PRIVATE EQUITY vs. STRATEGIC BUYERS:
DIFFERENCES IN DEAL PREMIUMS AND
TARGET CHARACTERISTICS.
*Jannick van der Horst University of Groningen Faculty of Economics and Business
June 2018
ABSTRACT
Using a sample of 3042 cash-only deals in the period 1996-2017, this study investigates the premium difference between private equity- and strategic buyers. While many studies examined the deal premiums of strategic buyers, academic research devoted little attention to private equity buyers. The first part of the research shows that private equity buyers pursue targets that are smaller, have lower R&D expenses, and hold less cash relative to targets bought by strategic buyers. The second part of this study shows that, after controlling for target- and deal characteristics, private equity buyers pay a lower premium.
Keywords: Private equity, deal premium, target abnormal returns JEL Classification: G30, G32, G34,
I.
INTRODUCTION
At no point in history has the private equity environment been as competitive as today. Since the year 2000 the number of private equity firms has more than tripled and the total amount of assets under management has grown from 600 billion in 2000 to almost 2500 billion in 2016 (Preqin, 2017). The combination of these enormous PE funds, a strong economic outlook and low interest rates has led to the “golden age” of private equity investment. This, in turn, caused a shifting pattern in the percentage of private equity buyers vs. strategic buyers. Even though the press emphasized the growing importance of the private equity industry, academic research has devoted little attention to these buyers.
After private equity buyers entered the stage in the 1980s, the M&A market is dominated by two main types of buyers, which Martos-Wila, Rhodes-Kroph, and Harford (2013) refer to as strategic buyers (operating companies) and financial buyers (private equity firms). Academics have suggested several reasons why private equity buyers might behave different from strategic buyers. One of the first was Jensen (1989) who states that the incentives of a private equity buyer are much more high-powered than those of strategic buyers, meaning that private equity buyers often look for undervalued firms, and try to fuel their returns with a determined focus on cash flow and aggressive use of debt. On the contrary, strategic buyers look for targets with potential synergies, and increase their returns by integrating the target into their own business. Traditionally, strategic buyers were considered to pay more than private equity buyers. The argument was that only strategic buyers could enjoy the synergies generated from the acquisition. In today’s M&A environment this way of thinking is somewhat outdated. Over the years private equity buyers became potent competitors. One of the most evident changes is that many private equity buyers have a portfolio company that is in the same industry as the target firm (Brooks, 2010). Hence, it is not unusual for a private equity buyer to be winner in a highly competitive deal process (Vild and Zeisberger, 2014).
and Teunissen (2012) corrects for methodological issues in earlier studies, and ultimately finds no significant difference in deal premiums between a strategic buyer and a private equity buyer. In view of these contradictory results, the question whether private equity buyers pay less for their targets remains open.
Given the growing role of private equity on the M&A market and the little empirical research on these buyers, this study aims to examine whether private equity buyers acquire targets at a lower premium. Why is there a private equity premium discount? The simplest explanation would be that they acquire different types of firms. Following this line of reasoning, the size of the deal premium may depend on target- and/or deal characteristics instead of the type of buyer. To correctly tackle this issue, this study consists of two parts. First, targets are analysed to find out which targets eventually end up with private equity buyers. Second, using target- and deal characteristics identified in prior literature, multiple regression analysis is performed to see if the difference in premiums paid still holds.
This study contributes to the existing literature in two ways. First, the few studies on deal premiums paid by private equity buyers use only one deal premium measure (Bargeron et al., 2008; Dittmar et al., 2012; Fidrmuc et al., 2012). This study brings research on deal premiums to a new level by analysing two premium measures. Second, as prior literature points out (e.g. Vild and Zeisberger, 2014), the M&A environment strongly changed. Even though the market for M&A is more crowded and company valuations and deal premiums reached all-time highs, private equity buyers are still gaining market share. This study adds new insight by being the first to analyse the competition between the two types of buyers after the financial crisis. The first part of the research shows that private equity buyers pursue targets that are smaller, have lower R&D expenses, and hold less cash relative to the targets bought by strategic buyers. Furthermore, the results suggest that private equity buyers are able to negotiate better deal terms than strategic buyers. The second part of the research reports the main finding of this study; private equity buyers pay a smaller premium than strategic buyers.
