inattention to merger announcements and strategic

Inattention to Merger Announcements and Strategic

Disclosure Timing

Fabian Dreher, 11085231

July 7, 2016

University of Amsterdam, Amsterdam Business School

MSc Business Economics, Finance Track

Master Thesis

Supervised by Dr. Florian Peters

Abstract

Do firms announce mergers strategically in response to investor inattention? This thesis

examines the market reaction of stocks to merger announcements made on days of increased

inattention. By focusing on mergers with a negative immediate market response of the ac-

quiring firm, I find that those firms perform significantly worse if the merger is announced

on a high inattention day than if the merger is announced on a normal day. Mergers an-

nounced on a day of a high number of earnings announcements show 2.5% lower cumulative

abnormal returns in the 30-day period after the announcement and mergers announced on a

day of a major sports event show 2.9% lower cumulative abnormal returns over this period.

These findings are in line with previous literature on strategic disclosure timing of managers

and support the claim that market inattentiveness is being exploited by managers around

merger announcements. Prior results to strategic disclosure timing on inattention days like

Fridays and days before national holidays can not be confirmed for merger announcements.

Acknowlegdements

I would like to express my gratitude to my supervisor Dr. Florian Peters for the useful

comments, remarks and engagement throughout the learning process of this master thesis.

Without his assistance and constant feedback this thesis would have not been accomplished

in the way presented.

Statement of Originality

This document is written by student Fabian Dreher who declares to take full responsi-

bility for the contents of this document.

I declare that the text and the work presented in this document is original and that no

sources other than those mentioned in the text and its references have been used in creating

it.

The Faculty of Economics and Business is responsible solely for the supervision of com-

pletion of the work, not for the contents.

Contents

1 Introduction 1

2 Literature Review 6

3 Methodology and Hypotheses 11

3.1 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.1.1 Hypothesis 1: Investors show less attention to merger announcements

when there is a high number of earnings announcements on the same

day and on days of major sports events . . . . . . . . . . . . . . . . 12

3.1.2 Hypothesis 2: Managers exploit investor inattention by announcing

less profitable mergers on days of a high number of earnings announce-

ments and on days of major sports events . . . . . . . . . . . . . . . 12

3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.3 Empirical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.4 Control Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4 Data and Descriptive Statistics 17

4.1 Announcement Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.1.1 Control Variables from Dataset . . . . . . . . . . . . . . . . . . . . . 17

4.1.2 Control Variable Outside of Dataset . . . . . . . . . . . . . . . . . . 18

4.2 Stock Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.2.1 Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.2.2 Returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.2.3 Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.2.4 Independent Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.2.5 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5 Empirical Results 24

5.1 Limited Attention to Merger Announcements . . . . . . . . . . . . . . . . . 25

5.1.1 Trading Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

5.1.2 Immediate Stock Return Response . . . . . . . . . . . . . . . . . . . 29

5.2 Strategic Disclosure Timing of Mergers . . . . . . . . . . . . . . . . . . . . . 31

6 Robustness Checks 37

6.1 Post-announcement Drift and Convergence . . . . . . . . . . . . . . . . . . 37

6.2 Extension of Observation Period . . . . . . . . . . . . . . . . . . . . . . . . 38

6.3 Terciles and Quartiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

7 Conclusion and Discussion 44

References 53

Appendix 57

1 Introduction

This research investigates strategic management behavior around the announcement of

mergers and acquisitions1. With respect to the large volume of empirical work on inatten-

tiveness in the stock market which has been done over the last several years, it is especially

interesting to see the implications of these findings on management decision making and

disclosure timing in particular. Prior literature in this field documents strategic disclosure

timing on Fridays and before national holidays (Niessner, 2015) and when shareholders are

distracted (Kempf, Manconi and Spalt, 2015). My research focuses on the announcement

of mergers and acquisitions on days of an increased number of earnings announcements or

days of major sports events. I propose that on these days markets are less attentive to

merger announcements and managers strategically announce worse mergers at that time. I

find clear evidence for this hypothesis, which is visible in a significantly worse performance

of mergers that are announced on these inattention days, compared to mergers announced

on normal days.

The underlying assumption of this research is that capital markets are not efficient. As

a result, managers have goals besides maximizing shareholder value. In the context of merg-

ers and acquisitions, corporate actions can also have a detrimental effect on the shareholder

value as managers favor private utility maximization over the shareholders’ preferences.

An example is a case of diversifying mergers which are usually taken out to stabilize cash

flows but are not in the best interests of shareholders who seek out diversification on an

individual level. Since in most cases, merger announcements are being kept secret until

their official publication, managers face a certain time window for the disclosure. I pro-

pose that managers prefer to choose days of high inattention for the announcement of less

profitable mergers (seen from an investor’s perspective) and days of low inattention for the

announcement of more profitable mergers. In specific, I propose two sources of inattention.

First, clustering of earnings announcements on a single day leads to distraction from merger

announcements and second, days of major sports events have the same distraction effect.

1From here on, the term “merger” will be used in order to reference to both “mergers” and“acquisitions”

1

My methodology relies on the idea that inattention is visible in an under-reaction of the

stock market, as shown by DellaVigna and Pollet (2008). A merger announced on a day

of high inattention should show a lower abnormal trading volume and a lower abnormal

stock return response. This is summarized in my first hypothesis, which states that merg-

ers announced on days of earnings announcement concentration or days of major sports

events show lower abnormal trading volume and lower immediate stock return response on

the day of the announcement compared to other (non-inattentive) days. My second hy-

pothesis is that managers preferably announce less profitable mergers on these inattention

days. I hereby look at the long-run performance of those mergers and apply a difference-in-

difference methodology to compare announcements on inattention days and normal days.

In an attempt to differentiate between “good” and “bad” mergers, the sample is split into

mergers showing a positive immediate stock return response and mergers showing a nega-

tive immediate stock return response. The difference in performance is to be observed from

lower cumulative abnormal returns in the bottom group.

I first document that even though abnormal trading volume and immediate stock return

response are lower on inattention days, these differences do not prove to be significant.

These results are in contrast to the literature which reports significant differences for merger

announcements on Fridays and other days (Louis and Sun, 2010). However, the sole focus

on immediate stock return response can not be used as a proof or disproof of inattention as

inattention is ultimately observable in a post-announcement drift (DellaVigna and Pollet,

2009). The findings for long-run returns in fact show a clearer result of investor inattention

as well as strategic disclosure timing. The difference in the bottom group between merger

announcements on days of earnings announcement concentration and normal days is 2.5%

30-days after the announcement and 2.9% between merger announcements on days of major

sports events and other days. These results also persist for an extension of the observation

period to 60 days following the announcement and for a refinement of the classification into

terciles and quartiles. Controlling for firm and deal specific characteristics, the difference

in performance of mergers announced on days of major sports events jumps to a significant

2

9.3% lower cumulative abnormal return. For days of earnings announcement concentration,

significance does not persist if controlled for firm and deal specific characteristics.

The organization of the thesis is as follows. In chapter 2, an outlay of the most relevant

literature is provided with the associated contribution to the existing literature. I position

the thesis in the field of behavioral finance and explain the main theories existing in this

field. Prior studies document different patterns of inattention. The work of Barber and

Odean (2008) shows that less sophisticated investors tend to focus on salient stocks. Cohen

and Frazzini (2008) point out that investors also miss subtle links. The findings are closely

related to the finding of less attention payment towards the distant future (DellaVigna

and Pollet, 2007) and inattention to earnings announcements on Fridays (DellaVigna and

Pollet, 2009). Louis and Sun (2010) were able to document a similar effect for merger

announcements. All these findings of investor’s inattentiveness rais the question of whether

this inattention has an impact on corporate actions. More recent literature by Niessner

(2015) provides evidence that managers strategically disclose negative corporate events on

Fridays. Also Kempf, Manconi and Spalt (2015) show that managers strategically announce

value-destroying mergers when shareholders are distracted. The aim of this research is to

contribute to the existing literature by observing strategic behavior in disclosure timing of

merger announcements more closely. I introduce two distraction variables which lead to an

under-reaction of stock markets. First, the findings of Hirshleifer et. al (2006) indicate that

multiple earnings announcements at the same time can lead to a distraction of investors

and prices to “react sluggishly to relevant news”. Second, as observed by Ehrmann and

Jansen (2012) major sports events can be a significant distraction to shareholders.

Chapter 3 describes the methodology used in this research. As mentioned earlier, the

main hypothesis I propose is a strategic merger announcement timing by managers. In

specific, less profitable mergers are announced on high-inattention days. Since this research

does not focus on Friday inattention2, for which inattention is evidenced quite clearly, there

2An extension of this analysis to Friday inattention and inattention before national holidays can be found

in the Appendix

3

is the need to introduce another hypothesis on which the main hypothesis will be based

on. This basic hypothesis is that investors show less attention to merger announcements

on days of a high number of earnings announcements and days of major sports events.

These two hypotheses are assessed by means of an event study. Cumulative abnormal

returns are calculated from the market model in the immediate time window around the

announcement (i.e. 3-days around the announcement) and in the long-run (30-days after

the announcement). I use a difference-in-difference methodology to see how the performance

of mergers announced on inattention days differs from mergers announced on other days.

In order to get around the problem of a cancellation of negative and positive returns, the

sample is split into “good” mergers, which show a positive immediate return response and

“bad” mergers, which show a negative immediate stock return response. The hypothesis of

strategic disclosure timing focuses on the bottom group as significantly worse performance

is expected here.

In chapter 4 I characterize my sample of merger announcements from January 1985 to

December 2014. Two variables of inattention are constructed. First, earnings announce-

ment concentration days are defined as all days that are in the top 10th percentile of total

quarterly earnings announcements made on a day over the sample. Second, major sports

event days are defined as the finals of NBA, MLB and NFL. I also construct necessary

control variables for robustness checks. Summary statistics can be found under this section.

Chapter 5 presents the results of the analysis. The first hypothesis can not be supported

by the difference in abnormal trading volumes. Even though a visual inspection of mean

abnormal trading volumes on days of inattention and normal days shows that trading volume

is in fact lower on the proposed days of inattention, the following regression results do not

support these findings, as no statistical significance can be found. Moreover, the positive

correlation of trading volume and earnings announcements makes an analysis of the pure

effect of merger announcements on trading activity difficult. There is also no clear evidence

for the first hypothesis to be found in the analysis of abnormal returns. The regression

results of abnormal return in a 3-day window around the merger announcement show a

4

negative effect of the proposed inattention days on returns but are not statistically robust.

The results for the second hypothesis are clearer. Both the visual inspection as well as

the OLS regression give evidence for strategic disclosure timing. Plotting the cumulative

abnormal returns over a 60-day window shows a worse performance of mergers announced

on inattention days which even persists in the long-run without catching up to the control

group. The results can be verified by OLS regression, which shows a significant worse

stock performance in the bottom group if mergers are announced on inattention days. The

difference is -2.5% for earnings announcement concentration days and -2.9% for days of

major sports events. With the introduction of control variables, no significant difference is

to be observed for announcements made on earnings announcement concentration days but

for mergers announced on days of major sports events. The difference for the latter one is

9.4% between the variable of interest and the control group in the bottom group.

