University of Amsterdam

Faculty of Business and Economics

An empirical analysis of the effect of payment

methods, deal and firm characteristics on the stock

return of retailers who just announced a takeover

Author: C.A.H. Beijlevelt

Student Number: 10763678

Supervisor: dhr. dr. Jan Lemmen

Study Program: Bsc Economics & Business

Specialization: Finance & Organization

Date: 26th June 2018

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Abstract

This paper empirically researches the effect of different payment methods, firm- and deal

characteristics within mergers and acquisitions on the cumulative abnormal stock return of

retailers who just announced a takeover. The expectation is that acquirers using cash as a

method of payment will gain positive cumulative abnormal returns around the

announcement date. Acquirers using a combination of payment methods will suffer

negative cumulative abnormal returns around the announcement date. It is expected that

other firm- and deal characteristics may increase or decrease these effects. To determine

these effects, a unique dataset has been set with observations from January 2010 till

December 2017. Eventually, 113 M&A deals have been selected. A robust regression

analysis shows that retailers suffer losses in their cumulative abnormal return when they

finance their deal with cash. This effect can be defended with results from papers of Firth

(1979) and Dodds and Quek (1985). The cumulative abnormal returns of the retailers in this

dataset have possibly been marked down because of a risk-adjustment by the market.

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Statement of Originality

This document is written by Student Coen August Hendrik Beijlevelt who declares to take

full responsibility for the contents of this document.

I declare that the text and the work presented in this document are 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

completion of the work, not for the contents.

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Table of Contents Abstract 1 1. Introduction 4 2. Literature Review 7 2.1 Economic Theory 7 2.1.1 Mergers and Acquisitions 7 2.1.1.1 Efficiency Theory 8 2.1.1.2 Monopoly Theory 8 2.1.1.3 Valuation Theory 9 2.1.1.4 Neoclassical Hypothesis 9 2.1.1.5 Behavioral Hypothesis 9 2.1.1.6 Empire-building Theory 10 2.1.2 Method of Payments 10 2.1.2.1 Agency Theory 11 2.1.2.2 The Benefit of Debt Theory 11 2.1.2.3 The Signaling Theory 12 2.2 Economic Literature 12 3. Data and Methodology 17 3.1 Methodology 17 3.1.1 Event Study 17 3.1.2 Cumulative Abnormal Return 18 3.2 Data 19 3.3 Linear Regression 20 3.3.1 Regression Model 20 3.3.2 Regression Analysis 22 3.4 Descriptive Statistics 22 3.5 Hypotheses 24 3.5.1 Method of Payment Hypothesis 24 3.5.2 Industry Hypothesis 25 3.5.3 Deal Value Hypothesis 25 3.5.4 Leverage Ratio Change Hypothesis 25 4. Results 26 4.1 Table Descriptive 27 4.2 OLS Regression with Robust Standard Errors 27 4.2.1 Regression Column (1) 27 4.2.2 Regression Column (2) 28 5. Conclusion 31 6. Discussion 32 6.1 Limitations and Improvements 32 6.2 Further Research 34 Bibliography 35 Appendix 1 38 Appendix 2: In depth interview with Miel Janssen 39

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1. Introduction

In November 2010 Mckinsey & Company published a report1 about the next wave in

mergers and acquisitions (M&A) in the US retail industry. In this report they set out the

expectation that after the financial crisis in 2008, the retail market will be waiting for a next

M&A wave. According to the analysts of Mckinsey, each of the past three economic

recessions in the US have led to waves in M&A and this time, 2010 and 2011 will be the

years where the M&A business picks itself up again. Figures of Thomson Reuters and

Bloomberg in the report prove this statement, because in the third quarter of 2010 the most

M&A deals in two years’ time were noted.

Although, the retail sector has not suffered from the financial crisis like other sectors

did, the sector still has gotten a blow from the crisis. The traditional retail did not suffer as

much from the financial crisis, because people still need to consume food and beverages.

However the secondary good retailers, such as technological, fashion and hard-good

retailers, did suffer from the financial crisis, mainly because consumers stopped spending

their disposable income on products they did not need. But underneath the surface,

retailers had to face a bigger problem. The past ten years retailers have been experiencing

that the traditional drivers of growth have not been satisfactory enough. Consumer

spending has seen a decrease since the financial crisis, but now the economy is recovering,

consumer spending still does not increase enough. A good indicator of this effect is the

increase in consumer savings in the past few years. Next to that, the forecasts of analysts

have not been much better. In 2010, experts of Mckinsey expected the Northern-American

disposable income to remain 50 to 60 percent lower than before the crisis. In addition, the

ability of retailers to pursue growth via the expansion of stores has been limited. Although,

retailers have to cope with the lack of growth in consumer spending, there is one big

advantage. Retailers have been cash-rich for many years now. Figures of Compustat2 prove

that the top ten retailers in the US had over $25 billion in cash in 2010.

One way for retailers to use this excess cash, was carrying out mergers and

acquisitions. The number of mergers and acquisitions in the retail sector started growing

again in the beginning of 2011, whilst the overall North-American M&A business started

1 Mckinsey & Company, November 2010: The next wave of M&A in retail 2 The Corporate Performance Analysis Tool of Mckinsey & Company

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increasing after 2013. The value of M&A in the retail sector even started increasing much

earlier, in 2009.

Although the figures suggest that mergers and acquisitions and the retail sector go

hand in hand, the reality is different. Retailers have been struggling for years to experience

the advantages of mergers and acquisitions. This was mainly because retail processes, which

have been developing in a technological way, are hard to combine after a takeover. It could

take retailers years to experience the advantages, after which they sometimes suffer from

being busy sorting out the takeover instead of doing the right investments in new

technological developments. Some retailers therefore prefer to pursue growth by

developing their technological advantage internally. Since the decision between doing

directed takeovers and growing internally is quite hard to make, it would be interesting to

see the effects of mergers and acquisitions on retailers’ short-term results.

The short-term effects of M&A on retailers’ stock returns have never really been

researched. Moatti et. al (2014) wrote a paper in which they compared M&A to internal

growth, but they did not focus on the deal and firm characteristics within mergers and

acquisitions. Therefore, it would be interesting to see if certain deal and firm characteristics

can explain the variance in the stock returns of retailers.

In this paper, the short-term effects of methods of payments on the cumulative

abnormal stock returns of retailers who just did a takeover deal will be researched. Next to

that, there will be a regression model set up which includes deal and firm characteristics to

explain more of the variance in the stock return of the acquirer.

The main question of this paper will be: What is the effect of different payment

methods in mergers and acquisitions on the stock returns of the acquirer after the

announcement in the retail sector in the U.S.A. from January 2010 till December 2017? To

investigate these effects, three databases have been used to retrieve data on the deals

(Zephyr), the stock returns (CRSP) and the deal and firm characteristics (Compustat).

Empirical analysis shows that cash financing has a negative effect on the cumulative

abnormal stock return of retailers. This contradicts economic literature written by Travlos

(1987) and Raad and Wu (1994) and economic theory on method of payments. On the other

hand, the findings are in line with literature written by Firth (1979) and Dodds and Quek

((1985). The main argument for the negative effect of cash-financing on the cumulative

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abnormal return of acquirers in retail, is the risk-adjustment of the market after a deal has

been announced.

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2. Literature Review

In this section we will discuss the existing theories and literature on payment methods and

its effects on the stock return of the acquirer. First, we will discuss the existing economic

theory. Economic theory will be split into economic theory about mergers and acquisitions

and payment methods. Second, we will discuss papers written about payment methods and

its effects on the cumulative abnormal stock returns of the acquirer.

