on a relatively new type of

conference calls—mergers and acquisition (M&A) conference calls that focus on specific

transactions. Since much of the variation in acquirer returns remains explained (Golubov et al.,

2015), we seek to answer the question: what information is inside an M&A call and how do

investors react to this information?

M&A conference calls are usually held in conjunction with or after the M&A announcement

as a means of voluntary disclosure. To the best of our knowledge, there are only three published

papers1 that examine M&A conference calls (Kimbrough and Louis, 2011; Siougle, Spyrou and

Tsekrekos, 2014; and Fraunhoffer, Kim and Schiereck, 2018). Using a sample of 1,228 M&A

deals in the US from 2002 to 2006, Kimbrough and Louis (2011) document that conference calls

are held when transactions are large and financed by stock. They also find the average market

reaction to a merger announcement is significantly more favorable when the deal is accompanied

by an M&A conference call. Using a sample of UK firms, Siougle, Spyrou, and Tsekerekos

(2014) find the existence of equity options decreases the willingness of managers to hold M&A

1 There is one more working paper by Rohrer (2017) that examine the participation of institutional investor in M&A

calls.

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calls as equity option markets already convey part of the information released during the call.

The most recent paper by Fraunhoffer, Kim and Schiereck (2018) investigates M&A calls in

Europe and shows that the premium return for M&A calls is only present in the UK and France

and in industries with a focus on research and development. However, none of these prior studies

examines the content (i.e. participants, tone, etc.) of M&A conference calls.

We collect M&A conference call data from 2011 to 2017 through Capital IQ and analyze

transcripts to extract information such as executive participants, sentiment, and quantitative

information of each call. After matching with the SDC Platinum M&A database, we identify 828

unique merger calls associated with 836 merger announcements. We show that percent of stock

payment increases the likelihood of both target CEO and target CFO participation in the M&A

call. This result is consistent with a tradeoff between realized and unrealized benefits for

managers of the target. We also estimate the market reaction to target management participation

and find a negative market reaction to the participation of target executives. We attribute this

negative market reaction to the retention of target executive.

We perform textual analysis on event transcripts to determine whether sentiment inside

M&A conference calls influences market reaction to a deal. We find that positive net tone has

both a statistical and economically significant negative influence on acquirer returns. We explore

two potential explanations for his negative relationship: managerial overconfidence and

information asymmetry between managers and investors. We employ three tests by dividing the

sample according to industry concentration, the listing status of the target, and information

environment, and find support for both of these explanations. Further, we show that content

within M&A calls significantly differs from both contemporaneous press releases and earnings

conference calls.

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Compared to private targets, a public target generally has much more available information

since public traded companies have mandatory disclosures required by listing exchanges and

regulatory authorities. Ceteris paribus, investors face less information asymmetry with regards to

how a public target will influence an acquirer’s valuation and can distinguish between genuinely

positive and contrived sentiment within management discussion. We find that benefits from

private targets mitigate negative effects by around 90% which is significant at 10% level.

Increased market power is another important motive for acquisitions (Borenstein, 1990).

Overly optimistic manager sentiment about an acquisition may be offset when investors

anticipate gains from increased pricing power of the firm. Ali, Klasa, & Yeung (2014) shows that

firms in more concentrated industries tend to have a more opaque information environment. We

use top quartile Herfindahl-Hirschman Index (HHI) to measure industry concentration in a given

year. Consistent with these explanations, we find that a one standard deviation increase in net

positive tone from concentrated (diffuse) industries is associated with a 15% (-42%) relative

increase in CAR from the mean.

Moreover, we also use information intensity (Zhao, 2017) to measure the information

environment for a firm. We find support for the information asymmetry explanation as a one

standard deviation increase in net positive tone for a low information intensity firm will increase

market reaction by 5% relative to the mean compared to a 62% decrease for high information

intensity firm.

We also extract other pertinent information including the level of quantitative information

within each M&A call. Prior research demonstrates that the impact of quantitative versus

qualitative information on the market is different (Hutton, Miller, and Skinner, 2003). In recent

work, Zhou (2017) proposes that quantitative information is more precise and has a more

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positive market reaction than qualitative information around quarterly earnings conference calls.

We predict and find that acquiring firm shareholders react more positively to M&A conference

calls with higher levels of quantitative information. Specifically, we find that a one standard

deviation increase in the percent of numbers spoken increase the market reaction by nearly 1.1%

(significant at 5% level) representing a $121 million increase relative to mean firm market

capitalization in our sample.

Previous research (Rodrigues and Stegemoller 2014; Gorbenko and Malenko, 2014)

employing novel data of M&A characteristics is often restricted by the limited sample size of

hand collected data. By using textual analysis, we build dictionaries to represent the

characteristics of each deal. Specifically, we identify relevant keywords in M&A calls and

broadly separate them into two groups: financial and strategic words. We find that the market

reacts more positively to more frequent occurrence of financial words and negatively to strategic

words. A one standard deviation increase in financial (strategic) words increases the market

value for mean size company in our sample by approximately $64 million (-$58 million). This

result is consistent with previous qualitative research identifying the difference between strategic

and financial mergers.

Our study makes several contributions to the literature. First, our findings provide insight

into variation within acquirer returns. Many prior studies examine M&A announcement returns

(Jensen and Ruback, 1983; Bradley et al.,1988; Moeller et al., 2005; Masulis et al., 2007; Phan,

2014), however, there is still a large proportion of unexplained variation in acquirer

announcement stock returns. Our composition analysis of M&A conference calls provides a

better understanding the gains/losses to acquirers.

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Second, our study contributes to the existing literature on voluntary disclosure by analyzing

the contents of management communication regarding a specific, often unexpected, corporate

activity. Other than previous research which mainly focuses on earnings conference calls, our

research highlights the usefulness of voluntary disclosure for an unscheduled corporate event

particularly when management overconfidence and/or information asymmetry about the event is

high (i.e. acquisitions of private targets).

Third, our research advances existing research on M&A calls by adding detailed content

analysis. Previous research (Kimbrough and Louis, 2011; Siougle, Spyrou, and Tsekerekos,

2014; Rohrer, 2017; Fraunhoffer, Kim and Schiereck, 2018) on this topic mainly focuses on the

determinants or outcomes of hosting a call. Our paper is the first, to the best of our knowledge, to

analyze the content of M&A calls and examine its impact on acquirer returns.

