The effect of mergers and acquisitions on the bid-ask spread

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The effect of mergers and acquisitions

on the bid-ask spread

Abstract

This thesis examines the effect that mergers and acquisitions have on the bid-ask spread, how this is affected by the crisis and if the existence of rumours have an effect. These effects are tested by using a sample of 127 merger and acquisition (M&A) deals completed in Western Europe between January 2000 and January 2015. The results reveal that there is no significant effect on the spread of both the acquirer and the target, but that the effect is significant on some other factors of the acquiring and target companies. Rumours show to have a significant effect on some of these factors as well. Furthermore, the crisis does not seem to have a significant effect on the spread.

Bo den Hollander 10451978 February 2016

BSc Business and Economics - Finance & Organization University of Amsterdam

Bachelor Thesis Supervisor: R.J. Doettling

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Statement of Originality

This document is written by Bo den Hollander who declares to take full responsibility for the contents of this document.

He declares 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 solely responsible for the supervision of completion of the work, not for the contents.

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Acknowledgments

First of all, I would like to thank my supervisor Robin Doettling for helping me to write this thesis. I am especially thankful for the guidance, available time and advice. Also, I would like to thank Philippe Versijp for his guidance and giving tips during the whole process. Finally, I would like to thank my parents for always cooking me dinner so I could have a quick meal and continue working on this thesis.

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1. Introduction

The stock market is the place where investors trade stocks and other financial instruments. Bidders and sellers never ask or buy for the exact same price. Therefore, there is a spread, the bid-ask spread. It is the difference between what a seller is asking for his stock or security and what a buyer is willing to pay at a point in time. So it is essentially the amount that the lowest ask price exceeds the highest bid price (http://www.investopedia.com).

The bid-ask spread consists of three components. The inventory holding cost, order processing cost and the adverse information cost. Each of these components have their own effect on the spread. Merger and acquisition (M&A) rumours and its announcement have an effect on the adverse information cost component. That is why this research will focus mainly on this particular component of the spread.

This research examined if mergers and acquisitions have an effect on the bid-ask spread. Previous research was done, such as Conrad and Niden’s (1992), on some of the effects that M&As have on the behaviour of traders. Moderately, about the bid-ask spread in particular, but more about liquidity itself and insider trading. The rumour date has not been highlighted before, which was done in this research. Expected was that rumours do not have such a big effect as the announcement has, because less people are aware of these rumours than of an announcement. So the effect is smaller on the adverse information component.

This is why it was also interesting to examine the effect of rumours, which was done in this thesis. Examining this will add extra information about the whole effect of M&As. Rumours were expected to result in extra adverse information before the announcement. That is because not all traders hear about these rumours. So this makes the adverse information component bigger before the announcement. Another effect that was examined in this research is the fiscal European crisis. The used time period of the years 2000 until 2015 covers this. This effect could go two ways. The crisis could result in less adverse information, because new rules and laws make insider trading more difficult. Or, it could lead to an increase in adverse information, because a lot of companies and governments became deregulated during the crisis.

This research continued on previous studies, one of which mentioned above. 127 M&A deals have been examined from Western Europe. An event study was done on price, return index, volume, dividend yield, turnover by value and market value to test whether M&As have a significant effect on the change of these factors. The effect of the components

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on the spread was examined by a regression with robust errors. The effect of the European fiscal crisis and the existence of rumours before the announcement date were also examined. Here the research of Conrad and Niden (1992) is partly followed, but some extra variables have been added to enhance their model.

The results of the event study revealed that both the rumour and announcement date do not have a significant effect on the bid-ask spread. In the conclusion of this study some explanations for these results and suggestions for future studies are given. One of the results is that price has a significant effect on the spread of the acquirer. Also, volume and turnover by value show to only have a significant effect on the spread of the target at the announcement date. If the effect of M&As could be successfully examined and understood, the inefficiency of the stock market could be decreased. That is because the adverse information components can then be reduced. This could be done by setting new and better regulations to counteract insider information.

The layout of this research starts with an introduction in section 1 with background information of the subject and the methods that were used to conduct this research. In section 2, other research will be discussed. Section 3 covers what data is used and how it is used. Section 4, 5 and 6 show the research methods and their results. Lastly, there will be a conclusion and some suggestions for future studies in section 7.