2.1 Economic Theory

In this subsection we will discuss the main economic theories about mergers and

acquisitions and method of payments. Since these two subjects have a lot of overlap, it will

be much easier to have a look at both subjects solely to get a structured view of the

economic theories.

2.1.1 Mergers and Acquisitions

Mergers and acquisitions have been a method for companies to chase growth opportunities

for many years. That is why mergers and acquisitions have been a subject of research for

many years as well. Therefore, researchers have discovered in the 90s that mergers and

acquisitions come in waves. For many years there have been researchers, such as Blair, Lane

and Schary (1991) and Brealey and Myers (1991), who had some suspicions about the wavy

character of mergers and acquisitions over time. Golbe and White (1993) were the first to

prove that mergers and acquisitions do indeed come in waves. Although, they stated that

their research was just the beginning of developing and testing hypotheses.

Now that we know that mergers and acquisitions do come in waves, it is important

to find out what could be the motives behind mergers and acquisitions. To get to know the

motives behind mergers and acquisitions, we will use the paper of Trautwein (1990) which

displays multiple economic theories about mergers and acquisitions. After that, we will use

the paper of Mariana (2012) which displays two tested hypotheses which can be linked to

theories in the papers of Trautwein (1990) and Muehlfield et al. (2012).

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2.1.1.1 Efficiency Theory

First we look at the efficiency theory, which is explained in the paper of Trautwein (1990).

The efficiency theory states that mergers and acquisitions mainly take place to find

synergies between the acquirer and the target. The theory splits three kinds of synergies:

financial, operational and managerial synergies.

Firstly, acquirers can achieve financial synergies by a decrease in the cost of capital.

This can be achieved by lowering the systematic risk of the company’s investment portfolio.

In addition, due to the firm’s size increase, it has bigger buying bower by which it can

decrease the cost of capital.

Secondly, acquirers can achieve operational synergies by lowering the operational

costs. If everything remains constant, this cost decrease will immediately affect the bottom

line in a positive way. In addition, acquirers can achieve operational synergies by sharing

knowledge.

Thirdly, acquirers can achieve managerial synergies by managing the target in a more

effective and efficient way than the former management did.

Although the efficiency theory is well-established, it is mainly criticized on the

theoretical part of the theory. Critics say that financial synergies cannot be achieved in a

perfectly efficient capital market.

2.1.1.2 Monopoly Theory

The second theory is the monopoly theory, which is also explained in Trautwein (1990). This

theory states that the main motive of mergers and acquisitions is achieving market power.

This strategy is mainly used in conglomerate mergers, where an acquirer uses its profits

from one market to acquire a target in another market. This way, acquirers can easily enter

new markets and because of that diversify their operational portfolio.

Although the monopoly theory is not highly supported by the evidence, it is a theory

which has been an overall accepted theory for many years. The evidence of the monopoly

theory is limited, because no acquirer will admit that it thrives for a monopoly position in

the market they are operating in.

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2.1.1.3 Valuation Theory

The third theory which is explained in the paper of Trautwein (1990) is the valuation theory.

This theory states that the biggest motive of mergers and acquisitions is that some

managers have a better view on the target’s value than the market has. Managers try to

take an advantage of this information asymmetry by moving rapidly.

Although this theory is quite well-established, it still gets some criticism. Critics state

that in a world with perfect information, which is incorporated in the share price, it will not

be possible to take a financial advantage out of information.

2.1.1.4 Neoclassical Hypothesis

The first hypothesis explained in the paper of Mariana (2012) is the neoclassical hypothesis,

which could also be called the economic motivation or the disturbance theory. This

hypothesis states that mergers and acquisitions are a reaction to macroeconomic events, for

example technological developments or industrial overcapacity. Since macroeconomic

shocks will cause an increase in the demand, companies have to scale up very quickly. One

way to do this is acquiring a target.

Furthermore, Mariana (2012) also refers to Manne (1965). In his paper about

mergers and the market for corporate control, he states that mergers and acquisitions are a

way of relocating assets. The same applies to the relocation of capital (Jovanovic and

Rousseau, 2002). After a takeover, it might be that the assets or the capital will come in the

hands of firms who can use it more effectively.

Lastly, Persons and Warther (1997) state in their paper about the adoption of

financial innovations that the wavy character of mergers and acquisitions might be

explained by a reaction mechanism. They state that firms who see their competitor doing a

takeover will be more likely to react to that by doing a takeover themselves.

2.1.1.5 Behavioral Hypothesis

The second hypothesis explained in the paper of Mariana (2012) is the behavioral

hypothesis, which could also be called the managerial motivation. The behavioral hypothesis

(Schleifer and Vishny, 2003) states that there is a connection between bond evaluations and

the frequency of mergers and acquisitions. According to this theory, the wavy character of

mergers and acquisitions can be explained by the characteristics of the players on the

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market. Clearly, the behavioral theory explains the waves in M&A from a microeconomic

point of view.

Schleifer and Vishny (2003) stated that managers play a huge role in the explanation

of waves in mergers and acquisitions. Managers can use the valuation of their own stock as

a tool to take M&A decisions. As managers see their stock price being overvalued, they

might want to swap their stock for tangible assets which generate cash flows. When the

stock market is in a boom, there will be a lot of shares which are overvalued. This way,

Schleifer and Vishny (2003) hypothesize that there is a positive relation between capital

market booms and waves in mergers and acquisitions.

2.1.1.6 The Empire-Building Theory

Another managerial theory states that managers see mergers and acquisitions as building an

empire: the empire-building theory. Although this theory is very old and was already started

by Berle and Means (1933), it is best explained in a paper by Trautwein (1990). This theory

states that some managers who like to build an empire will maximize their own utility

instead of the utility of the shareholders. To maximize their own utility, managers will close

riskier deals by which the company they are managing might come in severe danger.

Although the empire-building theory is just a theory and evidence on it is limited,

there are a few researchers who have found evidence that could support the theory. For

example, You et. al (1986) found that management ownership and the number of managers

in the board correlate negatively with merger success. This means that the more

management ownership and number of managers in the board, the less the probability a

merger succeeds. Next to that, Amihud and Lev (1981) found that high management

ownership is associated with conglomerate mergers, which are riskier.

2.1.2 Method of Payments

In this section we will discuss the existing economic theory on method of payments and its

effects on the stock return of acquirers. Before going into the economic theory on method

of payments, it is important to get an understanding of the agency theory and link this to

method of payments.

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2.1.2.1 Agency Theory

Agency theory, first developed by Fama (1980), provides us with the description of the

relationship between the managers of the firm and its shareholders. In this case, the

managers are the agents and the shareholders are the principals. Decisions about mergers

and acquisitions can cause conflicts between managers and shareholders.

Jensen (1986) used agency theory to come to the agency costs of mergers and

acquisitions. He states that the main issue between managers and shareholders is that

shareholders might want to see the profit of the business being paid out to them, but this

will negatively affect the financial resources disposable to managers. A side-effect might be

that when managers reduce their resources to their disposal, they might have to go to the

capital markets to obtain capital way earlier. Since financing via the capital markets causes a

lot of monitoring with third parties and a possible decrease in the stock price when stock is

issued, managers give the preference to use internally held capital. Next to that, managers

normally have the target to let the company grow. This is good for the company, but also

profitable for the managers because growth in executive compensation correlates positively

with growth in sales (Murphy, 1985). After all, Jensen (1986) states that all managers look

for free cash flows. But when companies have big free cash flows, the agency problem

becomes bigger and bigger, because the shareholders do not want the managers to invest

the free cash flows in below the cost of capital investments or waste it on organizational

inefficiencies. Most of the times, managers promise to increase the dividends on the shares,

but since dividends can be adjusted every single time, it is not so credible that managers will

keep their promise. In advance, managers can be punished by the market when they do not

keep their promise: the agency costs of free cash flows.