This paper is organized as follows. In section 2, we develop our hypotheses related to

information inside M&A calls. Section 3 describes the procedure of obtaining our M&A sample.

Empirical results are described in section 4 while section 5 concludes.

2 Hypothesis Development

2.1 Attendance of Top Executives

The participation of top executives in conference calls is probably one of the most

fundamental pieces of information contained in conference calls. In this paper, we try to discover

the driving factors of top acquirer and target executives attending a conference call about a

proposed transaction. Previous literature shows that in a stock-based merger, the target

executives face a tradeoff between the tax benefit of stock sales and the risk minimizing benefit

of cash consideration (Faccio and Masulis, 2005). When they choose to accept a stock offer,

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target managers have greater incentives to attend an M&A since their future value is linked to the

ongoing performance of the acquiring company. Target managers are also more likely to retain

positions within the acquiring firm when the deal has a higher stock component (Ghosh and

Ruland, 1998), and are thus more likely to participate in the conference call. We summarize our

first hypothesis accordingly.

Hypothesis 1: The attendance of top executives of the target is positively affected by the

percentage of stock in the M&A offer.

Moreover, Fich et al. (2016) point out that acquirers retaining the target CEO in M&A

transactions receive a significantly lower return in both short- and long-time periods. The

unexpected conclusion of Bargeron et al. (2017) partly supports this result by providing evidence

that the target shareholders gain higher acquisition premiums if the target CEO is retained after

the transaction. Therefore, if the main driving mechanisms we describe above are true, we would

also expect the market reacts negatively to target executives’ attendances.

Hypothesis 2: The attendance of top executives of the target has a negative impact on the

CAR of acquirer.

2.2 Net Tone and Market Reaction

The net tone of language used in information disclosures, i.e., the net usage of positive

words versus negative words, is probably the most common metrics used in previous literature

employing textual analysis. Early studies have examined the impact of net tone in different kinds

of information disclosures, including press releases (Davis et al. 2012), earnings conference calls

(Price et al. 2012), and MD&A disclosures (Davis and Tama-Sweet 2012). In this paper, we

investigate on how net tone of M&A conference calls affects their abnormal returns.

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Constant with previous literature, one would expect the marketplace to react positively to

positive tone of information disclosure events. However, in comparison to other scheduled

disclosure events like earnings conference calls, the level of information asymmetry is much

higher between the management and investor/analyst participants of an M&A conference call.

Moreover, managers of acquiring firms express overconfidence regarding to successful outcomes

of merger and acquisition transactions (Malmendier and Tate, 2007). As a result, words spoken

by an acquiring management team are expected to be overly positive which often means that the

sentiment they use to describe a deal is "too good to be true." Therefore, a potential question is

how much the market believes such information and whether the market can distinguish whether

managers are over-optimistic or not. While an earnings conference call is preceded an earnings

surprise that drives sentiment, there is no analogous metric for M&A events. Therefore, we

propose two opposite hypotheses, and let the data lead us to the correct one.

Hypothesis 3a: The net tone of an M&A conference call has a positive impact on the

market reaction.

Hypothesis 3b: The net tone of an M&A conference call has a negative impact on the

market reaction.

2.3 Information in Numbers

Early literature shows that disclosure of both qualitative and quantitative information is

valuable to investors. However, more recent studies point out that the impact of these two types

of information on the market is different. For example, Hutton, Miller, and Skinner (2003) find

that when there are no numbers in the headline of a press disclosure, the market tends to

underreact to the disclosure. Based on this fact, the authors argue that numbers in disclosure

serve as salient information.

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Since quantitative information has a stronger impact on the market, Chuprinin, Gaspar, and

Massa (2017) and Zhou (2017) point out that the proportion of quantitative information and

qualitative information also carries information. A higher percentage of quantitative information,

defined as tangible information in Chuprinin et al. (2017), is related to more precise and easier

interpretation.

It is therefore possible that information tangibility with M&A conference calls plays an

important role in explaining acquirer equity market reaction. In this paper, we follow the

approach used by Zhou (2017), which uses the percentage of numbers in a conference call as a

measure of information tangibility, and check its impact on market reaction to M&A conference

calls. Since higher tangibility of information implies higher quality disclosure, we predict that

the market reacts more positively to M&A conference calls with a higher percentage of numbers.

Hypothesis 4: The market reacts more positively to M&A conference calls with a higher

percentage of numbers.

2.4 Recognition of M&A Transaction Type via Conference Calls

Market reactions to M&A transactions have been studied extensively in the literature

(Travlos (1987), Moeller, Schlingemann and Stulz (2004), Masulis, Wang and Xie (2007),

Rodrigues and Stegemoller (2014)). Many of these prior works focus on how different firma nd

deal characteristics affect the market reaction. These results are often clear and strong. However,

in many cases, the size of the sample of deals non-standard characteristics is small due to the cost

associated with hand collected data. However, with M&A conference call transcripts and textual

analysis techniques, we might able to identify such characteristics and use them to better explain

the market reactions to acquisitions.

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It is commonly accepted that bidders in M&A transactions can be divided into two groups:

financial bidders and strategic bidders. Financial bidders seek undervalued firms and treat the

target firm as a part of their portfolio, while strategic bidders focus on long term operational

synergies and try to integrate the target into their own business (Gorbenko and Malenko, 2014).

Using a small sample of special purpose acquisition corporations, Rodrigues and Stegemoller

(2014) conclude that, compared to strategic M&A transactions, financial M&A transactions have

significantly higher announcement abnormal returns. We create two custom M&A motive

dictionaries using keywords (provided in Appendix III) of previous studies in financial and

strategic M&A transactions and use the proportion of words in these two dictionaries as

measures of how likely a deal is to being financial or strategically motivated. According to prior

literature, we expect that the market reacts more positively to higher percentages of financial

words and lower to percentages of strategic words.

Hypothesis 5: The market reacts more positively to more frequent use of financial words

and less frequent use of strategic words during M&A calls.

3 Data

3.1 M&A Conference Calls

We start the data collection process by extracting a list of all announced mergers and

acquisitions between January 2011 and December 2017 from SDC Platinum Database. 2011 is

the year in which merger call transcripts are widely available through Capital IQ.2 In screening

the deal in SDC database, we set several restrictions. We require that the deal value is at least 1%

of the acquirer's market value, that the acquirer is public, the acquirer can be linked to CRSP and

2 The first year in which M&A calls appears is 2009, but the coverage of M&A call transcripts in Capital IQ is low

for the first two years.

