is the event risk in merger arbitrage priced?...offer success and the pricing of the risk of...

Stockholm School of Economics Master’s Thesis in Finance

Is The Event Risk In Merger Arbitrage Priced?

Authors: Johan Koch Markus Sjöström Tutor: Patrik Säfvenblad Presentation: 10:15, Room Ruben, June 13 2003

Abstract The thesis examines whether the Swedish market prices the event risk in merger arbitrage. This is done by first developing a model predicting the probability that a tender offer will be successful and secondly use this model to relate the obtained probabilities to the risk arbitrage premium and the actual returns obtained from risk arbitrage strategies. The results from the model predicting the probability of tender offer success shows that the main factors influencing the success of a tender offer are the reaction of the target’s board, method of payment and the size of the largest owner in the target company. When applying this model in order to evaluate if the Swedish market prices the event risk in merger arbitrage it is found that that the Swedish market indeed prices this risk. Acknowledgments We thank Patrik Säfvenblad and Per-Olov Edlund at the Stockholm School of Economics for their valuable comments and Nils Forsberg and Oskar Nilsson for providing us with their database on Swedish mergers.

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1. INTRODUCTION............................................................................................................................................. 4

1.1 PURPOSE ....................................................................................................................................................... 4 1.2 CONTRIBUTION.............................................................................................................................................. 4 1.3 OUTLINE........................................................................................................................................................ 4

2. PREVIOUS RESEARCH................................................................................................................................. 5 3. INSTITUTIONAL SETTING.......................................................................................................................... 6 4. DEVELOPMENT OF PREDICTION MODEL ............................................................................................ 7

4.1 DEPENDENT VARIABLE ................................................................................................................................. 7 4.2 EXPLANATORY VARIABLES........................................................................................................................... 7

4.2.1 Premium ............................................................................................................................................... 7 4.2.2 Management Ownership....................................................................................................................... 8 4.2.3 Timing Effect ........................................................................................................................................ 8 4.2.4 Number of Block-holders...................................................................................................................... 9 4.2.5 Method of Payment............................................................................................................................... 9 4.2.6 Board Reaction................................................................................................................................... 10 4.2.7 Largest Owner .................................................................................................................................... 10 4.2.8 Toehold ............................................................................................................................................... 11

5 MODEL SPECIFICATION............................................................................................................................ 12 5.1 THE LOGISTIC REGRESSION MODEL............................................................................................................ 12 5.2 ASSESSING THE COEFFICIENTS OF THE MODEL ........................................................................................... 12 5.3 GOODNESS OF FIT OF THE MODEL............................................................................................................... 12

5.3.1 The Classification Table ..................................................................................................................... 12 5.3.2 Nagelkerke R2 ..................................................................................................................................... 13 5.3.3 Hosmer and Lemeshow Test ............................................................................................................... 13

6 DATA DISCUSSION....................................................................................................................................... 14 6.1 THE DATA SAMPLE ..................................................................................................................................... 14 6.2 VARIABLES ................................................................................................................................................. 14

6.2.1 Premium ............................................................................................................................................. 14 6.2.2 Management Ownership..................................................................................................................... 15 6.2.3 Timing Effects ..................................................................................................................................... 16 6.2.4 Number of Block-holders.................................................................................................................... 17 6.2.5 Method of Payment............................................................................................................................. 18 6.2.6 Board Reaction................................................................................................................................... 19 6.2.7 Largest Owner .................................................................................................................................... 21 6.2.8 Toehold ............................................................................................................................................... 21

7 REGRESSIONS ............................................................................................................................................... 23 7.1 INITIAL MODEL ........................................................................................................................................... 23 7.2 FINAL MODEL ............................................................................................................................................. 24 7.3 GOODNESS OF FIT OF THE FINAL MODEL .................................................................................................... 24

8 THE MARKET’S PRICING OF THE RISK OF ABANDONMENT ........................................................ 26 8.1 RISK OF ABANDONMENT AND THE RISK ARBITRAGE PREMIUM .................................................................. 26

8.1.1 Definition of the Risk Arbitrage Premium .......................................................................................... 26 8.1.2 Risk of Abandonment relative the Risk Arbitrage Premium ............................................................... 26

8.2 RISK OF ABANDONMENT AND ACTUAL RETURNS ....................................................................................... 27 8.2.1 Portfolio Test with Actual Returns ..................................................................................................... 28 8.2.2. Limitations of the results ................................................................................................................... 29

9 ANALYSIS ....................................................................................................................................................... 30 10 CONCLUSIONS ............................................................................................................................................ 31 11 FUTURE RESEARCH .................................................................................................................................. 31 12 REFERENCES............................................................................................................................................... 32

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APPENDIX I – STATISTICAL TESTS FOR CONTINUOUS AND DICHOTOMOUS VARIABLES .... 34 APPENDIX II - HOSMER LEMESHOW TEST............................................................................................. 36 APPENDIX III - SAMPLE ................................................................................................................................ 37

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1. Introduction At the time of a tender offer announcement, there is typically a spread, the risk arbitrage premium, between the current market price of the target company’s shares and the price to be paid for the shares in the offer. The reasons for the existence of this spread can be attributed to factors such as; supply and demand of capital, regulatory issues, time value of money and the probability of abandonment. Merger arbitrage is a strategy aiming at exploiting eventual price inefficiencies regarding this premium. Of the factors influencing the size of the risk arbitrage premium, the risk of abandonment, the so-called event risk, is considered to be the most important factor. In addition, the event risk can also be held to contain a substantial part of the potential price inefficiencies. It is therefore, as a first step, interesting to evaluate the relationship between this variable and the risk arbitrage premium in order to see if the market prices the risk of abandonment by demanding a higher risk arbitrage premium for offers with low predicted probability of success. If this is the case, it is interesting to proceed and evaluate if this higher premium compensates, or even overcompensates, for the event risk by looking at actual returns from portfolios investing in risk arbitrage strategies.

1.1 Purpose The purpose of the thesis is to develop a model predicting the probability that a tender offer is successful and use this model to explore whether the Swedish market prices the event risk in merger arbitrage.

1.2 Contribution Numerous studies regarding merger arbitrage in general have been conducted primarily in the U.S. However, to our knowledge, no studies have been made regarding the pricing of the event risk in merger arbitrage. This thesis provides an initial insight whether this risk is priced by the Swedish market.

1.3 Outline The remainder of this thesis is organized as follows: The previous research made in relevant areas will be presented in section II followed by some remarks regarding the Swedish setting in section III. Relevant variables for predicting the probability of tender offer success are described, along with the findings of previous research done on these variables, in section IV. The theory behind the logistic regression is discussed in section V. In section VI the data used in the thesis is described while the results from the logistic regression are presented in section VII. In section VIII, the model predicting the probability of a successful tender offer is applied to test whether the market prices the event risk in merger arbitrage. The results are analyzed in section IX and concluded in section X. Suggestions of future research is made in the final section.

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2. Previous Research There are to our knowledge no previous research combining a model which predicts tender offer success and the pricing of the risk of abandonment. However, there is research which covers risk arbitrage and other research covering factors influencing the probability of success of tender offers separately. The results from some of this previous research are presented in order to give a background to our topic. There are a large number of research papers dealing with risk arbitrage possibilities in mergers. Pulvino (2001) shows, based on a sample of 4,750 U.S. mergers from 1963 to 1998, that risk arbitrage generate excess returns of four percent per year. These findings are consistent with Baker and Savasoglu (2002) who find an excess return of 0.6-0.9% per month based on a sample of 1,901 mergers over the period of 1981-1996. Earlier research finds considerably higher returns from merger arbitrage, for example Dukes, Frohlich and Ma (1992) who find excess return of 0.47% per day, i.e. resulting in a yearly return that greatly exceeds more recent findings. To our knowledge there are no research regarding merger arbitrage possibilities in Sweden. Prediction of tender offer success has not been researched as extensively as merger arbitrage. In a qualitative study, Pickering (1983) identify four main causes of abandoned mergers; change of mind of either the bidder or target, lack of market support, operation of competitions authority and other acquisitions (for example competing bids). However, Pickering does not establish a model of predicting tender offer success, which has been developed in some of the quantitative studies found in the literature. In one of these quantitative studies, Hoffmeister and Dyl (1981) applies a discriminate analysis model based on 84 cash tender offers made between 1976 and 1977. The authors find that management reaction and firm size are the most important factors increasing the probability of success. Many factors thought to be important predictors for tender offer success, such as size of the bid premium, are actually not very influential according to this study. While Hoffmeister and Dyl apply a linear model, Walkling (1985) applies a logistic analysis. Based on a sample of 158 cash tender offers from 1972 to 1977, Walkling finds that variables that increase the supply of obtainable shares, such as increased bid premium, increase the probability of success. So does having a toehold. On the other hand, factors such as target management opposition decrease the probability of success. In line with Hoffmeister and Dyl, Flanagan (1998), who also applies a logistic analysis, finds, based on a sample of 991 tender offers between 1985 and 1994, that the size of the bid premium is not a significant factor for a successful tender offer. Flanagan also finds that toehold and relatedness of the two firms are important factors when predicting successful tender offers while target managers negative reaction to the offer and the existence of competing bidders has a negative impact. In the Swedish setting Forsberg and Nilsson (2000) find that the board reaction and the type of payment are important factors for predicting tender offer success. In conclusion, previous research finds that there are risk arbitrage possibilities in the U.S. setting. However, the size of these returns varies substantially. When it comes to predicting tender offer abandonment there are some differences regarding which variables that are most relevant for such an analysis. The theory regarding each of these variables will be further discussed in section 4. Also, to our knowledge, there are no previous research combining a model which predicts tender offer success and the pricing of the risk of abandonment