2. Literature review

In this theory section, the elements that are to be investigated will be further explained and discussed. There has not been any research about the the effect of M&As on rumours yet. To be able to get a better understanding of the used variables, some background information and theories are given.

2.1. Types of investors

There are three different types of investors on a stock market. First, there are the market

makers. They maintain the trading in stocks and make sure that there is a constant liquidity

on the stock market. They do this by constantly offering buy and sell prices. A market maker earns his money on the difference between the bid and ask prices. These investors try to avoid big inventory positions, because they are not well informed in what they trade. So they do not know if they trade in risky stocks or not.

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Secondly, there are informed traders. These traders know more about the traded stock, mostly because of insider information. They use this information and the public information that is available for trading. If they think that the prices are too low (high) regarding what they think is the fundamental value, then they buy (sell). Lastly, there are uninformed traders. These traders do not possess the amount of information that informed traders have and do this on their own 'feeling' or knowledge.

2.2. Components of the bid-ask spread

The market microstructure theory gives that the bid-ask spread consists of three components (Madhaven, 2002). The first component is the inventory holding cost. Previous research by Amihud and Mendelson (1980) conclude that this cost arises because of the need of

continuous liquidity on the stock market. The dealer that holds this inventory is being

compensated by being able to purchase shares below the fundamental value (bid price) and at the same time selling shares at the ask price, which is most of the time above the fundamental value. This creates a distance between the bid and ask price.

The second component is the order processing cost. On the stock market there are always inefficiencies and transaction costs. The order processing cost exists because the stock has to be transferred to the market, it has to be monitored, a match has to be found and

because of some other procedures. This component has been examined in greater detail by Demsetz (1968).

The last and most important component is the adverse information cost. This cost arises because not every trader or dealer has the same amount of information available. Insider information ensures more adverse information. A reason for the existence of the bid-ask spread is the compensation required by market makers to offset their potential losses on trades with better informed traders. According to Glosten and Harris (1988), “It is also called the adverse-selection component because market-makers face adverse information in their order flow” (p. 124). In fact, “Inventory and adverse information components are difficult to distinguish because quotes react to trades in the same manner under both” (Huang & Stoll, 1997, p. 997).

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2.3. Bid-ask spread

To be able to examine whether there was an effect of M&As on the bid-ask spread, data of Western European countries1_{were used. Data from the years 2000 until 2015 were examined.} Previous studies were primarily about the United States because U.S. deals dominated the scene. M&As in European banking started to catch up in the beginning of the century (Cybo-Ottone & Murgia, 2000) and continued to do so. They showed that relative quoted spreads of both acquirers and targets suggest that the market makers increase the future returns before the announcement. According to Stoll (1989), “The quoted bid-ask spread is the difference between the ask price quoted by a dealer and the bid price quoted by a dealer at a point in time. The realised bid-ask spread is the average difference between the price at which a dealer sells at one point in time and the price at which a dealer buys at an earlier point in time. The quoted spread consists of inventory holding costs, adverse information costs and order processing costs. The realized spread only consists of the latter two. Each of these components has a different effect on the bid-ask spread. Theories about the spread are of the quoted spread” (p. 115). This research made use of the quoted spread. The earlier mentioned European banking which is catching up and the fact that few literature is written about Western Europe, were reasons why this research focuses on this geographical area.

The spreads of targets on the announcement date and announcement date +1 decrease significantly (Huang & Tung, 2012). They also found a permanent increase in the relative quoted spread of the acquirer’s after the announcement, which implies that market makers pay more attention to the acquirer’s stock because there are more informed traders. Conrad and Niden (2012) also found that the level of the spreads of the targets persistently decline, which is caused from a dramatic increase in trading activity, during and after the

announcement. According to Kim (2014) both spread measures decrease after the

announcement. This is in conflict with this research. Here the spread of both the acquirer and the target increase after the announcement.

2.4. Background information about used variables

Previous literature shows that M&As have an effect on the stock price and bid-ask spread of the acquiring- and target company. Lipson and Mortal (2007) find that spreads decline as the number of shareholders, number of analysts, firm size, volume and number of market makers

1_{The countries that the database (Zephyr) uses for Western Europe are on alphabetical order: Austria,}

Belgium, Cyprus, Denmark, Finland, France, Germany, Great Britain, Greece, Ireland, Italy, Liechtenstein, Luxembourg, Malta, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and Turkey.