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COMPUSTAT, and that the deal status is completed. Applying these screening filters, we

identify 2,538 unique merger announcements.

For each of the 2,538 deal announcements, we manually collect M&A conference call

transcripts from Capital IQ. When matching the conference call with deal information in the

SDC database, we require that the date of the M&A conference call be within 7 days following

deal announcement to increase the accuracy of the matching process. After careful matching, we

identify 828 unique merger calls associated with 836 merger announcements.3 Among the

identified calls, we obtain 757 unique M&A conference call transcripts in Capital IQ.4 Merger-

related conference calls occur for about 34% of all the deals in our sample. Compared to

Kimbrough and Louis's (2011) sample, our coverage rate is higher since we use a 1% deal ratio

which is smaller than theirs (10% deal ratio). We report descriptive statistics for the full sample

in Table 1.

[Insert Table 1]

From Table 1, we find distinguishing differences between deals with and without M&A

conference calls. Consistent with prior research, we show that acquirers who choose to hold

conference calls are much larger, have a lower book to market ratio, and have higher analyst

coverage than firms that do not hold M&A conference calls. For deal level characteristics, we

find that firms with conference calls have higher payment in stock, a larger deal ratio, and the

3 One M&A call can involve discussion about several different deals. 4 Some of the transcripts are not available in Capital IQ since the company either did not open the call to the general

public or did not provide an event transcript to Capital IQ.

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target is less likely to be private. At the industry level, bidders are more likely to hold a call

when the target is in the same industry or the acquirer is in the finance industry.5

3.2 Information in M&A Calls

After obtaining individual merger call transcripts from Capital IQ, we parse and structure

the obtained HTML file for each conference call. We identify each participant's name and the

paragraph associated with that person in each part (presentation or Q&A) of the transcript using

Python’s Beautifulsoup package. We also identify the following information for each merger

call: the affiliation of each person (e.g. acquirer/target executive or analyst), tone, and numerical

content.

3.2.1 Participation of Executives

For identification of executive participant affiliation, we first use the summary page in each

M&A call to broadly separate participants into two groups: executives or analysts. For

executives, we first identify position by extracting keywords such as CEO (Chief Executive

Officer) and CFO (Chief Financial Officer) from each participant’s title in the executive

subgroup. If there is no information in the call summary page, we manually check each person’s

position through the content in the M&A call or through internet searches. We then use

Execucomp to check whether the executive is from the acquirer or target.7 In Table 2, we list the

top executive participant types in M&A calls.

[Insert Table 2]

5 Probit regression results for deal announcements that are followed by M&A conference calls are provided in

Appendix II. 7 For private targets, we assume all remaining unmatched executives are from the target firm.

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Table 2 tells us that the acquirer CEO is the most frequent participant n M&A calls. Since

mergers and acquisitions usually lead to a major change in company's future direction, acquirer

CEOs need to participate and explain to analysts and investors how the deal will positively

impact the future of the firm. Following the acquirer CEO, the acquirer CFO attends near 85% of

calls in our sample as the acquisition usually contains a great amount of payment and financial

restructuring details. On the target side participation is much lower: the target CEO (CFO)

participates in nearly 30% (5%) of M&A calls.

3.2.2 Content Inside M&A calls

After identifying the participants in M&A calls, we use Loughran and McDonald’s

dictionaries (2011) to separately measure positive and negative tone as the number of positive

(negative) words divided by the total number of words in merger calls. We use net positive tone

which is positive tone minus negative tone for our regression analysis. For quantitative

information inside the merger calls, we follow the same procedures as described by Zhou (2017).

We look for any number (excluding numbers more likely to be years) that begins with a space or

dollar sign ($) and followed by numbers (0-9), a comma (,), or a period(.). We calculate the

percentage of numbers as the total count of numbers divided by the total number of words in

each section of the transcript. Table 3 reports summary statistics for the information inside M&A

conference calls.

[Insert Table 3]

From Table 3, we find that the average number of words in an M&A transcript is around

5500. There are approximately 38% (62%) of words in the presentation (Q&A) section of a

typical merger call. Panel B shows that net positive tone is around 1.14% which means the

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average of the tone for a transcript is positive and there are around seven questions in a merger

call.

To compare the tone in M&A calls, we construct two related measurements from the

merger-related press release and earnings conference calls. We define the merger-related press

release as the nearest 8-K filling around the M&A announcement date with merger relevance

keywords inside the document. Moreover, we follow the same data collection procedure as Call,

Sharp, and Shohfi (2017) to obtiain the corresponding earnings calls for the firms in M&A

sample during the sample period.

[Insert Table 4]

Table 4 gives us a clear view that sentiment inside M&A calls is much higher than the other

two types of disclosure. Compared to an M&A press release, an M&A call expresses more

positive tone and less negative tone. The results is consistent with a press release in an 8-K being

a mandatory disclosure which contains higher legal risk and is therefore more conservative. We

also collect all related earnings conference calls from acquiring firms during the sample period.

As we can see in the third column of Table 4, the net positive sentiment is still significantly

higher for M&A calls. This is initial support for our argument that managers express far more

optimism during M&A calls compared to other forms of corporate disclosure.

3.3 Endogenous Choice of Hosting an M&A Call

There is a potential endogeneity concern regarding holding an M&A conference call in that

the decision to do so is made by the acquiring firm. This problem has been raised by previous

research (Kimbrough and Louis, 2011; Rohrer 2017). In our empirical approach, we address this

issue using the approach of Kimbrough and Louis (2011). We run a probit regression model to

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determine the probability of hosting an M&A conference call in our sample. We then calculate

the inverse mills ratio from the selection model. The result is shown in Appendix II. In

subsequent empirical analyses, we do not include industry membership in our regressions as it is

highly associated with the conference call decision but has no theorized effect on announcement

returns (Kimbrough and Louis, 2011).

4 Empirical Results

4.1 Determinants of Executive Appearance

We hypothesize that the percent of stock payment will positively affect the appearance of

target executives. Target executives are sellers who are faced with a tradeoff between the tax

benefits of stock and risk minimizing benefits of cash payment (Faccio and Masulis, 2005). It

follows that target executives will have greater incentive to attend the M&A conference call with

higher levels of stock payment.