2.1.2.2 The Benefit of Debt Theory

To tackle the problem of high agency costs of free cash flows, Jensen (1986) has developed a

theory which states that managers will keep their promise to pay out future free cash flows

to the shareholders if they use debt as a method to finance the deal. Jensen explains this as

follows: when issuing debt to buy back shares from current shareholders, the shareholders

might take the company to the court when the company goes bankrupt. This way,

shareholders can make managers keep their promise in paying out future cash flows to the

shareholders instead of wasting it on bad investments. Yook (2003) adds another advantage

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of debt. He states that due to a bigger possibility of bankruptcy, managers will have an

incentive to work harder. On the other hand, Jensen (1986) acknowledges that debt

issuance has its disadvantages, because increased leverage comes at a cost of debt, which

mainly consists of bankruptcy costs.

2.1.2.3 The Signaling Theory

Next to the benefit of debt theory there is another important theory about method of

payments. The signaling theory, described in a paper by Yook (2003), is a theory which

states that cash is likely to be used when acquirers invest in positive net present value

acquisitions. This theory makes two assumptions. The first assumption is that capital

markets are not fully efficient. The second assumption is that there is information

asymmetry between the managers of the firm and the market. Since this is the case,

managers may use that information asymmetry advantage to signal information to the

market using their method of payment. Not only can managers signal information to the

market by choosing a method of payment, they can immediately change the internal capital

structure as well. The theory states that managers will be more likely to issue stock when

their shares and assets are overvalued. When a firm issues new shares, which is seen as a

dilution of the voting power by the current shareholders, the share price will decrease due

to a supply increase. This strategy is also a way to protect the shareholders against a setback

once the market gets to know that the stock is overvalued.

On the other hand, when the shares and assets are undervalued, managers would

want to finance with cash, because they do not want their stock to decrease any further.

Offering cash in a merger or an acquisition will therefore signal to the market that the

acquirer is sure that their stock is undervalued and thus has more belief that the acquisition

will become a success. Furthermore, Yook (2003) quotes Myers-Majluf (1984) that when an

acquirer needs outside financing, it will choose debt over equity because a stock issue is less

favorable to current shareholders.

2.2 Economic Literature

Payment methods have been researched a lot in the past. Some literature focuses on the

stock return of acquirers, some on the stock return of the targets and some on both the

acquirer and the target. In this paper we will look at the stock return of the acquirer.

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Travlos (1987) was one of the first to find hard evidence that shareholders of bidding

firms who financed their takeover with stock suffered significant losses around the

announcement date. On the other hand, he showed that shareholders of bidding firms who

financed their takeover with cash gained normal returns around the announcement date.

Next to that, Travlos (1987) proves that stock financing causes significant losses no matter

what the outcome of the bid will be. These findings are consistent with the signaling theory

explained in the section above.

Asquith et. al (1990) follow these findings and add to it that the relative size of the

target and the bidder does influence the stock returns of the bidder. When combining a

stock offer with a big relative size measure, Asquith et. al (1990) find that the effect is even

more negative than with just a stock offer. They even argue that while the investment value

of the takeover is positive, it is not big enough to offset the negative consequences of the

combination between a stock offer and a big relative size. In addition, Asquith et. al (1990)

find that it does not matter whether a bid is a tender offer or a merger offer, but it does

seem that merger offers are most of the times financed with stock and tender offers are

most of the times financed with cash. On average, mergers do add value according to

Asquith et. al (1990).

After that, Raad and Wu (1994) studied methods of payments in combination with

management equity ownership and changes in the leverage of the acquirer. They found that

when firms finance their takeover with stock in combination with low management

ownership of equity, the losses the shareholders had to suffer would be larger. On the other

hand, stock-financing which led to a decrease in the leverage levels of the acquiring firm

decreased the losses of their shareholders. They also found that when acquirers financed

their takeovers with cash in combination with high management ownership and an increase

in leverage, the shareholders of the acquirer would gain significant abnormal returns. Raad

and Wu (1994) also chose to control for management ownership and leverage changes, by

which they found that high management ownership in combination with mergers which led

to an increase in the amount of leverage were associated with significant positive abnormal

returns. On the other hand, high management ownership in combination with mergers

which led to a decrease in leverage was associated with insignificant negative abnormal

stock returns.

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Chang (1998) focused his research mainly on the effect of payment methods in

takeovers of privately held targets. In his paper he found results which contradicted the

economic theory of positive abnormal returns in cash offers and negative abnormal return

in stock offers. He found that on average bidders who financed their bid with cash, gained

and lost no abnormal return. On the other hand, he found that bidders who tried to acquire

privately held targets using stock as a method of payment, gained significant positive

abnormal returns. Chang defends these findings with other results from the research. In the

same paper he finds evidence of positive correlation between bidding firm returns in stock

offers, the creation of a new large blockholder and the amount of stock issued to the

target’s shareholders. This can be explained by the fact that privately held targets are highly

concentrated, by which a takeover with stock creates a new large blockholder. Therefore, if

a new large blockholder is created, the cumulative abnormal stock return must go up.

Fuller, Netter and Stegemoller (2002) researched acquirers who did five or more

successful acquisition bids within three years’ time. They first researched the wealth effects

of acquiring either a private target, subsidiary or a public target. They found that

shareholders gain significant positive abnormal stock return when the target was a private

target or a subsidiary. In addition, they found that the gain will be larger if the target is

larger and when stock is used as a way of financing. When a public target was acquired, the

shareholders suffered losses. In addition, they found that shareholders of acquirers suffer

bigger losses when the public target is larger and when the acquirer used stock as a method

of payment.

On the other side of the research spectrum, there have been researchers who found

contradicting results to the results of Travlos (1987), Raad and Wu (1994) and Fuller et. al

(2002).

Firstly, Firth (1979) researched the profitability of takeovers in the United Kingdom

using an efficient markets theory framework. He analyzed the gains and losses of acquired

and acquiring firms and concluded that there was no gain or loss associated with a takeover,

just a shift of wealth. Therefore, the results back the idea that mergers and acquisitions are

just for growth purposes. Firth (1979) arguments his findings in two different ways. First, he

states that seemingly the market thinks the two combined companies are worth less than

the two separate companies. This might be because the market thinks that the acquiring

management has taken on too much work to make the combined company a success, in

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which they will not succeed. Second, Firth (1979) states that the market thinks that the fees

and expenses of takeover are too high and will therefore lead to less profit. In addition, Firth

(1979) found that takeovers financed with cash are associated with negative cumulative

abnormal stock returns. 79% of his sample of acquirers suffered risk-adjusted declines in

their share prices. He therefore concludes that the market regards takeovers as very

expensive and therefore marks down the stock price of the acquirer.

Secondly, Dodds and Quek (1985) researched post-merger profitability in the

industrial sector during the 70s. They found a positive short-term effect of takeovers on the

cumulative abnormal stock return of the acquirer. When considering the method of

payment used for the takeover they found something interesting. In the month around the

takeover they found negative cumulative abnormal returns for acquirers who used cash as a

method of payment. 75% percent of the whole sample suffered negative cumulative

abnormal return and the cumulative abnormal return was on average -1.92% for acquirers

using cash. For acquirers using stock, Dodds and Quek (1985) found positive cumulative

abnormal returns. The cumulative average abnormal return was 0,78%. On the other hand,

just 50% of the sample experienced positive cumulative abnormal returns after the

takeover, but on average the effect was positive. They motivated these findings by the fact

that the acquirers are over-stretching their scarce financial resources, which the market

found to risky. Next to that, he suggests that the market finds cash offers less desirable than

stock offers, by which the stock prices of the acquirers using is marked down.