In chapter 6 I conduct some robustness checks for the provided results. It can be shown

that there is no clear catching up effect of mergers that are announced on high inattention

days. This supports the claim of strategic disclosure timing, as a difference in performance

solely due to inattention and a slower incorporation of news into prices would have meant a

post-announcement drift resulting in a convergence of the two groups. As this convergence

does not occur, I propose that mergers announced on inattention days are in fact worse and

have strategically been disclosed by managers on these days. I also analyze the performance

difference in the long run (60-day period instead of 30 days) and find that the regression

results confirm my prior results. Finally, as the classification of mergers into positive and

negative immediate stock return responses is merely rough, the classification is refined into

tercile and quartile grouping. Also for this refinement, the prior results can be achieved

and statistical robustness be confirmed. For all that, the introduction of control variables

has a detrimental effect on the significance of the results.

Chapter 7 covers the conclusion drawn from this analysis. I infer that managers indeed

exploit investor inattention in their disclosure timing decision. Nevertheless, I rate the

economic relevance as low since the observed inattention days do not happen frequently

5

enough to be chosen for merger announcements on a regular basis.

In the Appendix I show the results found for Friday merger announcements and merger

announcements made before national holidays. The strategic announcement timing hy-

pothesis can not be confirmed for these two variables, which is in contrast to the previous

literature.

2 Literature Review

The intention of this research is to contribute to the existing literature in several ways. On

a broader level it goes into the field of Behavioral Finance. As discovered by Kahneman

and Tversky (1979), individuals do not always behave fully rational which is a controversy

to traditional economic beliefs and assumptions. Simon (1957) argues that people have

limited cognitive capacity and therefore can overlook or disregard important information.

This inattention can lead to under-reaction in the stock market as shown by Hong and Stein

(1999) and is the main assumption underlying my thesis.

Investor inattention has been discovered by different studies, challenging the the Efficient

Market Hypothesis. Barber and Odean (2008) find that less sophisticated investors, like

retail investors, tend to buy salient stocks and while disregarding less attention-catching

stocks. This is due to limited attention, whereas institutional investors for example can focus

on more stocks at the same time. In summary, retail investors are net buyers of attention-

catching stocks as opposed to institutional investors who are net-sellers of attention catching

stocks. This ultimately leads to an upward price pressure in salient stocks (Da, Engelberg

and Gao, 2011).

Huberman and Regev (2011) outline inattention to news by examining stock price re-

actions of a pharmaceutical firm which had very early positive results on a cure for cancer.

The stock reaction was less pronounced when the article was featured in a science magazine

than when the same article was published in the New York Times and The Wall Street

Journal.

Cohen and Frazzini (2008) investigate investor inattention to subtle economic links. By

6

observing customer-supplier links they find that investor inattention leads to predictable

supplier returns and a resulting arbitrage opportunity. The time needed for information to

fully incorporate into prices is about one year. Other researchers had similar findings (e.g.

Hong, Torous and Valkanov, 2007; Driesprong, Jacobsen and Maat, 2008; Cohen and Lou,

2012). Inattention exists also with respect to the distant future as shown by DellaVigna

and Pollet (2007) by looking at demographic shifts and the predictability of industry-level

stock returns.

These patterns continue to exist even in times of global information accessibility. A

model by Nieuwerburgh and Veldkam (2009) tries to explain the home country bias puzzle,

investor’s natural tendency to invest in the domestic market instead of global markets.

They find that investors do not choose to learn what foreigners know, due to the fact that

they profit more from knowing information which others do not know. Nieuwerburgh and

Veldkam’s conclusion is that learning amplifies information asymmetry.

The main inspiration for this thesis is the discovery of inattention of investors to Friday

earnings announcements by DellaVigna and Pollet (2009). DellaVigna and Pollet examine

the stock market reaction to earnings surprises, comparing Friday to Non-Friday announce-

ments to see whether the weekend acts as a distraction factor. The earnings surprise is

calculated as the difference between the actual earnings per share and the consensus analyst

forecast (median of forecasts), normalized by the stock price. The corresponding abnormal

return is calculated from the market model and used to observe the short-run stock market

response as well as the post-announcement drift. In order to disentangle negative and pos-

itive surprises, DellaVigna and Pollet sort the earnings surprises into quintiles. What they

find is that inattention on Fridays can lead to a 15% lower immediate response and 70%

higher delayed response to earnings surprises as well as lower trading volume. Moreover, a

riskless portfolio investing into the Friday drift and shorting the non-Friday drift earns sub-

stantial abnormal returns. Their findings open a very important question which will be the

main topic of this thesis. Do firms time their announcements strategically? This thesis tries

to fill the research gap of this “largely unexplored field”, according to Baker and Wurgler

7

(2012). Existing literature already suggests strategic disclosure timing, for example Patell

and Wolfson (1982) state that “good news is more likely to be released when the security

markets are open while bad news appears more frequently after the close of trading”.

Niessner and Madsen (2015) indicate the strategic behavior of managers who increase

advertising ahead of positive earnings announcements. The authors identify the importance

of advertising in financial markets by documenting an increase in Google searches for com-

pany tickers after the company publishes print advertisements. These advertising efforts

are strategically being increased in the weeks around earnings announcement if the earnings

surprise is positive.

This is in line with the findings of Solomon (2012) on selective publicity. By examining

media coverage of good and bad corporate news, he finds that investor relations firms

“spin their clients news” and generate a higher media coverage of positive press releases

than negative press releases, resulting in increased announcement returns. This effect is

weakened around earnings announcements. Solomon draws the conclusion that “IR firms

causally affect both media coverage and returns”.

The goal of this thesis is to investigate a specific and also one of the most important

companies news - the announcement of mergers and acquisitions. Louis and Sun (2010) take

a similar approach to DellaVigna and Pollet (2009) and show that investor inattention on

Fridays exists around merger announcements as well. Their analysis focuses on stock swap

announcements only, arguing that cash acquisitions have an “economically small” market

reaction. I believe that even if this is the case in their dataset, there is no economic argu-

ment supporting this. If a company announces a stock swap merger, the market reaction

is naturally more negative than a comparable cash acquisition because the selling company

8

takes part in the shareholder value added (SVA)34. My thesis therefore looks at cash acqui-

sitions as well. The results of Louis and Sun document a lower abnormal trading volume of

acquirers to Friday announcements and less pronounced abnormal stock returns. They also

find evidence for the so called Merger-Monday effect, a phenomenon on Wall Street that

mergers are preferably announced on Mondays for better publicity. These two findings do

not contradict each other, but indicate that investor attention varies over different week-

days. CEOs have the incentive to draw advantage of that, for example through stock-based

compensation.

Current research by Niessner (2015) provides evidence that managers “strategically ma-

nipulate their company’s information environment to extract private benefits”. She makes

use of the SEC requirement for managers to disclose certain corporate events within five

business days. Since managers still have the freedom to choose the day of the week, she

hypothesizes that bad news is preferably disclosed on high inattention days like Fridays and

before holidays. In fact, she finds that negative events are disproportionally disclosed on

these days which results in a significant under-reaction of the market lasting up to three

weeks. In a second step, Niessner observes how managers profit from their strategic disclo-

3Consider the following example: Suppose there is a buyer company and a seller company. Market

capitalization of the buyer is $5 billion, made up of 50 million shares priced at $100 per share. The sellers

market capitalization stands at $2.8 billion - 40 million shares each worth $70. The assumed synergy value

is $1.7 billion. If the buyer offers to buy all the shares of the seller at $100 per share, the value placed on the

selling company is $4 billion, representing a premium of $1.2 billion over the companys preannouncement

market value of $2.8 billion. The shareholder value added (SVA) is simply the expected synergy of $1.7

billion minus the $1.2 billion premium, or $500 million, in a cash acquisition. But if the buyer finances the

acquisition by issuing new shares, the SVA for its existing stockholders will drop. If the buyer offers one of

its shares for each of the sellers shares and the offer places the same value on the seller as did the cash offer,

upon the deals completion, the acquiring shareholders’ ownership in the buyer company will be reduced.

They will own only 55.5% of a new total of 90 million shares outstanding after the acquisition. So their

share of the acquisition’s expected SVA is only 55.5% of $500 million, or $277.5 million. The rest goes to

sellers shareholders, who are now shareholders in an enlarged company.4Example taken from https://hbr.org/1999/11/stock-or-cash-the-trade-offs-for-buyers-and-sellers-in-

mergers-and-acquisitions

9

sure. She confirms that manager insider trading activities take advantage of the disclosure

timing. I take her findings as a foundation for my analysis as I expect to find similar

patterns for merger announcements.

Finally, and most important for this thesis, Kempf, Manconi and Spalt (2015) observe

the implications of distracted shareholders on corporate actions, hereby constructing a

measure of distraction. Distraction measures are constructed by using exogenous shocks to

unrelated parts of the shareholders’ portfolios. In line with their findings of the distraction

effect being concentrated around diversifying mergers, I expect the announcement of less

well performing mergers being concentrated on days of high inattention. Still, this “Strategic

Disclosure Hypothesis” is not undisputed. Doyle and Magilke (2008) also analyze disclosure

timing by using firm-level tests that focus only on firms that switch their disclosure timing.

The underlying argument is that firms which consistently report earnings at the same time

can not be considered to act strategically. As a result, they find “no evidence that managers

opportunistically report worse news after the market closes or on Fridays”. My thesis is

supposed to contribute to the current work on behavioral corporate finance by filling the

gap of strategic manager behavior in the context of M&A announcements and help to solve

this ongoing argument.

I propose four types of inattention days which I am going to observe in my analysis. First

of all, I assume that a source of distraction is a concentration of earnings announcements

as proposed by Hirshleifer et al. (2006) who find that multiple earnings announcements at

the same time can lead to distraction of investors and prices “react sluggishly to relevant

news”. I propose that on days of many earnings announcements, investors pay less attention

to merger announcements than on other days, due to the fact that cognitive capacities are

limited. Second, I assume days of major sports events to be distracting and leading to higher

inattention. Since the days of these sports events are known upfront, firms can strategically

make merger announcements on these days to exploit investor inattention. Evidence for

investor inattention during major sports events is given by an analysis of the European

Central Bank on investor inattention during FIFA world cup matches by Ehrmann and

10

Jansen (2012). The authors find that when the national team is playing, the number of

trades drops by 45% and volumes are 55% lower. Moreover, they find that co-movement

between national and global stock market returns decreases by over 20% during matches

of the national team. A paper on investor inattention to earnings announcements during

major sports events in the US by Santos (2009) documents more negative abnormal two-day

returns of announcements falling on sports event days. The observed sport events are the

Super Bowl, World Series of Baseball and NBA finals which I am also going to use in my

analysis.

Thirdly, and in line with DellaVigna and Pollet (2009), inattention is supposed to be

higher on Fridays compared to other weekdays. This is due to the fact that investors get

distracted by the upcoming weekend and pay less attention than on other days. Finally,

days before national holidays should have the same or an even more negative effect on

investor attention than Fridays. Evidence is given by Pantzalis and Ucar (2014) who find

that earnings announcements released during the Holy Week (Easter) are associated with a

significant post-earnings announcement drift. Positive earnings surprises show a drift of up

to 4% and negative earnings surprises even up to 10%, suggesting that negative events are

even less thoroughly incorporated into prices on high inattention days than positive events.