To test our hypothesis related to the appearance of top executives, we use the following

probit regression model:

𝑃𝑟𝑜𝑏(𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒 𝑃𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑡𝑖𝑜𝑛) = 𝛼 + 𝛽1 × 𝑃𝑒𝑟𝑐𝑒𝑛𝑡 𝑜𝑓 𝑆𝑡𝑜𝑐𝑘 + 𝛽2 ×

𝐷𝑒𝑎𝑙 𝑅𝑎𝑡𝑖𝑜 + 𝛽3 × 𝐿𝑛(𝐴𝑛𝑎𝑙𝑦𝑠𝑡) + 𝛽4 × 𝑆𝑎𝑚𝑒 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 + 𝛽5 × 𝑃𝑟𝑖𝑣𝑎𝑡𝑒 +

𝛽6 × 𝐿𝑛(𝑆𝑖𝑧𝑒) + 𝛽7 × 𝐵𝑜𝑜𝑘 𝑡𝑜 𝑀𝑎𝑟𝑘𝑒𝑡 + 𝛽8 × 𝑅𝑒𝑔𝑢𝑙𝑎𝑡𝑒𝑑 𝐼𝑛𝑑𝑠𝑢𝑡𝑟𝑦 +

𝛽9 × 𝐻𝑖𝑔ℎ 𝑇𝑒𝑐ℎ 𝐼𝑛𝑑𝑠𝑢𝑡𝑟𝑦 + 𝛽10 × 𝐹𝑖𝑛𝑎𝑛𝑐𝑒 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 + 𝛽11 ×

𝐼𝑛𝑣𝑒𝑟𝑠𝑒 𝑀𝑖𝑙𝑙𝑠 𝑅𝑎𝑡𝑖𝑜 + 𝑌𝑒𝑎𝑟 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 + 𝜖 (1)

The dependent variable is an indicator variable equal to one if the M&A conference call includes

that executive position’s participation and zero otherwise. The main independent variable here is

the percent of stock which is measured as the stock value of the payment divided by the total

value of the M&A deal. We also include other variables that may affect the appearance of

executives. We include the ratio of deal to acquirer size, a dummy variable to indicate whether

15

target and acquirer are in the same industry, natural log of acquirer equity market value, acquirer

book to market ratio, and three industry specifications. A detailed description of each

independent variable can be found in Appendix I. Results from the estimation of equation (1) are

reported in Table 5.

[Insert Table 5]

We find evidence supporting our hypothesis. An increase in the percentage of stock payment

increases the likelihood of appearance by the target CEO and target CFO. In column 1, we test

whether the payment type increases the probability of participation of any target executives. The

result indicates that higher levels of stock payment increase executive participant from the target

firm. This result is consistent for both target CEO and CFO roles.

We also observe some interesting results for other independent variables. First, we find that

private target CEOs and CFOs are less likely to attend M&A calls. This result is consistent with

control power since private target management usually lose their control after the acquisition.

Unlike public targets, private target executives to not need deal approval from a dispersed

shareholder base. As a result, they will be less likely to attend the M&A calls. At the industry

level, we also find that the attendance of target CEOs is higher in finance industries (e.g. banks,

insurance companies, etc.).

4.2 Market Reaction to Executive Appearance

In the hypothesis section, we argue that the market will react negatively to the participant of

target executives during M&A calls. We test this hypothesis by estimating the following

regression:

𝐶𝐴𝑅𝑠(−1, +1) = 𝛼 + 𝛽1 × 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒 𝐴𝑝𝑝𝑒𝑎𝑟𝑎𝑛𝑐𝑒 + 𝛽2 × 𝑃𝑒𝑟𝑐𝑒𝑛𝑡 𝑜𝑓 𝑆𝑡𝑜𝑐𝑘 +

𝛽3 × 𝑃𝑟𝑖𝑣𝑎𝑡𝑒 + 𝛽4 × 𝐷𝑒𝑎𝑙 𝑅𝑎𝑡𝑖𝑜 + 𝛽5 × 𝐿𝑛(𝐴𝑛𝑎𝑙𝑦𝑠𝑡) + 𝛽6 × 𝐿𝑛(𝑆𝑖𝑧𝑒) +

16

𝛽7 × 𝐵𝑜𝑜𝑘 𝑡𝑜 𝑀𝑎𝑟𝑘𝑒𝑡 + 𝛽8 × 𝐼𝑛𝑣𝑒𝑟𝑠𝑒 𝑀𝑖𝑙𝑙𝑠 𝑅𝑎𝑡𝑖𝑜 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡 +

𝑌𝑒𝑎𝑟 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 + 𝜖 (2)

The main dependent variable here is cumulative abnormal returns from one day before and

one day after the call. The key independent variable is Executive Appearance, which is the

dummy variable that equals one when the indicated executive participates in the M&A call and

zero otherwise. We also include relevant control variables. The regression results of equation (2)

are shown in Table 6.

[Insert Table 6]

From Column (1) of Table 6, we show that participation of a target manager will decrease

the market reaction by 1.8%, which translates into an additional loss of $212 million in market

value the mean of our sample. This result suggests that the participation of target executives play

an economically material role in the market reaction. The result is similar if we only focus on

target CEO. In contrast, the result for target CFO does not have the significant result as expected.

4.3 Market Reaction to Call Content

4.3.1 Sentiment within M&A Transcripts

The first type of content level information that we examine is sentiment within M&A call

transcripts. As noted previously, we do not have an ex ante expectation of the effect of sentiment

due to the inherent ex-ante optimism of the management team(s) that undertake M&A deals. To

answer this empirical question, we employ the following equation (3):

𝐶𝐴𝑅𝑠(−1, +1) = 𝛼 + 𝛽1 × 𝑁𝑒𝑡 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 + 𝛽2 × 𝑃𝑒𝑟𝑐𝑒𝑛𝑡 𝑜𝑓 𝑆𝑡𝑜𝑐𝑘 +

𝛽3 × 𝑃𝑟𝑖𝑣𝑎𝑡𝑒 + 𝛽4 × 𝐷𝑒𝑎𝑙 𝑅𝑎𝑡𝑖𝑜 + 𝛽5 × 𝐿𝑛(𝐴𝑛𝑎𝑙𝑦𝑠𝑡) + 𝛽6 × 𝐿𝑛(𝑆𝑖𝑧𝑒) +

𝛽7 × 𝐵𝑜𝑜𝑘 𝑡𝑜 𝑀𝑎𝑟𝑘𝑒𝑡 + 𝛽8 × 𝐼𝑛𝑣𝑒𝑟𝑠𝑒 𝑀𝑖𝑙𝑙𝑠 𝑅𝑎𝑡𝑖𝑜 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡 +

𝑌𝑒𝑎𝑟 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 + 𝜖 (3)

17

The main dependent variable is cumulative abnormal return from one day before and one

day after the call. The main explanatory variable here is the net tone of the call. The

measurements of positive tone and negative tone are calculated using the procedure introduced

by Loughran and McDonald (2011). All other control variables are described in the Appendix I.