Since this paper is focused on the retail and wholesale sector, it is important to have

a look at a paper which specifically researched the retail market and its growth

opportunities. Moatti et. al (2014) disentangled two typical horizontal growth strategies:

mergers and acquisitions and organic growth. They researched the effect of those two

growth strategies on two indicators of firm performance: (1) firms bargaining power with

respect to suppliers and customers and (2) operating efficiency arising from scale

economies. For the sake of this paper, we mainly look at the outcome of the research on the

M&A side. In their findings Moatti et. al (2014) empirically prove that mergers and

acquisitions do have a significant positive effect on the bargaining power of a firm with

respect to suppliers and customers. They even find that mergers and acquisitions do affect

bargaining power in a more positive way than organic growth. On the other hand, they find

that the bargaining power advantage created by M&A lasts for just two years and then

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disappears. When looking at the effect of M&A on the operating efficiency, mergers and

acquisitions create a disadvantage compared to organic growth. Due to the high costs of

M&A and the fact that creating more operating efficiency after a takeover is way more

complex than through organic growth, M&A has a negative effect on the operating

efficiency. After all, Moatti et. al (2014) struggle to prove if mergers and acquisitions do

have a positive or negative effect on firm performance. What they do prove, is that when

measuring firm performance by return on assets or operating profit, mergers and

acquisitions do worse than organic growth on the short as well as the long term.

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3. Data and Methodology

3.1 Methodology

This section will explain the process of doing an event study which consists of three parts:

identifying the event, measuring expected stock returns and measuring the short-term

abnormal stock returns. Firstly, the start of the event study will be explained. Secondly, the

way of measuring and determining stock returns will be explained. Thirdly, this section will

give a look into the regression analysis done on the cumulative abnormal stock returns of

the acquirer. At the end of this section the hypotheses will be summed up.

3.1.1 Event Study

This paper uses a combination of the event study methodologies of Fuller, Netter and

Stegemoller (2002) and Raad and Wu (1994) to investigate the short-term effects of mergers

and acquisitions. Event study is mostly used in economic and financial research where an

effect of a certain event is investigated. For example, event study can be used to research

stock returns, firm profitability and operating income. The event study done in this paper

investigates the effect of different methods of payment, in combination with certain deal-

and firm characteristics, on the short-term stock return of the acquirer. To investigate these

effects, two time windows have been created: the event window and the estimation

window.

The event window is the period in which the event has appeared, which in this paper

will be the announcement of a merger or and acquisition. For the event window the

methodology of Fuller, Netter and Stegemoller (2002) will be used. In their research to

acquirers who did five or more acquisitions in three years’ time, they followed the standard

event study methodology of Brown and Warner (1985). Therefore, the event window will be

a five-day period (-2,2), which is exactly two trading days before and after the

announcement of the takeover. In this window the cumulative abnormal returns (CARs) will

be calculated, which eventually will be the dependent variable in the regression model.

The estimation window is the period in which the expected returns will be

calculated. For the estimation window the event study methodology of Raad and Wu (1994)

will be used. They used a 149-day period (-160,-11), which starts approximately eight

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trading months before and ends two trading weeks before the announcement. Since the

research of Raad and Wu (1994) is comparable to this paper, the estimation window (-160,-

11) of their research will be used.

3.1.2 Cumulative Abnormal Return

The cumulative abnormal return (CAR) on an acquirers’ stock can be explained by the

following. The CAR is the extra bit of return which is earned by shareholders due to the

outperformance of a benchmark combined with the expected return of the stock. In this

paper, Rit is the daily stock return of the acquirer and Rmt is the daily return on the S&P 500.

The S&P500 is the index of the 500 biggest companies in the United States of America,

which will be used as an indicator of the return on the market. In this case, beta (βi) is an

indicator of how much the stock of the acquirer will move when the S&P 500 index moves

up or down with 1%. Using these indicators, the abnormal return can be calculated as

follows:

ARit= Rit - αi -βiRmt (1)

As stated in the estimation period section, the expected return of the stock should be

calculated according to the return earned in the estimation period. To calculate the

expected return, the event study commands in Stata have been used. Using these

commands, Stata sets up a simple market model and calculates the expected returns

according to that market model. In the calculation of the abnormal returns, the market

model can be indicated by the calculation of the expected return in the estimation period:

E(Rit)=αi +βiRmt (2)

Here it can be seen that the expected stock return of the acquirer consists of a constant and

a certain reaction to the market movement. As indicated by formula (1), the expected

return will be subtracted from the stock return in the event window, which will eventually

give you the abnormal return.

To calculate the cumulative abnormal return, the abnormal returns on the stock of

the acquirer should be summed. This can be done in Stata as well by using the command to

sum the abnormal returns.

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The formula for the calculation of the cumulative abnormal return is as follows:

CAR(t1, t2) = ∑ARit (3)

In this case t1 and t2 indicate the first and last day of measuring the abnormal returns in the

event window. The cumulative abnormal return shows how much excess return has been

earned by the shareholders in the period around the announcement of the merger.

To calculate how much cumulative abnormal return has been earned by the

shareholders in the total dataset, the cumulative average abnormal return (CAAR) can be

calculated. The CAAR is the average of all the cumulative abnormal returns in the dataset. In

this case, it is an indicator how much return an investor can gain on average in the retail and

wholesale sector when a merger or an acquisition takes place.

The formula to calculate the CAAR is as follows:

CAAR = (1/N) *∑CAR(t1, t2) (4)

3.2 Data

The data for the empirical research has been retrieved from different databases. Firstly, the

Zephyr database has been used to gather a set of M&A deals. In Zephyr it is possible to

select different types of conditions which the M&A deals must fulfill. First of all, the deals

must all be mergers or acquisitions. Different forms of deals, such as a management buy-

outs or buy-ins, have not been considered. In addition, all acquirers must be listed at an

American stock exchange. Since listed companies have the obligation to share financial

information, it is easy to gather this financial information in databases.

The next condition states that the events should have taken place between the first

of January 2010 and the thirty-first of December 2017. In addition, targets must be listed or

unlisted. Moreover, the acquirers should operate in either the retail or the wholesale sector.

Targets do not have this constraint, since this paper also controls for the industry the target

is in. This will be done according to the industry measure, which measures whether the

target and acquirer are in the same sector or not.

The last condition is the minimum and maximum deal value. The minimum deal

value is stated on fifty million US dollars and the maximum deal value is stated on one

billion US dollars.

20

After all conditions have been met, the total size of the dataset is 142 M&A deals.

Since Zephyr just supplies the M&A deals, a different database had to be used for the stock

returns of the acquirer.

To gather all the stock data of the acquirers, the CRSP database has been used. To

get stock returns out of CRSP a text file had to be set up with all company Cusip codes. This

way, CRSP can recognize which company is which and whether the database has stock data

of these companies. Due to the lack of stock data of some acquirers, the dataset had to be

reduced to 116 M&A deals. Using Stata during the event study the M&A deals have been

linked to the stock data in the estimation and event window. After the stock data had been

retrieved from CRSP, data had to be gathered from Compustat.

The Compustat database can be used to retrieve financial information of the

acquirers, such as debt and equity book values. With these values the leverage ratios have

been calculated. The debt and equity values retrieved from Compustat were the book

values the fiscal quarter before and after the event. With these values it was possible to

calculate the percentage change of the leverage ratios. Due to some negative values in the

equity book values, three M&A deals had to be subtracted from the dataset. Finally, 113

M&A deals were left, which all have a value for the variables of the model.

3.3 Linear Regression

In this section the regression model will be explained. The intuition behind the regression

model has already been explained in the literature review, because all variables in the

regression model are based on earlier research. Later in this section, the regression analysis

done in Stata will be explained.