II.
LITERATURE REVIEW
In this section the theoretical background of this paper is discussed. First, a review on why firms pay a premium. The next section elaborates on the differences between private equity- and strategic buyers. Lastly, the impact of the financial crisis on premiums is discussed.
Premiums
When a bidder acquires a target it is often at a substantial cost above the market value of the target company. This extra cost of buying the target is referred to as deal premium. Over the last 20 years, the average deal premium in the US has been observed to be on average 36% to 52%. As a matter of fact, studies of Eckbo (2009) and Kengelbach and Roos (2011) report deal premiums well above 100%. Although several studies show that average buyer gains from M&A are neutral or negative (Loughran & Vihj, 1997; Eckbo, 2009), buyers continue to pay these substantial premiums. Many academics attempted to explain the motives of buyers by focusing on both firm- and deal-related characteristics. Motis (2007) divides the motives for a takeover into two groups, (i) takeovers that increase the value of the firm (i.e. efficiency gains, cost savings, and enhancement of market power) and (ii) takeovers that increase the wealth of the manager1. The existence of deal premiums is mostly explained through the first, value creation school of thought. All in all, the main motivation to pay a premium is that the combination of the bidder and the target is more valuable than if the two firms were independent.
Private equity buyer vs. strategic buyer
According to Williams (2007) the type of buyer affects every aspect of the deal; the negotiating process, price, tax, legal implication, and most importantly the future prospects of the company. Theory articulates that private equity buyers are cash rich and often have easy access to credit. While strategic buyers often look for targets to integrate into their own business. Financial buyers search for undervalued targets which they sell once exit opportunities become appealing, usually no more than ten years. According to Barber and Goold (2007) this is the fundamental reason behind private equity’s high rates of return. In their own words (p.1) “‘buying to sell’
generates a much higher return on investment than the public company practice of ‘buying to keep’”. Another fundamental difference between the two types of buyers is the corporate
governance structure. One of the first to examine this was Jensen (1989). He predicted that private equity buyers would eventually dominate because the mechanisms and structures used by private equity buyers are superior to those of strategic buyers. Namely, strategic buyers are often characterized by low leverage, weak corporate governance and dispersed shareholders. On the contrary, private equity is characterized by aggressive use of debt, high-powered incentives for target’s management, a determined focus on cash flow and margin improvement, and their ability to negotiate superior deal terms. Moreover, these differences in nature between strategic- and private equity buyers may indicate that the two buyers end up purchasing different targets. Prior literature shows that private equity buyers seem to pursue targets that have lower market-to-book ratios and lower R&D expenses (Shleifer and Vishny, 1992; Gorbenko and Malenko, 2009; Fidrmuc et al., 2012). Furthermore, private equity buyers offer deal terms that are more favourable, resulting in higher returns for the acquirer (Fidrmuc et al., 2012). Thus, for the first part of the research the following two hypotheses are proposed:
Hypothesis 1: Targets pursued by private equity buyers are different from those pursued by
strategic buyers.
Hypothesis 2: Deal terms offered by private equity buyers are more favourable to the acquirer
than those offered by strategic buyers.