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3. Institutional Setting The takeover activity in Sweden is considerably lower than in the U.S. According to Franks and Mayer (2001) the number of hostile takeovers is also much lower in the Swedish market. One of the reasons for this might be the fact that Swedish companies are characterized by a high concentration of ownership, making it difficult for hostile takeovers to take place. The feature of concentrated ownership is a characteristic which Sweden, according to Högfeldt and Högholm (2000), shares with other civil law countries. In these countries the ownership of listed firms is much more concentrated than in the common law countries such as the U.S. and the UK. This is to be expected since the setting in Sweden and other civil law countries facilitates the creation of large ownership holdings, Holmén and Högfeldt (2000). Two-tier offers, i.e. a tender offer for a fraction of the target's shares (often enough to give the bidder a majority) to be followed by a back-end merger using different consideration, a lower value, or both, is prohibited in Sweden but allowed in for example the U.S. The Swedish Equal Treatment Principle limits the possibilities to offer different terms to owners of the same type of stock. According to Högfeldt and Högholm (2000) this affects both the level of the tender offer price and the distribution of the takeover gain. Hence, it is likely that the bid premium in Sweden is of greater importance than in a country where two-tier offers are allowed.

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4. Development of Prediction Model In order to see if the Swedish market prices the event risk in merger arbitrage we first develop a model that predicts the probability that a tender offer will be successful. In this section the variables that, based on economic theory and previous research, are likely to affect the outcome of tender offers.

4.1 Dependent Variable The dependent variable in the prediction model is the dichotomous variable success. A successful tender offer is defined as an offer where the bidder obtains ninety percent or more of the voting rights in the target company. This definition is chosen since, according to Swedish corporate law, a holder of at least ninety percent of the votes has the right to compulsory acquire the remaining shares at the same price at which the other shares were acquired. An abandoned offer is defined as an offer where the ninety percent level is not achieved.

4.2 Explanatory Variables 4.2.1 Premium Theory As suggested by Walkling (1985) the bidders face a supply curve in their search for shares of the target when making an offer. However, this curve is not known by the bidder and must, therefore, be estimated. Based on this estimation the bidders then price their offers. When the required premium is underestimated the offer will fail. Thus, a higher bid premium would result in an increased amount of shares being tendered which in turn increases the probability of success. In line with the theory, Walkling finds that an increased bid premium increases the probability of success. Although the economic theory proposes that the size of the bid premium should relate to the tender offer success, a number of studies have found the opposite, i.e. that the size of the premium does not appear to affect tender offer success. Hoffmeister and Dyl (1981) and Flanagan, D’Mello and O’Shaughnessy (1998) found, contrary to the findings of Walkling(1985), that the size of the bid premium is not related to the outcome of a tender offer. Definition The bid premium in this study is specified as the percentage premium based on the market price five trading days before the first announcement of the takeover. The five trading days are used to prevent the noise from run-ups due to insider trading. Expected impact Although previous studies have presented opposing results regarding the effect of bid premium on tender offer success, we expect, intuitively and due to economic theory on supply and demand, that an increased bid premium increases the probability of tender offer success.

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4.2.2 Management Ownership Theory The research regarding how managerial ownership is related to the probability that a tender offer will be successful is somewhat divided. For a bidding firm, according to North (2001), there might be a downside to management ownership in that controlling more votes may allow managers to block unwanted acquisition attempts. In addition, managers who believe that a tender offer is in the best interest of their shareholders may, nevertheless, find themselves unemployed if the bid is successful. Thus, the best interest of agents may differ from the best interest of shareholders. These factors would indicate that a high level of management ownership would lower the likelihood of a tender offer being successful. This is in line with the findings of Stulz (1988). On the other hand, Walkling and Long (1984) find that managers with relatively high ownership are less likely to resist tender offer. Also, Mikkelson and Partch (1989) suggest that the probability of successful takeovers is unrelated to managers’ holdings. Definition Here, the management ownership is defined as the percentage of the votes controlled by management in the target firm at the end of the year preceding the offer. Since this point in time deviates from the point in times for the offer this variable might be a source of uncertainty. This will be further discussed in section 6. Expected impact Previous research does not agree on what effect managerial ownership has on the probability that a tender offer will be successful and, therefore, we are uncertain which sign to expect for this variable. 4.2.3 Timing Effect Theory It is reasonable to think that the timing of a tender offer, i.e., whether the offer is announced in a bull or a bear market, might influence the outcome of the offer. In the literature there has been no or little research done in this area and it is therefore hard to draw any conclusion about the impact the timing might have on the probability of tender offers being successful. Boström and Gustavsson (2000) states, that the bid premium is higher during bull markets, especially in the case for stock tender offers. This means that the timing effect at least can have an indirect impact on the probability of tender offer success and the variable is, therefore, included among the explanatory variables. Definition The timing effect is specified as the returns of the SAX index 6 months prior to the announcement of the takeover. Expected impact As suggested by Boström and Gustavsson (2000), a bull market results in higher premiums. This might in turn imply that a tender offer made in a bull market has a higher probability of being successful. On the other hand, targets might be cheaper in a bear market, which might increase the probability of a successful tender offer. Due to these unclear relationships we are uncertain which sign to expect for this variable.

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4.2.4 Number of Block-holders Theory Swedish corporate law makes it possible for a shareholder that owns a corner position, i.e. 10 percent of the votes or more, to block a takeover. This means that it is of great importance for the bidder that block-holders in the target firm find the tender offer favourable and, therefore, the influence of the block-holder on the tender offer success can be believed to be substantial. There are mainly two ways to look at how the number of block-holders can affect the probability that a tender offer will be successful. First, as the number of block-holders increases there are more people that can reject the offer which, therefore, lowers the probability of a successful offer. Second, it might be easier to negotiate with a few large stockowners than with a large number of small stockowners. The second factor is believed to make it easier for the bidder to fulfil a tender offer as the number of block-holders gets higher. Mikkelson and Partch (1990) suggest that companies with a block-holder who sits on the board are more likely to undergo a change in control than firms without such a block-holder. On the other hand, block-holders who do not sit on the board don’t seem to either encourage or discourage takeover attempts. The latter is also confirmed by North (2001). However, these findings don’t tell how block-holders influence the probability of tender offer success but indicate that it is a factor that may be of importance when calculating such a probability. Definition In this thesis we define the number of block-holders as the number of shareholders in the target company that holds a stake larger than or equal to ten percent of the votes. Expected impact We believe that the factor that a higher number of block-holders imply that more people can turn down a tender offer outweighs the factor that the success of probability gets higher because of a higher number of block-holders simplifies the negotiation procedure. Thus, we expect the number of block-holders to have a negative relation to the probability that a takeover will be successful. 4.2.5 Method of Payment Theory According to the asymmetric information hypothesis, introduced by Myers and Majluf (1984), decisions to issue stocks signal adverse information concerning the value of the company’s stock. That is, management is more likely to offer stocks as payment when the company is overvalued than if undervalued. This is of course known by the target company’s stockholders and might therefore lower the probability of a stock offer being successful relative to a cash offer. Further, as presented by Jung, Kim and Stulz (1995) and Martin (1996), the higher the growth opportunities1 of the acquirer the more likely the acquirer is to use stock to finance an acquisition. Conversely the likelihood of stock financing decreases with higher cash availability, higher institutional shareholdings and block holdings, and in tender offers. Other research regarding the method of payment shows that the acquirer’s management structure affects the decision of method of payment. Amihud, Lev and Travlos (1990) find that the larger the managerial ownership in the acquiring firms the more likely the use of cash financing. This is, according to the authors, due to the fact that management is reluctant to

