Is The Event Risk In Merger Arbitrage Priced?

Transcription

1 Stockholm School of Economics Master s Thesis in Finance Is The Event Risk In Merger Arbitrage Priced? Authors: Tutor: Johan Koch Markus Sjöström Patrik Säfvenblad Presentation: 10:15, Room Ruben, June

2 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. 1

3 1. INTRODUCTION PURPOSE CONTRIBUTION OUTLINE PREVIOUS RESEARCH INSTITUTIONAL SETTING DEVELOPMENT OF PREDICTION MODEL DEPENDENT VARIABLE EXPLANATORY VARIABLES Premium Management Ownership Timing Effect Number of Block-holders Method of Payment Board Reaction Largest Owner Toehold MODEL SPECIFICATION THE LOGISTIC REGRESSION MODEL ASSESSING THE COEFFICIENTS OF THE MODEL GOODNESS OF FIT OF THE MODEL The Classification Table Nagelkerke R Hosmer and Lemeshow Test DATA DISCUSSION THE DATA SAMPLE VARIABLES Premium Management Ownership Timing Effects Number of Block-holders Method of Payment Board Reaction Largest Owner Toehold REGRESSIONS INITIAL MODEL FINAL MODEL GOODNESS OF FIT OF THE FINAL MODEL THE MARKET S PRICING OF THE RISK OF ABANDONMENT RISK OF ABANDONMENT AND THE RISK ARBITRAGE PREMIUM Definition of the Risk Arbitrage Premium Risk of Abandonment relative the Risk Arbitrage Premium RISK OF ABANDONMENT AND ACTUAL RETURNS Portfolio Test with Actual Returns Limitations of the results ANALYSIS CONCLUSIONS FUTURE RESEARCH REFERENCES

4 APPENDIX I STATISTICAL TESTS FOR CONTINUOUS AND DICHOTOMOUS VARIABLES APPENDIX II - HOSMER LEMESHOW TEST APPENDIX III - SAMPLE

5 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. 4

6 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 % per month based on a sample of 1,901 mergers over the period of 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 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 5

7 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. 6

8 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 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. 7

9 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 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. 8

10 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 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 opportunities 1 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. 9

11 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 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 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 10

12 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 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. 11

13 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 P Z e = 1+ e ( event) Z where P is the probability that the event occurs and Z is the linear combination Z = β + β X + β X β n X n 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 chisquare 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 R 2 (or some other equivalent R 2 measure such as the McFadden Rho 2 ) and the Hosmer and Lemeshow test 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 12

14 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 Nagelkerke R 2 The Nagelkerke R 2 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 R 2 in a linear regression model, although the variation in a logistic regression model must be defined differently, Norušis/SPSS Inc. (1997) 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). 13

15 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 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 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. 14

16 Success Abandoned 30% 25% Frequency 20% 15% 10% 5% 0% 5% 15% 25% 35% 45% 55% 65% 75% 85% 95% 105% 115% Premium 125% 135% 145% 155% 165% 175% 185% 195% 205% 215% 225% 235% 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 Standard deviation Number of observations z-statistic p-value 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 15

17 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. Successful Abandoned 80,0% 70,0% 60,0% Frequency 50,0% 40,0% 30,0% 20,0% 10,0% 0,0% 5% 10% 15% 20% 25% 30% 35% 40% Management voting rights 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% 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. Abandoned Successful Mean Standard deviation Number of observations z-statistic p-value 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. 16

18 Successful Abandoned 12% 10% Frequency 8% 6% 4% 2% 0% -35% -30% -25% -20% -15% -10% -5% Maket return 6 months prior to the announcement 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 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. Abandoned Successful Mean Standard deviation Number of observations z-statistic p-value 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

19 Successful Abandoned 45,0% 40,0% 35,0% 30,0% Frequency 25,0% 20,0% 15,0% 10,0% 5,0% 0,0% 0 Blockers 1 Blocker 2 Blockers 3 Blockers 4 Blockers 5 Blockers 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. Abandoned Successful Mean Standard deviation Number of observations z-statistic p-value 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

20 Successful Abandoned 80,00% 70,00% 60,00% Frequency 50,00% 40,00% 30,00% 20,00% 10,00% 0,00% Cash Mixed Stock Method of payment 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. Proportion of population Number of observations Cash Mixed Stock Abandoned Successful Abandoned Successful Abandoned Successful 65.7% 68.3% 11.4% 19.9% 22.9% 11.8% z-statistic p-value 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

21 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. Successful Abandoned 100,0% 90,0% 80,0% 70,0% 60,0% Frequency 50,0% 40,0% 30,0% 20,0% 10,0% 0,0% Positive Neutral Negative Board reaction 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 11.4% 66.7% 40.0% 29.7% 48.6% 3.7% of Population Number of observations z-statistic p-value

22 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. Successful Abandoned 16,0% 14,0% 12,0% Frequency 10,0% 8,0% 6,0% 4,0% 2,0% 0,0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% Voting rights 60% 65% 70% 75% 80% 85% 90% 95% 100% 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. Abandoned Successful Mean Standard deviation Number of observations z-statistic p-value 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

23 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. Successful Abandoned 70,0% 60,0% 50,0% Frequency 40,0% 30,0% 20,0% 10,0% 0,0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Toehold Figure 8. Frequency of toehold for successful and abandoned tender offers. 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% 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. Abandoned Successful Mean Standard deviation Number of observations z-statistic p-value

24 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 Mixed Offer Negative Board n/a n/a n/a Positive Board Neutral Board Management? Toehold Premium Timing Effect? Largest Owner Block-holders Constant n/a

25 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 Neutral Board Largest Owner Stock Offer n/a n/a n/a Not a Stock Offer Constant n/a 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 Observed Successful Abandoned (Type I error) 22 (Type II error) 65.7% % Overall Percentage 87.9% The Nagelkerke R 2 for the final model is which means that 42.6 % of the variation in the outcome variable is explained by the logistic regression model. Further, from the Hosmer 24

26 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 In conclusion the results from the classification table, the Nagelkerke R 2 and the Hosmer and Lemeshow test all indicate that the final model fits the data reasonably well. 25

27 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 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 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 onesided test, where the sample variances, s 2, are used, was applied. 2 H 0 : µ x µ y 0 H : µ µ 0 1 x y < 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