A common view is that strategic buyers are willing to pay more than private equity buyers. (Gorbenko and Malenko, 2014). This stems from the fact that there are (operational) synergies generated from the acquisition that could not be enjoyed by financial buyers. Shleifer and Vishny (1992) report that private equity buyers fear overpaying since they do not have the industry-specific knowledge needed to correctly value the assets. Furthermore, they need to hire specialists to run the target firm’s assets for them and thus face agency costs. Taken together, if you want to sell your business, you will be able to negotiate a better deal with a strategic buyer than with a private equity buyer. Nonetheless, selling your business to private equity buyers brings other advantages. For example, their access to cheaper debt and their expertise in restructuring target firms.
announcement date. After controlling for target- and deal characteristics, they find that private equity buyers pay a smaller premium. They argue that strategic buyers are less selective in the price they want to pay. In addition, they mention that strategic buyers may have an empire-building mentality leading them to overpay for a target firm.2 Second, Gorbenko and Malenko (2014) report that strategic buyers have lower average valuations of target firms compared to private equity buyers. Practically, this means that private equity buyers pay a lower price and consequently a lower premium. An important implication of this result is that when both buyers compete for the same target, the strategic buyer would generally win the auction. Dittmar et al. (2012) continue with analysing the bidding competition between the two types of buyers and conclude that strategic buyers can benefit from following the first bid of a private equity buyer. Even though deal premiums are not the main focus of their paper, they confirm that the target- and deal characteristics cannot explain the difference in premiums. Based on these three studies, it seems that private equity buyers pay a lower premium. However, Fidrmuc et al. (2012) correct for methodological in earlier work, and ultimately find no significant difference in premiums. In line with this, Boone and Mulherin (2011) report an insignificant private equity dummy in their regression. Given (i) the mixed results in prior literature, and (ii) the fact that in recent years private equity buyers were able to win highly competitive deal processes (Vild and Zeisberger, 2014), the question whether private equity buyers pay a smaller premium remains open. Even though existing literature fails to provide a conclusive answer, the classic ‘’synergy’’ argument still seems valid. Hence, a private equity buyer is expected to pay a smaller deal premium.
Hypothesis 3: Private equity buyers pay a lower deal premium than strategic buyers Financial crisis
Historically, M&A activity comes in waves, of which six complete waves are evident in the last 125 years. Eisenbarth and Meckl (2014) find that every M&A wave was accompanied by sinking interest rates, increasing economic growth, and ended with an economic shock. In the years preceding the financial crisis the total number of deals and deal value reached record-breaking levels as a result of increasing world demand (Gaughan, 2007). The recent financial crisis3 turned the world upside down, leading many business experts to change their
2 This argument provided by Bargeron et al. (2008) would fall into the second category of Motis (2007); ‘takeovers that increase the wealth of the manager’. Important here is that management of a strategic buyer, generally speaking, does not bear the full costs of overpaying for the target.
assumptions and beliefs about industry- and firm value. Moreover, the crisis crippled the banking industry and depressed capital markets. For target firms this made it more difficult to raise and support debt as lenders were unsure how these firms would be impacted. Subsequently, target firms that wanted to survive this financial crisis, but suffered from this lack of available capital were willing to accept discounted offers (Shleifer and Vishny, 1992). Hence, one would expect that deal premiums during the crisis were lower than before- and after the crisis. However, the crisis created conditions of information asymmetry, and the ability of buyers to correctly valuate the target was compromised (Latham and Braum, 2011; Fralich and Papadopoulus, 2017). In addition, buyers tend to believe that a target is only temporarily undervalued, and that after the crisis the target value would return back to normal. Thus, the deal premiums paid during the crisis are expected to be higher than before and after the crisis.
Hypothesis 4: During the financial crisis, the deal premiums paid by both buyers were higher
than before- and after the financial crisis.
Control variables Target characteristics
Previous empirical research examines a large number of target-, acquirer- and deal related characteristics with mixed results. One of the most discussed deal premium drivers is the size
of the target. There is a vast amount of empirical literature documenting the positive relation
Another widely used deal premium driver is profitability. Or to put it differently; return on assets (ROA). Generally speaking, profitable targets are attractive, hence acquirers are willing to pay more for them. But, one could also argue that inefficiently managed targets are more appealing since buyers are better able to extract synergies (Palepu, 1986). Overall there is a large body of literature that report a positive relationship between takeover premiums (Walkling & Edmister 1985; Shawver, 2002; Eckbo, 2009) A next characteristic to compare different deals is the target’s leverage ratio. Leverage increases the tax savings, reduces the free cash flow problem and protects current investors from dilution. More importantly, highly levered firms often have more concentrated ownership, which in turn, forces the acquirer to pay a higher premium. This is confirmed by the research of Safieddine and Titman (1999) and Raad (2012). In conclusion, maintaining a high leverage ratio is in the interest of the target shareholders.