1 Measured as Tobin’s q-ratio or historical sales growth.

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dilute their holdings and risk loosing control as a result of financing the acquisition with stocks. Definition In this study cash offers are defined as either pure cash offers or offers where stocks are offered at an amount equal to a pre-set value. Stock offers are defined as offers where target shareholders are offered stocks, either in the acquiring company or in another company. When the target shareholders are presented with the alternative of either a stock or cash offer the offer is defined as mixed. Expected impact Based on previous research we expect stock offers to decrease the probability of success relative to cash offers and mixed offers. 4.2.6 Board Reaction Theory There are a number of international studies regarding the impact of the target management’s reaction to a tender offer. However, while these international studies discuss the management’s reaction to tender offers, it is the task of the board to evaluate offers in the Swedish setting. Even though there is a difference between management and board the findings regarding the impact of the management’s recommendation can, in our opinion, be held to have relevance also when discussing the recommendation of the board. Flanagan, D’Mello and O’Shaughnessy (1998) and Hoffmeister and Dyl (1981) both find management’s response to an offer to be the most significant factor in determining the outcome of a tender offer. In the studies a positive board reaction was found to have a positive impact. Definition In this study the board reaction is divided in three categories, positive, neutral and negative. For a board reaction to be considered negative or positive only clear statements made by the board as a whole has been considered. In the case where the board has stated that it will neither recommend nor not recommend the shareholders to accept the offer the reaction has been classified as neutral. In the case where no information has been provided by the board the reaction has also been classified as neutral. Expected impact As found in previous research we expect to find that a positive board recommendation increases the likelihood of a successful tender offer while a negative recommendation is expected to decrease the likelihood of success. The impact of a neutral reaction is uncertain, however, it is expected to be positive in relation to a negative recommendation. 4.2.7 Largest Owner Theory Walkling and Long (1984) suggest that a large owner can facilitate the negotiation process and it is therefore likely that a large owner increases the probability that a tender offer will be successful. Further, Shleifer and Vishny (1986) find that the larger the largest owner the higher probability of a takeover in the first place. Definition

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In this study ownership is based on voting rights as of the end of the year prior to the offer is made. Due to the fact that this point in time deviates from the time of the tender offer there will be some uncertainty regarding the relevance of the variable. This will be further discussed in section 6. Expected impact Due to the fact that a single large owner facilitates the negotiation possibilities and the lack of two-tier offers in the Swedish setting we expect, in line with Shleifer and Vishny (1986), to find a find a positive correlation between the size of the largest owner and the likelihood of a successful tender offer. 4.2.8 Toehold Theory By having a toehold the acquirer’s bargaining power is likely to increase due to the fact that the acquirer already has voting power and is able to affect the management/board of the target company. Walkling (1985) shows that the probability of a successful takeover is positively related to the ownership stake in the target company. According to Bulow, Huang and Klemperer (1999) empirical research show that a large portion of bidders own toeholds at the time they make offers. Further, the authors find that the existence of a toehold makes the bidder more aggressive in its acquisition effort and improves the acquirer’s chance of winning an eventual auction. Bris (2002) on the other hand states that only a small percentage of acquirers in the U.S. tender offers acquire toeholds in the target prior to the bid. The discrepancy between the two can be explained by the fact that Bris refers to toeholds acquired shortly before the takeover while Bulow et al refers to research regarding more long-term toeholds. Bris suggests that the lack of toehold acquisitions prior to the bid is due to that the bidder risks a run-up in price if the intentions are detected by the market. When looking at the Swedish takeover market Agnblad et al. (2001) find that the acquirer often has a long-term toehold in the target firm. Definition In this study toehold is based on voting rights as of the end of the year prior to the offer is made. Even though the research presented above suggests that toeholds often are long term rather than short term there will be some uncertainty regarding the relevance of the variable due to the timing difference. This will be further discussed in section 6. Expected impact In line with the above mentioned findings we expect that there is a positive correlation between size of toehold and the probability of success.

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5 Model specification

5.1 The Logistic Regression Model A number of multivariate statistical techniques can be used to predict a binary dependent variable from a set of independent variables. However, methods like for example multiple regression analysis pose difficulties when the dependent variable is of dichotomous art and can have only two outcomes, an event occurring or not occurring. For example, since the values predicted by multiple regression analysis cannot be interpreted as probabilities, i.e. they are not constrained to fall in the interval between zero and one, this type of regression is not suitable when dealing with dichotomous dependent variables. Logistic regression is well suited for analyzing dichotomous outcomes and with this method the probability of an event occurring is directly estimated, see further Menard (1995). The logistic regression model is defined as

( ) Z

Z

eeeventP+

=1

where P is the probability that the event occurs and Z is the linear combination

nn XXXZ ββββ ++++= ...22110 where n is the number of explanatory variables.

5.2 Assessing the Coefficients of the Model In order to determine which variables to be included in the final model the consistence of the sign with economic theory and the significance of the estimated coefficients can be used. To test that a coefficient is statistically significant from zero the Wald statistic, which has a chi-square distribution, is used. The Wald statistic is formed from the ratio of the estimated slope parameter over its standard error and from this ratio a significance level for the Wald statistic is calculated, Peng and So (2002). Unfortunately, this statistic has an undesirable property. For large absolute values of the regression coefficients the estimated standard error is inflated, which in turn, gives a Wald statistic that is too small. This can result in a failure to reject the null hypothesis when the null hypothesis is false, Menard (1995).

5.3 Goodness of Fit of the Model According to the literature, see for example Peng and So (2002), there exists some confusion about which methods that should be used to evaluate a logistic regression model. However, three commonly used methods are the classification table, the Nagelkerke R2 (or some other equivalent R2 measure such as the McFadden Rho2) and the Hosmer and Lemeshow test. 5.3.1 The Classification Table The classification table is used to tell how well the outcomes predicted by the logistic regression model fits the observed outcomes. The classification table is a 2x2 table in which the rows represent the two possible outcomes of the dichotomous variable and the columns

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present the predicted outcomes. The classification table is most appropriate when classification is a stated goal of the analysis (Hosmer and Lemeshow 2000), which is the case in our thesis. 5.3.2 Nagelkerke R2 The Nagelkerke R2 is a statistic that tries to measure the proportion of the variance that is explained by the logistic regression model. This statistic is similar in intent to the R2 in a linear regression model, although the variation in a logistic regression model must be defined differently, Norušis/SPSS Inc. (1997). 5.3.3 Hosmer and Lemeshow Test The Hosmer and Lemeshow test, based on a chi-square distribution, measures how closely the observed and predicted probabilities match. If the calculated chi-square value is above the chosen significance level, the null hypothesis that there is no difference between the observed and predicted values, can not be rejected (Norušis/SPSS Inc. (1997)). However, there are some limitations with the Hosmer and Lemeshow test. First, the test is conservative, lacking statistical power in certain cases to detect a model’s poor fit. Second, even when the test is significant, indicating that a model does not fit the data well, it does not explain where and why data are not well fitted by the model (Peng and So 2002).

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6 Data Discussion In this section the data sample which can be seen in Appendix III is presented. The statistical methods used in this section are presented in Appendix I.

6.1 The Data Sample The data used in this thesis is takeovers of Swedish listed companies in the years 1985-2002. Over the past 17 years the market for takeovers has most likely changed significantly, resulting in that the older observations in the sample can be of less relevance today. However, in order to obtain a sufficiently large sample in the rather small Swedish market a long sample period is unavoidable. When choosing between different types of stock series the most liquid stock has been used. From the initial sample observations have been excluded for the following reasons: • Missing data. • Non-Swedish bidders making a stock offer or cash offer denominated in a foreign

currency. • Failed bid when two competing bids are made and one bid is successful. When having

competing bids at least one must fail, which may not be due to the characteristics of the bid.

• When the bidder prior to the bid holds 90 % or more of the voting rights in the target company.

• Takeovers failed due to intervention based on competition regulations. • When a partial bid is made, i.e. the aim is not to acquire the entire target. After the observations that did not meet our criteria were excluded, 281 observations remained. Out of these, 246 tender offers were successful and 35 were abandoned.

6.2 Variables 6.2.1 Premium The data used for the premium is gathered from the Trust database, Dagens Industri and from the OM Stockholm Stock Exchange’s statistics. As mentioned in section 4 the premium is defined as the percentage premium based on the market price five trading days before the first announcement of the takeover. This is done in order to avoid problem with run-ups before the announcement of the offer. Further, when dealing with mixed offers the cash alternative has been used. In addition, in the cases where the premium has been increased as a result of a raised offer the premium has been calculated based on the raised offer. When the frequency for successful and abandoned offers is presented relative to the premium figure 1 is obtained. The figure indicates that the size of the premiums doesn’t differ considerably between the abandoned and successful offers. However, for the successful offers there are a number of really high premiums which is not the case for the abandoned offers.

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Figure 1. Frequency of bid premiums for successful and abandoned tender offers.

In order to see if there really is a significant difference between the mean of the premiums for the abandoned and the successful offers, a z-statistic was calculated. The calculated z-value indicates that the premium for the successful offers is significantly higher than the premium for the abandoned offers on a two percent level, as can be seen in table 1. Since there is a statistically significant difference in the mean for the abandoned and the successful offers the premium might be a good variable for predicting the probability that a tender offer will be successful. This is, as mentioned above, in line with the findings of Walkling (1985) who suggests that the size of the bid premium relates to the success or failure of a tender offer. Table 1. Descriptives and t-test of the bid premium.