Column (1) of Table 7 reports the results of the estimation of equation (3).

[Insert Table 7]

In the Column (1), we observe a significantly negative relationship between market reaction

and the net positive tone used in the M&A call without adding any control variables. This result

indicates that a standard deviation increase in net positive tone with an M&A call will decrease

the market reaction by 1.2% (significant at 5% level). This result is also economically significant

in that a one percent increase in positive tone is associated with a $141 million acquirer market

capitalization decline. After adding other controls variables, the coefficient for net positive tone

in column (2) is still significant at 5% level.

Generally, managers will exaggerate the benefits of a merger deal. Malmendier and Tate

(2008) point out that overconfident CEOs will overestimate the returns that they can generate on

their decisions and have a higher probability of making an acquisition. As a result, words spoken

by the CEO or management team will always be overly positive which often means that the

sentiment they use to describe a deal is "too good to be true." However, if information

asymmetry between the management team and investors is low (i.e. for public firms), investors

can make their judgements by checking available information to ascertain whether management

is telling the truth. On the other hand, when information asymmetry is high (private firms),

investors rely more heavily on the information presented by the manager. To resolve this

negative market reaction, we observe three possible channels in the next subsection.

18

4.3.2 Three Channels for negative market reaction

These three channels are related to the market power of the acquirer and information

asymmetry between market and the acquiring firm. Management overconfidence with regard to

deal success can be mitigated by increases in market power from the acquisition of a competitor.

Additionally, compared to competitive industries which have more public information about

products and cost, concentrated industries are dominated by one or two large companies who

control information share.

The major difference between equation (3) and this analysis is the introduction of new

indicator variables and an interaction term between these indicators and net positive tone. The

dummy variables of interest are target public/private status, industry Herfindahl-Hirschman

index (HHI), and firm’s information intensity which is measured by the number of 8-Ks filed by

the firm during past 12 months (Zhao, 2017), respectively. We define Private as the target listing

status in the SDC database; High HHI as an industry level indicator for the top quartile of HHI

measurement in a given year; and Low Information Intensity where firms lie in the bottom half of

the information intensity measurement during the past 12 months. The results are shown in Table

8.

[Insert Table 8]

Column (1) shows that positive tone regarding acquisitions of private targets mitigates

negative market reactions by around 90%, which is significant at 10% level. This result supports

our argument that information asymmetry is mitigation for the negative market reaction since

investor may not have available information to verify whether executives are over-optimistic or

not. Column (2) of Table 8 gives us a similar relationship between high HHI and market

reaction. When an industry is concentrated (diffuse), a one standard deviation increase in net

19

positive tone is associated with a 15% (-42%) increase in CAR. We observe a similar result in

Column (3) as a one standard deviation increase in net positive tone for a low (high) information

intensity firm will increase (decrease) the market reaction by 5% (62%). After examining the

tone in M&A calls, we can conclude that information asymmetry and market power play an

important role in determining the market reaction to an acquisition.

4.3.3 Quantitative Information

Zhou (2017) proposes that quantitative information is more precise and has higher market

reaction than qualitative information in an examination of quarterly earning conference calls.

Cohen et al. (2010) also document that investors tend to react more slowly to less salient and less

tangible information. As quantitative information reduces uncertainty, we propose that higher

levels of quantitative information in an M&A conference call will have a positive effect on

market reaction.

[Insert Table 9]

In Table 9, we identify a significantly positive relationship between the percentage of

numbers spoken and CAR at the 1% level. Column (1) indicates that a one standard deviation

increase in the percent of numbers spoken increases the market reaction by nearly 1.1% which

translates to a $121 million increase for the mean market capitalization in our sample. We also

add control variables in column (2), and the result continues to be both statistically and

economically significant. To sum, higher levels of quantitative information within an M&A call

are viewed favorably by the marketplace.

4.3.4 M&A Motive Dictionary

We next seek to examine the market reaction to two major motives of mergers and

acquisitions: financial and strategic. We use two customized M&A motive dictionaries to

20

represent each type of deal and count keywords appearing in each conference call. The main

independent variables here are the percent of financial (strategic) words in our dictionaries

divided by the number of total words in the merger call. The words lists of these two dictionaries

can be found in Appendix III and summary statistics are provided in the last two rows of Table 3.

[Insert Table 10]

The coefficient for percent of financial words in Table 10 is positive and statistically

significant at the 10% level in Column (1) and at 5% level in Column (3). This positive

coefficient supports the argument that financially orientated M&A is associated with a more

positive market reaction. This result is also economically significant, as a one standard deviation

increase in financial words increases acquirer market value by $64 million in our sample.

The impact of strategic words is also as expected. This negative relationship is significant at

the 10% level in both Column (2) and Column (3). The market reaction decreases by 28% from

the mean with a one standard deviation increase in strategic words which translate into $58

million in lost market value. These results indicate that market participants react differently to

various M&A motives communicated on merger conference calls..

5 Conclusion

This paper investigates information inside M&A conference calls and its impact on acquirer

returns. Although previous research (Kimbrough and Louis, 2011; Siougle, Spyrou, and

Tsekerekos, 2014; Rohrer, 2017; Fraunhoffer, Kim and Schiereck, 2018) has studied this special

type of conference call, none has analyzed the information content within each M&A call. Using

a sample of 757 merger calls from 2011 to 2017, we provide several pieces of evidence on

executives’ decision to participate in M&A conference calls, the market reaction to this

21

participation, textual sentiment and quantitative information within these calls, and introduce a

new dictionary that captures motives for M&A transactions. Overall, the evidence supports the

view that there is rich information within merger calls and that the marketplaces reacts to that

information.