Deal characteristics
Deal characteristics are one of the most studied determinants for premium and refers to the way in which the deal was consummated. There are essentially two ways to take over a publicly traded target, either through a tender offer or through a merger.4 In a merger, the bidder and the
target’s board agree on a price after which the target shareholders can vote in favour, or against the deal. In a tender offer, the buyer proposes buying shares from every target shareholder for a certain price at a certain time. The offer price in a tender offer is generally at a premium to the market price. In line with the findings of Huang and Walkling (1987), Flanagan and Shaughnessy (2003), and Offenberg and Pirinsky (2015) a tender offer is expected to yield higher announcement returns and premiums.
Equally important is the number of bidders. The majority of previous studies control for the presence of multiple bidders by including a competition dummy. Walkling and Edmister (1985), Flanagan and Shaughnessy (2003), and Alexandridis et al. (2010) all find a positive relation between bid competition and the premium paid. Following prior research in the field of M&A, there are two broad classes of takeovers; it is either categorized as friendly or hostile. Even though the end result might be the same, the takeover process is highly dependent upon the nature of a takeover. A friendly takeover occurs when the boards of both the target- and acquiring firm approve the deal. At the other end of the spectrum, when the target’s board does not approve the deal, it is considered a hostile takeover. The target firm can attempt to resist a hostile takeover by using defensive tactics. The Thomson ONE database contains 18 different
types of defensive tactics. This study will account for the following two techniques.5 First, a
target termination fee also known as a breakup fee, requires that the buyer pays the target a
fixed cash fee if the deal fails for reasons specified in the acquisition agreement. This fee incentivizes the buyer to complete the deal. The paper of Officer (2003) demonstrates that deals with target termination fees involve higher premiums than deals without such a clause. Closely related to the target termination fee is the lockup option.6 In such an option, the target company has been granted an option to buy shares in the acquiring company. Subsequently, the acquirer is then locked-up and is not free to sell shares to a party other than the designated target (Burch, 2001) Thus, a positive association between the lockup agreement and the premium is expected.
5 Note: a tender offer (discussed above) is historically also considered as a defensive tactic. But, the introduction of the ‘’Best Price Rule’’ in the late 1980s made tender offers more fair. Furthermore, the adoption of state antitakeover legislation in the early 1990s almost eliminated hostile tender offers completely (Offenberg and Pirinsky, 2015). Therefore, a tender offer is not viewed as a defensive tactic.
6 In this study, the term lockup option is from a bidder’s perspective. The Thomson ONE Database also contains a target lockup option, which is the exact opposite: in this case the controlling shareholder is locked up and not free to sell shares to a party other than the designated buyer.
7
In their paper Boone and Mulherin (2007) show that the actual competition sometimes occurs before the announcement of a deal. Thus, the competition measure used here might underestimate the actual degree of competition. However, this is beyond the main focus of this study.
TABLE I
Definition and expected sign of the target- and deal characteristics
This table summarizes the control variables used in the multiple regressions and robustness checks. The left column reports the variables, the middle column provides a description, and the right column shows the expected signs (based on the literature review). All of these control variables are available on a yearly basis. Following Bargeron et al. (2008) this study uses the data at the end of the year before the official SDC announcement. Both the target- and deal characteristics are obtained from the Thomson ONE database.