Abandoned Successful Mean 0.236 0.295

Standard deviation 0.131 0.238 Number of observations 35 246

z-statistic -2.205 p-value 0.014

6.2.2 Management Ownership In order to find how large the management ownership was in the target companies, data from Owners and Power in Sweden’s Listed Companies has been used. These books present the ownership structure of the listed companies in Sweden at the end of each year. That is, in our case the data will be based on the management ownership at the end of year preceding the offer. This result in that the data covering management ownership at the time for the offer can be somewhat different compared to the data presented in the literature. However, in most cases the management ownership is not believed to fluctuate considerably over time resulting in that the data should be relevant. Figure 2 shows the frequency of the management

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ownership for the abandoned and the successful offers. No considerable difference between the abandoned and successful offers regarding the size of the management ownership of the targets can be seen.

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Figure 2. Frequency of management voting rights for successful and abandoned tender offers.

The calculated z-statistic in table 2 confirms the conclusion drawn from the graph above, i.e. that there is no significant difference in management ownership between the abandoned and the successful tender offers. Hence, it can be expected that the management ownership is a poor predictor of the probability that a tender offer will be successful. This is in line with the findings of Mikkelson and Partch (1989). Table 2. Descriptives and t-test of the management’s voting rights.



z-statistic 0.034 p-value 0.513

6.2.3 Timing Effects As described in section 4 the timing effect is specified as the returns of the SAX index 6 months prior to the announcement of the takeover. To calculate this market return Affärsvärldens Generalindex has been used. Figure 3 shows the frequency of the market return six months prior to the offers. It is difficult to see any major differences between the abandoned and successful offers regarding market return prior to the announcement of the tender offer.

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Maket return 6 months prior to the announcement

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ySuccessful Abandoned

Figure 3. Frequency of market return 6 months prior to the announcement for successful and abandoned tender offers. The calculated z-statistic in table 3 supports the graphical observation above, i.e. that there is no significant difference in market return six months prior to the announcement between the abandoned and the successful tender offers. Therefore, it is questionable if this variable can help predicting the probability that a tender offer will be successful. Table 3. Descriptives and t-test of the market timing.




6.2.4 Number of Block-holders As presented in section 4 the number of block-holders is defined as the number of shareholders in the target firm that holds a position in the target company that is larger or equal to ten percent of the votes. Data regarding this variable was based on Owners and Power in Sweden’s Listed Companies. As mentioned before, this source presents the ownership structure of the listed companies in Sweden at the end of each year. This means that the data used might differ from the actual number of block-holders at the time for the offer. Figure 4 shows the frequency of the timing effects for the abandoned and the successful offers. It is difficult to identify any major differences between the abandoned and successful offers concerning the number of block-holders.

17

0,0%

5,0%

10,0%

15,0%

20,0%

25,0%

30,0%

35,0%

40,0%

45,0%

0 Blockers 1 Blocker 2 Blockers 3 Blockers 4 Blockers 5 Blockers

Freq

uenc


Figure 4. Frequency of number of block-holders for successful and abandoned tender offers.

The calculated z-statistic in table 4 gives the same result as the graphical observation above, i.e. that there is no significant difference in the number of block-holders between the abandoned and the successful tender offers. Thus, this variable might be a poor predictor when estimating the probability that a tender offer will be successful. Table 4. Descriptives and t-test of the number of block-holders.




6.2.5 Method of Payment Information regarding the method of payment has been obtained from the Affärsdata database and from the Stockholm Stock Exchange Fact books. The variable has been measured by using dummy variables indicating whether the payment is in cash, mixed or stock (for definitions see section 4). As can be seen in figure 5 stock offers seem to fail to a higher degree than cash offers, which is in line with economic theory. It can also be observed, in table 5, that cash offers are by far the most common form of offers in the Swedish market. The asymmetric information hypothesis mentioned above can offer an explanation to these results, which also are consistent with the findings of Agnblad et al (2001).

18

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

Cash Mixed Stock

Method of payment

Freq

uenc


Figure 5. Frequency of method of payment for successful and abandoned tender offers.

The calculated z-value in table 5 indicates that there is no significant difference between the abandoned and the successful tender offers when it comes to cash offers and mixed offers. There is however a significant difference between the abandoned and successful stock offers, which indicates that stock offers can be a good predictor of whether tender offers will be successful or not. Table 5. Descriptives and t-test of the method of payment. Cash Mixed Stock

Abandoned Successful Abandoned Successful Abandoned Successful Proportion

of population

65.7% 68.3% 11.4% 19.9% 22.9% 11.8%

Number of observations

23 168 4 49 8 29

z-statistic -0.306 -1.201 1.812

p-value 0.380 0.115 0.035

6.2.6 Board Reaction Information regarding the board reaction has been obtained from the Affärsdata database. Even though this information has been scrutinized thoroughly there might be some uncertainties regarding the reliability of these data. Further, in some cases there is a lag in time between the actual offer and the time for the recommendation of the board which might influence the predictive power of this variable. However, the board’s attitude towards the offer often has a tendency to reach the market in advance of the time when the board’s reaction is actually made public which makes it not completely unreasonable to assume that the board’s attitude is known by the market at the time for the offer. The variable has been measured by using dummy variables indicating whether the target company’s board

19

recommendation to the shareholders is positive, neutral or negative (for definitions see section 4). As can be observed in figure 6 the board recommendation is of great importance when evaluating the probability of whether a tender offer will be successful or not. In a substantial part of the positive board reactions the offers were successful while most of the offers followed by a negative board reaction were abandoned. The data regarding a neutral board reaction does not show any significant difference regarding the outcome of the offers. The importance of the board recommendation is in line with our expectations and previous research.

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

80,0%

90,0%

100,0%

Positive Neutral Negative

Board reaction

Freq

uenc

y


Figure 6. Frequency of board reaction for successful and abandoned tender offers.

Consistent with the results shown in figure 6 the z-statistics in table 6 show that there is a significant difference between abandoned and successful offers in the case of either a positive or negative recommendation from the board. This difference is however not, at a 10 % level, significant for neutral board reaction. Further, it can be noted that there are rather few takeover attempts where the target company’s board is negative, which is consistent with the Swedish setting described by Agnblad et al (2001). These results indicate that the board reaction can be a good predictor of whether tender offers will be successful or not. Table 6. Descriptives and t-test of the board reaction. Positive Neutral Negative

Abandoned Successful Abandoned Successful Abandoned Successful Proportion

of Population

11.4% 66.7% 40.0% 29.7% 48.6% 3.7%

Number of observations

4 164 14 73 17 9

z-statistic -6.236 1.236 8.580

p-value 0.000 0.108 0.000

20

6.2.7 Largest Owner Information regarding the ownership structure has been obtained from Owners and Power in Sweden’s Listed Companies the year preceding the offer. Hence, there might be some discrepancy between the data regarding the ownership at the time of the actual offer and our data. In the Swedish setting, with a rather high concentration of ownership and few hostile takeovers, it can be anticipated that the existence of a large owner will influence the probability of success positively. In figure 7 it can be observed that there is a higher frequency of low voting rights for abandoned tender offers than for successful offers.

0,0%

2,0%

4,0%

6,0%

8,0%

10,0%

12,0%

14,0%

16,0%

5% 10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

Voting rights

Freq

uenc

y


Figure 7. Frequency of the voting rights of the largest owner for successful and abandoned tender offers.

The conclusion drawn from figure 7 is further supported by the results from the statistic test seen in table 7. There is a statistical significant difference between the abandoned and successful offers with regard to the size of the largest owner. This variable might therefore be a good variable for predicting the probability that a tender offer will be successful. Table 7. Descriptives and t-test of the voting rights of the largest owner.


Standard deviation 0.210 0.215

Number of observations 35 246 z-statistic -2.508

p-value 0.006

6.2.8 Toehold Information regarding the size of an eventual toehold has been obtained from Owners and Power in Sweden’s Listed Companies the year preceding the offer. Figure 8 shows the

21

frequency of the toeholds for the abandoned and the successful offers. When observing the figure it is difficult to identify any major differences between the abandoned and successful offers concerning the size of the toehold.

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

5% 10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

Toehold

Freq

uenc

y


Figure 8. Frequency of toehold for successful and abandoned tender offers.

The calculated z-statistic in table 8 shows that there is no significant difference in the size of toeholds between the abandoned and the successful tender offers. This indicates that the toehold is a poor predictor when predicting tender offer success. Table 8. Descriptives and t-test of the toehold.




22

7 Regressions In order to obtain an initial model that predicts the probability that a tender offer will be successful, all the variables described in section 4 and 6 has been used. By including all the variables misspecification, resulting from arbitrarily choosing variables to exclude, can be avoided. After including all the variables in the initial regression the variables with the least significance level was eliminated one by one until a model only including variables significant at the 0.10 level, according to the Wald statistic, was obtained.