3.3.1 Regression Model

Finally, when all data has been retrieved and put into Stata, it will be possible to do a

regression analysis on the stock returns of all acquirers. The most important question here is

whether the variance in the independent variables can explain the variance in the

dependent variable: the cumulative abnormal stock return of the acquirer. The independent

variables chosen in this model mainly come from the independent variables used in the

papers of Travlos (1987), Fuller et. al (2002), Raad and Wu (1994) and Asquith et. al (1990).

To do the regression analysis, a regression model had to be set up.

21

The regression model is as follows:

CAR=β0+β1*Cash+β2*Combi+β3*Industry+β4*LN(DealValue)+β5*LeverageRatioChange+εi

Since this paper mainly focuses on the effect of different payment methods on the

cumulative abnormal stock return of the acquirer, the variables which represent these

payment methods will be placed first into the model.

The first independent variable cash is a dummy variable which will have value 1 if

cash is used as the method of payment and 0 if another method of payment is used.

The second independent variable combi is a dummy variable as well, which will have

value 1 if the acquirer uses a combi of payment methods which consists of the following

options: stock, cash, deferred payment and earn-out. The combi variable will have value 0 if

just one payment method is being used by the acquirer.

The third independent variable is an industry indicator, which is a dummy variable as

well. It indicates whether an acquirer acquires a target which is in the same industry as him

or not. This is important to know, because cross-industry acquisitions can be riskier, since

the acquirer might enter a new business area. The variable has value 1 if the target was in

the same industry as the acquirer. When the target is not in the same industry as the

acquirer, the variable will have value 0.

The fourth variable is the natural logarithm of the announced merger value in US

dollars. It will give an indication on whether the takeover is a big or small takeover. Since the

minimum and maximum deal value do lie quite far from each other, we used the natural

logarithm to bring all the observations closer to each other.

The fifth variable is the LeverageRatioChange, which will give an indication on

whether the acquirer has increased its leverage ratio during the takeover. The leverage ratio

is equal to the book value of the debt divided by the book value of the equity. Since the

market values of the debt were not available, the book values for both the debt and the

equity had to be used. This variable could be worthy, because most cash takeovers will in

the end be financed with the issuance of debt. When an acquirer issues more debt, its

leverage ratio will increase if the equity book values remain the same. It therefore might

explain something extra in the variance of the stock return of the acquirer. This last variable

concludes the regression model. All details about the variables can be found in tables 3 up

till 7 in Appendix 1.

22

3.3.2 Regression Analysis

After the regression model has been set up, the regression analysis can be done in Stata.

Linear regression analysis can be done in Stata in many ways. The most common used

regression analysis is the Ordinary Least Squares (OLS) regression analysis. OLS regression

regresses the dependent variable on the independent variables in such a way that the sum

of squares of the residuals of the dependent variable and the independent variables is as

small as possible. The OLS regression analysis is the best estimator when some conditions

are fulfilled: (1) consistency because regressors are exogenous, (2) unbiased because the

error terms are homoscedastic and serially uncorrelated and (3) error terms are normally

distributed. The OLS regression analysis can easily be done in Stata by selecting the linear

regression button using the standard errors which follow the OLS methodology.

Next to that, a regression analysis with robust standard errors can be done. A robust

regression will be used when the dataset has outliers which can compromise the validity of

the model. Robust regression is less dependent on all conditions which must be fulfilled

when using the OLS regression analysis. OLS regression analysis might give misleading

results when the conditions are not met properly, this way OLS regression analysis will not

work as well as it should work.

Since the dataset used in this paper is quite spread out, we used robust standard

errors. The main reason for this is to prevent heteroscedastic errors which compromise the

validity of the model. In addition, using robust standard errors will cause the standard errors

of the variables to be smaller. Eventually, this will give us more significant variables which

will be able to explain more of the variance in the dependent variable.

3.4 Descriptive Statistics

All the descriptive statistics of the variables discussed in section 3.3.1 can be found in table

3, which is placed in Appendix 1. The cumulative abnormal return has a mean of 2,04%,

which is quite normal for average cumulative abnormal returns. Especially, when we

compare this to the findings of Travlos (1987) and Raad and Wu (1994) who found average

cumulative abnormal returns within the event window around the 2%-mark as well. If we

then have a look at the standard deviation, we can see that the standard deviation is 6,74%.

This tells us that the cumulative abnormal returns are lying quite far from each other within

the dataset, which can also be noticed when we look at the minimum and maximum value

23

of cumulative abnormal return. These are respectively -24,35% and 35,59%. From this we

might conclude that some acquirers do much better than others.

The dependent variable cash is a dummy variable, so the mean and standard

deviation do not say anything. What we can conclude from table 4 is that most of the

acquirers choose to finance their acquisition with cash: 79 cash takeovers against 34 combi

takeovers.

The dependent variable combi is also a dummy variable, so we cannot conclude

anything out of the mean and standard deviation. In table 5 we can see that most acquirers

choose an alternative payment method instead of choosing a combination of payment

methods: 30 combi takeovers against 83 alternative method of payments.

As the variable industry is a dummy variable as well, we cannot conclude anything

out of table 3. Table 6 tells us that in this dataset, the acquirers mainly choose to acquire

targets which are in the same industry as them: 97 same industry targets against 16

different industry targets. This might tell us that big players in the retail and wholesale

market acquire smaller players to fight competition and therefore increase their market

share.

The variable change in leverage ratio has a mean of 15,20% and a standard deviation

of 46,38%, which shows us that the leverage changes do differ a lot amongst the acquirers.

This can also be seen in the minimum and maximum values of leverage ratio change. Since

these are already percentages, there is no need to take the natural logarithm to decrease

the differences.

Finally, we can see in table 3 that the natural logarithm of the deal value has a mean

of 19,12 and a standard deviation of 0,81. The minimum and maximum values are 17,73 and

20,64. The minimum and maximum value of the actual deal values are $50 million and $922

million. In table 7 we can see that the deal values are quite equally distributed, with most

deals lying between $100 and $250 million.

24

Table 1: Correlation Matrix

CAR Cash Combi Industry LnDealValue Leverage

CAR 1

Cash -0.1829 1

Combi 0.1489 -0.9164 1

Industry -0.0295 0.0103 0.0142 1

LnDealValue -0.0661 -0.0743 0.0743 -0.1610 1

Leverage 0.0550 -0.2165 0.2731 0.0103 0.0054 1

In Table 1 above we can see the correlation matrix. It is important that the variables do not

correlate too much with each other, because if they do, they will reduce each other’s

explanatory power. This phenomenon is called multicollinearity and we want to prevent

this. In the correlation matrix we can see that almost all correlations are far under the 0,7-

mark, which is the mark where multicollinearity appears. The only two variables who

correlate a lot with each other are cash and combi, but that is logical. This is the case,

because the cash and combi variables are dummy variables, so if one of the two has value

one the other variable must have value 0. This way they affect each other a lot, but this

should not be a problem, because it cannot be prevented.

3.5 Hypotheses

In this section we will look at the hypotheses of this paper. For every single independent

variable we will state a hypothesis. The idea is that every single independent variable should

have their own effect on the dependent variable.

3.5.1 Method of Payment Hypothesis

The null hypothesis will be that shareholders do not gain or lose any cumulative abnormal

stock return around the announcement date. The alternative hypothesis for cash-financing

will be that shareholders of acquirers using cash as a method of payment will gain positive

cumulative abnormal return.

25

The null hypothesis will be that shareholders do not gain or lose any abnormal stock

return around the announcement date. The alternative hypothesis for combi-financing will

be that shareholders of acquirers using a combination of payment methods will suffer

negative losses in their cumulative abnormal return.

