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Market Power, Toeholds and the Takeover Premium August 2013 Robert G. Bowman University of Auckland and University of Queensland Adrian Richards Goldman Sachs, Melbourne, Australia Keywords: takeover premium, market power, toehold JEL classification: G34, G14, L22 Market Power, Toeholds and the Takeover Premium Abstract We provide new evidence that market power is a motivation for takeovers. We develop a proxy based on the market concentration doctrine that measures whether a takeover is likely to cause an increase in the market power of the acquirer. In a test of the determinants of the takeover premium we find that it is over 10% higher when the acquirer expects to gain additional market power in the transaction. This result indicates that, contrary to the existing literature, firms do use the market for corporate control as a tool to increase their market power. We also consider the influence a toehold has on the takeover premium. We develop a non-linear model that allows for a more refined analysis of this affect. Accounting for the degree of influence a toehold provides the acquirer is important when conducting empirical research on the takeover premium. 1. Introduction The mergers and acquisitions market has been a rich source of study in finance and economics. Research has explored many aspects of this market for corporate control including the bidders, the targets, the pre and post transaction performance, bidding strategies and the takeover premium. We focus on two factors that influence the takeover premium – market power and toeholds. We provide new evidence that market power is a motivation for takeovers. In a test of the determinants of the takeover premium we find that premiums are over ten percent higher when the acquirer expects to gain additional market power in the transaction. This result indicates that firms use the market for corporate control as a tool to increase their market power. Existing literature has generally relied on a measure of abnormal returns to proxy for the takeover premium. We use offer price data to estimate the takeover premium, enabling it to account for private information. The results are found in a large sample of US takeovers between 1990 and 2011. Contrary to past results (Eckbo, 1983; Stillman 1983), this study provides evidence that market power is a motivation for horizontal acquisitions. Acquirers are willing to compensate targets for the additional market power they will acquire in a transaction. We also consider the influence a toehold provides the acquirer over the target in arriving at the takeover premium. While previous literature has documented the varying sizes of toeholds, it has failed to directly consider the influence it has on the takeover premium. We develop a non-linear model to capture the influence of different toehold levels. Accounting for the degree of influence a toehold provides the acquirer is important. When the toehold does not provide the acquirer with substantial control, the takeover premium is not significantly lower. This is not consistent with the extant literature. However, when the 1 toehold does provide the acquirer with substantial control, the takeover premium is significantly lower and qualitatively similar to the existing literature. 1.1. Research questions The amount offered in a takeover above the target’s market value is the takeover premium. This should reflect the target’s share of the increased value the acquirer expects to realize from the transaction. Holding the relative bargaining position of the deal parties constant, an increase in acquirer value due to an increase in its market power post acquisition increases the takeover premium. Therefore, the first research question of this study is whether acquirers offer a higher takeover premium in transactions that are expected to increase their market power. By extension, this question also asks whether firms use the market for corporate control as a tool to increase their market power. The second research question asks whether the magnitude of a toehold has a differential impact on the takeover premium. A number of researchers have developed models showing that the takeover premium will be smaller in the presence of toeholds. However, the empirical evidence is mixed. We show that there is a critical toehold level, where the acquirer has substantial influence, that is important in understanding the relationship between toeholds and the takeover premium. The remainder of this study is as follows: Section 2 provides an overview of the relevant literature; Section 3 develops and presents the main models and hypotheses; Section 4 describes the sampling methodology and provides an analysis of the descriptive statistics; Section 5 presents and analyzes the results; Section 6 provides robustness testing and Section 7 concludes. 2 2. Literature review 2.1. Industry organization and firm value A long history of research shows positive correlation between market power and firm profitability (Bain, 1951; Shepherd, 1972, among others). Together, these studies show market power allows firms to earn monopoly rents through higher product prices. Firms obtain this power through market imperfections such as concentrated seller markets and barriers to entry. However, these studies do not provide a direct link between market power and higher firm value. Thomakadis (1976) presents a model linking market power to firm value. He models firm value as the sum of the reproduction cost of existing assets and the capitalized value of future monopoly rents. When market power exists, firm value is the sum of existing assets and expected future excess profits from monopoly rents. He also employs his model to test a sample of 158 firms between 1961 and 1968 (Thomakadis, 1977). The results indicate firm value increases with market power (measured by market concentration). Furthermore, they indicate persistence in monopoly rents. Sullivan (1977) provides empirical evidence that the increased profitability of firms with market power is fully capitalized into their share price. The positive relation between market power and firm value is robust to alternative measures of market power. Lindenberg and Ross (1981) provide evidence based on Tobin’s q that market power is associated with higher firm value. Tobin’s q can be considered a proxy for monopoly rents as a q ratio greater than unity implies firm value greater than the replacement cost. Consistent with Sullivan (1977), the results indicate those firms with greater capacity to extract monopoly rents have higher q ratios, which are greater than one on average. 3 2.2. Market power as a motivation for takeovers The findings of Eckbo (1983) do not support the acquisition of market power as a motivation for takeovers. He argues rival firms will benefit from anticompetitive, horizontal mergers due to the higher probability of successful collusion. This results in higher product prices, which should then result in positive stock returns around the announcement date. However, if an antitrust regulator challenges a takeover, rival firms should experience negative returns. Eckbo examines 259 horizontal and vertical transactions in the mining and manufacturing sectors and finds that while rivals have a significant and positive excess return of 2.45% around the announcement date, they also experience significant and positive returns of 1.78% around the time of a regulatory challenge. Furthermore, these results are statistically independent. Therefore, he concludes that there is no support for the collusion hypothesis. Stillman (1983) also fails to find evidence that the acquisition of market power is a motive for takeovers. Similar to Eckbo (1983), he argues that the benefits of an anticompetitive takeover should result in positive share returns for rivals around the announcement date. To test this, Stillman examines 11 transactions between 1964 and 1972 that antitrust regulators challenged. Like Eckbo (1983) he finds no evidence that additional market power is a motivating factor in takeovers. Eckbo (1985) was the first to use the market concentration doctrine in the context of mergers; (higher) market concentration is directly related to (greater) market power. Using a sample of 266 mergers (196 horizontal, 70 non-horizontal), he finds market concentration does not increase the abnormal returns of rival firms. Therefore, he concludes that an increase in market power is not a motive for horizontal mergers. These results do not support the argument that firms use the market for corporate control as a means of increasing market power. 4 2.3. Toeholds as an advantage in takeovers Toehold positions offer a number of apparent advantages in the event of a decision to pursue a complete takeover. The larger the toehold, the less shares need to be acquired to achieve full ownership. Also, at some level, a toehold may confer information advantages in assessing the value of a prospective target. The existence of a toehold may deter competitive bidding. Similarly, at some toehold level, the target management will be familiar with the management of the toehold company. These factors may result in a lower takeover premium. Although numerous models indicate that toeholds are value adding for bidders, 1 most studies indicate that the level of toeholds in practice is low and declining. 2 Betton and Eckbo (2000) and Betton et al. (2008b) provide results showing a negative association between the level of toehold and the takeover premium for takeovers that are consummated. Betton et al. (2009) examine a sample of over 10,000 US control bids and show that the frequency of toeholds in takeover offers has steadily declined since the early 1980s, but when present, the toehold is relatively large. They find the optimal toehold is either zero or greater than a threshold, which they estimate averages 9%. Chemmanur et al. (2009) show that private information held by the bidders and targets in a takeover has implications for the method of payment, which in turn impacts on valuations, abnormal returns and the likelihood of competing bids. Toeholds have the potential to create private information, but Chemmanur et al. do not explore this issue. 1 See Grosman and Hart (1980), Chowdhry and Jegadeesh (1994), Burkart (1995), Bulow et al. (1996), Goldman and Qian (2005) and Dodonova (2012). 