First, we show that payment choice influences top executives’ decision to participate into

the M&A call for target firms. Percent of stock payment increases both target CEO and CFO

participation in the M&A call. This result is driven by a tradeoff between realized and unrealized

benefits for managers of the target. Regarding the market reaction to this participation, we find

the participation of the target CEO generates a negative market reaction, which is consistent with

previous literature that retaining top management reduces an acquirer’s announcement abnormal

return.

Second, we investigate the content of each call. We find a negative relationship between net

tone within the call and market reaction. After three channels tests, we attribute this negative

relation to the both managerial overconfidence and information asymmetry between manager

and investor. We also examine the level of quantitative information within a call and find a

statistically and economically significant relationship between quantitative information and

market reaction.

Finally, we construct a new M&A dictionary approach that quantifies previously only

qualitative information about deal motivation. We separate the dictionary into two broad groups

which contain financial and strategic words reflective of M&A motives that been proven to have

different market reactions in previous literature. Our result show that more financial

communication is associated with a positive market reaction and more strategic focus decreases

the market reaction.

22

Our analysis of M&A conference call content suggests that it is a valuable information

source for market participants. Managers of acquiring firms should be aware of what their

appearances and communication within these calls reveal information about overconfidence,

adverse selection, and M&A motives. Future research might explore specific investor and analyst

reactions to M&A conference calls, the impact of M&A call disclosure on long-term acquisition

outcomes (e.g. goodwill impairment, etc.), and spillover effects on other potential targets.

23

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26

Table 1

Summary Statistics for Full Sample

Table 1 reports descriptive statistics for the full sample and M&A call subsample, respectively. A detailed description of all variables can be found

in Appendix I. All variables are winsorized at 1% and 99% levels.

Full Sample Call Sample No Call Sample

(N=2538) (N=828) (N=1710)

Variables Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Diff T-stat for Mean

CARs(-1,+1) 0.012 0.055 0.018 0.065 0.008 0.048 0.01 3.8

Private Target 0.586 0.493 0.448 0.498 0.66 0.474 -0.212 10.59

Same Industry 0.615 0.487 0.697 0.46 0.571 0.495 0.126 8.27

Percent of Stock 18.94 33.3 26.38 36.96 14.98 30.46 11.4 17.17

Deal Ratio 0.195 0.283 0.321 0.338 0.129 0.221 0.192 6.81

Ln(Analyst) 1.877 0.886 2.043 0.785 1.79 0.923 0.253 6.26

Ln(Size) 7.491 1.714 7.768 1.702 7.344 1.703 0.424 6.05

Book to Market 0.567 0.391 0.523 0.354 0.591 0.408 -0.068 4.17

Regulated Industry 0.0642 0.245 0.0602 0.238 0.0663 0.249 -0.0061 0.64

High Tech Industry 0.343 0.475 0.358 0.48 0.335 0.472 0.023 1.1

Finance Industry 0.232 0.422 0.208 0.406 0.245 0.43 -0.037 2.18

27

Table 2

Top Participants in M&A Calls

Table 2 displays the top 6 management participant types in M&A calls. Column 1 shows the position of

participants; column 2 shows the number of times that person participated in the M&A call; column 3

represents the percentage of participants in all M&A calls.

Position Number of Times in M&A Calls Percentage of all M&A Calls

Acquirer CEO 723 95.5%

Acquirer CFO 643 84.9%

Investor Relations 485 64.1%

Acquirer COO 246 32.6%

Target CEO 214 28.4%

Target CFO 38 5.0%

28

Table 3

Textual Analysis Summary Statistics for M&A Call Sample

Table 3 reports descriptive statistics for the full sample and M&A call subsample, respectively. A detailed

description of all variables can be found in Appendix I. All variables are winsorized at 1% and 99%

levels.

Variable Name N Mean Std. Dev. 25% Median 75%

Total Words 757 5462 2191 3958 5140 6782

Percent of Positive Words 757 0.0185 0.00484 0.0151 0.0181 0.0216

Percent of Negative Words 757 0.0072 0.00225 0.00562 0.00681 0.00834

Net Positive 757 0.0113 0.00523 0.00751 0.0115 0.0146

Percent of Numbers 757 0.0191 0.00632 0.0148 0.0188 0.0227

Total Questions 757 7.144 5.124 4 6 10

Acquirer CEO 757 0.955 0.206 1 1 1

Target CEO 757 0.284 0.450 0 0 1

Acquirer CFO 757 0.849 0.358 1 1 1

Target CFO 757 0.049 0.217 0 0 0

Financial Words 757 0.0154 0.00675 0.0103 0.0145 0.0195

Strategic Words 757 0.0297 0.00839 0.0236 0.0293 0.0354

29

Table 4

Comparison among Different Disclosure Channels

Table 4 displays the comparison among M&A calls, press release and earnings conference calls. For press releases, we use the relevant 8k filling

around M&A announcement date. Regarding earnings conference calls, we use the corresponding earnings conference call in the same period for

firms with M&A calls. The difference represents the difference in mean between press releases or earnings calls and M&A calls. *, **, and ***

indicate significance at the 10%, 5%, and 1% levels, respectively.

Type of Disclosure M&A Call Press Release Earnings Call

N Mean Std. Dev. N Mean Std. Dev. Difference N Mean Std. Dev. Difference

Positive Tone 757 1.844% 0.470% 593 1.539% 0.747% 0.305%*** 1066 1.713% 0.419% 0.131%***

Negative Tone 757 0.716% 0.220% 593 1.075% 0.582% -0.359%*** 1066 0.981% 0.290% -0.265%***

Net Positive Tone 757 1.127% 0.565% 593 0.462% 1.060% 0.666%*** 1066 0.805% 0.553% 0.323%***

30

Table 5

Probability of Executive Participation

Table 5 reports the participation of key executives in M&A conference calls. We use probit regression

model for all of the dependent variables. The dependent variables are a series of dummy variables which

measure whether the position (Target Executive, Target CEO and Target CFO) attend the M&A

conference call. A detailed description of independent variables is provided in Appendix I. *, **, and ***

indicate significance at the 10%, 5%, and 1% levels, respectively.