3.5.2 Industry Hypothesis

The null hypothesis will be that shareholders do not gain or lose any cumulative abnormal

stock return around the announcement date. Based on the paper of Fuller et al. (2002), the

first alternative hypothesis will be that shareholders of an acquirer will suffer a negative

cumulative abnormal return when the target is in the same industry as the acquirer

provided that the target is listed. The second alternative hypothesis is: when a target is

unlisted, the shareholder of an acquirer will gain a positive cumulative abnormal return

when the target is in the same industry as the acquirer.

3.5.3 Deal Value Hypothesis

The null hypothesis will be that shareholders do not gain or lose any cumulative abnormal

stock return around the announcement date of a merger or an acquisition. Based on

economic theory, the alternative hypothesis will be that shareholders will suffer a negative

cumulative abnormal return as the deal value increases.

3.5.4 Leverage Ratio Change Hypothesis

The null hypothesis will be that shareholders do not gain or lose any abnormal stock return

around the announcement date of a merger or an acquisition. Based on the paper of Raad

and Wu (1994), the first alternative hypothesis will be that shareholders will gain a positive

cumulative abnormal return if the acquirer pays in cash and increases its leverage ratio. The

second alternative hypothesis will be that shareholders will suffer a negative cumulative

abnormal return provided that the acquirer pays with stock and decreases its leverage ratio.

26

4. Results

In this section the results of the regression analysis will be explained. Firstly, the results of

the regression with just the two payment methods as independent variables and the

cumulative abnormal return as the dependent variable will be explained. Secondly, the

results of the final regression with all independent variables included will be explained.

Table 2: Regression output

Table 2: results empirical analysis Cumulative Abnormal Return

Independent Variables (1) (2)

Cash -0.042** (0.019)

-0.044** (0.020)

Combi -0.018 (0.026)

-0.015 (0.029)

Industry -0.010 (0.033)

Ln (DealValue) -0.012 (0.007)

Leverage Ratio Change 0.006 (0.011)

Constant 0.055*** (0.019)

0.293** (0.148)

N 113 113

R-squared 0.036 0.0650

F-value 3.01* 1.86

Df 2, 110 5, 101

Robust standard errors are parentheses below the estimated coefficients. Significance is indicated as follows: *** if p<0,01, ** if p<0,05 and * if p<0,10. N is the total observations. R-squared indicates the percentage of the variance in the dependent variable explained by the variance in the independent variable(s). F-values indicate the significance of the model,

with below the degrees of freedom used.

27

4.1 Table Descriptive

The results of the regression analysis are displayed in table 2. On the left-hand side the

independent variables are shown. The dependent variable, the cumulative abnormal return,

is placed above the two regressions done. In column (1) the first regression can be seen. In

this regression, only the variables cash and combi are included. In this way, we can

distinguish the effect of payment methods in isolation from the other independent variables

and included in the final model with the other independent variables. These independent

variables are then regressed against the dependent variable: cumulative abnormal return. In

column (2) the final regression can be seen. In this regression, all the independent variables

are regressed against the dependent variable.

4.2 OLS Regression with Robust Standard Errors

4.2.1 Regression Column (1)

In the first column of table 2 the variables cash and combi are regressed against the

dependent variable: cumulative abnormal return. The model is significant at a 10% level.

According to the regression results, cash-financing has a negative impact on the

cumulative abnormal return. The coefficient of cash has a value of -0,042 with a standard

error of 0.019. The coefficient is significant at the 5%-level. From this we can conclude that

the cumulative abnormal return will decrease when an acquirer uses cash as a method of

payment. This contradicts the alternative hypothesis, which states that acquirers using cash

as a method of payment will gain positive abnormal returns. Although this might seem

strange, other researchers have found the same result for cash acquisitions. Firth (1979)

found significant negative cumulative abnormal returns after a takeover bid when the

acquirer proposed a cash offer. According to Firth (1979) this is due to risk-adjustment by

the market. Like Firth (1979), Dodds and Quek (1985) also found negative cumulative

abnormal returns after cash offers. They also concluded that this could be the case due to a

mark-down of the market, but they added that this would be the case if the market found

that the acquirers were dealing too risky with their financial sources. This could be a reason

why in this paper a negative effect of cash-financing has been found. Possibly, the market

found the takeovers done in this dataset too risky to pursue. If this is the case, it is in line

28

with existing literature of Firth (1979) and Dodds and Wuek (1985) that cash-financing has a

negative effect on the cumulative abnormal stock return of the acquirer.

The coefficient of the combi variable has a value of -0,018 with a standard error of

0,026, but is insignificant. The direction of the coefficient is in line with the alternative

hypothesis that picking a combination of payment methods does have a negative impact on

the cumulative abnormal return of the acquirer, but this cannot be concluded with 1, 5 or

10% significance. It could be the case that due to the multi-collinearity between the cash

and combi variables, the combi variable loses explanatory power and thus has a lower

significance.

4.2.2 Regression Column (2)

The second column of table 2 shows the total model in which the payment methods and the

other independent variables are regressed on the cumulative abnormal return of the

acquirer. This model is not significant at a 1,5 or 10% level.

The cash variable has a coefficient of -0,044, which is more negative than in the first

regression, and has a standard deviation of 0,020. It is significant at the 5% level. This tells

us that when an acquirer chooses cash as the method of payment, his cumulative abnormal

return will decrease by 0,044. As stated in the first column explanation, this finding is in line

with the findings of Firth (1979) and Dodds and Quek (1985). It might be that in a high-

competitive market as the retail and wholesale market, the market adjusts for the risk of a

takeover much faster than in other markets. Because the effect is negative, we cannot reject

the null hypothesis.

The combi variable has a coefficient of -0,019 and a standard deviation of 0,029.

Although it is not significant, the direction of the coefficient is in line with the literature

stating that using a combination of payment methods will have a negative effect on the

cumulative abnormal stock return of the acquirer. The insignificance might be caused by the

multicollinearity, because the combi variable correlates heavily with the cash variable. On

the other hand, if this would be the case, then the cash variable should lose explanatory

power as well. It might also be that the disparity in the data causes this variable to be

insignificant. Because the combi variable is not significant at 1,5 or 10%, we cannot reject

the null hypothesis.

29

The industry variable has a coefficient of -0,010 with a standard deviation of 0,033, it

is not significant at any level. This might be the case because the industry measure is not

evenly distributed. There are way more acquirers who chose to acquire a target which is in

the same industry. Therefore, the variance might be too big, by which it loses explanatory

power. The direction of the coefficient does match the findings of Fuller et. al (2002) partly.

Their findings suggest that when an acquirer and a target are in the same industry and the

target is either listed or a subsidiary, the cumulative abnormal return will be affected

negatively. On the other hand, they find that a takeover of a private target in the same

industry sorts in a positive abnormal return for the acquirer. Since the industry variable is

not significant at any level, we cannot reject the null hypothesis.

The deal value variable, which is the natural logarithm of the actual deal value, has a

coefficient of -0,012 and a standard deviation of 0,007. It is almost significant at a 10% level

with a p-value of 0,108. The direction of the coefficient is in line with economic theory,

which states that a higher deal value will sort in a lower cumulative abnormal return. As the

natural logarithm has a positive relation to the amount under the log, the outcome will

increase when the amount under the log increases. Therefore, if the deal value increases,

the cumulative abnormal return will decrease. This is quite logical, because a bigger deal

value requires more financing, which might require more risk. If a stock gets riskier, less

investors will buy the stock if one assumes that investors are risk-averse. Therefore, more

investors will supply the stock instead of demanding it, by which the stock price will drop.

Since the natural logarithmic deal value variable is not significant at 1,5 or 10%, we cannot

reject the null hypothesis.