7.1 Initial Model When including all the variables it can be seen in figure 9 that the board reaction, type of payment and largest owner are significant at a 0.10 level. All of the variables signs are in line with the signs expected, except for the variable block-holders. The number of block-holders has a positive sign which indicates that a higher number of large owners facilitate the negotiation process involved in the tender offer, as in the case of the largest owner, rather than being an obstacle in the tender offer process. However it should be noted that the block-holder coefficient is not very significant. The degree of managerial ownership seems to have a negative impact on the probability of tender offer success but the coefficient for this variable is not significant either. This can be compared with the findings of North (2001) and Walkling and Long (1984) who both point out that management might block tender offers. The board reaction and the type of offer variables are used as categorical variables with negative board reaction and stock offer as reference variables.

Table 9. Variables from the initial model. Variable Expected Sign Coefficient p-value Stock Offer n/a n/a n/a Cash Offer + 1.143 .073 Mixed Offer + 1.750 .043 Negative Board n/a n/a n/a Positive Board + 4.483 .000 Neutral Board + 2.179 .000 Management ? -1.564 .137 Toehold + .029 .977 Premium + 1.471 .338 Timing Effect ? 1.178 .309 Largest Owner + 3.065 .029 Block-holders - .225 .385 Constant n/a -3.487 .000

23

7.2 Final Model After excluding, one by one, the variables with the least significant coefficients until only variables significant at a 0.10 level remained, the final model is obtained. As predicted, the board’s reaction has a significant impact on the probability of success, so has the presence of a large owner. When it comes to the method of payment it seems that, as indicated in section 6, offers not solely consisting of stocks are more likely to be successful. The method of payment has therefore been divided into two categories, stock offers and not stock offers. All of the coefficients of the variables in the final model show the expected signs and are significant at a 0.10 level. As in the initial model, the board reaction and the type of offers are used as categorical variables with negative board reaction and stock offer as reference variables.

Table 10. Variables from the final model. Variable Expected Sign Coefficient p-value Negative Board n/a n/a n/a Positive Board + 4.444 .000 Neutral Board + 1.998 .000 Largest Owner + 2.043 .081 Stock Offer n/a n/a n/a Not a Stock Offer + 1.281 .035 Constant n/a -2.465 .002

7.3 Goodness of Fit of the Final Model As can be seen in table 11 the predictive power of the final model is 91.1% of the successful offers and 65.7% of the abandoned offers, resulting in an overall predictive power of 87.9 %, which must be considered to be satisfactory.

Table 11. Classification Table for the final model. Predicted

Abandoned Successful Percentage

Correct

23

12 (Type I error)

65.7%

Obs

erve

d Su

cces

sful

Aba

ndon

ed

22

(Type II error)

224

91.1%

Overall Percentage 87.9% The Nagelkerke R2 for the final model is 0.426 which means that 42.6 % of the variation in the outcome variable is explained by the logistic regression model. Further, from the Hosmer

24

and Lemeshow test, presented in table 12 (see also appendix 2), it can be seen that the null hypothesis that there is no difference between the observed and predicted values, can not be rejected. Table 12. Hosmer and Lemeshow test.

Step Chi-square Df Sig. 1 6.771 8 0.561

In conclusion the results from the classification table, the Nagelkerke R2 and the Hosmer and Lemeshow test all indicate that the final model fits the data reasonably well.

25

8 The Market’s Pricing of the Risk of Abandonment By applying the model developed in previous sections it can now be tested whether the market prices the event risk in merger arbitrage. First we will test if the risk arbitrage premium, see definition below, is higher for the tender offers with a low predicted probability of success than for the tender offers with a low predicted probability of success. If this is the case, which we expect, we can proceed to evaluate if this higher premium compensates for the event risk by looking at actual returns from portfolios investing in risk arbitrage strategies.

8.1 Risk of Abandonment and the Risk Arbitrage Premium In this section the average arbitrage premium of the observations with highest and lowest predicted probability of success has been compared. This is done in order to make an initial evaluation whether the market prices the probability of abandonment by attaching a higher risk arbitrage premium to tender offers with a low predicted probability of success than to tender offers with a high predicted probability of success. 8.1.1 Definition of the Risk Arbitrage Premium The risk arbitrage premium is defined as the spread between the current market price and the price to be paid for the shares in the deal at the time of the tender offer announcement. In our case the risk arbitrage premium is based on data from the trading day after the tender offer was made public. By choosing the trading day one day after the announcement we avoid being dependent on the exact timing of the announcement. In the cases where the premium has been increased as a result of a raised offer, the risk arbitrage premium has been calculated based on the raised offer. The data used for the premium is gathered from the Trust database, Dagens Industri and from the OM Stockholm Stock Exchange’s statistics. 8.1.2 Risk of Abandonment relative the Risk Arbitrage Premium In order to evaluate whether the market prices the probability of an abandoned merger a ranking procedure, where the tender offers were ranked after their probability of failure estimated by the developed logistic regression described above, was used. This was followed by a statistical test where the z-statistic was calculated to test if there is a significant difference in the average arbitrage premium of the observations with highest and lowest predicted probability of success. Since it is expected that the market prices the probability of success we believe that a higher degree of probability of success will result in a lower premium. Therefore, the following one-sided test, where the sample variances, s2, are used, was applied.2

0:0 ≥− yxH µµ 0:1 <− yxH µµ

where x denotes the observations with highest predicted probability of success and y denotes the observations with lowest predicted probability of success.

2 The test requires that the samples are normally distributed. However, in the case where the sample sizes are large, above 30, the test is still applicable. See further Newbold (1995).

26

The following decision rule was used

Reject H0 if: αZ

ns

ns

yx

y

y

x

x

−<

+

−22

In the first case, the 30 observations with the highest predicted probability of success, was compared to the 30 observations with the lowest predicted probability of success. This means that the mid range of the sample has been excluded from the test in order to obtain a distinct difference between the high and low probability of success. The result can be seen in table 13. Table 13. Descriptives and t-test for the ten percent of the observations with highest respective lowest predicted probability of success.

The 30 Observations with the Lowest Predicted Probability of Success

The 30 Observations with the Highest Predicted Probability of Success

Average Premium Variance Average Premium Variance 0,093 0,012 0,025 0,0005

z-value 4,177 p-value 1,48E-05

The calculated z-value is 4.177 and the p-value is 1,48E-05 which shows that there is a significant difference between the average arbitrage premiums of the two groups, indicating that the market prices the probability of abandonment since a higher risk arbitrage premium is attached to the observations with lowest predicted probability of success. To confirm that these results are valid we repeat the test with more observations included. In this second case, the top third of the observations, i.e. the observations with the highest predicted probability of success, was compared to the lowest third of the observations. The result can be seen in table 14. Table 14. Descriptives and t-test for the third of the observations with highest respective lowest predicted probability of success.

The Third of the Observations with the Lowest Predicted Probability of Success

The Third of the Observations with the Highest Predicted Probability of Success

Average Premium Variance Average Premium Variance 0.063 0.006 0.034 0.003

z-value 2.921 p-value 0.002

The calculated z-value is 2.921 and the p-value is 0.002 which shows that there is a significant difference between the average arbitrage premiums of the two groups, once again indicating that the market prices the probability of abandonment.

8.2 Risk of Abandonment and Actual Returns The results in section 8.1 indicate that the market prices the risk of abandonment by demanding a higher risk premium. This leads us to the question to what extent the market compensates, in comparison to offers with high predicted probability of success, for the risk

27

involved when investing in tender offers with lower predicted probability of success. That is if a portfolio investing in offers with low predicted probability of success generates an equal or higher annual return than a portfolio investing in offers with high predicted probability of success. 8.2.1 Portfolio Test with Actual Returns When testing if a portfolio investing in offers with low predicted probability of success generates an equal or higher annual return than a portfolio investing in offers with high predicted probability of success actual returns, and not only the initial risk arbitrage premium, must be taken into consideration. That is, the potential downside involved in abandoned mergers is now incorporated into the calculations. The return for the abandoned offers were calculated as the percentage decrease, or in some cases the increase, in the share price between the day after the tender offer was announced and the day after the tender offer was abandoned. This return was then annualized. For the successful offers the return equals the risk arbitrage premium. This return was annualized based on the period from the day after the tender offer was announced and the last day of the acceptance period. Since the acceptance periods for the offers prior to 1997 were not available through the Stockholm Stock Exchange an average acceptance period was calculated based on the data from 1997 and forward. This average was then used as a proxy for the successful offers acceptance period. After the annualized returns were obtained the tender offers were ranked after their probability of success estimated by the logistic regression model described above. The sample was then split up into portfolios according to the predicted probability. First, a portfolio consisting of the 50 percent of the observations with the lowest predicted probability of success was compared to a portfolio consisting of the 50 percent of the observations with highest predicted probability of success. Secondly, a portfolio consisting of the 25 percent of the observations with the lowest predicted probability of success was compared to a portfolio consisting of the 75 percent of the observations with highest predicted probability of success. Since we want to test if the portfolio consisting of the observations with the lowest predicted probability of success yields a higher return than the portfolio consisting of the observations with highest predicted probability of success the following one-sided test was applied:3

0:0 ≥− yxH µµ 0:1 <− yxH µµ

where x denotes the observations with highest predicted probability of success and y denotes the observations with lowest predicted probability of success. The following decision rule was used

Reject H0 if: αZ

ns

ns

yx

x

x

x

x

−<

+

−22

where s2 is the sample variance and n the number of observations. 3 The test requires that the samples are normally distributed. However, in the case where the sample sizes are large, above 30, the test is still applicable. See further Newbold (1995).