The latter two variables are covered in the appendix.

3 Methodology and Hypotheses

The thesis examines strategic behavior of managers around their timing of merger announce-

ments. My hypothesis consists of two parts, which will be further explained in the following.

The research focuses on two sources of inattention/ distraction. First, a high number of

earnings announcements and and second, days of major sports events.

11

3.1 Hypotheses

3.1.1 Hypothesis 1: Investors show less attention to merger announcements

when there is a high number of earnings announcements on the same

day and on days of major sports events

Hypothesis 1 aims to find inattention to merger announcements on certain days of assumed

inattention. First, when a lot of firms announce their earnings, the attention is shifted

towards those and reduced attention is available for merger announcements. This hypoth-

esis is based on Simons’ (1957) argument that the human minds capacity is limited. The

same accounts for days of major sports events, for which I assume investors being more

distracted than on other days and showing less attention towards merger announcements.

This hypothesis is closely related to the work of DellaVigna and Pollet (2009), but instead

of earnings announcements I analyze merger announcements. It is also related to the work

of Louis and Sun (2010) but looks at different inattention factors (for Friday announcements

see Appendix).

To evidence inattention, I am looking at trading volumes and initial stock returns. I

directly compare abnormal trading volume and cumulative abnormal returns around the

announcement of mergers on the proposed days of inattention and other days.

3.1.2 Hypothesis 2: Managers exploit investor inattention by announcing less

profitable mergers on days of a high number of earnings announcements

and on days of major sports events

If inattention exists around days of a high number of earnings announcements and on days

of major sports events, I hypothesize that managers take advantage by announcing less

profitable mergers on these days. There are two main reasons for that, first, major sports

events and earnings announcements are set in advance and managers can thereof plan their

merger announcements accordingly. Secondly, the managers are aware of this inattention

and act strategically to “hide” less profitable mergers on days of high inattention. Even

though there might be a post announcement drift and eventually a market correction, they

12

can profit in the short run. To evidence this hypothesis, I look at the performance of

mergers which are announced on days of inattention and compare it to mergers announced

on normal days.

Since there is the likelihood of a cancellation of positive and negative returns in the

aggregate, there is the need to to differentiate between “good” and “bad” mergers. Because

of that I split my sample into mergers showing a positive immediate stock return response

and mergers that showing a negative immediate stock return response. The difference in

performance should be seen from lower cumulative abnormal returns in the bottom group.

3.2 Methodology

The basic idea to assess the proposed hypotheses is to observe the effect a merger announce-

ment has on the stock return by means of an event study.

Before looking at stock returns, I start with the analysis of trading volumes and how

they differ between days of announcement. The inattention hypothesis suggests that trading

volume is lower on days of inattention than on other days. To assess this difference, I

examine abnormal trading volumes, i.e. the difference between the normal trading volume

and the increased volume due to the announcement of the merger. First a plotting of

abnormal trading volume will show whether it is lower on days of high inattention or not.

The findings are quantified in a regression of the following type:

∆v(0,1)t,k = α+ β1X + controls

where ∆v(0,1)t,k is the abnormal trading volume for firm k on the day of the merger

announcement and the subsequent day. α is the constant in the regression and β1 is the

coefficient for the inattention variable X which either indicates whether an announcement

was made on a day of high inattention (1) or not (0) or whether an announcement was

made on a day of a major sports event (1) or not (0).

To further assess hypothesis 1, I look at the immediate stock response by means of the

abnormal stock return around the day of the merger announcement. The event window to

13

be observed is -1/+1 day (3-day window) of the announcement. The abnormal return is

calculated from the market-adjusted model by taking the difference of the market return

and the stock return.

Ri = Rmt + εit

where Ri is the stock return, Rmt is the market return in period t and εit is the abnormal

return.

To measure the abnormal return over the observed time period, the cumulative abnormal

return (CAR) is calculated over the windows -1 to +1 leading to

CARi (−1,+1) =1∑

t=−1

εit

To see a difference in announcements made on days of inattention and normal days, this

thesis will borrow from the Difference-in-Difference methodology where an announcement

on a day of inattention is seen as the “treatment”. A dummy variable indicates whether an

announcement was made on a day of high inattention (1) or not (0). Announcements made

on a normal day act as a control group, as they are not affected by the treatment of interest,

but similarly affected by the announcement. Under the Efficient Market Hypothesis, the

announcements made on an inattention day should behave like the announcements made

on a normal day.

The OLS specification is

R(−1,+1)t,k = α+ β1X + controls+ εt,k

where R(−1,+1)t,k is the abnormal return of company k at time t one day before the

merger announcement, the day of the announcement and the following day. β1 measures

the difference in the variables observed. X constitutes a dummy variable for the inattention

variables being observed. In the case of earnings announcements, it is a dummy for high

earnings announcement concentration days and in the other case a dummy for days of major

sports events as before.

14

Moreover, if managers do not act strategically in their timing of merger announcements,

mergers announced on inattention days should perform similarly to mergers announced on

normal days without significant difference.

Finally, the long-term response is defined as the cumulative abnormal return over the

period -1 to +30 days around the announcement:

CARi (−1,+30) =1∑

t=−1

εit

The effect will be inspected in a graphical form by plotting the mean outcome per period

over the event time for the treatment and the control group.

Finally, a regression shows whether there are differences in the announcement days and

whether the mergers perform differently.

3.3 Empirical Model

To quantify the findings, I examine the stock performance of acquiring companies whose

merger announcement led to a negative immediate stock return response. The OLS speci-

fication is

R(−1,30)t,k = α+ β1X + β2bottom.group+ β3 (X ∗ bottom.group) + controls+ εt,k

where R(−1,30)t,k denotes the abnormal stock returns for company k at time t between 1

day before the announcement and 30 days after the announcement. α is the constant in the

estimated regression. X is the independent variable of interest which indicates the source of

inattention. In the case of earnings announcements, it constitutes a dummy variable taking

the value 1 for days of high number of earnings announcements (90th percentile) and 0

if not (below 90th percentile). The coefficient β1 measures the abnormal return difference

between those two announcement days. In case of a major sports event, X takes the value 1

if an announcement was made on such a day and 0 otherwise. The coefficient measures the

abnormal return generated due to an announcement on a day of a major sports event. The

second variable bottom.group indicates whether the merger announcement had a negative

15

immediate stock market response (1) or not (0). Most important, X ∗ bottom.group is

an interaction variable of the bottom group indicator and the inattention variable. The

coefficient β3 shows the difference in abnormal returns due to the fact that an announcement

was made on an inattention day.

3.4 Control Variables

In contrast to other corporate events like earnings announcements, merger announcements

are much more complicated to assess by the market. Different control variables are needed

to control for other factors which influence the market reaction to merger announcements.

First of all, the profitability of the target as well as the acquirer have to be controlled

for. This is self-explanatory as more profitable targets should have a more positive effect on

the stock price. Since the profitability is usually priced into the bid price it is also necessary

to control for the transaction value.

The size of the acquirer plays an important role in the magnitude that a deal of a

certain size can have on the stock price. I hereby control for the market size of the acquirer

four weeks prior to the announcements in order to have a less influenced price (due to

information leaks for example). A firm that holds high amounts of cash is also more likely

to do value-destroying acquisitions (Harford, 1999), which is why a control variable for cash

is also necessary. It has been shown that mergers which are done over different industries

are less profitable compared to mergers inside industries, so-called diversifying mergers.

These ones will be controlled for as well.

The likelihood of the outcome is a very important factor on the market reaction but

can not be directly observed at the time of the announcement. I control for this effect by

considering the actual outcome of the merger announcement, assuming that the market can

assess the likelihood of the announcement on the day of the announcement correctly.

Finally, it is necessary to control for certain factors which effect inattention. Firstly,

the number of bidders can give an idea of how important a deal is and how much attention

will be given to this deal. Secondly, the public awareness of a company shows how much

16

possible inattention can exist. A control variable for this is the analyst coverage as I expect

a company with higher analyst coverage to be less affected by inattention than companies

with lower analyst coverage.

4 Data and Descriptive Statistics

4.1 Announcement Data

The source for merger announcement data is Thomson One. I restrict the whole sample

of merger announcements to non-financial firms, since in most cases, financial firms do

acquisitions as part of their business model. Consequently, strategic behavior should not be

observed for financial firms. The sample is also restricted to publicly traded firms located

in the US. The minimum deal size taken into consideration is 5 million USD. Smaller deals

are less likely to have an observable impact on the market value. The deal size restriction

also leads to ignorance of all mergers for which no transaction value has been reported.

Finally, the data range is set to a 30-year period from 01/01/1985 to 12/31/2014.

The resulting sample consists of 41,662 announcements made in this time period.

4.1.1 Control Variables from Dataset

In addition to the announcement data the following control variables are introduced. The

first control variable is the status of the merger which is reported in Thomson One as

“Status”. The string variable is converted into a dummy variable, which takes the value 1

for completed mergers and 0 otherwise (including “Withdrawn”, “Intented”, “Unknown”,

etc.). The next control variable is the type of merger, separating into diversifying and non-

diversifying mergers. For this variable the Thomson Financial Macro Code (“TF Macro

Code”) of the target is being matched with the acquirer’s code and a dummy results taking

the value 1 for diversifying mergers (i.e. mergers over different macro industries) and 0 for

non-diversifying mergers (i.e. mergers within the same macro industry). Another control

variable is the number of bidders for a single deal. To control for the size of the transaction,

17

I calculate the ratio of the acquirer’s market value to the transaction value. The acquirer’s

market value is defined as the stock price times shares outstanding, four weeks prior to the

announcement. In addition, I calculate the acquirer’s market to book ratio by dividing the

market value (as described above) by the book value (total assets as reported in the balance

sheet). As a control variable for the profitability of the acquirer and the target, I take the

earnings per share as reported in Thomson One. Finally, I create a control variable for the

acquirer’s cash holdings by dividing the cash position by the total value of assets. This

variable and the market to book ratio are winsorized at the 5% and 90% level. All other

variables are winsorized at lower levels of 1%-5% to 95%-99%.

4.1.2 Control Variable Outside of Dataset

To control for the analyst coverage of an acquiring firm, I create another variable, namely

analyst coverage. Analyst coverage is defined as the number of analysts reporting a year

end earnings estimate for a company in a certain year. This data is taken from IBES and

matched with the announcement data using a link table5.

4.2 Stock Data

Corresponding data for stock prices is retrieved from CRSP. In a first step, the CUSIPs

from Thomson One are matched with the CRSP permanent number (PERMNO). I then

take stock data for the period 01/01/1984 to 12/31/2015 in order to also have lagged data

and forward-looking data.

4.2.1 Volume

In order to calculate daily volumes, I calculate an average daily price by taking the average

of the highest ask price and the lowest bid price during a day. The volume is calculated as

the product of the average price and the total number of shares traded that day.

5The link table was provided by supervisor Dr. Florian Peters.

18

The abnormal volume is defined as the difference of the volume of the day of announce-

ment and the normal volume of a stock. Normal volume is defined as the average arithmetic

volume over the time period -20 to -11. I calculate the abnormal volume for the time frame

0 to +1 of the announcement.