Variable Description Expected sign
Target characteristics
Size The size of the company is measured by the book value of
total assets, in regressions included as a natural logarithm +/- Profitability (ROA)
The profitability of the target is calculated as net income to total assets for the financial year ending before the deal announcement
+
Leverage The leverage ratio is calculated as the book value of debt to the book value of total assets +
Tobin's Q Tobin's Q is defined as the firm’s market value divided by
the book value of assets +
Deal characteristics
Tender offer Dummy which equals one if the deal is a tender offer and
zero otherwise +
Competition7 Dummy which equals one if there is more than one bidder and zero otherwise. +
Termination fee Dummy which equals one if the deal involves a termination
III. METHODOLOGY
This section presents the methods underlying the research approach. The methodology is broadly based on the two parts of this study; (i) univariate analysis and (ii) multiple regression. First, the univariate analysis is used to find out if the targets pursued, and the deal terms offered by private equity buyers are different from those of strategic buyers. Next, the differences in premiums are analysed at a univariate level. The second part of the research employs a multiple regression to see if the difference in premiums still holds when controlling for the target- and deal characteristics identified in the literature review. Figure I shows a graphic representation of how the research method is structured.
FIGURE I
Graphic representation of research method
Univariate analysis
As explained in the literature review the nature of the deal and its characteristics are highly dependent upon the type of buyer. First, targets are analysed to find out which targets eventually end up with private equity versus strategic buyers. Following Bargeron et al. (2008) univariate methods of analysis are used to assess the differences in premiums, announcement returns, target-, and deal characteristics. First, the Jarcque-Bera test is performed to check whether the premiums, target-, and deal characteristics follow a normal distribution. Since the Jarque-Bera test indicates non-normality and the parametric t-test relies on the assumption of a normal distribution, this study uses two tests to assess the difference in means; the Welch’s t-test8, and the non-parametric Mann-Whittney test.9 test. This is an adaptation of the well-known student
8 When calculating the Welch’s t-test we use the Satterthwaite-Welch adjustment for degrees of freedom 9 Unlike the t-test the Mann-Whitney test does not require the assumption of normal distributions and it is nearly as efficient as the student’s t-test.
US-based deals with public targets between 1996 and 2017
All other deals Deals that meet the conditions on page 14 Direct deal premium and target CAR Target firm characteristics OLS regression with target- and deal specific control variables Mann-Whitney test and Welch’s t-test.
Sample selection First part Second part Dependent variables
t-test which is more reliable when the two samples have unequal variances and an unequal number of deals.
Multiple regression analysis
The results of the univariate analysis give an intuition about the differences in premiums between strategic- and private equity buyers. However, this difference can be due to different target- and deal characteristics. Hence, to analyse whether these differences can explain the difference in deal premiums, several regressions are performed. The dependent variable is the deal premium (both target CAR and direct premium) and the independent variables are the target- and deal characteristics discussed in the literature review above. Following Bargeron et al. (2008) an indicator variable for private equity buyers is included.
The equation that will be estimated reads:
where ! is an index for the deal, "# is a constant, $% and $& are the coefficients for PREMIUM
and CONTROLS. PREMIUM refers to the deal premium in percentage terms. In the first two columns of Table VI the deal premium is defined as the 4 week direct premium. In column [3], [4], [5], and [6] the deal premium is defined as the cumulative abnormal returns for respectively the 3-day, 5-day, and 11-day event window. PRIVATE EQUITY is an indicator variable which equals one if the target is acquired by a private equity buyer and zero otherwise. CONTROLS are the eight control variables presented in Table I.
If these target- and deal characteristics explain the differences in premiums, the private equity indicator variable should turn out to be insignificantly different from zero in the regressions. Before running the multiple regressions, the assumptions of the OLS are checked. First, the White heteroscedasticity and the Breusch-Godfrey LM serial correlation tests are performed. Secondly, the Jarque-Bera test is used to check whether the residuals are normally distributed. The results of these tests show no signs of serial correlation. However, the White tests reject the null hypothesis of homoscedasticity. To correct for this the OLS is performed using White’s heteroscedasticity consistent standard errors. In addition, the dataset is checked for outliers. Since the outliers are not due to errors in the data, they are not removed.