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increase or as volatility decreases. This research showed that volume has a significant effect on the spread. This coincides with research of Lipson and Mortal. The item that conflicts from this research with theirs, is that volatility does not have a significant effect on the target’s spread. It even has a negative effect, so that means that an increase in volatility caused the spread to decrease.

Other studies revealed that in the short-term the bidding firm does not improve after the acquisition; this was also the case in the long-term. According to Chatterjee (1992), Datta et al. (1992), King et al. (2004), Moeller et al. (2003) and Seth et al. (2002) the value of the firm of the acquirers decreases after the acquisition. As the results of this research show, this coincides. For both the rumour and the announcement date the acquirer had a decrease in market value. The target however had a significant positive change in the market value after both dates. This could be explained by the premium that generally is paid by acquirers to acquire targets. Asquith and Kim (1982), Datta et al. (1992) and Hansen and Lott (1996) confirm this and conclude that target shareholders often experience considerable positive returns. Study of Cybo-Ottone and Murgia finds that the stock market value has a significant positive increase for the average merger at the time of the announcement.

Seth and Dastidar (2009) find in their research; evidence of synergy after the M&A. The acquirer can create higher value by combining the assets of the two firms. Exhibit 1 shows that the acquirers have a negative return after the rumour and announcement date. This is in conflict with research of Huang and Tung (2012) that finds positive returns, and the research of Seth and Dastidar (2009).

Negative abnormal returns on the announcement day should be interpreted as evidence that traders expect the takeover will be unprofitable. This is because the

expectations of future earnings are reflected in the share price at the announcement date and the price change of the acquiring and target company is the expected net present value (Baker & Anderson, 2010).

From this section it can be concluded that the existence of the three types of investors tend to play a big role in the bid-ask spread. Also, the bid-ask spread has three components and they all have their own effect on the bid-ask spread. Furthermore, previous studies have mixed outcomes on the bid-ask spread and on other variables that were used in this research.

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3. Data

This section briefly presents a data description and the model that is used. All the data about the M&A deals was collected from the database of Zephyr. In this research M&A deals from Western Europe are used. Some research was already done in the United States of America, but little in Europe. Europe in its entirety is not used to make sure that the results could not be biased because of cultural, economic or any other differences. Both the acquirer and target company have been selected from the same geographical area, Western Europe, for this same reason. The sample period was from January 1, 2000 through January 1, 2015. This was done to make sure that the sample was big enough and to be able to see if the European fiscal crisis had an influence on the results. To be sure that the data was available and correct only the deal status announced and completed confirmed were used. This gave a total of 281 M&A deals. To require all variables, the add-on of Excel, DataStream, was used. In order to retrieve all data, only listed acquirers and target companies were used. This was because DataStream only works with listed companies. To conduct this research, the following variables were used:

Symbol Term Definition (Reuters)

Pa Price- ask This is the asking price quoted at close of market. Pb Price- bid This is the bid price offered at close of market. P Price (Adjusted – Default) Represents the official closing price.

RI Total Return Index This shows a theoretical growth in value of a shareholding over a specified period, assuming that dividends are re-invested to purchase additional units of an equity or unit trust at the closing price applicable on the ex-dividend rate.

VO Turnover by Volume This shows the number of shares traded for a stock on a particular day (in thousands).

DY Dividend Yield This expresses the dividend per share as a percentage of the share price.

VA Turnover by Value This shows the value of all trades for a stock on a particular day (in thousands). Also calculated by multiplying Price by Volume. MV Market Value Is the share price multiplied by the number of ordinary shares in

issue.

From the database of Zephyr, the rumour and announcement date of all the M&A deals of all the acquirers and target companies were retrieved. 20 days before and 20 days after those

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dates were used, to make sure that a change in the ask spread could be visible. The bid-ask spread was expected to slightly increase after the rumour date, because adverse information would increase. Not everyone knows about these rumours. It was expected that after the announcement date the bid-ask spread would be smaller, because an announcement is public information, so the adverse information would decrease. In order for this research to be more unbiased the same M&A deals for the rumour and announcement dates were used. After collecting all the data, this research ended up with a sample size of 127 M&A deals. Not all data could be retrieved from the other 154 M&A deals. That is, companies could not be found, there was no data for all variables, there was no data for all dates before and/or after the rumour and announcement date or the bid price was available on more dates than the ask price or vice versa. Note, this research was based on Western European countries, so the results are only descriptive for this geographical area.