Consequently, the measure for abnormal volume is

∆v(0,1)t,k =

1∑u=0

log(V ut,k) −

−11∑u=−20

log(V ut,k

)/10

Where (Vt,k)u is the value of shares traded on the u-th trading day after the merger

announcement of company k at time t. ∆v(0,1)t,k is the immediate abnormal volume on the

day of the merger announcement and the following day.

4.2.2 Returns

I calculate the abnormal return according to the market model:

εit = Ri −Rmt

Where Ri is the daily stock return and Rmt is the value-weighted CRSP market return

on the same day. The short run response is calculated as the cumulative abnormal return

over days -1 to +1 around the announcement:

CARi (−1,+1) =1∑

t=−1

εit

I calculate the long-run response as the cumulative abnormal return over days -1 to +30

CARi (−1,+30) =30∑

t=−1

εit

For the return calculation I create a workweek calendar which only takes into account

trading days, consequently the lagged value of a Monday return is the prior Friday return.

19

4.2.3 Data Preparation

For the merge of the announcement data and the stock data the CRSP permanent numbers

(PERMNO) are used as identifiers. 3,341 observations can not be matched and are neglected

as the effect on the outcome is assumed to be insignificant. In line with DellaVigna and

Pollet (2009), supposedly illiquid stocks like penny stocks are dropped as well. The threshold

is set to a $5 minimum stock price. Finally, a small number of announcements made on

Saturday and Sunday are dropped as well. The resulting sample consists of 41,622 merger

announcements with corresponding stock data.

4.2.4 Independent Variables

Earnings Announcements. As stated before, a concentration of earnings announcements

is assumed to be a factor of distraction. I retrieve earnings announcement data via I/B/E/S

on quarterly earnings announcements. This data is contracted by day which results in the

total number of earnings announcements made on a single day. Since at some points of

this analysis real numbers are not handy enough to draw conclusions, a dummy variable is

created to indicate announcement concentration days. Therefore, the top 10% in numbers of

announcements are tagged as announcement concentration day (1) and the remaining 90%

non-announcement concentration day (0). The calculated threshold is 339 announcements.

20

Figure 1: Number of Earnings Announcements made per Day

Notes: The graph shows the total number of earnings announcements per day over the period from January 1985 to December 2014.

Quarterly earnings announcement data is downloaded from I/B/E/S and contracted by day. The turquoise line indicates the threshold

for the 90th percentile (highest 10% above this line).

Major Sports Events. Sport plays an important role in the US and most of the US

citizens follow the national leagues. I assume that major sports events are also factors of

distractions if they fall on a trading day. The most important events for the biggest sports

are the Super Bowl in American Football, the World Series of Baseball and the NBA finals in

Basketball. These events are considered in the analysis. Since the Super Bowl traditionally

falls on a Sunday night, I assume that the following Monday will be hallmarked by higher

inattention.

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Table 1: Distribution of Sports Event Variable by Event Type

Notes: The table shows the number of major sports events in the period from 01/01/1985 to 12/31/2014 falling on the same day of at

least one merger announcement. Data is retrieved from sports-reference.com. NBA Finals are the championship series of the National

Basketball Association, World Series of Basketball the championship series of the Major Baseball League. Super Bowl is the final game

of the National Football League. For NBA and MLB games, only the days falling on a weekday are taken into account, dropping

all weekend games. Since the Super Bowl falls on a Sunday, the following Monday is considered as an inattention day. The column

“Number” shows the absolute number of events by type, the column “Percentage” shows the percentage falling onto each type of event.

Number Percentage

NBA Finals 116 52.489

World Series of Baseball 76 34.389

Super Bowl 29 13.122

Total 221 100

4.2.5 Descriptive Statistics

In Table 2, I present summary statistics for the two variables of interest, earnings announce-

ments and major sport events, and the control variables used in this analysis. 7.2% of the

merger announcements in the sample were made on a day of a high number of earnings

announcements. Considering the fact that the dummy variable for earnings announcement

concentration days covers 10% of all days in the sample, this number is lower than expected.

3.2% of the merger announcements were made on days of major sports events which is high

considering that only 2.8% of days in the observation period hosted a major sports event.

Finally, 42.3% of the merger announcements are categorized into the bottom group which

means that most merger announcements showed a positive immediate market response.

Approximate location for Table 2

Table 3 shows the differences in firm characteristics by announcement day. I merge the

variables Announcement Concentration and Major Sports Event into a single inattention

variable. As a result, one can observe differences in firm characteristics of firms announcing

mergers on inattention days and firms announcing on non-inattention days. Surprisingly,

the target as well as the acquirer have higher earnings per share for mergers made on the

22

Table 2: Summary Statistics

Notes: The table shows summary statistics of the independent and control variables. Data on merger announcements is taken from

Thomson One and covers the period from 01/01/1985 to 12/31/2014. Announcements on Saturdays and Sundays are not included the

sample. “Announcement Concentration” is a dummy variable indicating whether the number of quarterly earnings announcements on a

single day was in the 90th percentile of all daily quarterly earnings announcements (data for earnings announcements is retrieved from

I/B/E/S). “Major Sports Event Dummy” is a dummy variable indicating whether the announcement was made on a day of a major

sports event (NBA finals, MLB finals, Super Bowl). “Bottom Group Dummy” is a dummy variable indicating whether the immediate

stock return response (+1/+1 day of the announcement) was negative (1) or not (0). “EPS Target” are the earnings per share of the

target (sell-side) firm. “EPS Acquirer” are the earnings per share of the acquirer (buy-side) firm, respectively. “Log (Pre-announc. MV)

Acquirer” is the natural logarithm of the acquiror’s market value, defined as share price times number of shares outstanding in million

US$, 4 weeks prior to the announcement. “Deal Value to Acquirer Size” is the ratio of the deal (transaction) value to the acquirer size

(“Pre-announc. MV Acquirer” as defined above, not logarithmized). “Log (MV/BV) Acquirer” is the natural logarithm of the ratio of

the market value of assets (“Pre-announc. MV Acquirer”) to the book value of assets of the acquirer. “Acquirer Cash Holdings” is the

ratio of cash assets to net total asset value of the acquirer. “Number of Bidders” is the number of competing bidders for the target

company. “Dummy for completed Mergers” is a dummy variable that takes the value 1 for completed mergers and 0 for any other status.

“Dummy for diversif. Mergers” is a dummy variable which takes the value 1 for mergers over different macro industries (according to

the Thomson Financial Macro Hierarchy) or 0 otherwise. “Analyst Coverage” is the number of analysts who publish quarterly earnings

estimates of the acquiring company in the year of announcement. Data for this variable is taken from I/B/E/S.

observed inattention days. Also the market value of acquirers who make announcements

on inattention days is significantly higher by almost 100%. The deal size is more or less

equal. The market-to-book ratio of acquirers announcing on inattention days is higher than

for the counterpart, possibly due to the high observed market value. Cash holdings and

number of bidders are almost equal for both groups. Mergers that are announced on high

inattention days are completed less frequently and are only in 19% of the cases diversifying,

compared to a 22% of the rest of the sample. Interestingly, analyst coverage is higher for

the companies who announce their mergers on inattention days. This could be interpreted

as an indicator of strategic behavior. In particular, firms who are already at the center of

23

attention might try to make use of inattention days as they are aware of the attention they

usually receive.

Table 3: Differences in Company Characteristics

Notes: The table shows the differences in company characteristics (control variables) on whether the company announced a merger on

a high inattention day or not. Data on merger announcements is taken from Thomson One and covers the period from 01/01/1985 to

12/31/2014. Announcements on Saturdays and Sundays are not included the sample. Column (1) shows the arithmetic mean for the

variable on the left. The variables “Earnings Announcement Concentration Dummy” and “Major Sport Event Dummy” are summed

up into a single “Inattention” variable, a dummy variable that indicates whether a merger announcement was made on a day of a high

number of earnings announcements or on a day of a major sports event (column 1) or not (column 2). Column 3 shows the difference

in means with the corresponding significance level presented as p-values in column 4. “EPS Target” are the earnings per share of the

target (sell-side) firm. “EPS Acquirer” are the earnings per share of the acquirer (buy-side) firm, respectively. “Log (Pre-announc. MV)

Acquirer” is the natural logarithm of the acquiror’s market value, defined as share price times number of shares outstanding in million


(“Pre-announc. MV Acquirer” as defined above, not logarithmized). “Log (MV/BV) Acquirer” is the natural logarithm of the ratio

of the market value of assets (“Pre-announc. MV Acquirer”) to the book value of assets of the acquirer. “Acquirer Cash Holdings”

is the ratio of cash assets to net total asset value of the acquirer. “Number of Bidders” is the number of competing bidders for the

target company. “Dummy for completed Mergers” is a dummy variable that takes the value 1 for completed mergers and 0 for any

other status. “Dummy for diversif. Mergers” is a dummy variable which takes the value 1 for mergers over different macro industries

(according to the Thomson Financial Macro Hierarchy) or 0 otherwise. “Analyst Coverage” is the number of analysts who publish

quarterly earnings estimates of the acquiring company in the year of announcement. Data for this variable is taken from I/B/E/S.

5 Empirical Results

In this section I present the empirical results. As explained under Methodology, there are

two main hypotheses. First, there is limited attention to merger announcements on days

of high number of earnings announcements and days of major sports events. The empirical

results for this hypothesis can be found under 5.1. I first look at abnormal trading volumes

24

on the day of the merger announcement and in a second step at the immediate stock return

response.

Second, the hypothesis that mergers are announced strategically on days of higher inat-

tention is covered under 5.2 by looking at the performance of different mergers.

5.1 Limited Attention to Merger Announcements

This section provides evidence that investors are distracted by a high number of earnings

announcements and major sports events. First, I look at the abnormal trading volume on

days of inattention and on normal days. I then examine whether the immediate stock return

response differs between inattention days and normal days.

5.1.1 Trading Volume

Trading volume is the underlying mechanism that causes prices to adjust (DellaVigna and

Pollet, 2009). If there is inattention in the market, this should also be visible in a lower

than normal trading volume. DellaVigna and Pollet find lower abnormal trading volumes

for earnings announcements on Fridays and interpret it as evidence for investor distraction.

These results are confirmed by Louis and Sun (2011) for merger announcements on Fridays.

Following these findings, I try to show limited attention for the proposed inattention days

by means of abnormal trading volume.

Graphical evidence. Figure 2 plots the average abnormal volume of acquiring compa-

nies to merger announcements made on days of a high number of earnings announcements

and days of a low/normal number of earnings announcements over the day of announce-

ment and the subsequent day. The vertical axis shows the logarithmic abnormal trading

volume, defined as the difference between the normal volume (arithmetic volume over the

time period -20 to -11) and the day of observation. The abnormal volume on the day of the

announcement is shown by the dark blue bar and the abnormal volume on the following

day by the turquoise bar. The sum of the abnormal volume on these two days is shown

by the grey bar. On the left side of the horizontal axis one can see the average abnormal

25

trading volumes for days of a high number of earnings announcements. On the right side of

the horizontal axis one can see the average abnormal trading volumes on days of a lower/

normal number of earnings announcements. It can be seen that abnormal volume is about

25% lower on days of a high number of earnings announcements, which is in line with prior

findings to inattention on Fridays.