Dependent variables
For the purposes of robustness checks and maximizing potential data points, this study will address two deal premium calculation techniques.
Deal premium
The most widely used definition of the deal premium is the percentage difference between the deal value per share and the unaffected share prior to the original announcement date. When using this definition, it is important to realize that for almost every deal there are rumours which drive up the share price of a target before the official announcement. These investors engage in so-called merger arbitrage activity; buying up target stock to profit from the conversion of the target price to the final offer price. With this in mind, the date on which the market value of the firm is measured should be unaffected by these rumours. Thus, it should be far enough from the original announcement date, yet close enough so that the market value reflects a correct valuation. Following Walkling and Edmister (1985) and Bargeron et al. (2008), this study calculates deal premiums based on the target share price 4 weeks prior to the original announcement date.
'()*- = ;#− ;%?
;%? (2)
where '()*- is the deal premium, ;# is the offer price per share and ;%? the market value of the target 4 weeks before the original announcement date.
Target CAR
Thomson ONE database, leakage of information prior to the announcement is not likely. Therefore, consistent with other event studies in the field of M&A, we use relatively short event windows: CAR11, CAR5, and CAR3. The timeframes used are respectively –5 to 5, -2 to 2, and -1, to 1. Lastly, the day of the official announcement, which is the first press release, is set to be the event date, referred to as day 0.
Using the dates of the deal announcements, daily stock returns, and the S&P 500 Index. The abnormal returns are defined as the excess returns above the normal returns of the standard Capital Asset Pricing Model of Sharpe (1964):
2(-@ = (-@− "- − $-(A@ (3)
Where 2(-@ is the abnormal return, ! is an index for the firm’s stock, B is a time index, C is the market index (S&P 500), (-@ is the return of the stock ! on day B, (A@ is the return of the market index (S&P 500) on day B, "- is the estimated intercept of the relationship of stock !, and $- is the estimated slope of stock ! with the market index (S&P 500).
Independent- and control variables
IV. DATA
This section presents the data this paper uses. First, there is a step-by-step explanation of how the data is collected. Next, the sample description and summary statistics are discussed. Finally, the correlation matrix of the (in)dependent variables is presented.
Data collection
The sample is collected from the Thomson ONE Mergers and Acquisitions database. This study focuses on deals during the period 1996 and 2017. To be included in the sample, the following conditions must be satisfied:
(i) All acquisitions in the United States of America between 1996 and 2017. (ii) The acquirer owns 100% of the shares after the transaction.
(iii) The target has a public status with stock return data available on Datastream (iv) The takeover is on a cash-only basis
(v) Spinoffs, recapitalizations, self-tenders, exchange offers, repurchases, self-tenders, exchange offers, repurchases, minority stake purchases and privatizations are excluded.
(vi) Private Equity buyer:
‘Acquirer is a financial sponsor flag’ and ‘Acquirer is a Leveraged Buyout Firm flag’ are applied.
Strategic buyer:
‘Acquirer is a public firm’
premiums are constructed by using information from both Datastream and the ThomsonONE database. Even though this study integrates two databases, there is still some missing data. These variables are carefully examined and seem to be missing at random.
Sample description
Table II shows an overview of the sample. The yearly distribution of the deal value and premium is provided in the left column of the table. There are two important trends worth mentioning. First, 2015 is, since the financial crisis, by far the best year for M&A. This is confirmed by the 10% of total deal value reported in Table II. Even though in the real world this growth continued in the consecutive years, this is not reflected in the unrestricted sample as well as the sample used in this study. This suggests that there is some unavailability of deal information in the Thomson ONE database. Second, the financial crisis caused a serious downfall in the number of deals, and it seems that premiums increase during the financial crisis. As discussed in the literature review the financial crisis impacts both deal value and premiums, this will be discussed in more detail later on.