2 See Bradley et al. (1988), Jennings and Mazzeo (1993), Betton and Eckbo (2000), Povel and Sertsios (2013), and especially Betton et al. (2009). 5 Eckbo (2009) provides an excellent review of takeover bidding strategies, including the role of toeholds. He reports (p175) that “… toeholds are large when they exist and that they occur mostly in hostile bids.” 2.4. Takeover premiums in the M&A literature 2.4.1. Measuring the takeover premium Studies of takeover premiums predominantly use abnormal returns as the measure of the takeover premium. For example, Schwert (1996) presents the takeover premium as the sum of the runup and a markup, where runup is the change in the share price over a period prior to announcement and markup is the increase in the share price on the announcement date. The Takeover offer is not included in this calculation. Other studies measure the takeover premium as the ratio of the offer price relative to the target’s share price at some point prior to the announcement date (Betton and Eckbo, 2000; Bange and Mazzeo, 2004; Betton et al., 2009). These studies use varying lengths of time prior to the takeover announcement. It is reasonable to assume that in any takeover transaction there is significant information asymmetry between deal parties and the market. Acquirers have an incentive to increase the information asymmetry between themselves and the market, resulting in share prices that are less likely to reflect expected gains from a transaction. When the acquirer has been in discussions with the target prior to announcement, the target will have a better understanding of the expected gains from the transaction. This implies that information asymmetry is lower between the deal parties than between the acquirer and the market. We believe the difference between the market price of the target immediately before the takeover announcement and the offer price is the best reflection of the value expected to be gained from the transaction. This is our measure of the takeover premium. 6 2.4.2. Determinants of the takeover premium The focus of this study is the initial offer premium. Therefore, when considering the determinants of the takeover premium it is important to ensure that information about the determinants is likely to be known prior to announcement. 3 In this context, the impact of target, acquirer and deal characteristics on the takeover premium are important. The existing literature has investigated the relation between several key target characteristics and the takeover premium. Betton et al. (2008b) were the first to show that a runup increases the markup (and by necessity the premium). A $1 runup increases the offer price by approximately $0.84. Acquirers do not respond fully to changes in the target’s share price over the runup period. Target size and book-to-market are typically included as control variables in studies of the takeover premium. Empirical results indicate the former is negatively and the latter positively correlated with the takeover premium (Officer, 2003; Betton et al., 2008b, 2009). The existing literature also investigates the relation between several key acquirer characteristics and the takeover premium. Betton and Eckbo (2000) and Betton et al. (2009) estimate the effect of toeholds on the offer premium. They find negative correlation between toeholds and the offer premium. However, while both studies document that toeholds range in size, neither consider the potential implications of this on the takeover premium. We fill this gap. Furthermore, Bargeron et al. (2008) find that public acquirers tend to offer larger premiums compared to private acquirers. They attribute the result to over bidding by public acquirers due to lower managerial ownership. The existing literature also considers deal characteristics. Huang and Walkling (1987) find evidence that higher abnormal returns characterize tender offers. In a sample of 204 control contests abnormal returns in tender offers are 4.7% higher. However, when using a premium 3 Studies that consider post-bid information include Officer (2003, 2004) and Bates and Lemmon (2003) who examine the role of deal protections, and Betton and Eckbo (2000) who examine the effect of bid jumps. 7 based on the offer price Betton et al. (2009) find no relation between offer premiums and tender offers. Schwert (2000) finds that hostile responses increase the takeover premium, arguing this result is consistent with more aggressive bargaining by target managers. Gilson et al. (1988) argue cash-offers will increase the takeover premium as the acquirer must compensate the target’s shareholders for higher capital gains tax. However, while Eckbo and Langohr (1989) fail to find compelling evidence to support this argument they, along with de la Bruslerie (2013), find a positive empirical relation. 2.5. Summary The existing literature shows that market power translates into market value. Furthermore, while the previous literature rejects market power as a motivation for takeovers, we provide a new approach that supports the market power dimension of takeovers. We also investigate the impact of toeholds on the takeover premium using a non-linear model. We show that the level of the toehold is critical in estimating the impact. Low levels of toehold do not seem to influence the premium, but substantial levels, above 20%, do reduce the premium. 3. Model development and hypotheses 3.1. The takeover premium The takeover premium is our primary dependent variable. It represents the amount above the target’s market price the acquirer is willing to pay to acquire control. We measure the premium as the difference between the initial offer price per share made by the acquirer (t = 0) and the target’s share price one day prior to announcement (t = -1), relative to the target’s share price one day prior to announcement. That is: 𝑃𝑟𝑒𝑚𝑖𝑢𝑚 = 𝑖𝑛𝑖𝑡𝑖𝑎𝑙 𝑜𝑓𝑓𝑒𝑟 𝑝𝑟𝑖𝑐𝑒−𝑡𝑎𝑟𝑔𝑒𝑡 𝑠ℎ𝑎𝑟𝑒 𝑝𝑟𝑖𝑐𝑒𝑡−1 𝑡𝑎𝑟𝑔𝑒𝑡 𝑠ℎ𝑎𝑟𝑒 𝑝𝑟𝑖𝑐𝑒𝑡−1 (1) Measuring the takeover premium in this way provides two advantages: (1) it standardizes the takeover premium measure for the target size, providing easily interpretable and comparable 8 results across events; and (2) it provides a very short period from which to obtain an objective measure of the takeover premium. We discuss this in more detail in Section 3.2.1. 3.2. Target, acquirer and deal related variables Consistent with Betton et al. (2008a), we place each independent variable in one of three groups; target characteristics, acquirer characteristics or deal characteristics. We provide an explanation of the calculation of each variable, its reason for inclusion and the expected direction of its relation with the takeover premium. 3.2.1. Target characteristics The first target characteristic included in the model is the target’s size (Size). This variable is measured by the market value of the target four calendar weeks prior to the announcement of the deal on a fully diluted basis. The expected relation between Size and Premium depends on the relative strength of two effects, the ‘bargaining power effect’ and the ‘absolute premium effect’. The ‘bargaining power effect’ refers to the superior bargaining position large target firms hold in deal negotiations due to their capacity to hire sophisticated legal and financial advisors and the experience of their management and board. This power allows the target to capture a greater proportion of the incremental value of the transaction and hence, a higher premium. On the other hand, the ‘absolute premium effect’ refers to the greater proportion of the acquirer’s equity value an increase in the premium represents when the target is large. This implies that acquirers will be less willing to offer a higher premium the higher the target’s size. A conflict exists between these two effects resulting in the expected direction of the relation between Size and Premium being indeterminable ex ante. The second target characteristic included in the model accounts for a target’s stock exchange listing (Major Exchange). This binary variable is one if the target is listed on the American Stock Exchange, the NYSE or the NASDAQ, and zero otherwise. It should be positively correlated with the Premium variable for two reasons. First, firms listed on these 9 exchanges are more likely to have sophisticated boards and management, giving them greater bargaining power than those listed on lesser known exchanges. Second, due to the greater exposure of these exchanges to retail investors, it is likely that the share register of firms listed on these markets is more diffuse. This creates the potential for the free-rider issue identified by Grossman and Hart (1980). When there is no major shareholder to discipline management, shareholders will free-ride off the value enhancing actions of the bidder as they know the value of control to the bidder is greater than zero. Therefore, shareholders will demand a premium over the current market value of their shares up to the value the bidder expects to generate by having that control. We expect the Major Exchange variable to be positively correlated with the Premium variable. The third target characteristic included in the model is the target’s equity book-to-market ratio (BM). Book value is the target’s accounting equity as of the date of the most current financial information prior to the announcement, and market value is the value of the target’s equity four calendar weeks prior to the announcement of the deal. Low BM firms are considered growth firms as the firm’s relatively high market value implies the market’s expectations about the firm’s future earnings are high. In the context of contests for corporate control we assume if a firm has more growth opportunities then the value of being able to control these is higher, and the premium an acquirer would be willing to pay would be higher. Therefore, as a higher BM implies that the firm has relatively fewer growth opportunities, the relation between the BM and Premium variables should be negative. The fourth target characteristic included in the model is the target’s runup (Runup). This variable is calculated as the difference between the target’s stock price one day prior to announcement and its stock price four calendar weeks prior to announcement, relative to its stock price four weeks prior to announcement. That is: 𝑅𝑢𝑛𝑢𝑝 = 𝑡𝑎𝑟𝑔𝑒𝑡 𝑠ℎ𝑎𝑟𝑒 𝑝𝑟𝑖𝑐𝑒𝑡−1 −𝑡𝑎𝑟𝑔𝑒𝑡 𝑠ℎ𝑎𝑟𝑒 𝑝𝑟𝑖𝑐𝑒𝑡−28 𝑡𝑎𝑟𝑔𝑒𝑡 𝑠ℎ𝑎𝑟𝑒 𝑝𝑟𝑖𝑐𝑒𝑡−28 (2) 10 If the offer price is set some time before the announcement date and the share price increases over the runup period, it should not have a significant impact on the offer price, but the takeover premium will be relatively lower. Therefore, we expect negative correlation between this variable and the takeover premium. 