Figure 2: Abnormal Volume at Announcement by Earnings Announcement Concentration

Days

Notes: The figure shows abnormal trading volume per day, differentiated by days of a high number of earnings announcements and days

of a low/normal number of earnings announcements. Merger announcements in Thomson One from January 1985 until December 2014

are matched to stock prices and volumes in CRSP. Time 0 in event time is the day of the merger announcement. The abnormal trading

volume is the average logarithmic volume on the day of the merger announcement and the following day, minus the average log volume

for the period -20 to -11. “High # of Earn. Announc.” indicates whether the number of quarterly earnings announcements on a single

day is in the 90th percentile of all daily quarterly earnings announcements (data for earnings announcements is taken from I/B/E/S).

Figure 3 shows the same graph for days of major sports events. Abnormal trading

volume is also positive, showing that merger announcements in general lead to an increased

trading volume. Comparing the two different types of announcement days, namely days

of major sports events and normal days, a lower abnormal trading volume can be seen for

26

acquiring firms who announce their mergers on days of major sports events. The difference

is about 10%, which is lower than the difference previously observed.

Figure 3: Abnormal Volume at Announcement by Major Sports Event Days

Notes: The figure shows abnormal trading volume per day, differentiated by days of major sports events and normal days. Merger

announcements in Thomson One from January 1985 until December 2014 are matched to stock prices and volumes in CRSP. Time 0

in event time is the day of the merger announcement. The abnormal trading volume is the average logarithmic volume on the day of

the merger announcement and the following day, minus the average log volume for the period -20 to -11. “Day of Major Sports Event”

indicates whether the announcement was made on a day of a major sport event (NBA finals, MLB finals, Super Bowl). Data for this

variable is taken from sports-reference.com.

Regression. To quantify the findings above I regress the abnormal trading volume

on days of merger announcement onto the type of announcement day, i.e. the inattention

source. The OLS specification is




coefficient for the inattention variable X, which is either a dummy variable for days of

27

earnings announcement clustering or a dummy variable for days of major sports events.

The coefficient β1 shows the percentage difference in abnormal trading volume between

merger announcements made on inattention days and announcements made on normal days.

Table 4 shows the regression results. Column 1 shows a highly significant coefficient for

days of an increased number of earnings announcements. The coefficient of 0.139 means

that on average, abnormal trading volume in acquiring firms is 13.9% higher if mergers are

announced on days of earnings announcement clustering. This is in contrast to prior findings

on inattention like DellaVigna and Pollet (2009), who find a negative effect of inattention

days on trading volume. A possible explanation is that trading activity generally increases

with the number of earnings announcements (Frazzini and Owen, 2007) and consequently

the inattention effect is getting diluted. An example would be a company who announces

their earnings and a merger at the same time, making a disentangling of trading volume

due to the merger announcement and trading volume due to the earnings announcement

impossible. Another explanation is correlation existent in the stock market, which is also

reflected by trading volume. If many companies announce their earnings, trading volume

increases also around companies who don’t announce earnings on that day, for example

because of ETF trading. The introduction of control variables for merger and company

specifications in column 2 changes the coefficient into a negative 10.7% which is more in

line with the inattention hypothesis. But with a standard error of -1.17 it is not statistically

significant.

In the case of major sports events (columns 3 and 4), the coefficients which show the

difference in abnormal trading volume depending on the day of announcement are also

positive but neither significant with nor without control variables. One can conclude that

the abnormal trading volume in merger announcing companies is not dependent on whether

the announcement was made on a day of a major sports event.

Columns 5 and 6 show regressions with both independent variables with and without

controls. The picture drawn is similar to the previous results described.

28


Summary. A visual inspection of mean abnormal trading volumes on days of inattention

and normal days shows that trading volume is in fact lower on the proposed days of inat-

tention, a finding that is in line with previous literature. Regardless, the regression results

do not support these findings, as no statistical significance can be found. Moreover, the

positive correlation of trading volume and earnings announcements makes a pure analysis

of the effect on merger announcement trading activity difficult.

5.1.2 Immediate Stock Return Response

Next to abnormal trading volume, also the responsiveness of stock returns is affected by

inattention. The logical reasoning is similar to abnormal trading volume. If investors are

distracted, the immediate stock return response should be lower than on days without

distraction.

Regression. To observe this effect, I regress abnormal return, defined as the stock

return subtracted by the market return, on the inattention variables earnings announcement

clustering and days of major sports events. This is similar to the regression I did for

abnormal trading volume, except that the dependent variable is now the abnormal return

instead of the abnormal trading volume.

The OLS specification for the immediate stock response is

R(−1,+1)t,k = α+ β1X + controls+ εt,k

whereR(−1,+1)t,k is the abnormal stock return for company k at time t between 1 day before

the merger announcement and 1 day after the merger announcement. The sample includes

all merger announcements. α is the constant in the regression and β1 is the coefficient for the

inattention variable X which is either a dummy variable for days of earnings announcement

clustering or a dummy variable for days of major sports events. Under Hypothesis 1 of

6Table 4 can be found on page 47

29

inattentive markets, β1 should be significantly lower than 0 in the short-run. This means

that the impact, a merger announcement has on the acquiring company’s stock is less

pronounced on inattention days than on normal days.

The OLS specification allows for a set of control variables, which are described in section

4.1.

The regression results are presented in table 5. Column 1 and 2 show the results for

the variable earnings announcement concentration without and including control variables.

Column 3 and 4 show the regression results for the variable major sports events without and

including control variables respectively. Finally, columns 5 and 6 show regression results

for all variables.

As seen in column 1, the abnormal return of acquiring companies in a 3-day window

around the merger announcement is 0.3% lower on days of earnings announcement con-

centration days than on normal days. This figure is significant in a statistical sense. Even

though the economic impact is low, mostly due to the fact that positive and negative returns

cancel each other out, there is a negative effect of the inattention variable on returns on

average. Introducing control variables, the effect is lower but not significant anymore. The

same accounts for major sports events, which lead to a 0.4% lower immediate abnormal

return than on normal days. Still, the explanatory power of the regressions is low consid-

ering the adjusted R-squared figures. Even with the introduction of control variables, the

percentage variation, which can be explained by the regression is only 1.8%.


Summary. The regression results of abnormal return in a 3-day window around the merger

announcement show a negative effect of the proposed inattention days on returns. These

can be seen as an indicator for inattentiveness, but is not statistically robust. A refinement

of the results can be found under Chapter 6, robustness checks.


30

5.2 Strategic Disclosure Timing of Mergers

The previous section gave evidence that earnings announcement clustering and major sports

events, in fact, act as a distraction factor to investors around merger announcements. This

leads to the second hypothesis I propose, namely that managers take advantage of this

inattention by announcing less profitable mergers on these days. If the hypothesis is true,

the long-term stock return of acquiring firms should be more negative if the announcement

was made on a day of inattention, than on other days. What I expect is that managers

preferably announce bad mergers on high inattention days, to exploit the fact that investors

are distracted and having a less pronounced negative effect on the company’s stock price.

On the other hand, if managers do not expect an major negative market reaction, they are

indifferent of the day of the announcement.

For a better differentiation I split the sample into two groups of merger announcements.

First, the ones that showed a positive immediate response (i.e. the mergers which had an

abnormal return smaller than 0 in the period -1 to +1 day of the announcement). I name

this group the top group. Second, the merger announcements which lead to a negative

immediate stock return response are grouped in the bottom group. I expect the hypothesis

of strategic disclosure timing to be verified in the bottom group.

First of all, I present graphical evidence by plotting the long-term cumulative abnormal

returns of mergers announced on inattention days and mergers announced on normal days.

Next, I present an OLS regression which shows the quantified difference in announcement

days.

Graphical evidence. The figures show the development of cumulative abnormal re-

turns (CARs) of the acquiring companies over the time period of 1 day before the announce-

ment to 60 days after the announcement. The calculation for the CARs can be found under

section 4.2.2. Each graph shows two lines for which each either presents the development

of stock returns of mergers made on inattention days or the development of stock returns

in the control group.

Figure 4 and 5 show the results for the variable earnings announcement concentration.

31

Figure 4 shows the stock reaction over time for announcements made on days of a high

number of earnings announcements compared to other days in the top group. Figure 5

shows the same development for the bottom group. In the top group, a clear performance

difference is visible. Announcements made on days of earnings announcement concentration

days show a better performance from day 1 onwards. The difference in returns is about

1 to 1.5% in cumulative abnormal returns. Interestingly, there is no catch-up in the long

run, but mergers in the top group announced on inattention days continue to outperform

mergers in the control group even after a period of 60 days. A possible explanation for this

is that strategic behavior might be two-sided. A concentration of earnings announcements

on a single day might have a distraction effect on some firms, but also leads to a shift

in attention to other firms. A company that is in the spotlight due to positive earnings

announcements might have the incentive to also announce a profitable merger at the same

time for better publicity.

Figure 5 shows the same development of CARs in the 60-day period following the merger

announcement for companies in the bottom group. The picture drawn is similar to the

findings in the top group, whereas the difference in returns varies more over time. Starting

from a difference of about 1.5% in immediate stock return response, it looks like there is

a catching up after around 20 days. But looking at the longer time periods of 30 and 60

days, the difference even increases more. After 60 days the CARs for mergers announced on

earnings concentration days is still about 2% lower than in the control group with no sign of

a catching up. With respect to the explanation above, I see evidence for strategic behavior

of managers in the bottom group as these companies try to hide their less profitable mergers

in a distraction environment when the focus is shifted to other (earnings announcements

reporting) companies.

Figure 6 and 7 show the same graph for days of major sports events. In figure 6 the

plots show the development of CARs of acquiring firms in the top group which announced

a merger either on a day of a major sports event (dark green line) or on a normal day (light

green line). In contrast to the findings for days of earnings announcement concentration,

32

Figure 4: Response to Merger Announcements on Earnings Announcement Concentration

Days From -1 to 60 (Top Group)

Notes: The figure shows performance of mergers in the top group (positive immediate stock return response). Merger announcements

in Thomson One from January 1985 until December 2014 are matched to stock prices in CRSP. Saturdays and Sundays are not included

in the sample. Time 0 in event time is the day of the merger announcement. Cumulative abnormal returns (CAR) are calculated from

the market model. The vertical axis shows the mean CAR and the horizontal axis the days after the merger announcement. The blue

line shows the performance for mergers announced on days of a high number of earnings announcements and the turquoise line shows

the performance of mergers announced on other days.

the difference in CARs in the top group are much smaller. In the 20-day period after the

announcement, the CARs between companies announcing on days of major sports events

do not differ from the control group. Only after 30 and 60 days, announcements made on

days of major sports events show a better performance, but without a clear trend as for

earnings announcement concentration days. The regressions below will show whether the

difference is significant in a statistical sense.

Figure 7 shows the development over time in the bottom group. Here the difference

is quite visible, starting from a similar performance of both mergers announced on days

of major sports events and mergers announced on other days, the CARs drift apart after

around 10 days. It can be seen that the performance of mergers which have been announced

on a day of major sports event is worse than the performance of the control group. This

finding underlines the hypothesis of strategic disclosure timing.