28

The results from the tests can be observed in table 15 and table 16. Table 15. Descriptives and t-test for the 50 percent of the observations with the lowest probability of success and the 50 percent of the observations with the highest probability of success.

The 50 percent of the Observations with the Highest Predicted Probability of Success

The 50 percent of the Observations with the Lowest Predicted Probability of Success

Average Return Variance Average Return Variance 0.285 0.635 0.268 0.688


The calculated Z-value is 0.183 and the p-value is 0.427 which indicates that there is no significant difference in average annualized returns for the 50 percent of the tender offers with the lowest predicted probability of success and the average annualized returns for the 50 percent of the tender offers with the highest predicted probability of success. Table 16. Descriptives and t-test for the 25 percent of the observations with the lowest probability of success and the 75 percent of the observations with the highest probability of success.

The 75 percent of the Observations with the Highest Predicted Probability of Success

The 25 percent of the Observations with the Lowest Predicted Probability of Success

Average Return Variance Average Return Variance 0.297 0.663 0.217 0.657


The calculated Z-value is 0.878 and the p-value is 0.190, which once again indicates that there is no significant difference in average annualized returns for the portfolio consisting of the tender offers with low predicted probability of success and the portfolio consisting of the tender offers with a high predicted probability of success. These findings indicate that the market prices the event risk in merger arbitrage correctly. The higher premium for the offers with lower predicted probability of success compensates, but does not overcompensate, for the negative returns resulting from the actually abandoned tender offers. 8.2.2. Limitations of the results When the results from the test above are analyzed it is important to point out some limitations. First, when the returns have been calculated transaction costs have not been taken into consideration. However, this fact does to a large extent affect the different strategies in the same way and does therefore not affect the relative performance of the different strategies. Secondly, the liquidity factor has not been taken into consideration. Some of the companies that have been subject for takeovers are not frequently traded on the Stockholm Stock Exchange, which means that the liquidity in those stocks is low. It is unclear how this affects the relative performance of the portfolios above and therefore our conclusions. Thirdly, as mentioned above, a proxy has been used when determining the time period for the successful offers. This might affect the annualised returns of the successful offers, which in turn can affect the final result.

29

9 Analysis The empirical findings regarding the model which predicts the probability of tender offer success suggests that board reaction, largest owner and method of payment are the most influential factors for such a prediction. The fact that the board reaction is important can to some extent be contributed to the fact that there might be a lag in time between the actual offer and the time for the recommendation of the board. Also, since the board is appointed by and should represent the stock-holders, its reaction can be seen as an indicator about how the stock-holders will react to the offer. The importance of the board recommendation is supported by the findings of Flanagan, D’Mello and O’Shaughnessy (1998) and Hoffmeister and Dyl (1981). The size of the largest owner is also an important factor; we find a positive correlation between the size of the largest owner and the predicted probability of tender offer success. A large owner might facilitate the negotiation process and it is unlikely that an offer is made without preliminary discussions between large owners and the bidder has taken place. This is in line with the findings of Shleifer and Vishny (1986) but contradictory to Stulz (1988). However, based on the Swedish setting where two-tier offers are prohibited, hostile takeovers are few and the concentration of ownership is higher relative to the U.S, it is to be expected that large owners facilitates the tender offer process. The method of payment was also found to be an important factor. Consistent with the previous research and the adverse information hypothesis, stock offers decrease the likelihood of successful tender offers. Some factors thought to be important determinants of a takeover were however rejected by the study. Most surprising is perhaps that a higher premium, which intuitively should increase the probability of a successful offer, does not seem be of significant importance for the outcome. The fact that two-tier offers are prohibited in the Swedish should further emphasize the importance of the size of the premium. Some previous research data has found similar evidence regarding the lack of impact of the size of the bid premium. One explanation to the minor importance of the bid premium can be that the effect of the bid premium is incorporated in some of the other variables such as the board’s reaction. The fact that management ownership, the toehold and the number of block-holders seem to have no significant impact on the tender offer success can be influenced by that information regarding these factors have been obtained from Sundin and Sundqvist (1985-2002) at the end of the year preceding the offer. Hence, there might be some discrepancy between the data regarding these factors at the time of the actual offer and our data. However, when it comes to the toehold Agnblad et al. (2001) find that the acquirer in the Swedish market often has a long term toehold in the target firm which would suggest that this is of minor importance in the Swedish setting. Finally, the timing effect shows no significant effect on the tender offer success indicating that the market climate has no substantial effect on the tender offer success. The empirical results from the model examining whether the market prices the risk of abandonment show that the market indeed prices the risk of tender offer abandonment. When comparing the risk arbitrage premium for the thirty observations with the highest predicted probability of abandonment with the thirty lowest, a highly significant difference between the average arbitrage premiums for the two groups could be seen. This significant difference was also found when the size of the sample was expanded to the thirds of the observations with highest respectively lowest predicted probability of abandonment. That the market prices the event risk in merger arbitrage was found when taking actual returns into consideration. No difference in annual returns for portfolios investing in tender offers

30

with high and low predicted probability of success could be found. There are limitations regarding primarily transaction costs and the liquidity in the market. However, these factors should to a large extent affect the different portfolios equally and therefore not substantially influence our results. The markets pricing of the event risk is in line with the economic theory regarding risk and return.

10 Conclusions The most important factors in predicting whether a tender offer will be successful or not is the board reaction, type of payment and the size of the largest owner. A positive and neutral board reaction of the target company increases the likelihood of tender offer success relative a negative board reaction. Relative to stock offers, cash and mixed offers increase the likelihood of tender offer success. Further, as the size of the largest owner increases so does the probability of tender offer success. When the predicted probabilities of tender offer success are related to the event risk in merger arbitrage, it is found that the Swedish market prices this risk.

11 Future research This thesis has provided an initial insight regarding if the Swedish market prices the event risk in tender offers. As a suggestion for further research the probability model can perhaps be improved in order to better predict which tender offers that will succeed. Other variables, such as analysts’ recommendations and financial ratios, might improve the model further. The trading model can also be improved by taking for example transaction costs and liquidity into consideration. Further, this thesis has not focused on the absolute returns generated from risk arbitrage premiums. However, the returns from the portfolios in the trading model suggest that risk arbitrage strategies can generate high absolute returns. It can therefore be interesting to study how high returns that can be achieved from risk arbitrage strategies in the Swedish market when taking factors such as transaction costs and liquidity into consideration.

31

12 References Agnblad, J., E. Berglöf, P. Högfeldt and H. Svancar (2001). Ownership and Control in Sweden: Strong Owners, Weak Minorities and Social Control, eds. Barca and Becht, The Control of Corporate Europe, Oxford University Press. Amihud, Y., B. Lev, and N.G. Travlos (1990). Corporate Control and the Choice of Investment financing: the Case of Corporate Acquisitions. Journal of Finance 45, 603-616. Baker M. and S. Savasoglu (2002). Limited Arbitrage in Mergers and Acquisitions. Journal of Financial Economics 64, 91-115. Boström, K. and A-S. Gustavsson (2000). What Determines the Premium in Swedish Takeovers? An Empirical Study on Data 1999-1998. Master Thesis in Financial Economics, Stockholm School of Economics. Bris, Arturo (2002) Toeholds, takeover premium, and the probability of being acquired. Journal of Corporate Finance 8, 227–253 Bulow, J., M. Huang and P. Klemperer (1999). Toeholds and Takeovers. Journal of Political Economy 107, 427-454. Dukes W., C. Frohlich and C. Ma (1992). Risk Arbitrage in Tender Offers: Handsome Rewards-and not for insiders only. Journal of Portfolio Management 18:4, 47-55. Flanagan, D.J., J.P. D’Mello and O’Shaughnessy K.C. (1998). Completing the Deal: Determinants of Successful Tender Offers. Journal of Applied Business Research 14, 21-32. Forsberg, N. and O. Nilsson, (2000). Determinants of Tender Offer Success. Master’s Thesis in Financial Economics. Stockholm School of Economics. Hoffmeister, R. J. and E. A. Dyl. (1981). Predicting Outcomes of Cash Tender Offers. Financial Management 10, 50-58. Holderness C.G. and D.P. Sheehan (1988). The Role of Majority Shareholders in Publicly Held Corporations: An Exploratory Analysis. Journal of Financial Economics 20, 317-346. Holmén, M. and P. Högfeldt. (2000). A law and finance analysis of initial public offerings, Working Paper, Department of Finance, Stockholm School of Economics. Hosmer, D.W., Jr. and S. Lemeshow (2000), Applied Logistic Regression, New York: Wiley. Högfeldt P. and K. Högholm. (2000). A law and finance theory of strategic blocking and preemptive bidding in takeovers. Journal of Corporate Finance 6, 403-425. Jung, K., Y-C, Kim and R.M. Stulz (1995). Timing, Investment opportunities, Managerial discretion, and the Security Issue Decision. Journal of Financial Economics 42, 159-185