3.2.2. Acquirer characteristics The first acquirer characteristic included in the model is the toehold variable (Toehold). We measure this variable as the percentage of the target’s outstanding shares the acquirer owns prior to the announcement of the transaction. The expected relation between Toehold and the Premium variable is negative. As discussed earlier, when a target has a diffuse share register, shareholders may be able to demand a premium over the current market value of their shares up to the value the bidder expects to generate by having control. Therefore, when an acquirer has a toehold in the target, it reduces the degree to which they need to compensate the free-riding shareholders (Shleifer and Vishny, 1986; Hirshleifer and Titman, 1990). Betton and Eckbo (2000) provide empirical evidence consistent with a negative relation between these variables. The second acquirer characteristic identifies publicly listed acquirers (Public). This binary variable is one for a publicly listed acquirer and zero otherwise. Consistent with the findings of Bargeron et al. (2008), the expected relation between this variable and the offer premium is positive. As discussed in Section 2.4.2, they find that public acquirers tend to overbid for targets, relative to private companies. Managers’ ownership stakes in their company is low. This tendency to overbid causes takeover premiums to be higher when the acquirer is public. The third acquirer characteristic identifies horizontal takeovers (Horizontal3). This binary variable is one if the acquirer has the same three-digit SIC code as the target and zero otherwise. We expect this variable to be positively correlated with the takeover premium due to increased potential for synergy gains and less information asymmetry between bidder and 11 target. This will increase the maximum premium the acquirer is willing to pay and improve the target’s ability to determine the synergy value. Therefore, the premium is expected to be higher as the target’s bargaining position improves and the value available to target shareholders increases. 3.2.3. Deal characteristics The first deal characteristic included in the model accounts for the type of consideration offered. This binary variable (Cash) is one if the entire consideration offered by the acquirer is cash and zero otherwise. We expect this variable to have a positive relation with the takeover premium. As discussed in Section 2.4.2, Gilson et al. (1988) argue that acquirers need to compensate target shareholders for the relatively higher capital gains tax paid in cash offers. While, Eckbo and Langohr (1989) fail to find compelling evidence for this argument they, along with de la Bruslerie (2013), find a positive empirical relation between the two. The second deal characteristic included in the model accounts for the form of the transaction (Tender). This binary variable is one if the deal is a tender offer and zero otherwise. We expect the relation between Tender and Premium to be positive. Tender offers are a more aggressive style of takeover, which requires more aggressive bidding. This could lead to a strategic choice by the acquirer to increase the initial offer price to reduce the probability of a competing bid. Kohlers et al. (2007), however, show tender offers are more likely to be all cash. Therefore, given the expectation that cash offers are associated with higher premiums, it may be the positive effect of cash driving the result. In any case, Eckbo and Langohr (1989), Franks and Harris (1989), and Nathan and O’Keefe (1989) find that tender offers are correlated with higher offer premiums. The third deal characteristic accounts for the expected target response (Hostile). This binary variable is one if the initial response of the target to the acquirer’s offer is hostile and zero otherwise. We expect this variable to correlate positively with the Premium variable. 12 Importantly, as the acquirer decides upon its initial offer premium the target’s response is not known. Therefore, this variable accounts for the acquirer’s expectations about the target’s reaction. As a hostile response is likely to reduce the probability of a successful bid, an acquirer may compensate for this by increasing the offer price. Table 1 provides a summary of our expectations for the relations the target, acquirer and deal characteristic variables have with the takeover premium. -------------------------------------Insert Table 1 about here -------------------------------------3.3. Market power related variables We expect that market power will be important in explaining the takeover premium. A number of variables capture elements of market power. Of interest to us is the additional market power that the acquirer expects to gain from the transaction. This is consistent with the nature and measure of the takeover premium as the amount offered above the target’s market value. Measures of the existing market power of the target or acquirer are useful only as an aid in the estimation of the additional market power generated by the transaction; they are not directly associated with the premium. 3.3.1. Herfindahl-Hirschman Index The Herfindahl-Hirschman Index (HHI) is a commonly used measure of market concentration. This measure proxies industry concentration by summing the squared market shares of each firm in an industry where market share is the firm’s proportion of the total industry sales (by three-digit SIC code). That is: 𝑠 𝐻𝐻𝐼𝑆𝐼𝐶 = ∑𝑛𝑖=1( 𝑆𝑖 )2 (3) where si is the total sales of firm i, and S is the total sales of the industry. A measure close to the lower bound of zero indicates a highly competitive industry, and a measure close to the upper bound of one indicates a highly concentrated industry. 13 Importantly, HHI is a measure of existing market concentration, not a measure of the expected change in the market power due to the transaction. 3.3.2. Top four firms The acquirer’s existing market position may be an indicator of the potential change in market power due to the transaction. The four-firm concentration ratio measures the level of industry concentration based on the market share of the top four firms in a four-digit SIC code industry. That is: 𝑠 4𝐹𝐶𝑅𝑆𝐼𝐶 = ∑4𝑖=1( 𝑆𝑖 )2 (4) Like HHI, it is bounded by approximately zero and one. A convenient characteristic of this measure is that if the acquirer is a top four firm in its industry and the target is in the same industry, industry concentration, and hence market power, must increase. A binary variable (Acquirer top 4) is one if the acquirer is a top four firm (by four-digit SIC code) and zero otherwise. Similarly, Target top 4 is one if the target is a top four firm and zero otherwise. The top firms in an industry are likely to be large, so we expect correlation between Acquirer top 4 and Size. Large acquirers are likely to have superior bargaining power, so we anticipate positive correlation with the Premium variable. Therefore, to interpret this variable as a measure of the incremental market power expected from the transaction, we remove the effect using an interaction variable as explained in Section 3.6. 3.3.3. Regulatory challenge Section 7 of the Clayton Act prohibits a merger or acquisition “if in any line of commerce or in any activity affecting commerce in any section of the country, the effect of such acquisition may be substantially to lessen competition, or to tend to create a monopoly.” Sections 1 and 2 of the Sherman Act, and Section 5 of the Federal Trade Commission Act, have similar provisions to ensure that companies do not use the market to materially increase their market power (Department of Justice and Federal Trade Commission, 2010). 14 The regulatory agencies that seek to enforce these laws are the Department of Justice and the Federal Trade Commission. They use several analytical techniques to, “identify and challenge competitively harmful mergers while avoiding unnecessary interference with mergers that are either competitively beneficial or neutral” (Department of Justice and the Federal Trade Commission, 2010). These techniques include both quantitative (such as the cross-price elasticity of products and the HHI) and qualitative measures (such as discussions with customers and other interested parties). This implies that, when a regulator challenges a transaction it has estimated the expected effect of the transaction on the market. Therefore, whether a transaction is challenged by a regulator is a proxy for transactions that are expected to lead to a material change in market power. However, using it in this way is limited by some real world complications. The pragmatic realities of regulation prohibit the use of a regulatory challenge as a direct measure of the expected increase in market power. Regulators’ resources are small relative to the resources required to consider all mergers where market power might materially change. Also, when deciding whether to investigate a transaction, regulators often rely on interested parties to request a challenge to the transaction (Posner, 1969). Together, these limit the use of a regulatory challenge as a direct proxy for an increase in market power as challenges may be based on considerations other than the expected change in market power. 3.3.4. Measuring incremental market power Of the available options to measure the expected change in market power as a result of a transaction, we regard whether the acquirer is one of the top firms in its sector as the best candidate. It is an objective measure of the expected increase in market power as a result of the transaction. However, as discussed in Section 3.3.2 the market power of the acquirer is most likely to change when it is acquiring a firm in the same industry. Therefore, we interact the Acquirer top 4 variable with a new binary variable, Horizontal2, that is one if the acquirer 15 and target have the same two-digit SIC code and zero otherwise. This interacted variable (Acquirer top 4*Horizontal2) proxies for the incremental market power created in a transaction through related operations. 