33


Days From -1 to 60 (Bottom Group)

Notes: The figure shows performance of mergers in the bottom group (negative immediate stock return response). Merger announcements




line shows the performance for mergers announced on days of a high number of earnings announcements and the turquoise line shows

the performance of mergers announced on other days.

Regressions. The second step in examining strategic behavior looks at the long-term

stock response by means of an OLS regression.

The OLS specification for the long-term stock response is

R(−1,+30)t,k = α+ β1X + β2bottom.group+ β3 (X ∗ bottom.group) + controls+ εt,k

where R(−1,+30)t,k is the abnormal stock return for company k at time t between 1 day

before the merger announcement and 30 days after the merger announcement. The sample

includes all merger announcements. Coefficient β1 measures the return to mergers made

on an inattention day as described above. The coefficient β2 measures the return difference

due to classification into the bottom group and is not a variable of interest. β3 indicates

the difference in returns between mergers in the bottom group that are announced on a

day of inattention and mergers that are not announced on a day of inattention. Under

Hypothesis 2 of strategic behavior of managers, β3 should be negative for the long-term

response (R(−1,+30)t,k ) as mergers announced on days of inattention perform worse in the

34

Figure 6: Response to Merger Announcements on Days of Major Sports Events From -1 to

60 (Top Group)

Notes: The figure shows performance of mergers in the top group (positive immediate stock return response). Merger announcements




line shows the performance for mergers announced on days of a major sports event and the turquoise line shows the performance of

mergers announced on other days.

long run.

As stated above, and for a better analysis of whether managers announce mergers strate-

gically, I observe more closely the mergers that showed a negative immediate market re-

sponse. To analyze the difference to non-inattention days, I introduce an interaction variable

of the bottom group indicator and the inattention variable.

Table 6 shows the regression results for the long-term (30-day) stock return response. As

before, columns 1 and 2 show the regression results for the variable earnings announcement

concentration without and including control variables. Columns 3 and 4 show the same

regressions for the independent variable major sports event days. Finally, columns 5 and

6 show regression results of all independent variables. In line 4 and 5 we can see the

coefficients for our main variable of interest, the interaction variable of the bottom group

dummy and the inattention variable.

35

Figure 7: Response to Merger Announcements on Days of Major Sports Events From -1 to

60 (Bottom Group)

Notes: The figure shows performance of mergers in the bottom group (positive immediate stock return response). Merger announcements




line shows the performance for mergers announced on days of a major sports event and the turquoise line shows the performance of

mergers announced on other days.


What can be observed for earnings announcement concentration days is that the bottom

group shows a significant 2.5% worse performance if mergers were announced on these

days. With the introduction of control variables, the significance vanishes. Nevertheless,

the credibility of the first regression is already quite high with an R-squared of 7.5% and

only increases slightly with the introduction of control variables to 9.4%.

A similar effect can be observed for mergers announced on a day of a major sports

event. Performance in the bottom group is 2.9% worse than in the control group and highly

significant. With the introduction of control variables, the significance persists and the

difference becomes even higher, namely 9.4%.


36

A regression with both variables and the interaction variables comes to a similar result

as shown in columns 5 and 6.

Summary. Both the visual inspection as well as the regression give evidence for strate-

gic disclosure timing. The graphs show a worse performance of mergers announced on

inattention days which even persists in the long-run without catching up to the control

group. The results can be verified by OLS regression, which shows a significant worse

stock performance in the bottom group if mergers are announced on inattention days. The

difference is -2.5% for earnings announcement concentration days and -2.9% for days of

major sport events. With the introduction of control variables, the significance is not given

anymore for announcements made on earnings announcement concentration days, but still

persists for mergers announced on days of major sports events. The difference for the latter

one is 9.4% between the variable of interest and the control group in the bottom group.

6 Robustness Checks

6.1 Post-announcement Drift and Convergence

Chapter 5.2 evidences strategic behavior of managers around their timing of mergers. In

order to prove a worse performance of mergers announced on inattention days, I divide the

full sample into a group of “good” mergers (i.e. mergers that showed a positive immediate

stock return response) and “bad” mergers (i.e. mergers that showed a negative immediate

stock return response. To better understand the inattention argument, I show how the

CARs of the whole sample (without classification) develop over time.

Figure 8 and 9 show the stock return reaction over time for announcements made on

high inattention days (2c for earnings announcement concentration and 3c for major sports

events) compared to other days over the whole sample.

Figure 8 shows a clear delayed response for announcements made on earnings announce-

ment concentration days which persists until 20 days after the merger announcement. In-

terestingly, the CARs drift apart again after around 60 days, which could be either due to

37

random variation or a general worse performance of mergers announced on these inattention

days (and the market needs time to discover this fact).


Days From -1 to 60 (Whole Sample)

Notes: The figure shows performance of mergers over the whole sample. Merger announcements in Thomson One from January 1985

until December 2014 are matched to stock prices in CRSP. Saturdays and Sundays are not included in the sample. Time 0 in event time

is the day of the merger announcement. Cumulative abnormal returns (CAR) are calculated from the market model. The vertical axis

shows the mean CAR and the horizontal axis the days after the merger announcement. The blue line shows the performance for mergers

announced on days of a high number of earnings announcements and the turquoise line shows the performance of mergers announced

on other days.

For merger announcements made on days of major sports events the difference in returns

is even more obvious as seen in figure 3c. A catching up effect is starting to develop after

around 30 days. Still, even after 60 days, the CARs for mergers announced on days of

major sports events have not reached the level of the control group but show a difference

of around 4%.

6.2 Extension of Observation Period

The core findings of table 6 in chapter 5.2 show the stock return response regression for the

event time -1 to +30 days around the merger announcement. Since merger announcements

are not as easy to process by the market and merger negotiations and the closing process

can take up much more time, it is necessary to see whether the findings persist over an even

38


Days From -1 to 60 (Whole Sample)

Notes: The figure shows performance of mergers over the whole sample. Merger announcements in Thomson One from January 1985

until December 2014 are matched to stock prices in CRSP. Saturdays and Sundays are not included in the sample. Time 0 in event

time is the day of the merger announcement. Cumulative abnormal returns (CAR) are calculated from the market model. The vertical

axis shows the mean CAR and the horizontal axis the days after the merger announcement. The blue line shows the performance for

mergers announced on days of a major sports event and the turquoise line shows the performance of mergers announced on other days.

longer time period.

Regression. The following regression will look at the long-term stock response to

merger announcement in the period of 60 days after the announcement of the merger.




before the merger announcement and 60 days after the merger announcement, instead of

the previous 30-day period. The sample includes all merger announcements. As before,

coefficient β1 measures the return to mergers made on an inattention day as described above.

The coefficient β2 measures the return difference due to classification into the bottom group

and is not a variable of interest. β3 indicates the difference in returns between mergers in

39

the bottom group that are announced on a day of inattention and mergers that are not

announced on a day of inattention. If the findings of strategic behavior of managers also

apply for the extended time period, β3 should be negative for the 60-day return response

(R(−1,+60)t,k ).

All specifications from before remain the same, mergers that showed a negative imme-

diate market response are observed more closely and an interaction variable of the bottom

group indicator and the inattention variable shows the difference which is of interest.

Regression results are presented in table 7. As before, columns 1 and 2 show the regres-

sion results for the variable earnings announcement concentration without and including

control variables. Columns 3 and 4 show the same regressions for the independent variable

major sports event days. Finally, columns 5 and 6 show regression results of all independent

variables. In line 4 and 5 we can see the coefficients for our main variable of interest, the

interaction variable of the bottom group dummy and the inattention variable.



group shows a significant 3.2% worse performance if mergers were announced on these days

which is even worse than the 2.5% difference in the 30-day period. With the introduction

of control variables, the significance vanishes, which is the case in the previous regressions

as well.

For mergers announced on a day of a major sports event, the performance in the bottom

group is 2.6% worse than in the control group and significant at the 5% level. This is slightly

better than the difference in the 30-day period of 2.9%. With the introduction of control

variables, the significance does not persist.




40

Summary. The OLS regression for the 60-day period shows similar results to the

previous findings. Merger announcements in the bottom group perform 2.6% to 3.2% worse

if announced on high inattention days even after 60 days. The regression results are only

significant if not controlled for company and deal specific characteristics.

6.3 Terciles and Quartiles

The results under chapter 5.2 are based on a separation of the merger announcement sample

into two groups of positive immediate stock returns and negative immediate stock returns.

To check the validity of these results I refine the separation into even finer groups. First,

I split the sample into terciles based on their immediate stock return response. The first

tercile includes the highest 1/3rd of immediate stock return responses, the second tercile

the middle 1/3rd and the last tercile the bottom 1/3rd. The focus here is on the bottom

tercile, in line with the hypothesis of strategic disclosure timing of worse mergers.


R(−1,+30)t,k = α+ β1X + β2bottom.tercile+ β3 (X ∗ bottom.tercile) + controls+ εt,k


before the merger announcement and 30 days after the merger announcement. As before,

coefficient β1 measures the return to mergers made on an inattention day as described

above. The coefficient β2 measures the return difference due to classification into the bottom

tercile (i.e. the lowest 1/3rd of immediate stock return responses) and is not a variable of

interest. β3 indicates the difference in returns between mergers in the bottom tercile that

are announced on a day of inattention and mergers that are not announced on a day of

inattention. I expect a negative coefficient to prove the findings under chapter 5.1.2. The

sample does not contain values in the middle tercile but only the top tercile and bottom

tercile.



41




interaction variable of the bottom tercile dummy and the inattention variable.




which is slightly higher than the 2.5% difference observed earlier. With the introduction of

control variables, the significance vanishes, which had been the case earlier as well.

For mergers announced on a day of a major sports event the performance in the bottom

group is 2.7% worse than in the control group and significant at the 5% level. This is slightly

better than the difference in the 30-day period of 2.9%. With the introduction of control

variables, the significance does persist at the 5% level.



Secondly, I split the sample into quartiles based on their immediate stock return re-

sponse. The first quartile includes the highest 1/4th of immediate stock return responses,

the second quartile the second highest 1/4th and so on. The focus here is on the bottom

quartile, in line with the hypothesis of strategic disclosure timing of worse mergers.


R(−1,+30)t,k = α+ β1X + β2bottom.quartile+ β3 (X ∗ bottom.quartile) + controls+ εt,k


before the merger announcement and 30 days after the merger announcement. As before,

coefficient β1 measures the return to mergers made on an inattention day as described


42

above. The coefficient β2 measures the return difference due to classification into the bottom

quartile (i.e. the lowest 1/4th of immediate stock return responses) and is not a variable

of interest. β3 indicates the difference in returns between mergers in the bottom quartile

that are announced on a day of inattention and mergers that are not announced on a day

of inattention. I expect a negative coefficient β3 to prove the findings under chapter 5.1.2.

The sample does not contain values in the middle quartiles (second and third highest) but

only the top quartile and bottom quartile.






interaction variable of the bottom quartile dummy and the inattention variable.




which is slightly higher than the 2.5% difference observed earlier. With the introduction of

control variables, the significance vanishes.