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Kahn, S., (2002). Merger Arbitrage: A Long-Term Investment Strategy. The Journal of Wealth Management, Volume 4, Issue 4, 76-81. Martin, J.K. (1996), The method of Payment in Corporate Acquisitions, Investment Opportunities, and Management Ownership. Journal of Finance 51, 1227-1246. Mikkelson, W. H. and M. M. Partch (1989). Managers’ Voting Rights and Corporate Control, Journal of Financial Economics 25, 263-90. Menard, S. (1995). Applied Logistic Regression Analysis. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-106. Thousand Oaks, CA:Sage. Myers, S. and N. Majluf (1984). Corporate Financing and Invest Decisions when Firms have Information Investors do not have. Journal of Financial Economics 13, 187-221. Newbold, P (1995), Statistics for Business & Economics, 4th Edition, Prentice-Hall, New Jersey. North, D. S. (2001). The Role of Managerial Incentives in Corporate Acquisitions: the 1990s Evidence. Journal of Corporate Finance 7, 125-149. Norušis M.J./SPSS Inc. (1997). SPSS Professional Statistics 7.5. Peng, C. Y. J. and T. S. H. So. (2002). Logistic Regression Analysis and Reporting: A Primer. Understanding Statistics, 1(1), 31-70. Pickering J. F. (1983). The Causes and Consequences of Abandoned Mergers. Journal of Industrial Economics 31, 267-281. Stulz, R. M. (1988). Managerial Control of Voting Rights: Finance Policies and the Market for Corporate Control. Journal of Financial Economics 20, 25-54. Sundin, A. and S-I. Sundqvist. Owners and Power in Sweden’s Listed Companies, Stockholm: Dagens Nyheter, 1985-2002. Walking, R., (1985). Predicting Tender Offer Success: a Logistic Analysis. Journal of Financial and Quantitative Analysis 20, 461–478. Walkling, R. A. and M. S. Long (1984). Agency Theory, Managerial Welfare, and Takeover Bid resistance. Rand Journal of Economics 15, 54-68.

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Appendix I – Statistical Tests for Continuous and Dichotomous variables In order to test if there are any differences in means and population proportions for the different variables a null hypothesis saying that there is no difference between abandoned and successful takeovers has been tested. The statistical methods for the continuous variables and the dichotomous variables can be seen below, for further reference see Newbold (1995). Continuous Variables In order to test if there are any differences in the continuous variables between the successful and abandoned takeovers the following null-hypothesis has been tested.

0:0 =− yxH µµ against the alternative hypothesis

0:1 <− yxH µµ or 0:1 >− yxH µµ H0 is rejected if

αz

ns

ns

yx

y

y

x

x

−<

+

−22

or αz

ns

ns

yx

y

y

x

x

>

+

−22

Where

xµ and yµ are the means of the successful and abandoned takeovers

xn and are the number of observations ynx and y are the sample means

2xs and are the sample variances and 2

ysα is the significance level Dichotomous Variables In order to test if there are any differences in the dichotomous variables between the successful and abandoned takeovers the following null-hypothesis has been tested.

0:0 =− yx ppH against the alternative hypothesis

0:1 <− yx ppH or 0:1 >− yx ppH H0 is rejected if

( )αz

nnnn

pp

pp

yx

yx

yx −<

+−

−

00 ˆ1ˆ

ˆˆ or

( )αz

nnnn

pp

pp

yx

yx

yx >

+−

−

00 ˆ1ˆ

ˆˆ

34

Where

yx

yyxx

nnpnpn

p++

=ˆˆ

ˆ0

and where denotes the proportion of for example stock offers in a random sample of observations from a population of abandoned takeovers with a proportion of cash offers

and is the proportion of stock offers in an independent sample of n observations from a population of successful takeovers with a proportion of stock offers.

xp̂

xn xp

yp̂ y

yp

35

Appendix II - Hosmer Lemeshow Test A Hosmer and Lemeshow test was performed in order to test how closely the observed and predicted probabilities match. This is a chi-square test with the following hypothesis: H0: There is no difference between the observed and predicted values H1: There is a difference between the observed and predicted values We can reject the null hypothesis if χ2

obs > χ2critical. The χ2

obs value is calculated by SPSS and can be seen in table 17. Table 17. Hosmer Lemeshow statistics

Hosmer and Lemeshow Test

6,771 8 ,561Step1

Chi-square df Sig.

51.15205.0,8,

2,, == criticaldfcritical χχ α

This means that χ2

obs < χ2critical and the null hypothesis can therefore not be rejected at the 5%

significance level. We cannot reject that there is no difference between the observed and predicted values. This can also be seen by the high significance level in table 17.

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Appendix III - Sample Target Bidder 1985 Schatullet Douglas-sfären * Yngve Kullenbergs STC Venture Iro Argentus Citadellet Stockholms Badhus Uddeholm AGA Guldfynd Lagonda Nederman Active Topflight Strålfors* Säfveån Catena Nils Weibull Faluhus* Kylmaterial (Kylma) Sekretären Balken Invest Pronator Carnegie Saba Småland Invest Finnveden Invest* Hilleshög Volvo** Cardo Volvo Bahco Promotion Scansped Bilspedition Järnbron Skåne-Gripen* Skåne-Gripen Active Parlamentet Citadellet Stockholms Ångslup Skanska* 1986 Anesco Sporrong* Ahlsell Boliden Monitor Argonaut* Kuben Aritmos* Tresor AGA GAB Herakles-Proventus Haki Österlen Almedahl Skandia Nessim Home Hotel Adamsons Bilspedition Stiab Malmros Höganäs Kanthal Leo Pharmacia* Barkman Joar Invest Ivars bil Philipson Bil Papyrus Stora* LKB Pharmacia

Rang Invest Öhmans, IKEA, SHB & A. Lindgrens stiftelse

Ljungdahls Munksjö* Tre konsulter (3K) 3K Holding* JM Bygg Skanska Hasselfors Munksjö 1987 Enator Pronator Stockholms Badhus Skandia Kebo Beijer Invest (Wall)** Sjölander J & W Beijer Invest Argentus (Anders Wall) Rockhammar Rottneros* Edstrand Familjen Edstrand Boliden Trelleborg* PLM Industrivärden Skrinet Wermia 1988 Sannadal Diös Norra* PM Capital Markets Jämtlands Folkbank ABV Nordstiernan Hemglass Hexagon Svenska Fläkt ABB Bohus Catena Swedish Match Stora Holmen MoDo* Iggesund MoDo* Broströms ASEA Data Logic Cap Gemini Ellos ICA Kontorsutveckling Esselte Printcom Östlund Fagerhult Almedahl Johnson Pump Skrinet Sardus Opus Swanboard Rottneros Gunnebo Gunab Intressenter

Target Bidder Radiosystem Ericsson Sporrong Prosparitas Cranab Valmet-Scantrac Pendax PX Intressenter Transatlantic Bilspedition Enström Esselte Carnegie (Saba) Nobel Asken Nobel Independent Finans Fermenta Opus Custos Scapa Apax & Skandia) Round Office Polarator Besam Bahco Datatronic Proventus Data Logic Cominvest*** 1989 Bruks Mercurius Nisses AP-fond (1-3) Hötorget BGB Hexagon Consolidator Anza Jordan Devehissar Schindlers Datacentralen Sapia Skåne-Gripen Cardo Edebe WM-Data Duni Marieberg Betong Industri BTG-Invest Svedbergs Svedbergs Intressenter** Skaraborgsbanken Gota Wermlandsbanken Gota Beijer Industries Kongsbo Industrier Nordbanken PK-Banken Pharmacia Procordia/Volvo Dacke Invest Industrivärden UV shipping N&T Indevo Infina Hexagon Axel Johnson 1990 HNB HNB Intressenter Marabou Freia AW Andersson AWA-Konsult* Allhus Familjen Nordqvist Databolin Databolin Intressenter VIAK VBB Movexa J & W Fortet Plinius Beväringen SPP Convexa BGB Hasselblad, Viktor Incentive* Incentive ASEA Enator Modulföretagen* HP-Färg BPA Constructa Olsson släkten** Indevo Indevo Holding Tax Free Invent Management IDK Data Frontec Hexagon Munksjö Adepten Independent AB Adamas Industrier Proton Invest Munksjö Munksjö Intressenter Thorsman Bahco Eneqvist Eneqvist Intressenter Memory Data Memory International Depenova Skrinet, Felländer & Möllefors Livsmedelskompaniet LMK* Åkermans VME Group Norden Export Norden International Ernström Gruppen Armagruppen Polarator Office Stikkan Andersson & Trefond** Mercurius Gruppen Privata Placeringar Swepart Swepart Intressenter Esselte Mobilia, Ratos