3.4. Toeholds The extant literature on takeovers and market power frequently include a toehold variable. An implicit and significant assumption in the existing treatment of toeholds is that a toehold of any size will allow the acquirer to influence the size of the offer premium. For example, Betton et al. (2009) find that, across their entire sample, if the acquirer increases its toehold by 1% the initial offer premium is reduced by approximately 10%. This implies that, whether the acquirer increases their toehold from 1% to 2% or from 10% to 11% they can expect to reduce the offer premium by 10%. However, it seems likely that the latter increase will have more impact on the acquirer’s influence, and hence more effect on the offer premium. Assuming a toehold of any size will provide the acquirer with comparable influence over the target is not likely to be correct. The existing literature potentially overstates the value of a small toehold and understates the value of a large toehold. To provide an objective measure of the influence of a toehold, we adopt the accounting guidance in the Statement of Financial Accounting Standards No. 115, Investments in Debt and Equity. The standard does not consider a toehold of less than 20% to have substantial influence. 4 It considers companies that own more than 20% but less than 50% of another company to have substantial influence. Companies that own 50% or more of another company are considered to have control over the company. We exclude these from our sample. We use these definitions to provide a non-linear measure of the importance of toehold in explaining the takeover premium. 4 The accounting regulation uses the term ‘significant influence’. However, to avoid confusion when discussing the statistical results, we use the term ‘substantial influence’. 16 To account for toehold influence we create the variable Small Toehold. This binary variable is one if the toehold is greater than 0% but less than 20%. This will differentiate between toeholds based on whether they provide the acquirer with substantial influence over the target. We expect that the previously observed value of toeholds will exist only when the toehold is greater than 20%. 3.5 Variable transformations The statistical properties of the variables presented above may not conform to the assumptions of our test procedures. For each key variable, we consider standard transformations and adjustments. We chose the form of the variable that has the least violation of the assumptions of our methodologies. The changes made here will not alter the directional expectations and economic reasoning presented in Section 3. 3.5.1. Premium The Premium variable is not normally distributed – there is positive skewness and significant excess kurtosis. We consider a logarithmic (log) transformation, which materially improves its distributional qualities, confirmed by the improvement in the Jarque-Bera statistic and the skewness and kurtosis measures. Therefore, the transformed variable, lnPremium will replace Premium in our tests. Transforming the Premium variable reduces the effect of significant outliers without their explicit removal. This allows the information in these variables to be captured in testing procedures, but dampens any potential undue influence they may otherwise have on the results. 3.5.2. Target size The distribution of Size is highly, positively skewed with a significant excess kurtosis. This implies that, while the majority of the observations in the sample have a market value four calendar weeks prior to announcement of between US$50 million and US$200 million, there 17 are a few very large firms that are causing the distribution to violate the normality assumption. Consistent with much of the finance literature, we use lnSize in our tests. 3.5.3. Runup The Jarque-Bera statistic indicates that the distribution of the Runup variable is significantly different from a normal distribution, with positive skewness and kurtosis. Therefore, we again test the log transformation of one plus the Runup variable. Unlike the Premium and Size variables, the transformation process does not improve the distribution and characteristics of the Runup variable. While the process improves the skewness (1.17 to 0.47), there is an increase in kurtosis (8.03 to 10.01) and the Jarque-Bera statistic deteriorates from 2322 to 3759. As there is no material improvement in the statistical properties of Runup, the transformed variable we will not adopt a transformation. 3.5.4. Toehold It is evident from the distribution of the Toehold variable for the Small Toehold Sample that the use of a continuous variable for toeholds when the acquirer holds less than 20% is inappropriate. There is a small number of observations that are greater than zero. We will use a binary variable, DToehold, that will be one when the acquirer’s toehold is greater than 0% but less than 20% and zero otherwise. When considering the Toehold variable in the Full Toehold Sample, it is the incremental additions that are of concern. There are only 34 transactions where the acquirer’s toehold was greater than 20% and less than 50%. From our analysis, it is clear that that the distribution of the Large Toehold variable, with a Jarque-Bera statistic of 1.58, is not statistically different from a normal distribution. 3.6. Models and hypotheses Section 3.2 provided a description of the key variables relevant in determining the size of the takeover premium. Section 3.3 presented proxies for transactions where the acquirer’s 18 market power is expected to increase. Section 3.4 discussed the role influence plays in toeholds. Section 3.5 considered whether transformations are appropriate for the variables. The variables and associated discussion in these sections form the basis of our models. The first model estimates the relation between the additional market power an acquirer is expected to receive in a transaction and the takeover premium. The second model builds on the first to improve the method by which the toehold is accounted for by incorporating the role of toehold influence. The third model provides a statistical justification for the use of the variables used to measure market power. 3.6.1. Model 1 and hypotheses The relative bargaining power of the deal parties must be accounted for to ensure the market power proxy provides the incremental effect of market power on the premium. Therefore, the Acquirer top 4 variable is included separately. This ensures that the market power poxy Acquirer top 4*Horizontal2 represents the incremental effect of a top four firm acquiring a firm in the same industry, absent any bargaining power effect due to acquirer size. Furthermore, to account for the relative bargaining power of the target, we interact the incremental market power proxy with the Size variable. This accounts for any confounding effects caused by a large target with a strong bargaining position. Model 1 𝑙𝑛𝑃𝑟𝑒𝑚𝑖𝑢𝑚 = β0 + β1 𝑙𝑛𝑆𝑖𝑧𝑒 + β2 𝐵𝑀 + β3 𝑅𝑢𝑛𝑢𝑝 + β4 𝑀𝑎𝑗𝑜𝑟 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 + β5 𝐷𝑇𝑜𝑒ℎ𝑜𝑙𝑑 + β6 𝑃𝑢𝑏𝑙𝑖𝑐 + β7 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙3 + β8 𝐶𝑎𝑠ℎ + β9 𝑇𝑒𝑛𝑑𝑒𝑟 + β10 𝐻𝑜𝑠𝑡𝑖𝑙𝑒 + β11 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 + β12 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 ∗ 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙2 + β13 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 ∗ 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙2 ∗ 𝑙𝑛𝑆𝑖𝑧𝑒 + ε Our first hypothesis is: (5) H1a: when an acquirer is a top four firm and is acquiring a target in the same industry it will offer a higher takeover premium. 19 A significant positive relation between Premium and Acquirer top 4*Horizontal2 will confirm this hypothesis. As discussed in Section 3.4, it is unlikely that toeholds of less than 20% will provide the acquirer with substantial influence over the target. Therefore, our second hypothesis is: H1b: when the acquirer has a toehold of less than 20% it will not be able to reduce the takeover premium. In the context of Model 1, there will not be a significant relation between Premium and Toehold. 3.6.2. Model 2 and hypotheses As discussed in Section 3.4, by failing to account for the degree of influence the toehold provides the acquirer, the benefit of having a toehold may be over or under stated. Therefore, we develop a model that explicitly accounts for the degree of influence the toehold provides. This model is the same as Model 1 but with an adjustment to account for toehold size. Model 2 𝑙𝑛𝑃𝑟𝑒𝑚𝑖𝑢𝑚 = β0 + β1 𝑙𝑛𝑆𝑖𝑧𝑒 + β2 𝐵𝑀 + β3 𝑅𝑢𝑛𝑢𝑝 + β4 𝑀𝑎𝑗𝑜𝑟 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 + β5 𝐷𝑇𝑜𝑒ℎ𝑜𝑙𝑑 + β6 (1 − 𝐷𝑇𝑜𝑒ℎ𝑜𝑙𝑑) ∗ 𝑇𝑜𝑒ℎ𝑜𝑙𝑑 + β7 𝑃𝑢𝑏𝑙𝑖𝑐 + β8 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙3 + β9 𝐶𝑎𝑠ℎ + β10 𝑇𝑒𝑛𝑑𝑒𝑟 + β11 𝐻𝑜𝑠𝑡𝑖𝑙𝑒 + β12 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 + β13 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 ∗ 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙2 + β14 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 ∗ 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙2 ∗ 𝑙𝑛𝑆𝑖𝑧𝑒 + ε This model allows us to test the following hypothesis in relation to the toehold variable: (6) H2: only toeholds that provide the acquirer with substantial influence will reduce the takeover premium. To confirm this hypothesis, there will be not be a significant relation between Premium and Small Toehold*Toehold, and there will be a significantly negative relation between Premium and (1-Small Toehold)*Toehold. 