For mergers announced on a day of a major sports event the performance in the bottom

group is 2.8% worse than in the control group and significant at the 10% level. This is

slightly lower than the difference in the 30-day period of 2.9%. With the introduction of

control variables, the significance jumps to the 1% level for a difference of 12.3%.



In summary, the refinement of the sample in terciles and quartiles shows the same results

as stated under chapter 5.2.


43

7 Conclusion and Discussion

At the outset, I positioned this thesis among related literature regarding strategic disclosure

timing of managers in response to investor inattentiveness and proposed two main hypothe-

ses: (1) investors are less attentive to merger announcements if they fall on days of high

number of earnings announcements or days of major sports events and (2) managers exploit

this inattention by announcing less profitable mergers on these days.

How well could these hypotheses be confirmed by empirical analysis? With respect to

the first hypothesis I find evidence for inattention on the proposed inattention days by

means of a lower abnormal trading volume observable through a plotting of mean abnormal

trading volumes around the day of the merger announcement. These findings can not be

confirmed by OLS regression due to a lack of significance. Moreover, the correlation of a

number of earnings announcements and trading volume makes a differentiation of trading

volume due to the announcement of a merger difficult. A similar picture is drawn by the

analysis of immediate stock return responses. Even though an expected negative effect of

inattention days on immediate stock returns can be found, the results do not hold if control

variables are introduced and are of low economic relevance.

Regarding the second hypothesis my findings are more robust. I show that a separation

of merger announcements into a top group (i.e. mergers that showed an immediate positive

stock return response) and bottom group (i.e. mergers that showed an immediate negative

stock return response can prove the hypothesis of managers announcing less profitable

mergers on days of high inattention. Mergers in the bottom group showed 2.5% lower 30-

day CARs if the announcement was made on a day of earnings announcement concentration

and 2.9% lower CARs if the announcement was made on a day of a major sports event. The

difference in long-term performance is also visible by plotting the performance of mergers

announced on inattention days and mergers announced on normal days.

As expected, the results are in line with previous literature on strategic disclosure timing

but have to be interpreted with caution. Niessner (2015) for example shows that managers

strategically announce bad news on high inattention days. My results show that this is

44

also the case for bad mergers, which are announced preferably on high inattention days. In

spite of that, the inattention variables as proposed by Niessner (2015), namely inattention

on Fridays and days before national holidays, do not lead to the same results for merger

announcements. I find no significant difference in performance of mergers announced on

Fridays or before national holidays and other days (shown in the Appendix). Other than

that, the findings on inattention around earnings announcement concentration and days of

major sports events can support the results of Manconi, Kempf and Spalt (2015) who find

that firms are more likely to announce bad mergers (i.e. diversifying mergers) when their

shareholders are distracted. I can even extend their claim that these mergers are “bad” by

analyzing cumulative abnormal returns in response to the merger announcement.

As mentioned before, the research results have to be considered with caution. Especially

the claim of inattention on days of high number of earnings announcements and days of

major sport events can not be proven with certainty. The predominant methodology for

showing inattention is to observe pre-announcement drift as used for example by DellaVigna

and Pollet (2009) and Louis and Sun (2010). My analysis does not document a clear

announcement drift and catching up effect in response to inattention. The ongoing worse

performance of mergers announced on the observed inattention days is in my opinion owed to

strategic disclosure timing as described earlier. This finding proves to be statistically robust

also in the long run (60-day period) and after a refinement of the grouping into terciles and

quartiles. I propose that there are two possible interpretations for those findings. First,

the unclear evidence of inattention might be simply owed to the fact that inattention only

gets discovered over time. In other words, the immediate trading volume and stock return

responses do not show a significant difference over announcement days because the true

nature of the merger has not yet been fully processed by the market. Only after some time

and as more information gets discovered, the difference in profitability becomes obvious. On

the other hand, it is also possible that there is no real inattention on days of high number of

earnings announcements and days of major sports events, but managers intuitively assume

there is. Consequently, they prefer to announce their less profitable mergers on these days

45

and that is why a performance difference becomes visible.

In summary, I see the biggest drawback in the low economic relevance. Looking at

the variable construction and resulting descriptive statistics it can be seen that only 3%

of all announcements happened on earnings announcement concentration days and 7% of

announcements on days of major sports events. Even though there is clear evidence for

strategic disclosure timing on these days, there is a certain limitation to which extend this

inattention is exploitable. Usually, mergers are not planned that far ahead, nor is it possible

to keep a major announcement secret from the market for too long a time period. As a

consequence, announcements of worse mergers might be preferably announced on these

inattention days if the decision falls into a period around one of these days, but in general

managers do not usually have that flexibility.

The main implications of the findings in this thesis are that investors should raise aware-

ness to merger announcements on days of earnings announcement concentration and days of

major sports events. The findings can also be used for an arbitrage strategy by means of a

long-short portfolio. Still, since mergers are much more complex and are heavily dependent

on other various factors besides the day of the announcement, this arbitrage strategy might

not prove as successful as other strategies that exploit market imperfections.

Promising avenues for future research include the analysis of management incentives for

strategic disclosure timing. There is also the question of whether the findings are indicative

of a certain management style.

46

Table 4: Abnormal Trading Volume Regression

Notes: *significant at 10%; **significant at 5%; ***significant at 1%. Merger announcements in Thomson One from January 1985 until

December 2014 are matched to stock prices in CRSP. Saturdays and Sundays are not included in the sample. Time 0 in event time

is the day of the merger announcement. The abnormal trading volume is the average logarithmic volume on the day of the merger

announcement and the following day, minus the average log volume for the period -20 to -11. “Announcement Concentration” is a dummy

variable indicating whether the number of quarterly earnings announcements on a single day is in the 90th percentile of all daily quarterly

earnings announcements (data for earnings announcements is taken from I/B/E/S). “Major Sports Event Dummy” is a dummy variable

indicating whether the announcement falls on a day of a major sports event (NBA finals, MLB finals, Super Bowl). “EPS Target” are the

earnings per share of the target (sell-side) firm. “EPS Acquirer” are the earnings per share of the acquirer (buy-side) firm, respectively.

“Log (Pre-announc. MV) Acquirer” is the natural logarithm of the acquirer’s market value, defined as share price times number of shares

outstanding in million US$, 4 weeks prior to the announcement. “Deal Value to Acquirer Size” is the ratio of the deal (transaction)

value to the acquirer size (“Pre-announc. MV Acquirer” as defined above, not logarithmized). “Log (MV/BV) Acquirer” is the natural

logarithm of the ratio of the market value of assets (“Pre-announc. MV Acquirer”) to the book value of assets of the acquirer. “Acquirer

Cash Holdings” is the ratio of cash assets to net total asset value of the acquirer. “Number of Bidders” is the number of competing

bidders for the target company. “Dummy for completed Mergers” is a dummy variable that takes the value 1 for completed mergers and

0 for any other status. “Dummy for diversif. Mergers” is a dummy variable which takes the value 1 for mergers over different macro

industries (according to the Thomson Financial Macro Hierarchy) or 0 otherwise. “Analyst Coverage” is the number of analysts who

publish quarterly earnings estimates of the acquiring company in the year of announcement. Data for this variable is taken from I/B/E/S.

47

Table 5: Immediate Stock Return Response Regression



is the day of the merger announcement. Immediate stock return response is the cumulative abnormal return in the period -1 to +1

around the announcement, calculated from the market model. “Announcement Concentration” is a dummy variable indicating whether

the number of quarterly earnings announcements on a single day is in the 90th percentile of all daily quarterly earnings announcements

(data for earnings announcements is taken from I/B/E/S). “Major Sports Event Dummy” is a dummy variable indicating whether the

announcement falls on a day of a major sports event (NBA finals, MLB finals, Super Bowl). “EPS Target” are the earnings per share of

the target (sell-side) firm. “EPS Acquirer” are the earnings per share of the acquirer (buy-side) firm, respectively. “Log (Pre-announc.

MV) Acquirer” is the natural logarithm of the acquirer’s market value, defined as share price times number of shares outstanding in

million US$, 4 weeks prior to the announcement. “Deal Value to Acquirer Size” is the ratio of the deal (transaction) value to the

acquirer size (“Pre-announc. MV Acquirer” as defined above, not logarithmized). “Log (MV/BV) Acquirer” is the natural logarithm

of the ratio of the market value of assets (“Pre-announc. MV Acquirer”) to the book value of assets of the acquirer. ’Acquirer Cash

Holdings’ is the ratio of cash assets to net total asset value of the acquirer. “Number of Bidders” is the number of competing bidders for

the target company. “Dummy for completed Mergers” is a dummy variable that takes the value 1 for completed mergers and 0 for any




48

Table 6: Long-term Stock Return Response Regression


December 2014 are matched to stock prices in CRSP. Saturdays and Sundays are not included the sample. Time 0 in event time is the

day of the merger announcement. Long-term stock return response is the cumulative abnormal return in the period -1 to +30 around

the announcement, calculated from the market model. “Announcement Concentration” is a dummy variable indicating whether the

number of quarterly earnings announcements on a single day is in the 90th percentile of all daily quarterly earnings announcements


announcement falls on a day of a major sports event (NBA finals, MLB finals, Super Bowl). “EPS Target” are the earnings per share of

the target (sell-side) firm. “EPS Acquirer” are the earnings per share of the acquirer (buy-side) firm, respectively. “Log (Pre-announc.

MV) Acquirer” is the natural logarithm of the acquirer’s market value, defined as share price times number of shares outstanding in

million US$, 4 weeks prior to the announcement. “Deal Value to Acquirer Size” is the ratio of the deal (transaction) value to the

acquirer size (“Pre-announc. MV Acquirer” as defined above, not logarithmized). “Log (MV/BV) Acquirer” is the natural logarithm

of the ratio of the market value of assets (“Pre-announc. MV Acquirer”) to the book value of assets of the acquirer. “Acquirer Cash

Holdings” is the ratio of cash assets to net total asset value of the acquirer. “Number of Bidders” is the number of competing bidders for

the target company. “Dummy for completed Mergers” is a dummy variable that takes the value 1 for completed mergers and 0 for any




49

Table 7: Long-term (60-day) Stock Return Response Regression


December 2014 are matched to stock prices in CRSP. Saturdays and Sundays are dropped from the sample. Time 0 in event time is the


the announcement, calculated from the market model. “Announcement Concentration” is a dummy variable indicating whether the

number of quarterly earnings announcements on a single day is in the 90th percentile of all daily quarterly earnings announcements


announcement falls on a day of a major sport event (NBA finals, MLB finals, Super Bowl). “Bottom Group” is a dummy variable

indicating whether the immediate stock return response (3-day window) was below zero. “(Bottom Group)*(Announc. Conc.)” and

“(Bottom Group)*(Sports Event)” are interaction variables. “EPS Target” are the earnings per share of the target (sell-side) firm.