(1) (2) (3)

VARIABLES Pr (Target Executive) Pr (Target CEO) Pr (Target CFO)

Percent of Stock 0.706*** 0.542*** 0.669***

(4.04) (3.11) (2.78)

Deal Ratio -0.339 -0.427 1.065**

(-0.92) (-1.13) (1.99)

Ln(Analyst) 0.019 0.015 0.240

(0.18) (0.14) (1.59)

Same Industry -0.222* -0.174 -0.134

(-1.85) (-1.42) (-0.71)

Private -0.239* -0.303** -0.706***

(-1.90) (-2.39) (-3.00)

Foreign Target -0.116 -0.095 -0.049

(-0.85) (-0.67) (-0.24)

Ln(Size) 0.011 -0.010 -0.027

(0.21) (-0.20) (-0.40)

Book to Market -0.079 -0.066 -0.130

(-0.44) (-0.35) (-0.46)

Regulated Industry 0.324 0.374 0.342

(1.35) (1.54) (1.16)

High Tech Industry 0.190 0.153 -0.249

(1.49) (1.17) (-1.21)

Finance Industry 0.422** 0.475** -0.131

(2.34) (2.55) (-0.49)

Inverse Mills Ratio -1.091*** -1.225*** 0.774

(-2.59) (-2.81) (1.20)

Constant 0.712 0.899 -2.692**

(0.84) (1.04) (-2.05)

Year FE YES YES YES

Observations 757 757 757

Pseudo R-squared 0.1382 0.1358 0.0963

31

Table 6

Market Reaction to Participation

Table 6 reports the market reaction to the participation from target firm. The dependent variable is the

cumulative abnormal return around the event date (0 days to +1 days). Column 1 reports the result for the

participation of either executive from target firm. Column 2 and 3 represents the involvement of target

CEO and CFO separately. A detailed description of other independent variables are provided in the

Appendix I. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

(1) (2) (3)

VARIABLES CAR(-1,1) CAR(-1,1) CAR(-1,1)

Target Executive Appearance -0.018***

(-3.01) Target CEO Appearance -0.017***

(-2.74) Target CFO Appearance -0.004

(-0.30)

Percent of Stock -0.026* -0.027* -0.029**

(-1.77) (-1.85) (-2.01)

Private 0.015* 0.014 0.014

(1.72) (1.65) (1.60)

Deal Ratio 0.022 0.023 0.024

(0.93) (0.95) (1.01)

Foreign Target -0.009 -0.009 -0.008

(-1.52) (-1.46) (-1.33)

Ln(Analyst) -0.003 -0.003 -0.003

(-0.55) (-0.53) (-0.49)

Ln(Size) -0.008*** -0.008*** -0.009***

(-3.60) (-3.58) (-3.68)

Book to Market -0.030*** -0.030*** -0.030***

(-3.13) (-3.14) (-3.22)

Date Difference -0.002** -0.002** -0.002**

(-2.07) (-2.31) (-2.38)

Inverse Mills Ratio -0.032 -0.031 -0.025

(-1.32) (-1.25) (-1.01)

Constant 0.143*** 0.140*** 0.132***

(3.13) (3.05) (2.85)

Industry FE YES YES YES

Year FE YES YES YES

Observations 757 757 757

R-squared 0.135 0.132 0.125

32

Table 7

Market Reaction to Tone in Transcripts

Table 7 reports the market reaction to the tone in M&A conference calls. And the dependent variable is

the cumulative abnormal return around the event date (-1 days to +1 days). The key independent variables

here are the tone used in the transcripts. We measure the tone as the percent of positive words minus the

percent of negative words in the whole transcripts. Industry fixed effects are 2-digit SIC. A detailed

description of other independent variables are provided in the Appendix I. *, **, and *** indicate

significance at the 10%, 5%, and 1% levels, respectively.

(1) (2)

VARIABLES CARs(-1,1) CARs(-1,1)

Net Positive -1.227** -1.097**

(-2.23) (-2.09)

Percent of Stock -0.026**

(-1.97)

Private 0.011

(1.44)

Deal Ratio 0.021

(0.69)

Foreign Target -0.010

(-1.57)

Ln(Analyst) -0.001

(-0.18)

Ln(Size) -0.009***

(-3.16)

Book to Market Ratio -0.017*

(-1.67)

Inverse Mills Ratio -0.026** -0.027

(-2.45) (-0.91)

Constant 0.060*** 0.140**

(3.70) (2.31)

Industry FE YES YES

Year FE YES YES

Observations 757 757

R-squared 0.153 0.222

33

Table 8

Market Reaction to Different Channels of Sentiment

Table 8 reports the market reaction to the tone in M&A conference calls for different channels of

sentiment (information asymmetry and overconfidence). The dependent variable is the cumulative

abnormal return around the event date (-1 days to +1 days). The key independent variables here are the

tone used in the transcripts. We measure the tone as the percent of positive words minus the percent of

negative words in the whole transcripts. A detailed description of other independent variables are defined

in the Appendix I. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

(1) (2) (3)

VARIABLES CARs(-1,1) CARs(-1,1) CARs(-1,1)

Net Positive -1.837*** -1.501** -2.220***

(-2.65) (-2.55) (-2.81)

Net Positive*Private 1.716*

(1.70) Net Positive*High HHI Index 2.024*

(1.78) Net Positive*Low Information Intensity 2.403**

(2.17)

High HHI Index 0.002

(0.09) Low Information Intensity -0.032**

(-2.24)

Private -0.009 0.011 0.015*

(-0.62) (1.49) (1.89)

Percent of Stock -0.026** -0.025* -0.038***

(-1.99) (-1.92) (-2.67)

Deal Ratio 0.021 0.019 0.006

(0.71) (0.63) (0.21)

Foreign Target -0.012* -0.011* -0.013*

(-1.73) (-1.66) (-1.84)

Ln(Analyst) -0.002 -0.002 -0.001

(-0.26) (-0.34) (-0.22)

Ln(Size) -0.009*** -0.009*** -0.011***

(-3.06) (-3.05) (-3.35)

Book to Market Ratio -0.017* -0.017* -0.022**

(-1.68) (-1.69) (-1.99)

Inverse Mills Ratio -0.027 -0.028 -0.045

(-0.90) (-0.93) (-1.45)

Constant 0.147** 0.141** 0.198***

(2.45) (2.34) (3.20)

34

Industry FE YES YES YES

Year FE YES YES YES

Observations 757 757 653

R-squared 0.225 0.229 0.242

35

Table 9

Market Reaction to Quantitative Information in M&A Calls

Table 9 reports the market reaction to quantitative information used in M&A conference calls. We use

cumulative abnormal return around the event date (-1 days to +1 days) as the dependent variable. The key

independent variable here is the proportion of numbers used in the transcripts. A detailed description of

other independent variables is provided in Appendix I. *, **, and *** indicate significance at the 10%,

5%, and 1% levels, respectively.