20 3.6.3. Probability of a regulatory challenge As discussed in Section 3.3.3, regulators judge whether a transaction will materially affect market power. Therefore, we develop a model that estimates the key variables that explain a regulator decision to challenge a transaction. We use a probit model to provide an indication of the target, acquirer and deal characteristics that increase the probability of a regulatory challenge. By extension, this indicates which variables aid in determining whether a transaction will increase the market power of the acquirer. Model 3 𝐶ℎ𝑎𝑙𝑙𝑒𝑛𝑔𝑒𝑑 = α0 + α1 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 + α2 𝑇𝑎𝑟𝑔𝑒𝑡 𝑡𝑜𝑝 4 + α3 𝐷𝐻𝐻𝐼 + α4 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙2 + α5 𝑆𝑡𝑎𝑡𝑒 + α6 𝐷𝐵𝑀 + α7 𝐷𝑆𝑖𝑧𝑒 + α8 𝐷𝑅𝑂𝐴 + α9 𝑀𝑎𝑗𝑜𝑟 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 + α10 𝐷𝑇𝑜𝑒ℎ𝑜𝑙𝑑 + α11 𝐷𝐿𝑎𝑟𝑔𝑒 𝑇𝑜𝑒ℎ𝑜𝑙𝑑 + α12 𝑃𝑢𝑏𝑙𝑖𝑐 + α13 𝐶𝑎𝑠ℎ + α14 𝐻𝑜𝑠𝑡𝑖𝑙𝑒 + α15 𝑇𝑒𝑛𝑑𝑒𝑟 + ε Where new variables are: Challenged (7) is 1 if the deal is challenged by the Department of Justice or the Federal Trade Commission and 0 otherwise, DHHI is 1 if the HHI of the target’s three-digit SIC industry is greater than 0.2 (indicating moderate to high concentration) and 0 otherwise, State is 1 if the target and the acquirer are headquartered in the same state and 0 otherwise, DBM is 1 if the target’s book-to-market is greater than the industry average (by three-digit SIC code) and 0 otherwise, DSize is 1 if the target’s market value four weeks prior to announcement is greater than the industry average (by three-digit SIC code) and 0 otherwise, DROA is 1 if the target’s return on assets is greater than the industry average (by three-digit SIC code) and 0 otherwise, DLargeToehold is 1 if the toehold is greater than 20% but less than 50% and 0 otherwise, 21 3.7. Summary We develop three models in this section. The first model is to determine whether an acquirer in a corporate transaction pays a higher premium when it is expected that the acquirer’s market power will increase due to the transaction. We do this by establishing a set of explanatory variables based on the existing literature and positing a proxy for the expected increase in market power due to the transaction. We then refine the model to directly account for the degree of influence a toehold provides the acquirer. Lastly, by exploiting the regulatory environment, we present a model that provides a more robust basis on which to assert the appropriateness of the market power variables posited in Model 1 and Model 2. 4. Sampling 4.1. Transaction data We sourced the transaction data for this study from the ThomsonONE database. To be included in our sample: (1) both the target and acquirer must be US companies, (2) the target must be a public company, (3) the deal type must not be a recapitalization, exchange offer or repurchase, (4) any toehold must be less than 50%, and (5) the transaction must have been announced between January 1990 and December 2011. These restrictions ensure that: (1) no cross border transactions are included in the sample, (2) takeovers are not included if the bidder already has control of the target, (3)information regarding the target’s stock price and financial position is available, and (4) transactions are included regardless of whether they are ultimately successful. To ensure transactions are an attempt to acquire the entire target and the target is a separate entity to the acquirer, we remove all buybacks and acquisitions of specific assets. We exclude all mining, financial and telecommunications companies and transactions where the equity value of the target four calendar weeks prior to the announcement date was less than US$50 million. We also removed transactions if the initial offer price, the target’s share price one day 22 prior to the announcement date, the target’s share price four calendar weeks prior to the announcement date or the book value of the target were missing. To construct the market power variables described in Section 3.3, we collected data from the Compustat database. Finally, we removed any transaction where the takeover premium was less than 0% or greater than 200%. An acquirer offering a target something less than its current market price either has a dominant bargaining position or is not making a serious takeover attempt and a target being offered three times its current market price has either a dominant bargaining position or is substantially smaller than the acquirer and hence, the absolute value of the premium relative to the acquirer’s assets is small. Such transactions are not representative of a normal transaction and simply introduce unnecessary noise. For testing Model 1, we exclude transactions where the acquirer’s toehold in the target was greater than 20%. This is the Small Toehold Sample where we assume none of the acquirers have substantial influence over the target. For Model 2 we include transactions where the acquirer had a toehold greater than 20% but less than 50%. This is the Full Toehold Sample. The sample selection process deleted observations from the sample based on three criteria: (1) the transaction was not representative of a normal transaction; (2) data which materially affected the calculation of key variables we missing; and (3) the transaction could not be matched correctly across the two databases. 4.2. Description of samples 4.2.1. Small toehold sample Panel A of Table 2 provides descriptive statistics for the Small Toehold Sample. In this sample, the average takeover premium is 33.81% of the target’s share price one day prior to announcement. Taking the median firm size of US$282.27 million, this represents an average 23 premium of US$95.45 million. 5 Including the share price runup, the takeover premium based on the target’s share price four calendar weeks prior to announcement is 40.05%. Overall, these statistics indicate that the sample of takeover premiums for this study is qualitatively similar to those collected in the previous literature -------------------------------------Insert Table 2 about here -------------------------------------The target characteristics indicate the average firm in the sample is medium sized and 95.4% are listed on a major US exchange. The target book-to-market ratio of 0.5126 indicates that the targets would not generally be considered growth firms. Lastly, the average runup over the 27 days prior to the base period (6.23%) is consistent with some market anticipation of the transaction. Overall, the target characteristics are consistent with medium size firms that are past their growth phase. The bidder characteristics indicate that toeholds uncommon in this sample with only 5.65% of acquirers having a stake in the target. Furthermore, when a toehold is present, the average size is 9.06%. This supports the use of a binary variable when accounting for the toehold. Other bidder characteristics indicate that the majority of acquirers are public companies (65.39%) and less than half (39.65%) of transactions occur between firms in the same sector. The deal characteristics indicate that approximately half the transactions are all cash offers and approximately a quarter are tender offers. As these variables are binary, we can interpret their means as the percentage of the sample that exhibits the characteristic. Interestingly, only 3.38% had a hostile response from the target, a percentage that is low given previous literature. However, the period of the study (1990 to 2011) was not marked by aggressive takeover activity. 5 We use median firm size due to the obvious skew in the distribution of firm size. 24 4.2.2. Full toehold sample To test Model 2 we add those transactions where the toehold is greater than 20% but less than 50% to the sample. This resulted in a sample of 1,840 observations. Table 2 Panel B provides a summary of the descriptive statistics and how they changed relative to the Small Toehold Sample. Table 2 Panel B indicates that, except for Toehold, there is no material change in the means of the variables. This indicates that the observations where substantial influence was present do not differ materially from the observations in the Small Toehold Sample except for the size of their toehold. Panel B also shows that when an acquirer does have an influential toehold, they tend to have substantially more than 20% with the average influential toehold in the sample being 33.76%. 4.3. Summary This section described the method we used to collect the primary samples for this study. It has also provided an analysis of the descriptive statistics of the sample. The section considered the statistical properties of each of the continuous variables except for BM. Inspection of the distributions of these variables suggested that several transforms were appropriate. This resulted in three new variables being adopted; lnPremium, lnSize and DToehold. Finally, the models in Section 3.5 were revised to reflect these changes, none of which resulted in a change in the economic interpretation of the results. 5. Results 5.1. Univariate analysis Table 4 Panel A provides correlations between variables in the Small Toehold Sample and Panel B correlations in the Full Toehold Sample. Several observed correlations between lnPremium and the model variables are of interest. The negative relation between lnSize and lnPremium provides evidence that the ‘absolute premium effect’ is stronger than the 25 ‘bargaining power effect’. Second, the positive relation between BM and lnPremium is inconsistent with expectations. This may be explained by the correlation between BM and lnSize as larger targets tend to have lower BM ratios and also tend to have lower takeover premiums. Third, the negative relation between the Runup and lnPremium suggests that acquirers base the offer price on the target’s share price some time before the announcement. Several correlations among the explanatory variables are also of interest. First, public acquirers tend to make fewer all cash offers. This may be because the target shareholders do not find exchanging their public and liquid shares for illiquid shares in the bidder. Second, the runup in a target’s share price is higher when the acquirer is public, a result potentially due to higher disclosure requirements of public firms increasing the probability of a leak. Third, acquirers who are a top four firm are likely to be publicly listed and larger firms. 5.2. Model 1 This section provides the results from Model 1 posited in Section 3.5.1. When interpreting these results it is important to remember that the coefficient estimates are the relations with lnPremium. When considering the relation to the takeover premium this transformation must be reversed. For convenience, the converted estimates are provided in the results table and referred to throughout the discussion. 