The leverage ratio change variable, which indicates whether an acquirer increases its

leverage ratio before a takeover, has a coefficient of 0,006 and a standard deviation of

0,011. This variable is not significant, but if we look at the direction of the coefficient we can

say that it matches the literature. As Raad and Wu (1994) found in their paper: an increase

in the leverage ratio is positively related to the cumulative abnormal return provided that

the acquirer pays in cash. If the acquirer chooses stock or a combination as the method of

payment, a leverage decrease will make the negative cumulative abnormal return worse

according to Raad and Wu (1994). The effect of the coefficient will be positive on the

cumulative abnormal return, but this cannot be said with 1,5 or 10% significance. It might be

that this variable has data which lie too far out of each other, by which its variance is too

30

high and it loses explanation power. Next to that, it could be possible that the data sample is

just too small for the leverage ratio change variable to be significant. It has been tried to

winsorize the data, but this did not sort in a better or significant effect. Since the leverage

ratio change variable is not significant, we cannot reject the null hypothesis.

31

5. Conclusion

In this paper the effect of different payment methods in combination with deal and firm

characteristics on the cumulative abnormal stock return of acquiring retailers has been

investigated. To research the effect of financing a deal with cash or a combination of

different payment methods, a dataset from Zephyr has been used. After linking the deals

from Zephyr to the stock returns found in CRSP, an empirical analysis has been done with

just the payment methods as independent variables. The main finding of this empirical

analysis is that cash-financing can have a negative effect on the cumulative abnormal stock

return of the acquirer. This finding is significant at a 5%-level. Using a combination of

payment methods does not have a significant effect on the cumulative abnormal return of

the acquirer.

After retrieving more deal and firm data, it was possible to set up a second

regression analysis. In the second multiple regression analysis, the effect of payment

methods, an industry measure, the deal value and the leverage ratio change on the

cumulative abnormal return of the acquirer has been researched. The variable cash is

significant at a 5%-level. It still has a negative relation to the cumulative abnormal return of

the acquirer.

These findings are in line with the findings of Firth (1979) and Dodds and Quek

(1985), who also found negative effects of cash-financing on the stock return of the

acquirer. Firth (1979) defended his findings by stating that the market adjusted the stock

prices of the acquirers for the risk of the acquisition. Dodds and Quek (1985) supported the

finding of Firth (1979) adding that the market would risk-adjust the stock price when the

market thought the acquirer was dealing too risky with its financial resources. It might be

that in this paper, where the retail and wholesale sector is used, this is the case as well. It

could be plausible that the market adjusts for a certain kind of risk, because the North-

American retail and wholesale sector is highly competitive. As such, investing in different

technologies or investing in other retailers, is perceived as a “high risk” activity. As stated in

the interview with Miel Janssen, retailers experience a period of turmoil and anxiety

following a takeover. Since turmoil and anxiety can have negative effects in the long-term, it

is possible that the stock market incorporates this effect immediately into the stock price. In

the discussion section, we will discuss this matter further.

32

6. Discussion

In this section we will discuss the limitations of this paper, suggest some improvements

which could be implemented and sum up some subjects which would need more research.

6.1 Limitations and Improvements

This paper has its limitations. Firstly, the amount of observations is a bit low. In this paper

113 observations have been used to construct the regression model. When doing an

empirical regression analysis, it is key that the amount of observations is minimally 100

observations. This to prevent a lack of explanatory power in the variables, because a small

sample cannot explain phenomena of a big population.

In this paper we used acquirers who operate in the retail and wholesale sector, but

the findings cannot say anything about other sectors. Next to that, it is not possible to state

that the findings in this paper represent the total retail and wholesale sector, because we

only studied North-American retailers. The possible lack of observations can be one of the

reasons that the regression model is not as significant as one might want. This lack of

observations is mainly caused by the sector itself. When searching for deals in the retail and

wholesale sector, the maximum amount of deals found was 142 deals. It could be that in

other sectors the maximum amount will be much higher. This could also be explained

following the paper of Moatti et. al (2014), who found that creating internal growth was

way more profitable in the long-term than doing an acquisition. Therefore, retailers will

decide to do a takeover less often than in other sectors might be the case. The data of the

Institute of Mergers Acquisitions and Alliances3 shows us that the retail sector is not really a

sector which does a lot of M&A compared to other sectors, such as the high tech or the

financial sector. Since 1985 the retail sector has had almost 5% of all merger and acquisition

deals. Compared to the financial sector, with 12% one of the highest, the retail sector does

little M&A deals.

The lack of observations could also be due to the time period in which this paper has

measured its variables. In eight years (2010-2017) 142 deals have been found, which is not

that much. On the other hand, this period has been chosen on purpose. For the sake of this

paper it had to be prevented that variables were measured during the financial crisis,

because this might affect the explanatory power in a negative way, since the American

3 Institute of Mergers Acquisitions and Alliances: M&A Statistics of the retail industry

33

economy was still recovering back then. A longer period could have benefited the amount

observations, but on the other hand it could have confused the regression model and its

variables.

The lack of observations could also be due to the databases used. The Zephyr

database has a lot of M&A deals, but does not have all of them. In addition, the CRSP

database did not have all the stock data of the acquirers selected in Zephyr. Next to that,

the Compustat database which has been used to calculate the leverage ratios did not have

all the data as well. Probably, when the amount of observations would have been greater,

the variables would be way more significant by which the model would have had better

explanatory power. Therefore, if the data is available, further research should gather much

more observations than this paper.

Secondly, the way of calculating the leverage ratio could be done differently if the

data would be available. In this paper, the book values of both equity and debt have been

used, but it would have been much better if the market values of the debt and the equity

could have been used. Especially, when it is possible to measure the market values on a

daily basis, we can construct a way better model. That way, we could have linked an

increase of debt to an M&A deal in a much better way. Now we estimated the change in the

leverage ratio by picking fiscal quarter book values from before and after the takeover.

Further research, if the data is available, might want to use the market measures to get a

better result. An alternative way to calculate the actual values of debt is to calculate the

present values of the book values of the debt issued. In this paper it has not been possible

to apply this method, because the interest rates of debt issuance are not known. We could

have used the risk-free rate, but since the interest rates on treasury bonds is near zero, this

would not have had an effect on the present values.

Thirdly, this paper uses a high level of significance (10 percent) for the measurement

of significance of the first regression model. The problem with picking a higher level of

significance is that the probability of a type I error gets bigger. The probability of a type I

error is described as the probability of rejecting a null hypothesis while it is in fact true.

When the significance level is higher, we will reject the null hypothesis earlier, so the

probability of rejecting a true null hypothesis will be bigger. To reduce the probability of a

type I error, we should measure with a lower level of significance. To then get a significant

model, we will need more observations.

34

6.2 Further Research

Lastly, more research is needed to investigate the advantages mergers and acquisitions

might have on the retail sector. Since this is a sector which is highly competitive, has low

margins and is exposed to a lot of development, more research is needed whether mergers

and acquisitions are a good and profitable way to pursue growth. Moatti et. al (2014)

proved in their paper that M&A is just a short-term success story, but it would be interesting

to research whether and in what ways M&A could also be a long-term success story for

retailers.

Next to that, more research will be needed to investigate in what ways mergers and

acquisitions can be made a better fit for the retail sector. For example, the retail sector is

now trying to develop an omnichannel shopping experience. It could be attractive to

research the effects of retailers acquiring tech companies who have the assets to deliver

such a shopping experience on their firm performance. It may be questioned whether tech

companies can easily be integrated in the retail sector and what development the retail

sector should make to adjust to these technological improvements.