“EPS Acquirer” are the earnings per share of the acquirer (buy-side) firm, respectively. “Log (Pre-announc. MV) Acquirer” is the

natural logarithm of the acquirer’s market value, defined as share price times number of shares outstanding in million US$, 4 weeks

prior to the announcement. “Deal Value to Acquirer Size” is the ratio of the deal (transaction) value to the acquirer size (“Pre-

announc. MV Acquirer” as defined above, not logarithmized). “Log (MV/BV) Acquirer” is the natural logarithm of the ratio of

the market value of assets (“Pre-announc. MV Acquirer”) to the book value of assets of the acquirer. “Acquirer Cash Holdings” is

the ratio of cash assets to net total asset value of the acquirer. “Number of Bidders” is the number of competing bidders for the





50

Table 8: Long-term Stock Return Response Regression Terciles

Notes: *significant at 10%; **significant at 5%; ***significant at 1%. Merger announcements in Thomson One from January 1985


time is the day of the merger announcement. Long-term stock return response is the cumulative abnormal return in the period

-1 to +30 around the announcement, calculated from the market model. ’Announcement Concentration’ is a dummy variable in-

dicating whether the number of quarterly earnings announcements on a single day is in the 90th percentile of all daily quarterly

earnings announcements (data for earnings announcements is taken from I/B/E/S). “Major Sports Event Dummy” is a dummy

variable indicating whether the announcement falls on a day of a major sport event (NBA finals, MLB finals, Super Bowl). “Bot-

tom Tercile” is a dummy variable indicating whether the immediate stock return response (3-day window) was in the lower 1/3rd

bracket. The second tercile (middle tercile) is not included in the regression. “(Bottom Tercile)*(Announc. Conc.)” and “(Bot-

tom Tercile)*(Sports Event)” are interaction variables. Control variables are standard controls, as used in all previous regressions.

51

Table 9: Long-term Stock Return Response Regression Quartiles



time is the day of the merger announcement. Long-term stock return response is the cumulative abnormal return in the period -1

to +30 around the announcement, calculated from the market model. “Announcement Concentration” is a dummy variable indicat-

ing whether the number of quarterly earnings announcements on a single day is in the 90th percentile of all daily quarterly earnings

announcements (data for earnings announcements is taken from I/B/E/S). “Major Sports Event Dummy” is a dummy variable indi-

cating whether the announcement falls on a day of a major sports event (NBA finals, MLB finals, Super Bowl). “Bottom Quartile”

is a dummy variable indicating whether the immediate stock return response (3-day window’ was in the lower 1/4th bracket. The

second and third quartile (middle quartiles) are not included in the regression. “(Bottom Quartile)*(Announc. Conc.)” and “(Bot-

tom Quartile)*(Sports Event)” are interaction variables. Control variables are standard controls, as used in all previous regressions.

52

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Appendix

Previous literature documents inattention around Fridays and before national holidays. The

following part will examine this proposition around merger announcements. In general, I

use the same methodology and hypotheses as earlier. The previous inattention variables

are now changed for Fridays and days before national holidays.

Variable Construction. The dataset is the same as constructed in Chapter 4. The

new variable “Friday” is a dummy variable taking the value 1 for merger announcements

on Fridays and 0 for merger announcements on other days12. The second variable “Days

before National Holidays” is a dummy variable taking the value 1 for merger announcements

on days before national holidays and 0 for merger announcements on other days. Since in

the U.S. public holidays with a corresponding day off vary over different states, I follow

the holidays observed by the Federal Reserve System. This is due to the reliance of banks

and financial institutions on the U.S. Federal Reserve and usually follow their holidays.

The holidays considered in this analysis are New Year’s Day, Birthday of Martin Luther

KingJr., Washington’s Birthday, Memorial Day, Independence Day, Labor Day, Columbus

Day, Veterans Day, Thanksgiving and Christmas. The general rule for holidays falling on

Saturday is that the day off is moved to the preceding Friday and for holidays falling on a

Sunday, the day off is moved to the next Monday. Consequently, the day before a national

holiday falling on a Saturday is Thursday and the day before a national holiday that falls

on a Saturday is Friday.

Volume Regression. Table 10 shows the regression results for the OLS specification




coefficient for the inattention variable, which is either a dummy variable for Fridays or a

dummy variable for days before national holidays.

12Note that announcements made on Saturdays and Sundays are not considered in this analysis

57

The coefficient β1 shows the percentage difference in abnormal trading volume between

merger announcements made on inattention days and announcements made on normal days.

Column 1 in table 10 shows a highly significant negative coefficient for Friday merger

announcements of -0.177. This means that on average, abnormal trading volume in acquir-

ing firms is 17.7% lower if mergers are announced on Fridays. Controlling for firm and deal

specific characteristics, the difference is even higher with 30% lower abnormal trading vol-

ume. This is in line with the findings of DellaVigna and Pollet (2009), Louis and Sun (2010)

and Niessner (2015). The findings for days before national holidays are similar. Acquiring

firms announcing mergers on these days observe a 50.5% lower abnormal trading volume,

or 47.6% lower if controlled for firm and deal specific characteristics. Both regressions show

a high credibility with an adjusted R-squared of around 22% including control variables.


Immediate Stock Return Response Regression. Table 11 shows the regression

results for the OLS specification

R(d,D)t,k = α+ β1X + controls+ εt,k

where R(d,D)t,k is the abnormal stock return for company k at time t between 1 day before

the merger announcement and 1 day after the merger announcement. The sample includes

all merger announcements. α is the constant in the regression and β1 is the coefficient for the

inattention variable X which is either a dummy variable for Fridays or a dummy variable for

days before national holidays. As stated earlier, under Hypothesis 1 of inattentive markets,

β1 should be significantly negative in the short-run.

As seen in column 1 in table 11, this is not the case. The regression coefficient for

the Friday dummy is not significantly different from 0. The same applies for days before

national holidays.


58


Long-term Stock Response Regression. I try to find strategic management behav-

ior by looking at the long-term stock response by means of an OLS regression. The focus

lies on the bottom group of merger announcements. For a deeper explanation please refer

to Chapter 3 Methodology.




before the merger announcement and 30 days after the merger announcement. The sample

includes all merger announcements. Coefficient β1 measures the return to mergers made

on an inattention day as described above. The coefficient β2 measures the return difference

due to classification into the bottom group and is not a variable of interest. β3 indicates the

difference in returns between mergers in the bottom group that are announced on a day of

inattention and mergers that are not announced on a day of inattention. Under Hypothesis 2

of strategic behavior of managers, should be negative for the long-term response (R(−1,+30)t,k )

as mergers announced on days of inattention perform worse in the long run.

Regression results are presented in table 12. For Friday merger announcements, the

performance difference is not significantly different from merger announcements on other

days. Surprisingly, mergers in the bottom group announced on days before national holidays

show a significantly better performance of 9.4% higher cumulative abnormal returns over

the 30-day period following the announcement.


14Table 11 can be found on page 6215Table 12 can be found on page 63

59

Summary. The regression on abnormal volume showed clear evidence for market inat-

tention on Fridays and days before national holidays with significant lower abnormal trading

volumes for announcements on these days. In contrast, abnormal returns turned out to be

not significantly different between the proposed inattention days and normal days in the

3-day window around the announcement. Moreover, the long-term performance in the

bottom group also showed no significant differences, except for announcements on days

before national holidays, which actually showed up to perform significantly better than an-

nouncements made on normal days. My conclusion is that there is no evidence for strategic

disclosure timing of mergers on Fridays or days before national holidays.

60

Table 10: Abnormal Trading Volume Regression Additional Variables



is the day of the merger announcement. The abnormal trading volume is the average logarithmic volume on the day of the merger

announcement and the following day, minus the average log volume for the period -20 to -11. “Friday” is a dummy variable indicating

whether the announcement is made on a Friday. “Before Holiday” is a dummy variable indicating whether the announcement is made

on a day before a national holiday (following the Federal Reserve calendar). “EPS Target” are the earnings per share of the target

(sell-side) firm. “EPS Acquirer” are the earnings per share of the acquirer (buy-side) firm, respectively. “Log (Pre-announc. MV)

Acquirer” is the natural logarithm of the acquirer’s market value, defined as share price times number of shares outstanding in million


(“Pre-announc. MV Acquirer” as defined above, not logarithmized). “Log (MV/BV) Acquirer” is the natural logarithm of the ratio

of the market value of assets (“Pre-announc. MV Acquirer”) to the book value of assets of the acquirer. “Acquirer Cash Holdings”

is the ratio of cash assets to net total asset value of the acquirer. “Number of Bidders” is the number of competing bidders for the





61

Table 11: Immediate Stock Return Response Regression Additional Variables



time is the day of the merger announcement. Immediate stock return response is the cumulative abnormal return in the period -1

to +1 around the announcement, calculated from the market model. “Friday” is a dummy variable indicating whether the announce-

ment is made on a Friday. “Before Holiday” is a dummy variable indicating whether the announcement is made on a day before

a national holiday (following the Federal Reserve calendar). “EPS Target” are the earnings per share of the target (sell-side) firm.

“EPS Acquirer” are the earnings per share of the acquirer (buy-side) firm, respectively. “Log (Pre-announc. MV) Acquirer” is the

natural logarithm of the acquirer’s market value, defined as share price times number of shares outstanding in million US$, 4 weeks

prior to the announcement. “Deal Value to Acquirer Size” is the ratio of the deal (transaction) value to the acquirer size (“Pre-

announc. MV Acquirer” as defined above, not logarithmized). “Log (MV/BV) Acquirer” is the natural logarithm of the ratio of

the market value of assets (“Pre-announc. MV Acquirer”) to the book value of assets of the acquirer. “Acquirer Cash Holdings” is

the ratio of cash assets to net total asset value of the acquirer. “Number of Bidders” is the number of competing bidders for the





62

Table 12: Long-term Stock Return Response Regression Additional Variables


December 2014 are matched to stock prices in CRSP. Saturdays and Sundays are not included in the sample. Time 0 in event time is the


the announcement, calculated from the market model. “Friday” is a dummy variable indicating whether the announcement is made on a

Friday. “Before Holiday” is a dummy variable indicating whether the announcement is made on a day before a national holiday (follow-

ing the Federal Reserve calendar). “Bottom Group” is a dummy variable indicating whether the immediate stock return response (3-day

window) was below zero. “(Bottom Group)*(Friday)” and “(Bottom Group)*(Before Holiday)” are interaction variables. “EPS Target”

are the earnings per share of the target (sell-side) firm. “EPS Acquirer” are the earnings per share of the acquirer (buy-side) firm, respec-

tively. “Log (Pre-announc. MV) Acquirer” is the natural logarithm of the acquirer’s market value, defined as share price times number of

shares outstanding in million US$, 4 weeks prior to the announcement. “Deal Value to Acquirer Size” is the ratio of the deal (transaction)

value to the acquirer size (“Pre-announc. MV Acquirer” as defined above, not logarithmized). “Log (MV/BV) Acquirer” is the natural

logarithm of the ratio of the market value of assets (“Pre-announc. MV Acquirer”) to the book value of assets of the acquirer. “Acquirer

Cash Holdings” is the ratio of cash assets to net total asset value of the acquirer. “Number of Bidders” is the number of competing bid-

ders for the target company. “Dummy for completed Mergers” is a dummy variable that takes the value 1 for completed mergers and 0

for any other status. “Dummy for diversif. Mergers” is a dummy variable which takes the value 1 for mergers over different macro indus-

tries (according to the Thomson Financial Macro Hierarchy) or 0 otherwise. “Analyst Coverage” is the number of analysts who publish


63

inattention to merger announcements and strategic

Documents