5.2.1. Explanatory variables The results from Model 1 run of the Small Toehold Sample are provided in Table 5. Before considering the additional market power variables, we discuss the explanatory variables described in Section 3.2. Of the target characteristics, three of the four variables are significantly different from zero. First, lnSize is significantly different from zero and negative. This implies that the ‘absolute premium effect’ is greater than the ‘bargaining power effect’ described in Section 3.2.1. Second, BM is significant and positive, a result that is contrary to our expectations. As 26 discussed above, a possible explanation for this is the correlation between BM and lnSize. Target size is negatively correlated with the takeover premium and with BM. It may that the negative relation between BM and Premium is driven by a ‘size relation’ as larger firms tend to have lower BM, implying a positive relation between BM and Premium. Third, Runup is negative and significant, a result consistent with expectations. This implies that acquirers do not adjust the offer price in response to target share price movements over the runup period. This supports the argument that acquirers set the offer price some time before the announcement of the transaction. Lastly, the Major Exchange variable is only weakly significantly different from zero in the Full Toehold Sample. Only five percent of targets are not listed on a major exchange so there is little dispersion in the variable. -------------------------------------Insert Table 4 about here -------------------------------------The relations between the acquirer characteristics and the takeover premium are as expected. DToehold is not significant. This is consistent with Hypothesis H1b that only toeholds that provide the acquirer with substantial influence result in a lower takeover premium. Secondly, consistent with expectations, Public is significantly different from zero and positive. Public companies are more likely to offer higher premiums, perhaps due to agency issues. Lastly, Horizontal3 is significant and positively correlated to the takeover premium. This is consistent with the argument that targets operating in the same industry as the acquirer have superior bargaining power due to a reduction in information asymmetry. Some of the expectations regarding the deal characteristics were not supported. Cash is significant and negatively correlated with Premium (-0.0256). This is inconsistent with the discussion in Section 3.2.3 and the empirical evidence in the extant literature. A potential explanation for this is that targets in transactions where 100% of the consideration is cash tend to be smaller. This is consistent with the negative correlation between Cash and lnSize. 27 Secondly, the relation between Tender and Premium is significant and positive (0.0428). This result is consistent with the expectation that tender offers attract higher offer premiums due to their aggressive nature. Lastly, Hostile is not significantly different from zero. This may result from only 3.38% of the transactions being hostile. Together, these results indicate that the size of the deal parties is an important issue when considering the drivers of the takeover premium. This provides support for the use of the interaction variables when estimating the relation between the additional market power expected to be generated in the transaction and the takeover premium. Furthermore, these results provide evidence that the toehold only allows the acquirer to reduce the takeover premium when it does not have substantial influence. 5.2.2. Market power variables As mentioned, the results provide support for the need to control for the effect of target and acquirer size when considering the effect of additional market power. The result for Acquirer top 4 is mildly significant and positive (0.0246). This is as expected and is likely due to the ‘absolute premium effect’ previously discussed in relation to public acquirers. The interactive Acquirer top 4*Horizontal2*lnSize is significant and negative (-0.0175). This is consistent with the correlations observed and the discussion above with regards to target size. The results provide support for Hypothesis H1a and the aim and motivation of this research. We found the expected significant and positive relation between the expected additional market power proxy, Acquirer top 4*Horizontal2. These results indicate that when an acquirer is a top four firm and it is acquiring a firm in the same industry it, on average, offers a takeover premium 11.76% higher than that offered by other acquirers who are top four firms and are acquiring targets of a similar size. This result is not just statistically significant; it is also economically significant. It implies that for the average target, an acquirer is willing to pay over US$143 million above the target’s market value. 28 5.2.3. Summary This section has presented and discussed the results of Model 1 posited in Section 3.5.1. Overall, the results of the explanatory variables are largely as expected. Even, where the results diverge from expectations, there are reasonable explanations for each. Furthermore, these results indicate the role of size in determining the takeover premium. This highlights the importance of controlling for any potentially confounding size effects when estimating the effect of market power on the takeover premium. The results in this section provide support for hypothesis H1a. The results indicate that a transaction where the acquirer is a top four firm and is acquiring a firm in the same industry it will on average have a higher takeover premium. The positive relation between Premium and Acquirer top 4*Horizontal2 provides support. The results in this section also provide support for hypothesis H1b. On average, having a toehold of less than 20% in the target does not reduce the takeover premium offered by the acquirer. This is evidenced by the insignificant relation between Premium and DToehold. 5.3. Model 2 This section presents and discusses the results from Model 2 posited in Section 3.5.2. As discussed in Section 4.2 the Full Toehold Sample implemented does not materially differ from the Small Toehold Sample except for the toehold variable and additional observations. Table 5 presents the results of this model. -------------------------------------Insert Table 5 about here -------------------------------------5.3.1. Explanatory variables (excluding Toehold) Except for Major Exchange, the estimates of the explanatory variables between Model 1 and Model 2 are not materially changed. This result is consistent with the discussion in Sections 4.2 regarding the differences between the two samples; they only differ on the 29 toehold variable. This indicates that it is the change in the toehold that is driving the change in the results observed in the other variables of interest. 5.3.2. Toehold variables The results provide evidence for Hypothesis H2. Table 5 shows that those toeholds that provide the acquirer with substantial influence over the target are negatively correlated with the takeover premium. This is evidenced by the negative and significant relation between (1DToehold)*Toehold and Premium. This relation implies that when an acquirer has a toehold of between 20% and 50%, takeover premium is reduced by an average 14.15%. Furthermore, the continued insignificance of DToehold provides further evidence that the degree of influence a toehold provides the acquirer matters. The change in the market power variable provides some further support for hypothesis H2. This can be seen in the reduction in the statistical significance of the additional market power proxy, Acquirer top 4*Horizontal2 from significance at the 5% to the 10% level. This result implies transactions where the acquirer already has substantial control over the target are not expected to increase the market power of the acquirer. A reasonable explanation for this is that the substantial control allows the acquirer to capture some gains from additional market power prior to the transaction. For example, the acquirer may be able to gain access to otherwise confidential information regarding customers, or it may be able to better manage supply agreements. This also suggests acquirers may have already captured other synergy gains from a takeover which would reduce the takeover premium. 5.3.3. Summary This section has discussed the results of Model 2. These results provide evidence for hypotheses H2 that the degree of influence the toehold provides the acquirer is an important distinction to make when conducting empirical research on takeover premiums. Only toeholds 30 that provide the acquirer with substantial influence reduce the premium. Toeholds that do not provide substantial influence are not significantly correlated to the takeover premium. 5.4. Model 3 This section presents the results for Model 3 posited in Section 3.5.3. We developed the model as potential support for the method we use to proxy the expected additional market power acquired in a takeover. Table 6 presents the results for this model. 5.4.1. Results The results presented in Table 6 provide support for the method used to account for the additional market power expected to be acquired in a transaction. This is evident as the only market power related characteristics that significantly increase the likelihood of a transaction being challenged by an antitrust regulator are Acquirer top 4 and Horizontal2. Antitrust regulators are more likely to challenge transactions where the acquirer is a top four firm or the transaction is between firms in the same industry,. The significant and positive result for Size provides further evidence of the need to account for target size in Model 1 and Model 2. -------------------------------------Insert Table 6 about here -------------------------------------Interestingly, both DToehold and DLargeToehold significantly reduce the probability of a challenge. Perhaps if the acquirer already has a stake in the company it is less likely that antitrust regulators will have an issue with an acquisition of the remaining shares. This also provides more evidence that the degree of influence a toehold provides is important, as the estimates for DToehold and DLargeToehold in the Full Toehold Sample decrease when the toehold is influential. This implies that if the acquirer already has meaningful influence over the target, the regulator is less likely to challenge a bid for the remaining shares. The results also show that several control variables affect the probability of a challenge. When the acquirer is public it is more likely to have its bid challenged. Public acquirers are 31 generally larger than private acquirers. As large companies are likely to have a larger customer base, acquisitions between large acquirers would be of greater interest to antitrust regulators, making them more likely to investigate the transaction. Second, tender offers increase the probability of a challenge. As such offers are made directly to shareholders there may be less involvement by the regulator prior to the announcement of the transaction. It may be the case that some potential mergers are more likely to be stopped by the regulator in the private discussion stage of a transaction. Lastly, the negative relation between Hostile and Challenged implies that when a target is hostile to an acquirer’s bid, the regulator is less likely to challenge the transaction. Hostile bids have a lower probability of success, and therefore regulators may be less likely to devote scarce resources to a challenge. All other control and industry variables are insignificant, a result that is not particularly disconcerting. 