4. Event study

4.1. Methodology

The first part consists of an event study. This event study is conducted to test if the chosen variables change significantly after a rumour and/or announcement date. It is then obvious if M&As have an effect if these changes are significant. The effect could be different on each variable. This research partly uses the research method of Conrad and Niden (1992). In their research they make use of price, return and volume. In this paper some extra variables were added to enhance their model. This was done by adding dividend yield, turnover by value and market value. These variables could all have a significant effect on the spread. Market value could give a better image of the size effect of the companies. To compensate for the size of the stock price the relative quoted bid-ask spread is used. According to Stoll (1989), “The quoted bid-ask spread is the difference between the ask price quoted by a dealer and the bid price quoted by a dealer at a point in time” (p. 115). To calculate the quoted relative bid-ask spread, the bid-ask spread for every day is calculated:

𝑃𝑎 − 𝑃𝑏

Then an average of all the bid-ask spreads before the rumour and announcement date, and an average of the days after is taken:

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𝑆𝑝𝑟𝑒𝑎𝑑_*

+

*,-𝑁

Where 𝑆𝑝𝑟𝑒𝑎𝑑_* is the bid-ask spread on day i (where i=1,2,3..,n). The next step is to calculate the difference between the before and after spread:

𝑆𝑝𝑟𝑒𝑎𝑑_/− 𝑆𝑝𝑟𝑒𝑎𝑑₀

Where 𝑆𝑝𝑟𝑒𝑎𝑑₀ is the average bid-ask spread before the rumour or announcement date and 𝑆𝑝𝑟𝑒𝑎𝑑/ is the bid-ask spread after the rumour or announcement date. To make these results

relative, the outcome is divided by 𝑆𝑝𝑟𝑒𝑎𝑑₀ to get the percentage change in the bid-ask spread from before and after the rumour and announcement date. Thus, the relative quoted bid-ask spread:

Δ𝑆𝑝𝑟𝑒𝑎𝑑

𝑆𝑝𝑟𝑒𝑎𝑑₀ = 𝛥𝑆𝑝𝑟𝑒𝑎𝑑567

Where Δ𝑆𝑝𝑟𝑒𝑎𝑑 is the absolute change in the bid-ask spread between before and after the rumour and announcement date. 𝛥𝑆𝑝𝑟𝑒𝑎𝑑₅₆₇ is the change in the relative quoted bid-ask spread2. The same calculations have been done on price, return index, volume, dividend yield, turnover by value and market value. This change is calculated to see if the rumour and/or announcement date have an effect on these variables.

The hypotheses used in this study are based on research of Conrad and Niden (2012), Huang and Tung (2012) and previous literature.

Hypothesis 1: The relative quoted spread of the acquiring company will change after the announcement date.

Hypothesis 2: The relative quoted spread of the target company will change after the announcement date.

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Expected is that the spread of the acquirer will increase after the announcement date and the target’s spread will decrease. Based on these two hypotheses and previous literature, two other hypotheses have been made that will be examined in this research.

Hypothesis 3: The relative quoted spread of the acquiring company will change after the rumour date.

Hypotheses 4: The relative quoted spread of the target company will change after the rumour date.

Expected is that both spreads will increase after the rumour date. Mainly because of increased adverse information.

4.2. Event Study Results

After retrieving all results and doing all calculations the following t-values are calculated:

T-values Spread Price Return

Index Volume Dividend Yield Turnover by Value Market value Rumour date: Announcement date: Acquirer Target Acquirer Target 0.53 -0.46 0.61 0.67 -0.78 3.20*** -1.77* 3.26*** -0.52 3.41*** -1.55 3.49*** 3.24*** 3.61*** 3.16*** 2.62*** 1.76* 0.22 1.64 -0.01 2.52** 2.88*** 2.87*** 2.00** -0.45 3.06*** -1.16 3.00***

Coefficients followed by one, two, and three stars are significantly different from zero at the 10%, 5%, and 1% level, respectively. All terms are relative changes. (Exhibit 1)

The change in the spread of the acquirers and target companies is not significant, there is no direct effect of the M&A rumour and announcement date. Therefore, all four hypotheses are rejected. Looking at the results, there is only weak evidence. The effect of the spread of the acquirers is positive, implying that the spread becomes bigger after the rumour and announcement date. This can be confirmed with previous literature. In research of Huang and Tung (2012) the relative quoted spread is significantly positive. They explain that the