In addition, research has to be done on whether the findings of this paper contain in

a bigger sample, because the findings in this paper contradict the economic theory and

some economic literature. It might be that the retail sector is a sector which experiences

different effects of deal and firm characteristics within M&A. Next to that, if the findings in

this paper still contain in a bigger sample, it would be interesting if further research could

investigate why retailers are still using method of payments within their takeovers which are

not profitable at the short-term.

35

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38

Appendix 1

Appendix 1 will show all the details about the variables used in the model, including

descriptive statistics which are explained in section 3.4.

Table 3: Descriptive statistics of the dependent and independent variable(s)

Variable Obs Mean Std. Dev. Min Max

CAR 113 .0203883 .0674202 -.2434798 .3559248

Cash 113 .699115 .4606857 0 1

Combi 113 .2654867 .443559 0 1

Industry 113 .8584071 .3501851 0 1

Change in Leverage Ratio

113 .1520411 .4638462 -.9035894 2.981045

Ln Deal Value 113 19.12242 .8106533 17.72753 20.64255

Table 4: Method of Payment Cash Variable

Method of Payment N

Cash (=1) 79

Other (=0) 34

Table 5: Method of Payment Combination Variable

Method of Payment N

Combination (=1) 30

Other (=0) 83

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Table 6: Industry Measure Variable

Target Same Industry or Not N

Same Industry (=1) 97

Other Industry (=0) 16

Table 7: Deal Value in US Dollars Variable

Deal Value in US Dollars N

Deal ≥ $500 Million 24

$250 Million ≥ Deal < $500 Million

16

$100 Million ≥ Deal < $250 Million

46

Deal < $100 Million

26

Appendix 2

In appendix 2 we will place the interview which has been done with Miel Janssen.

Miel Janssen is a former partner at the consultancy firm Accenture B.V. Originally, he is an

econometrist who started working at Accenture in the 80s. After becoming partner at the

firm, he led the consultancy team for the retail and wholesale department. He has advised

many big Dutch clients who operated across the whole world. His expertise lies in finding

new opportunities to penetrate the market for his clients who are operating in high

competition markets. After retiring from Accenture in 2016, he now advises start-ups and

board members of big corporates. For me, as an economics and business student, it was a

great opportunity to interview Miel, mainly because of his expertise in the retail sector. This

interview will provide a deeper view into the business strategies, growth opportunities and

new developments in the retail sector. I would like to thank Miel for taking the time to do

this interview.

40

How did you, but especially your clients, experience the growth opportunities in the retail

sector in the past 10 to 15 years?

“If you look at the retail market with a closer eye, one can divide the retail market into two

different markets. Firstly, there are saturated markets, the classical food market for

example. This means that there is little growth, but also that this market is much less

dependent on the cycle in which the economy is in. In this market, the way of pursuing

growth is mainly outperforming your competitors and up-trading the margin on your sold

products. Food retailers have been experiencing growth issues for years, so to pursue growth

they should look for innovation. For example, food retailers build on-the-go and digital

concepts, by which consumers can buy their food and drink on the go. Albert Heijn to go and

AH.nl are good examples of this phenomenon. This way, retailers can maximize their access

to the spending income of the consumer. Secondly, there are the growth markets, fashion

brands for example. In growth markets, retailers try out opportunities abroad in the hope of

becoming a global retailer. H&M is a good Dutch example of a brand which pursues the

growth market. Especially when there is a strong mother company with a proven formula

behind the daughter firms who pursue opportunities abroad, it is possible to be successful

when you enter these growth markets.’’

How would you describe the competition your clients had to face? In what way did your

clients fight this competition?

“The competition in the saturated retail markets is very high, mainly because there are a few

big players who are looking for the biggest piece of the cake. Since growth in the saturated

markets is tough, my clients really had to fight hard to outperform the competition. It was

dependent on the market or on the client which strategy they used to beat the competition.

For example, the food retail market used a lot of price wars, which we have seen for many

years now. Other clients used channel and product differentiation. After all, there are many

strategies which can be used to fight the competition.”

How did your clients experience the entry into North-American growth markets when you

look at the competition they face?

“For example, Ahold Delhaize is investing in growth in the North-American retail market, in

which they succeed quite well actually. Food retail is still very much a local and regional

41

business; there little national and international food retailers yet. They grow their North-

American presence, which is a saturated market, with a big and efficient back-office behind

them. If they start opening stores or acquiring smaller retailers, they leverage their back-

office only adding variable cost. Fixed cost are shared across a large revenue base. In the end

this will turn out into more profit for the holding. Off course, the competition in the North-

American food market is tough to face with big corporates as Kroger, Target and Walmart,

but Ahold Delhaize proves that it is not impossible to go abroad and be successful.”

Have you seen a certain change or development in the way your clients penetrated the

market to pursue growth before and after the financial crisis?

“First, it is important to define the difference between cyclical and non-cyclical retail. Food

retail is typically not cyclical, because people keep on consuming their morning and evening

meals. The firms who focus on this market did not really have to develop a different

penetration of the market after the crisis, because they did not really suffer from the

negative effects of the financial crisis. Non-food retail, such as fashion, electronics and hard-

good retail, did really suffer from the financial crisis. For example, the do-it-yourself market

suffered massively from the financial crisis, because the housing market dropped during that

period. Economically it is logical that when people have less spendable income, they will cut

the costs which weigh the highest and can most easily be deferred, that is why many non-

food good retailers suffered. Therefore, the cyclical retail firms had to cut a lot of costs to be

able to remain in business. Overall, you can see that all retailers invested a lot of money in

the online market during the financial crisis. Especially, because big online shops, such as

Bol.com, Alibaba and Amazon, stole a part of the revenue in the saturated market. To fight

this competition, classic retailers had to invest in technology to pursue the online markets.

They started developing their own online platforms instead of buying little firms who already

had these platforms. Nowadays, all big retailers are creating physical tech centers to

advance their technologies they have developed over the years. They then combine the

development of these tech centers with the acquisition of little tech companies who can

provide extra technology and have assets which can help the retailers pursue more growth,

but that is just a development of the past three to five years. The coming years, I expect the

retailers to invest in creating a truly omnichannel shopping experience. For example, virtual

reality and artificial intelligence can help creating that new experience.”

42

Have you ever advised your clients about doing mergers and acquisitions to pursue

growth? If so, what do you think is the big contribution of M&A in the short-term to the

retail market and what could be the downside of M&A?

“In the years I worked at Accenture, I guided a few mergers and acquisitions of my clients. In

the short-term the big advantage of doing a takeover is that you immediately create bigger

buying power. When a takeover is done, all contracts with suppliers will be put on a pile and

compared to each other. Since the company is bigger after a takeover and the contracts with

suppliers differ from each other, you will be able to negotiate much better deals with your

suppliers. This method is called “strategic sourcing” and is very handy to cut costs which will

immediately cause higher profits. The main disadvantage of M&A is anxiety within the

management of the firm. Top and senior level managers will mainly focus on what their role

will be in the newly integrated company, by which the focus of managers will be mainly on

the inside instead of keeping focus on the market. Operations do not suffer from this, but

innovations do suffer from M&A. It could be that the negative effects of not being

innovative, due to difficulties with the changes during a takeover, express themselves a few

years later than when the actual takeover took place.”

If you have advised your clients about doing a deal, would your clients really consider the

effects of payment methods on their future stock returns?

“For the internal business processes, it does not matter how the deal is constructed, but the

board does really think about how to construct such a deal. Within the process of doing an

acquisition the Chief Financial Officer and the Chief Executive Officer are busy discussing how

to construct the deal in such a way that it would be optimal for their company. For

managers, it is a hard question how to finance such a deal. Obviously, the method of

payment is part of this discussion. Especially, my clients who were listed really had to

consider that the method of payment could massively affect their stock prices in the future.

For unlisted clients, this was less of a problem, but it is still a subject of discussion.”