5.4.2. Summary This section has provided and discussed the results of Model 3. The primary finding supports the method used to proxy additional market power in Model 1 and Model 2. Furthermore, it provided some interesting results regarding other factors that tend to either increase or decrease the probability of a challenge by an antitrust regulator. In particular, the results support the argument that influence matters when considering toeholds, as the probability of a challenge falls when the toehold becomes influential. 5.5. Summary This section has provided the results of the models presented in Section 3.5 and revised in Section 4.4. They show evidence that in transactions where the acquirer is expected to increase their market power, the takeover premium is 11.76% higher. The results show that accounting for the degree of influence a toehold provides the acquirer is important when conducting empirical research on the takeover premium. The results of both models indicate that when the toehold does not provide the acquirer with substantial control, the takeover 32 premium is not significantly lower. However, when the toehold does provide the acquirer with substantial control the takeover premium is significantly lower and qualitatively similar to the existing literature. Furthermore, there is statistical support for the method used to account for transactions where the market power of the acquirer is expected to increase. 6. Robustness tests 6. 1. Self-selection bias Self-selection bias is an issue in M&A research as firms choose, or are chosen, to be acquirers or targets. That is, acquirer’s select targets from the population based on their characteristics. This can result in a sample of targets that is not randomly selected from the population, biasing results. We employ a statistical technique to account for the possibility of self-selection bias. 6.1.1. Correcting self-selection bias We use a two stage Heckman model to correct any self-selection bias in the results from Section 5. This involves a first stage probit model which estimates the probability of a firm being selected as a target. Importantly, the error term in this model (also called the Inverse Mills Ratio or IMR) can proxy for private information (Li and Prabhala, 2007: 42-45). We then re-run Model 1 with the IMR included. This corrects any self-selection bias as the IMR proxies for omitted factors that might have caused inclusion of the target in the sample. To conduct these tests, we collected a new sample which proxies the population of potential targets. To achieve this, firms were matched on two characteristics: (1) they share the same three-digit SIC code as the target and (2) their revenues are within +/-10% of the target’s revenue in the year of the transaction. This matching process created a new sample of 13,547 firms. This sample is used for the first stage probit model. 𝑇𝑎𝑟𝑔𝑒𝑡 = α0 + α1 𝐷𝐵𝑀 + α2 𝐷Size + +α3 𝐷𝑅𝑂𝐴 + α4 𝐷𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + α5 𝑀𝑎𝑗𝑜𝑟 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 + α6 𝑇𝑖𝑔ℎ𝑡𝑙𝑦 𝐻𝑒𝑙𝑑 + α7 𝐵𝑖𝑔4 + 𝜆 (8) 33 Where new variables are: Target is 1 if the firm is a target and 0 otherwise, DLeverage is 1 if the firm’s leverage is greater than the industry average (by three-digit SIC code) and zero otherwise Tightly Held is 1 if there are less than 500 shareholders in the company and 0 otherwise, Big4 is 1 if the firm’s auditor is a big four firm or Arthur Anderson and 0 otherwise, and 𝜆 is the error term which is the Inverse Mills Ratio. The revised Model 1 with the IMR included is then: Model 1 (IMR): 𝑃𝑟𝑒𝑚𝑖𝑢𝑚 = β0 + β1 𝑆𝑖𝑧𝑒 + β2 𝐵𝑀 + β3 𝑅𝑢𝑛𝑢𝑝 + β4 𝑀𝑎𝑗𝑜𝑟 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 + β5 𝑇𝑜𝑒ℎ𝑜𝑙𝑑 + β6 𝑃𝑢𝑏𝑙𝑖𝑐 + β7 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙3 + β8 𝐶𝑎𝑠ℎ + β9 𝑇𝑒𝑛𝑑𝑒𝑟 + β10 𝐻𝑜𝑠𝑡𝑖𝑙𝑒 + β11 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 + β12 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 ∗ 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙2 + β13 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 ∗ 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙2 ∗ 𝑆𝑖𝑧𝑒 6.1.2. Results + β14 𝐼𝑀𝑅 + ε (9) Table 7 presents the results of Model 1 (IMR). The results show that self-selection bias was not materially affecting in the results in Section 5. Panel A shows that all variables in the first stage model significantly affect the probability of a firm becoming a target. Firms are more likely to become targets if they are underperforming growth firms, if they are larger, more leveraged firms and are more likely to be listed on a large stock exchange. Unexpectedly there is a positive relation between Tightly Held and Target (0.2936) and a negative relation between Big4 and Target (-1.0718). The results presented in Panel B indicate that self-selection bias is not significantly affecting the results in Section 5. This is evident as the parameter estimates of Model 1 are not materially changed after accounting for self-selection bias. Interestingly, there is a positive 34 and significant relation between IMR and lnPremium. This suggests that private information increases the takeover premium. --------------------------------------------Insert Table 7 about here --------------------------------------------6.2. Excessive influence of observations Belsley, Kuh and Welsch (1980) suggested tests for determining if any observations have excessive influence on test results. We used the RStudent, 6 DFFITS 7 and COVRATIO 8 to determine the most influential observations in the sample (unreported). Considering the results of the three statistics, the undue influence is not extreme. 43 of the 50 top observations are the largest premiums in the sample, indicating that the effect of large premiums outweigh the effect of small premiums. This supports our decision to remove transactions where the premium was greater than 200%. However, removal of transactions at a lower premium threshold would reduce the variability of the data. We believe the explanatory power lost by removing these observations outweighs the gains from removing them. 6.3. Summary This section has provided the results of two robustness tests conducted on the results in Section 5. Section 6.1, using a Heckman model has shown that the results of Model 1 are robust to self-selection bias. The results indicate that private information has a significant and positive relation with the takeover premium. Section 6.2 provided the results of an undue influence test. These test show that although larger premiums tend to influence the results more, the effect is not strong enough to warrant major concern. 6 The error variance in the studentized residual RStudent is estimated without the ith observation. Observations with RStudent larger than 2 in absolute value may warrant attention. 7 The DFFITS statistic is a scaled measure of the change in the predicted value for the ith observation and is calculated by deleting that observation. A large value indicates that the observation is very influential. A cutoff of 2 is commonly used. 8 The COVRATIO statistic measures the change in the determinant of the covariance matrix of the estimates by deleting the ith observation. 35 7. Conclusions 7.1. Summary of key findings This study analyses a large sample of takeover transactions in the US over the 1990 to 2011 period. We show that acquirers offer a higher takeover premium in transactions that increase their market power. By extension it has also provided evidence that firms use the market for corporate control as a tool to increase their market power. The evidence suggests that in transactions where the acquirer expects to increase its market power the takeover premium is 11.76% higher. We conduct an additional test to determine if the method used to measure whether a transaction would lead to an increase in the market power of the acquirer was appropriate. We use a probit model of the probability of a proposed takeover being challenged by antitrust regulators. The model provides statistical justification for the method we use to proxy the additional market power expected to be acquired in a takeover. The study also considers the effect of substantial toehold influence on the takeover premium. We show that accounting for the degree of influence a toehold provides the acquirer is important when conducting empirical research on the takeover premium. The results of two models indicate that when the toehold does not provide the acquirer with substantial influence, the takeover premium is not significantly lower. However, when the toehold does provide the acquirer with substantial influence the takeover premium is significantly lower and qualitatively similar to the existing literature. Additionally, the results were robust to both self-selection bias and influence from extreme variables. Interestingly, the results of the two stage Heckman model suggest a significant positive relation between private information and the takeover premium. Regardless, the results of this test show the additional market power proxy does not change when we account 36 for self-selection. Furthermore, the results of the extreme influence tests indicate that transactions with large premiums have greater influence over the results. These results will be of interest to academics, regulators and practitioners. For academics, they provide evidence that market power is a significant motivation for horizontal acquisitions and that empirical tests of takeover premiums should account for the degree of influence a toehold provides the acquirer. For regulators, it indicates they may be failing to prevent transactions that increase market power at the expense of consumers. For practitioners, it indicates that acquirers are willing to compensate targets for the additional market power they expect to gain from the transaction. We also show the importance that private information can have on reducing the takeover premium. 