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increase in the relative quoted spread implies that market makers pay more attention to the acquirers’ stocks because there are more informed traders. Looking at the results of the target companies the spread becomes smaller after the rumour date and it becomes bigger again after the announcement date. This is in conflict with previous literature. That showed that the spread of target companies decreases after the announcement date (Augustin, Brenner & Subrahmanyam, 2014). An explanation for this is that after the announcement adverse information is less. There has not been any research about the rumour date yet. What can be expected after rumours is that adverse information increases. Only a few people know about these rumours and thus the spread increases. This is probably indeed the case at the acquirer, but the result of the target shows that it decreases.

The table shows that the results of price and return index are close to each other. This may indicate that they are correlated with each other. As can be seen in the explanation of the used variables, the return index is based on the price and dividend yield, so this explains this high correlation. For both, the rumour and announcement date have a significant positive effect on the target. A reason for this is that the premium, that an acquiring company pays for the target company, increases the value of the target company. The price of the share reflects the value of the company, so it will also increase.

The volume increases after the rumour and announcement date for both the acquirer and the target, which can be seen in Exhibit 1. Sanders and Zdanowicz (1992) also test on volume and they also find an increase in volume after the announcement date. Again, no research has been done on the rumour date, but it can be approached in the same way as the announcement date. After the rumour date some more people know about the M&A and will therefore trade more which will increase the volume. According to Conrad and Niden (1992) the increase in volume is due to a larger number of average-sized orders.

There is a significant negative price effect on the acquiring company after the announcement date. According to Bradley et al. (1988), Houston et al. (2001) and Leeth and Borg (2000) acquisitions produce neutral or negative returns for the acquiring firm. This also has to do with the premium that has been paid by the acquiring company. Hereby the acquiring company loses a part of its value and thus decreases the share price. Rumours also have a negative effect, but not significantly. The weak evidence that can be found here is that the better informed traders will evaluate the acquiring company as less valuable after the M&A deal.

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The dividend yield barely explains anything. It is just significant at the 10% level at the rumour date of the acquiring company. This could be because the dividend yield does not change on intra-day level and not all companies that have been researched issued dividend.

As earlier described the turnover by value is the value of all trades for a stock on a particular day. For both dates the results are positively significant for the target. For the acquirer the positive effect of the volume probably offsets the negative effect of the price. According to Stoll (1989) greater adverse information could be caused by large values of turnover and thus a larger adverse cost information component. This is in conflict with previous literature that states that after the announcement the adverse information decreases. As can be seen from the results of the market value, the size of the target company has a significant effect. The size of the acquiring company has no effect.

5. Regression

5.1. Methodology

The second part consists of an OLS regression on the change in the spread around the rumour and announcement date to examine the effect of the chosen control variables on the change in the spread. Here the research of Conrad and Niden (1992) is partly followed again. For market value, an average of the entire time series of 20 days before and 20 days after the rumour and announcement date for each company is taken. This is done to test if smaller companies have more adverse information than bigger companies. Market value is thus an absolute value and not a relative change. Note, this is different than the MV in the event study.

Two dummy variables are used. A lot of companies had the same announcement date as the rumour date. This means that there have not been any rumours before the announcement date. If there are rumours before an announcement it is expected that the adverse information increases. That is because not every trader is aware of these rumours. For this, the first dummy is used. If a company does not have rumours, it will get a value of 1 and 0 if there were rumours. The second dummy will test for an effect of the European fiscal crisis. This crisis is used, because the researched geographical area lies in Europe. This particular crisis had the biggest impact on the companies in this area. The European fiscal crisis lasted roughly five years from the beginning of 2009 until the end of 2014. The dummy will get a value of 1 if the M&A rumour or announcement date was during these years and a

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0 if these dates were before 2009 or after 2014. In contrary to the event study, the variables return index and dividend yield were not used. Return index has a high correlation with price. Dividend yield has almost no effect. Some test regressions have been done to see if volume and turnover by value correlate and cause the regression to be biased. A reason to use both these variables, is that turnover by value is calculated by multiplying price times volume. In the regressions where volume and turnover by value were used, the results are more explanatory than with just one. The R9_{and the F-value were bigger and the t-values for both}

increased. The standard deviation only increased slightly. This gives the following equation used for regression:

ΔSPREAD= β0 + β1*ΔPRICE + β2*ΔVO + β3*ΔVA + β4*MV + β5*DATE + β6*CRISIS + Ɛ

Where: SPREAD, PRICE, VO and VA are as defined earlier. Again, these are all relative changes. MV is the average value of each company. DATE is the dummy used for if the M&A announcement has rumours before the announcement or not. CRISIS is the dummy that corresponds with M&A deals within the years 2009-2014. Ɛ is the error term.