37

38

Target Bidder 1991 Alfa-Laval Tetra Pak Malmros Malmros Intressenter Thomée-Hörle Thomée-Hörle Intressenter Wermia Ahlmark & Co. Prosparitas Midway Holding Svenska Energinät AB Graningeverkens AB Essve Produkter Engros AB Ferro Fundia Fundiaintressenter Avena Förvaltning Coronado Intr. & Obligentia AB* Componenta Svedala Industri Bahco Industrivärden Inter Innovation De La Rue Plc Pulsen Familjen Bartholdson Scandiafelt Scapa Group Providentia Investor Hadronen Luxonen Anticimex Skandia, Länsförsäkringar & Wasa Taurus Salenia Taurus Traction 1992 Jeppsson Pac Superfos Emballage* Programator Cap Gemini Edata Storage Technology Corp. Gota AB Trygg-Hansa SPP Holding AB Aranäs Luxonen Korsnäs AB Industriförvaltnings AB Kinnevik* Protorp Förvaltnings AB Volvo + Skanska Almedahl-Fagerhult Investment AB Latour Custos Volvo, Skanska*** Riksbyggen Fastigheter AB RF HoldingAB Volvo Procordia 1993 BCP Volvo* Finansrutin Data Förenade Liv** Gorthon-Invest Cardo AxTrade Axel Johnson AB NK NCC Ncb AssiDomän* Nobel Industrier Akzo 1994 Sydsvenska Dagbladet Marieberg Bastionen Syd Klövern* Vide-Invest Spira Invest* Export-Invest Investor Kramo Securum** Cardo Incentive ESAB Charter Plc Gnosjö-Gruppen Hidef Anderson Fastigheter AB SIFAB Enator Celsius Aritmos Proventus Råckstahus AB Stena AB* Scribona Andersson & Bennet + Bure Fermenta Dala-Hebi 1995 Proventus Weil Invest Swegon Latour Produra Capital Atle Partnerinvest Atle KapN Atle Skåne-Gripen Gripen Intressenter Owell WM-Data Abu Garcia Berkely* Brukens Nordic EQT Industrier LIC Care Getinge Industrier Hilab Exab** Stancia Prifast Fristads Kansas E.O. Arjo Getinge Industrier Frigoscandia ASG Pharmacia Pharmacia & Ujohn Inc.* Hasselfors AssiDomän Karolin Invest Atle Skåne-Gripen EKP Invest

Target Bidder 1996 Gambro Incentive* Synectics Medical AB Medtronic United Tankers Broströms Swedspan Industrier AB IKEA Skåne-Gripen Skanska Swepart Hexagon Horda Trelleborg Hemstaden Diös Forsheda AB TI Group Sifab Tornet* Orrefors Kosta Boda AB OKB-Holding AB Terra Mining William Resources Lodet Fastighets AB Svenska Bostäder M2 Fastigheter Wihlborg & Son + Bergaliden* Stadshypotek Svenska Handelsbanken Stadshypotek Skandia*** Trustor ON-Invest ERRCE EIRA Kanthal Sandvik 1997 Östgöta Enskilda Bank Den Danske Bank AS Frontline Frontline Ltd* FABEGE Näckebro AB GOTIC Vasakronan Acrimo Newell com. ICB Shipping ICB Shipping Ltd* Spira Konsortium LRF, KF Invest mfl IPC Sands Petroleum AB* Nordbanken Nordbanken Holding AB* Sandblom & Stohne Sandblom & Stohne Intressenter Linjebuss CGEA Transport SA FORCENERGY Forcenergy Inc.* CynCrona OEM International VBB Gruppen SWECO (FFNS) SIAB NCC Föreningsbanken Sparbanken* Atlantica Invik & Co VenCap Industrier Grimaldi Industrier AB Hufvudstaden Diligentia* Skoogs Trelleborg Klövern Wihlborgs* Trygg Hansa SE-Banken Nordström & Thulin Argonaut ICB Shipping Frontline*** FABEGE Hufvudstaden*** Verimation Norman Data* 1998 Peak Performance Carli Gry International A/S* Marieberg, Tidnings AB Bonnierföretagen STORA ENSO Oyj* Tryckinvest i Norden Carl Bennet* Verimation NetSys Technology Group Tryckinvest i Norden Quebecor Printing Inc.* PLM Rexam Ltd Astra Zeneca Ltd* NK Cityfastigheter Hufvudstaden Storheden Wihlborgs Näckebro Drott Benima Ferator Engineering Sigma Caran WM-Data Kalmar Industries Partek Oy Liljeholmen Duni***

39

Target Bidder 1999 ABB ABB Ltd* Liljeholmens Blyth Inc. Scancem Heidelberger Zement AG SCANIA Volvo AB* AGA Linde AG Wilkenson Handskmakarn Wedins Norden AB Celsius SAAB AB Graningeverkens Graninge* Emil Lundgren GTIE S.A. Måldata Sigma AB* Pharmacia&Upjohn Monsanto Company* Althin Medical Baxter Sweden Spectra-Physics Thermo Instrument Inc. JP Bank Matteus BTL Stinnes AG Dahl EQT + Ratos Enator Tieito Corp. Oy* PriFast Balder ASTICUS IVG Holding AG Scandinavian PC Systems PC-Systemer ASA ASG Danzas AG BPA Procuritas Capital Partners II Gibeck Hudson RCI Sorb Industri Carl Bennet* Eldon EQT Scandinavia BV Meto Checkpoint Inc.* Martinsson Atle Humlegården Länsfastigheter N&T Argonaut Simbel Monark Stiga Grimaldi Industrikoncern Guide Framtidsfabriken Graphium Argynnis Industrier 2000 Diligentia Castellum AB*** Piren Rodamco N.V. Cell Network Mandator AB Balder Drott AB Diligentia Balder AB*** Kjessler & Mannerstråle Traction Evidentia Claesson & Anderzen Invest Connecta Information Highway AB Diligentia Skandia Liv Naturkompaniet Friluftsbolaget Mandator/Cell Network Pixelpark AG* BT Industries Toyoda Automatic Loom Works, Ltd Perstorp Perstorp Intressenter Provobis Scandic Hotels AB Folkebolagen Lindab AB Zeteco Partek Oyj Abp Entra TietoEnator AB Lifco Carl Bennet Kjessler & Mannerstråle Jacobson & Widmark AB Svedala AB Metso Abp SEC NetCom AB* IRO Van de Wiele Norrporten NS Holding AB Allgon AB LGP Telecom Holding AB Gylling Optima Batteries AB Johnson Controls Inc. Resco Fi SYSTEM* Arete AB TurnIT Bulten AB Finnveden AB Diös AP Fastigheter AB Allgon AB REMEC, Inc* Avesta Sheffield AB Outokumpo Steel * FB Industri Holding AB Bergman & Beving AB Stena Line Stena AB*

Target Bidder 2001 Artema Medical AB Cardiac Science Inc.* Segerström & Svensson AB Sanmina Corp.* Independent Media Group Sweden AB ( IMG) Vision Park Entertainment AB ATLE AB Ratos AB och 3i Group plc Sydkraft AB E.ON Energie AG* Perstorp AB Sydsvenska Kemi Platzer Fastigheter AB Fastighets AB Tornet*** Matteus AB NH Nordiska Holding AB Eniro AB SEAT PAGINE GIALLE S.p.A* Scandic Hotels AB Hilton Group PLC* Spendrups Bryggeri AB Spendrups Invest AB* SAS Danmark A/S, SAS Norge ASA, SAS Sverige AB SAS AB* Jacobson & Widmark AB WSP Group PLC Lindab AB Lindab Intressenter Matteus AB Aragon FK AB*** Jobline International AB TMP Worldwide Inc. Friluftsbolaget Ekelund & Sagner AB Fjällräven AB FöreningsSparbanken AB SEB* Platzer Fastigheter AB Ernströmgruppen AB Lundin Oil AB Talisman Energy Inc* Vision Park Entertainment AB KF Media Ångpanneföreningen AB Sweco AB AssiDomän AB Sveaskog Netwise AB Trio AB* Icon Medialab International AB IconMedialab Holding* Scandinavia Online AB Eniro AB AU-System AB Teleca AB Johnson Pump AB TMT One AB Kipling Holding AB Dimension AB MediTeam AB Meda AB 2002 Munksjö AB Smurfit Holdings IMS Data AB Martinsson Gruppen Realia AB Columna Esselte AB JWCA Nobel Biocare AB Nobel Biocare Holding AG* Pronyx AB Teleca AB AvestaPolarit Abp Outokumpu Oyj*

* Excluded due to missing data etc. ** Excluded due to toehold > 90% *** Excluded due to competing bids

is the event risk in merger arbitrage priced?...offer success and the pricing of the risk of...

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