37 References Bain, S., 1951. Relation of profit rate to industry concentration: American manufacturing. Quarterly Journal of Economics 65, 293-324. Bange, M., Mazzeo, M., 2004. Board composition, board effectiveness, and the observed form of takeover bids. Review of Financial Studies 17, 1185-215. Bargeron, L., Schlingemann, F., Stulz, R., Zutter, C., 2008. 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Variable Expected direction Target characteristics Target Size nd Target book-to-market + Runup + Major Exchange + Bidder characteristics Toehold - Public + Horizontal3 + Deal characteristics Cash + Tender + Hostile + Market power Top 4 firm + Top 4 firm*Horizontal2 + Top 4 firm*Horizontal2*Size - Toeholds Small Toehold nm Large Toehold - Where 'nd' and 'nm' are not determinable and not meaningful respectively. 41 Table 2: Descriptive Statistics This table presents descriptive statistics for the Small Toehold Sample and the Full Toehold Sample of US transactions between January 1990 and December 2011. Panel A provides the descriptive statistics for the Small Toehold Sample of 1,806 transactions where the acquirer’s toehold in the target was less than 20%. Panel B provides the descriptive statistics of the Full Toehold Sample of 1,840 transactions where the acquirer’s toehold in the target was less than 50%. Panel A: Small toehold sample Variable Mean Median Std Dev Premium 0.3381 0.2840 0.2566 0.2788 Target Size BM 1216.80 282.27 4475.39 737.47 0.5126 0.4512 0.4486 0.4254 Runup 0.0623 0.0384 0.1829 0.1737 Major Exchange 0.9540 Bidder characteristics Toehold 0.0051 0.0000 0.0232 0.0000 Toehold (when > 0%) 0.0906 0.0903 0.0421 Public 0.6539 Horizontal3 0.3965 Interquartile Range Target characteristics Deal characteristics Cash 0.5161 Tender 0.2442 Hostile 0.0338 Number of Observations 1806 Panel B: Full toehold sample Variable Mean Median Std Dev Interquartile Range Premium 0.3374 0.2834 0.2564 0.2802 Target Size BM 1204.31 280.84 4436.24 735.80 0.5127 0.4511 0.4492 0.4253 Runup 0.0617 0.0379 0.1819 0.1732 Major Exchange 0.9451 0.0000 Target characteristics Bidder characteristics Small Toehold 0.0113 0.0000 0.0516 Large Toehold Public 0.3376 0.3387 0.0856 Horizontal3 0.3913 0.6489 Deal characteristics Cash 0.5185 Tender 0.2462 Hostile 0.0332 Number of Observations 1840 42 Table 4: Correlations Acquirer top 4 Hostile 0.0509** 0.0189 0.0969*** -0.0918*** 0.0127 0.1918*** -0.099*** 0.0978*** 0.0305 0.0644*** -0.0587** 0.0385 -0.0751*** 0.0539** Tender -0.1292*** 0.134*** -0.1055*** -0.0393* 0.0236 Cash -0.0558** 0.0713*** 0.0932*** 0.0032 Horizontal3 -0.0196 0.0478** 1 Runup 0.0699*** 0.1181*** -0.1718*** 0.1458*** -0.0062 Dtoehold 0.0965*** -0.0191 1 BM Public -0.0312 -0.1958*** -0.073*** Runup Major Exchange 0.1087*** -0.2565*** BM 1 -0.115*** 1 lnSize lnSize lnPremium lnPremium Panel A: Small Toehold Sample 1 0.0061 -0.0175 0.0263 -0.0062 0.0427* 1 Public -0.0368 0.2198*** 0.0564** 0.4016*** 0.0523** 0.0152 0.0977*** -0.4063*** -0.0904*** 0.3064*** 0.0571** -0.1245*** -0.0265 1 Major Dtoehold Exchange 0.0517** -0.0074 0.0215 -0.1529*** 1 Horizontal3 -0.0324 -0.0029 0.2255*** 1 Cash 0.0687*** 0.1577*** 1 Tender 0.0309 1 Hostile 1 Acquirer top 4 T his table provides the correlations between the key variables in this study. Panel A provides the correlations for the variables in Model 1 in the Small T oehold Sample of 1,806 transactions where the acquirers toehold in the target is less than 20%. Panel B provides the correlations for the variables in Model 2 in the Full Toehold Sample of 1,840 transactions where the acquirers toehold in the target is greater than 20% but less than 50%. 43 0.0013 0.0921*** -0.0256 -0.0547** Acquirer top 4 Hostile Tender Cash 0.0465** 0.0193 -0.1038*** 0.0126 -0.0578** 0.038 -0.0768*** 0.0574** 0.1932*** -0.0974*** 0.0668*** 0.0979*** 0.0242 -0.1333*** 0.134*** 0.0973*** -0.0937*** -0.0366 0.0312 0.0706*** -0.0361 -0.0182 0.0045 Horizontal3 -0.022 DLarge Toehold 0.046** 1 Runup 0.0673*** 0.1231*** -0.1723*** 0.1489*** -0.0054 DToehold 0.0851*** -0.0206 1 BM Public -0.0273 -0.1943*** -0.0698*** Runup Major Exchange 0.1095*** -0.2548*** BM 1 -0.1172*** 1 lnSize lnSize lnPremium lnPremium Panel B: Full Toehold Sample 44 1 -0.0332 1 DLarge Toehold 0.0173 -0.0087 0.0214 -0.0126 -0.0353 0.2205*** 0.0545** 0.0956*** -0.0389* -0.0254 0.034 0.0353 0.0514** -0.0872*** -0.0769*** 0.0677*** -0.1204*** -0.0766*** -0.2859*** -0.0146 1 Major DToehold Exchange 0.4037*** 0.0535** 0.0107 -0.4081*** 0.3098*** 1 Public 0.058** -0.0054 0.0148 -0.1567*** 1 Horizontal3 -0.0347 -0.0038 0.2301*** 1 Cash 0.0649*** 0.1549*** 1 Tender 0.0317 1 Hostile 1 Acquirer top 4 Table 5: Results T his table provides the results of Model 1 and Model 2. T hese models were developed in Section 3.5 and revised in Section 4.4. T hey are run on two samples. T he Small T oehold Sample consists of 1,806 transaction between January 1990 and December 2011 where the acquirer had a toehold of less than 20%. T he Full T oehold consists of 1,840 transaction between January 1990 and December 2011 where the acquirer had a toehold of greater than 20% but less than 50%. Numbers in parentheses are p-values. Small Toehold Sample Variable Full Toehold Sample Expected direction lnPremium Premium lnPremium Premium 0.5662*** Intercept 0.761503 (0.0000) 0.5752*** 0.7775 (0.0000) Target characteristics lnSize nd -0.013*** -0.0130 (0.0001) BM - 0.0413*** Runup - -0.2193*** 0.0421 0.0414*** -0.1969 -0.2193*** -0.0286 0.0423 (0.0000) (0.0000) + -0.0136 (0.0000) (0.0000) Major Exchange -0.0137*** -0.1970 (0.0000) -0.0282 (0.1272) -0.0343* -0.0338 (0.0533) Bidder characteristics Toehold_D nm -0.1106 -0.1047 (0.5203) (1-Toehold_D)*Toehold -0.0117 -0.0117 (0.5025) - -0.1526* -0.1415 (0.0765) Public + 0.0216** 0.0218 (0.0408) Horizontal3 + 0.0225** 0.0211** 0.0213 (0.0436) 0.0228 (0.0154) 0.023** 0.0233 (0.0127) Deal characteristics Cash + -0.026*** Tender + 0.0419*** -0.0256 -0.0254*** 0.0428 0.0432*** (0.0038) (0.0044) (0.0000) Hostile + -0.0016 -0.0251 0.0442 (0.0000) -0.0016 (0.941) -0.0013 -0.0013 (0.9541) Market Power variables Acquirer top 4 + 0.0246* Acquirer top 4*Horizontal2 + 0.1111** 0.0249 (0.085) - -0.0176** 0.0919* 0.0962 (0.0936) -0.0175 -0.0151* (0.0331) (0.0652) Number of Observations 1,806 1,840 Adjusted R 2 F-value 0.0920 0.0918 15.06 14.28 Where nd and nm mean 'not determinable' and 'not meaningful' '***', '**' and '*' indicates significance at the 1%, 5% and 10% levels respectively 0.0250 (0.0837) 0.1176 (0.0452) Acquirer top 4*Horizontal2*lnSize 0.0247* -0.0150 45 Table 6: Challenge Probit Model Results T his table provides the results of Model 3. T his model was developed in Section 3.5 and run on two samples. T he Small T oehold Sample which consists of 1,806 transaction between January 1990 and December 2011 where the acquirer had a toehold of less than 20% and the Full T oehold consists of 1,840 transaction between January 1990 and December 2011 where the acquirer had a toehold of greater than 20% but less than 50%. Small Toehold Sample Full Toehold Sample Variable Challenged Challenged Intercept -1.1485*** -1.1*** 0.2142*** 0.2254*** Target top 4 -0.0151 0.0120 DHHI -0.0694 -0.0693 0.1952*** 0.197*** -0.0428 -0.0587 Market power variables Acquirer top 4 Horizontal2 State Industry variables DBM 0.1107 0.1183* DSize 0.3947*** 0.3705*** DROA 0.1033 0.0955 0.2553 0.2219 -0.5072*** -0.5006*** Control Variables Major Exchange DToehold DLarge Toehold -0.6439** Public 0.1615* 0.1646** Cash 0.0458 0.0237 Hostile -0.4843** -0.4769** Tender 0.5255*** 0.5058*** 1,806 1,840 0.0620 0.0631 Number of Observations McFadden R 2 '***', '**' and '*' indicates significance at the 1%, 5% and 10% levels respectively 46 Table 7: Self-selection bias T his table shows the results of the two stage Heckman model. Panel A shows the results of the probit model run on a sample of 13,457 firms. Panel B shows the results of the second stage OLS model the same as Model 1 except for the addition of the IMR variable from the first stage model. T his model was run on the Small Toehold Sample of 1,806 transactions. Numbers in parentheses are p-values. Panel A: Probit Selection Model Variable Target Intercept -1.217*** DBM -0.0718** DSize 0.9706*** DROA -0.3945*** DLeverage 0.1126*** Major Exchange 1.0047*** Tightly Held 0.2936*** Big4 -1.0718*** Number of Observations McFadden R 2 13,547 0.1957 Panel B: Second Stage OLS Variable IMR lnPremium lnPremium 0.0457*** (0.002) 0.5356*** 0.5662*** (0.0000) (0.0000) Target Size -0.0131*** -0.013*** (0.0001) (0.0001) Target book-to-market 0.0409*** 0.0413*** (0.0000) (0.0000) -0.2179*** -0.2193*** (0.0000) (0.0000) -0.0291 -0.0286 (0.1203) (0.1272) -0.102 -0.1106 (0.5522) (0.5203) Public 0.0209** 0.0216** (0.0471) (0.0408) Horizontal3 0.0231** 0.0225** (0.0125) (0.0154) -0.0259*** -0.026*** (0.0039) (0.0038) 0.0417*** 0.0419*** (0.0000) (0.0000) -0.0036 -0.0016 (0.87) (0.941) Intercept Target characteristics Runup Major Exchange Bidder characteristics DToehold Deal characteristics Cash Tender Hostile 47 Table 7: Self-selection bias continued Market Power variables Acquirer top 4 0.0259* 0.0246* (0.0695) (0.085) Acquirer top 4*Horizontal2 0.1114** 0.1111** (0.0442) (0.0452) Acquirer top 4*Horizontal2*lnSize -0.0176** -0.0176** (0.0331) (0.0331) Number of Observations 1,806 1,806 Adjusted R 2 F-value 0.0963 0.0920 14.74 15.0620 '***', '**' and '*' indicates significance at the 1%, 5% and 10% levels respectively 48