(1) (2)

VARIABLES CARs(-1,1) CARs(-1,1)

% Numbers 1.740*** 1.265***

(3.57) (2.65)

Percent of Stock -0.021

(-1.62)

Private 0.010

(1.33)

Deal Ratio 0.024

(0.79)

Foreign Target -0.013*

(-1.86)

Ln(Analyst) -0.001

(-0.11)

Ln(Size) -0.009***

(-2.89)

Book to Market Ratio -0.018*

(-1.80)

Inverse Mills Ratio -0.020* -0.016

(-1.89) (-0.55)

Constant 0.010 0.088

(0.63) (1.43)

Industry FE YES YES

Year FE YES YES

Observations 757 757

R-squared 0.162 0.224

36

Table 10

Market Reaction to M&A Motives

Table 10 reports the market reaction to our M&A motive dictionaries. The dependent variable is the

cumulative abnormal return in three days around the event date (-1, +1). The main independent variables

here are the percent of financial and strategic oriented, respectively. A detailed description of other

independent variables is provided in Appendix I. *, **, and *** indicate significance at the 10%, 5%, and

1% levels, respectively.

(1) (2) (3)

VARIABLES CARs (-1,+1) CARs (-1,+1) CARs (-1,+1)

Percent of Financial Words 0.864* 0.918**

(1.87) (1.99)

Percent of Strategic Words -0.620* -0.532*

(-1.85) (-1.67)

% Stocks -0.026* -0.027** -0.027**

(-1.94) (-2.01) (-2.04)

Private 0.011 0.013* 0.012*

(1.52) (1.67) (1.66)

Deal Ratio 0.029 0.016 0.027

(0.99) (0.52) (0.93)

Ln(Analyst) -0.012* -0.011* -0.012*

(-1.86) (-1.67) (-1.88)

Ln(Size) -0.001 -0.002 -0.001

(-0.15) (-0.35) (-0.11)

Book to Market -0.009*** -0.009*** -0.009***

(-3.46) (-3.19) (-3.48)

Inverse Mills Ratio -0.014 -0.030 -0.016

(-0.48) (-0.98) (-0.53)

Constant 0.103* 0.153** 0.121*

(1.70) (2.40) (1.94)

Industry FE YES YES YES

Year FE YES YES YES

Observations 757 757 757

R-squared 0.163 0.159 0.166

37

Appendix I

Variable Definitions

Variable Name Definition % Executive Numbers Percentage of numbers spoken by executives in the merger call

% Executive PRE Numbers Percentage of numbers spoken by executives in the presentation section

% Executive Q&A Numbers Percentage of numbers spoken by executives in the Q&A section

% Numbers Percentage of numbers spoken in the merger call

Acquirer CEO An indicator variable for acquirer CEO participation

Acquirer CFO An indicator variable for acquirer CFO participation

Book to Market Book Value of Equity / Market Value of Equity

CARs (-1, +1) 3 days Cumulative abnormal return around the conference call date

Date Difference Date difference between announcement date and M&A conference call date

Deal Ratio Value of deal / Market value of acquirer

Finance Industry An indicator variable for two-digit SIC codes in the range of 60-69

High HHI An indicator variable for the firm in an (two-digit SIC) industry that is within the top quartile of HHI in a given year

High Tech Industry An indicator variable for two-digit SIC codes equal to 28, 35, 36, 73, or 87

Ln(Analyst) Natural Log of number of analysts follows

Ln(Size) Natural Log of market value

Low Information Intensity An indicator variable for the number of 8k in the past 12 month within the lower half of all the companies in a given year

Net Positive Positive tone minus negative tone

Percent of Financial Words Percentage of financial words in M&A motive dictionary described in Appendix III

Percent of Numbers Percentage of numbers spoken in the merger call

Percent of Stock (% Stocks) Percentage of stock payment to the total payment of a deal

Percent of Strategic Words Percentage of strategic words in M&A motive dictionary described in Appendix III

Percent of Words in Presentation Percentage of words in presentation to total words

Percent of Words in Q&A Percentage of words in Q&A to total words

Private Target An indicator variable for private target

Regulated Industry An indicator variable for two-digit SIC codes equal to 48 or 49

Same Industry An indicator variable for bidder and target having the same two-digit SIC code

Target CEO An indicator variable for target CEO participation

Target CFO An indicator variable for target CFO participation

Total Questions Total number of questions in an M&A call

Total Words Total number of words in an M&A call

38

Appendix II

Probability Model of Holding an M&A Conference Call

Appendix II replicates the probability regression model from Kimbrough and Louis (2011). The

dependent variable is an indicator variable which measures whether there is a conference call

associated with a given M&A transaction. A detailed description of independent variables is provided

in Appendix I. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

(1)

Dependent Variable Call Dummy

Percent of Stock 0.005***

(5.39)

Deal Ratio 1.637***

(14.75)

Ln(Analyst) 0.109**

(2.53)

Same Industry 0.177***

(2.97)

Private -0.297***

(-4.95)

Ln(Size) 0.100***

(4.38)

Book to Market -0.307***

(-3.69)

Regulated Industry -0.331***

(-2.73)

High Tech Industry 0.067

(1.00)

Finance Industry -0.178**

(-2.17)

Constant -1.695***

(-8.58)

Year FE YES

Pseudo R2 0.1576

Observations 2,528

39

Appendix III

8 This “book” means “book to *” (e.g. market) ratio

M&A Motives Dictionary

Appendix III shows the custom M&A motives dictionaries that we employ. Each entry of the table is a stem, not a word, in English. Panel A

reports the stems for financial and Panel B reports strategic motives.

Panel A: Finance Words

Groups Definition Stems

Financial Information All related to firms’ financial performance capit contract credit debt financ loan return tax cash

bank flow

Financial Ratios All the ratios that are mentioned in the calls ebitda ebit roa roe book8

Short-term Action Describe the short-term actions that perform

by the firm after the M&A annual invest purchas volum

Panel B: Strategic Words

Groups Definition Stems

Future Orientated Describe the future of the firm or the deal confid develop opportun project growth

Business Strategy Words that describe the strategy synerg structur patient merger

Governance Describe the governance of the firm after the

M&A lead leader leadership manag oper organ

Operating Describe the operating performance after the

M&A

custom expens manufactur margin market produc sale

suppli