An extra factor has been added to the regression of the target’s announcement date. This has been done because after doing regressions with the ‘old’ model, not a lot of results showed significance. Another factor that could influence the spread is volatility. It has been calculated by getting the standard deviation of the return index of every company. After the calculations a new regression has been done with the new model:

ΔSPREAD= β0 + β1*ΔPRICE + β2*ΔVO + β3*ΔVA + β4*MV + β5*ΔVOL + β6*DATE + β7*CRISIS + Ɛ

All variables are as defined earlier and VOL is the volatility. This addition of volatility (exhibit 5) has only been done on the target companies at the announcement date, because it did not give significant results. Expected is that it also does not in the other situations. Adding volatility barely influences the other control variables or the spread, it only gives weak evidence. The negative effect is puzzling, other studies revealed that an increase in volatility caused an increase in the spread. A wider spread is the result of an increase in

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volatility. This is because dealers want compensation for them being less informed than informed traders (Foucault, Pagano & Röell, 2013).

5.2. Regression Results

The regression results for the acquiring companies are shown in Exhibit 2 and 3. Besides price, no other factor has a significant effect, so there is only weak evidence for these other factors. In both Exhibits the price had a positively significant effect which coincides with previous literature. According to Conrad and Niden (1992), “The positive relation between changes in price and changes in spread is consistent with the positive relation between spread and price level predicted by the inventory carrying cost theory and documented by prior studies” (p. 29).

The volume has in both Exhibits a negative effect on the change in the spread. To be noted, close to significance. This means that when the volume increases, the spread decreases. According to previous research by Madhavan (2000) a short holding period for market makers is the result of high volume and thus, it gives low inventory control costs. Previous research confirmed that effective spreads are lower in higher volume securities because dealers achieve faster turnaround inventory, which reduces their risk.

In both Exhibits the effect of turnover by value tends to be negative on the change in the spread. This can be explained by the effect of volume. Which apparently had a bigger effect than price had on turnover by value.

Market value has a negative effect in both Exhibits. This means that bigger companies have a smaller change in the spread than smaller companies have. An explanation for this could be that more information is publicly available about bigger companies, thus the bigger the company gets, the smaller the adverse information component becomes.

The date dummy for the rumour date had a slight positive effect on the change in the spread. This is puzzling, because if there is no rumour date, as is the case if the rumour and announcement date have the same date, then the change in the spread should become lower. That is because after the announcement there is more information available and thus this decreases the adverse information component. The date dummy for the announcement date had a positive effect on the change in the spread, which makes sense. Normally when there is a rumour date before the announcement date, the spread increases. Because there has not been a rumour date, the spread did not increase extra before the announcement, so the change in the spread is less. In other words, the spread would be relatively less decreased.

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In both Exhibits the crisis dummy has a positive effect on the change in the spread. An explanation for this could be that in the beginning of the crisis a lot of companies and foundations were deregulated and this could have affected the spread by making it bigger.

Acquirer (Rumour date) (1) (2) (3) (4)

Characteristics: Price Volume Turnover by Value Market Value Date dummy Crisis dummy R9 N 0.064 (1.87)* 0.146 (-1.46) 0.476 (-0.71) 0.0126 127 0.076 (1.79)* 0.242 (-1.18) 0.994 (0.01) 0.856 (0.18) 0.188 (1.32) 0.0254 127 0.065 (1.86)* 0.199 (-1.29) 0.720 (-0.36) 0.483 (-0.70) 0.0127 127 0.077 (1.78)* 0.306 (-1.03) 0.858 (-0.18) 0.991 (0.01) 0.865 (0.17) 0.191 (1.32) 0.0255 127

The coefficients between the parenthesis are the corresponding t-values. Coefficients followed by one, two, and three stars are significantly different from zero at the 10%, 5%, and 1% level, respectively. Market value is an absolute value and the other terms are relative changes. The errors are standard robust. (Exhibit 2)

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Acquirer (Announcement date) (1) (2) (3) (4) Characteristics: Price Volume Turnover by Value Market Value Date dummy Crisis dummy R9 N 0.093 (1.69)* 0.154 (-1.44) 0.499 (-0.68) 0.0107 127 0.094 (1.69)* 0.294 (-1.05) 0.883 (-0.15) 0.561 (0.58) 0.327 (0.98) 0.0191 127 0.093 (1.69)* 0.167 (-1.39) 0.880 (-0.15) 0.504 (-0.67) 0.0107 127 0.093 (1.69)* 0.313 (-1.01) 0.992 (0.01) 0.884 (-0.15) 0.563 (0.58) 0.329 (0.98) 0.0191 127

The regression results of the target companies are shown in Exhibit 4 and 5. There is no significant evidence around the rumour date that any of the used control variables had an effect on the change in the spread, there was just weak evidence (Exhibit 4). In Exhibit 4 and 5 the price has a small positive effect on the change in the spread, which can be explained in the same way as has been done earlier in this paper for the acquiring companies.

In Exhibit 5 the volume is significant so that gives good evidence that it had an effect on the change in the spread. Around both the rumour and the announcement date the volume had a negative effect on the change in the spread. Again, this can be explained in the same way as has been done earlier for the acquiring companies.

Also, the turnover by value is significant in Exhibit 5, this gives strong evidence that this factor had an effect on the change in the spread. In both Exhibits the effect of turnover by value was positive. This coincides with previous literature. According to Stoll (1989), “Turnover has some theoretical justification in that it ought to reflect the degree of informational trading and should therefore reflect adverse information costs. Thus, large

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values of turnover might imply greater adverse information and thus a larger adverse information component” (p. 130).

The market value has in both Exhibits a positive effect which can be interpreted that, the bigger the company, the bigger the change in the spread. This is a bit puzzling, because it can be expected that the bigger a company gets, the more liquid it will become and thus have a smaller spread change.

The effect on the change in the spread of the date dummy was negative for the rumour date and positive for the announcement date. This makes sense and can be explained in the same way as has been done earlier in this paper for the acquiring companies.

At both the rumour and announcement date, the dummy for the crisis had a slight negative effect on the change in the spread. An explanation for this could be that during the fiscal crisis all the companies were more under the microscope and more attention was paid to insider trading, so there already was less adverse information. That is why the relative spread change was less.

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Target (Rumour date) (1) (2) (3) (4) Characteristics: Price Volume Turnover by Value Market Value Date dummy Crisis dummy R9 N 0.521 (0.64) 0.607 (-0.52) 0.893 (0.14) 0.0025 127 0.478 (0.71) 0.706 (-0.38) 0.811 (0.24) 0.372 (-0.90) 0.581 (-0.55) 0.0065 127 0.608 (0.51) 0.542 (-0.61) 0.631 (0.48) 0.909 (0.11) 0.0029 127 0.547 (0.60) 0.711 (-0.37) 0.774 (0.29) 0.824 (0.22) 0.397 (-0.85) 0.586 (-0.55) 0.0066 127

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Target (Announcement date) (1) (2) (3) (4) Characteristics: Price Volume Turnover by Value Market Value Volatility Date dummy Crisis dummy R9 N 0.481 (0.71) 0.253 (-1.15) 0.394 (0.85) 0.545 (-0.61) 0.0078 127 0.318 (1.00) 0.405 (-0.84) 0.142 (1.48) 0.690 (-0.40) 0.270 (1.11) 0.800 (-0.86) 0.239 127 0.536 (0.62) 0.004 (-2.92)*** 0.027 (2.24)** 0.362 (0.91) 0.608 (-0.51) 0.0109 127 0.379 (0.88) 0.034 (-2.15)** 0.077 (1.78)* 0.127 (1.54) 0.787 (-0.27) 0.257 (1.14) 0.839 (-0.20) 0.0281 127

6. Split-up

It is possible that the size of a company has an effect on the spread. That is why in this next step the market value has been divided into three categories to account for the size of the companies. The first category is ‘small’, with a market value of 0 to 999. The second category is ‘medium’, that goes from 1000 to 9999. The last category is ‘large’, which is 10000 and bigger. Another event study has been done with these new categories to see the change in the t-value of the spread:

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T-value Market Value Old situation Small Medium Large Rumour date: Announcement date: Acquirer Target Acquirer Target 0.53 -0.46 0.61 0.67 1.28 0.49 0.65 -0.70 -0.47 -0.14 0.38 -0.05 -0.96 -0.20 -1.04 0.67

Coefficients followed by one, two, and three stars are significantly different from zero at the 10%, 5%, and 1% level, respectively. All terms are relative changes. (Exhibit 6)

Where each category has the following size:

N Small Medium Large

Rumour date: Announcement date: Acquirer Target Acquirer Target 64 76 64 78 31 44 32 42 32 7 31 7

This extra event study is done so that it could be seen more clearly how the companies with different sizes react to the rumour and/or announcement date. What strikes is that still no result is significant. So again, there is only weak evidence. For all dates and types of company the category ‘small’ gives better estimations, they are closer to significance. There are not a lot of ‘large’ target companies, which could be explained. To acquire a large company more capital is required, so it is not as easy to do as to acquire a small company. As can be seen at the rumour date, the effect of the ‘small’ category has a different direction than the ‘medium’ and ‘large’ categories. This is very interesting, because this shows that the spread of smaller companies, changes in a totally different way than the bigger companies. Another interesting result is that the acquiring companies at the announcement date, that are in the category ‘large’, change in a totally different way than the smaller companies. The difference of the effect of M&As on companies with these different sizes is interesting for

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future studies. This could be used to get a better understanding about how the adverse information of each company size reacts.

7. Conclusion

This research uses a sample of 127 M&A deals from Western Europe to test if M&As have an effect on the spread of acquiring and/or target companies. Previous research was mainly conducted in the U.S. and the subject being only the effect of the announcement date. In this research the effect of the rumour date has also been examined. Firstly, an event study is conducted to see if the spread, price, return index, volume, dividend yield, turnover by value or market value change significantly after the rumour and/or announcement date. Secondly, a regression has been done of the change of the spread’s relation with price, volume, turnover by value, market value and later also with volatility. Two dummy variables have been added to allow for an effect because of the European fiscal crisis and if there have been rumours before the announcement date.

Using daily data, the event study reveals little evidence for a change in the spread after the rumour and announcement date. This is in conflict with most other studies. They reveal that the spread of the target companies decreases after the announcement and the results of the effect on the acquirer’ spread are diverse. This event study shows by some of its significant results that it has, that the volume and turnover by value increase after the rumour and announcement date. The price decreases after the announcement date which coincides with previous studies. It also shows that price, return index, volume, turnover by value and market value of the target companies increases after the rumour and announcement date. The other factors do not change significantly.

Previous literature reported mixed results about the change of the spread after the announcement date. Again, these results report something else. An explanation for this could be high-frequency trading. It could also be an explanation for the reason that the spread is not significant in this research. The event study also shows that after the rumour date some factors change significantly, a reason for this could be because it did not hold into account that some rumour dates were the same as the announcement date. So this could have given a bias to the results. The results would get more significant. That is why a date dummy has been added to the regression. Section 6.1. could give evidence that there is a bigger change in the spread of smaller companies.

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To get a better idea of the effect of M&As on the spread, research using a bigger sample could be conducted. An example could be Europe in its entirety. Then other factors should also be taken into account, like culture, economical state, etc. Then there should also be tried to create a factor that reflects high-frequency trading. Another method for research could be to use dummies for pre, during and after the rumour and announcement date, like Conrad and Niden (1992) did. This way the effects of the factors on the spread could be better examined. Then, for example, a run-up effect could be visible before the date, because of insider information. The rumour date should also get more attention to get a better view of the degree of insider trading. To have more detailed results from what the crisis had for effect, some time fixed effects could be used. This can be done by giving each year a separate dummy.

Furthermore, this research did not find significant results between small, medium and large companies, but there is a big chance that there is. That is why it would be good for future studies to examine this. This way it could be visible that maybe there is more insider trading in large companies and this could result in the creation of new legislations to counter this.

Finally, another thing that could be done in future studies is to use a dummy for pre, during and after the crisis. When there is just a dummy for during the crisis or not, it does not show the difference of before and after. The crisis resulted in new rules and legislations that made insider trading more difficult and thus less. So after the crisis this should be different than before.

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