TABLE II
Sample characteristics by announcement year and industry
This table reports the composition of the sample which includes 3042 completed, cash-only deals that took place in the US between 1996 and 2017, that result in 100% ownership of the buyer. The left column ‘premium’ shows only the first premium measure (the 4-week direct deal premium) per year of the Thomson ONE announcement date. On the right side, the distribution of deal premiums per industry. The ‘Deal value’ column shows the number of deals, the total deal value, and the percentage of total deal value respectively. Again, the distribution per year on the left and the distribution per industry on the right. The aggregated deal value is in trillions of US dollars.
Premium Deal value Premium Deal value
Year % No. Total % Industry % No. Total %
1996 42% 159 98 2% Consumer products and services 53% 101 30 1% 1997 38% 158 120 3% Consumer staples 53% 90 210 5% 1998 53% 161 198 5% Energy and power 44% 185 338 8% 1999 56% 203 210 5% Financials 43% 1152 1185 28% 2000 49% 198 231 5% Healthcare 62% 303 536 13% 2001 50% 177 153 4% High technology 43% 463 511 12% 2002 65% 142 36 1% Industrials 42% 231 335 8% 2003 50% 149 69 2% Materials 63% 114 230 5% 2004 36% 153 151 4% Media and entertainment 28% 118 386 9% 2005 33% 156 310 7% Real estate 75% 86 60 1% 2006 36% 176 365 9% Retail 29% 85 116 3% 2007 34% 181 278 6% Telecom 46% 114 349 8% 2008 48% 94 87 2% 2009 68% 80 214 5% 2010 60% 111 90 2% 2011 61% 82 187 4% 2012 49% 100 119 3% 2013 39% 113 148 3% 2014 44% 104 365 9% 2015 39% 128 423 10% 2016 49% 137 288 7% 2017 41% 80 148 3% Total 3,042 4,288 100% Total 3,042 4,288 100%
TABLE III Summary statistics
This table shows the summary statistics for the total sample. The full sample contains 3042 completed, cash-only deals that took place in the US between 1996 and 2017 that result in 100% ownership of the buyer. Target- and deal characteristics are retrieved from the Thomson ONE database. The CAR premium is calculated using stock- and market returns retrieved from Datastream. Enterprise value, Equity value, Total Assets, Total Debt, and Transaction value are in millions of US dollars. The EBITDA Multiple is defined as the target firm’s enterprise value divided by their EBITDA. The Net Sales 5y growth is the growth, in percentage terms, of net sales over the 5 years preceding the SDC announcement. R&D is defined as the target’s research and development expenses for the 12 months prior to the announcement over the book value of assets. Cash is defined as the target’s cash holdings over the value of total assets. Leverage is defined as total debt over total assets. Q is defined as the market value of the firm over the book value of assets. Profitability is defined as net income over total assets. Loss is an indicator variable equal to one if profitability is negative and zero otherwise. Offer price to EPS is the ratio of the offer price to Earnings Per Share for the 12 months prior to the announcement. Transaction value is in millions of US dollars and is calculated by subtracting liabilities from the transaction value and by adding net debt. Tender, lockup, and termination fee are indicator variables equal to one (and zero otherwise) if the deal respectively is a tender offer, involves a bidder lockup provision, or includes a target termination fee. The number of bidder is defined as the number of entities (including the acquirer) bidding for the target firm. The 4 week premium is the first type of premium measure and is defined as the offer price to target closing stock price 4 weeks prior to the announcement date, expressed as a percentage. CAR11, CAR5, and CAR3 are the second type of premium measure and are defined as respectively the 11-day, 5-day, and 3-day cumulative abnormal return (in percentage points) calculated using the market model.
Variable No. Mean Median Std.
Deviation Max. Min. Skew. Kurt.
Panel A: Target characteristics
Enterprise value 2,952 1,763.99 280.75 5,685.20 83,932 -30.08 8.08 83.53 Equity value 2,963 1,381.43 211.36 4,443.08 68,445 0.00 8.16 87.51 Total Assets 2,797 1,584.12 284.50 6,029.73 143,819 0.21 12.20 214.74 Total Debt 2,305 615.97 73.83 2,501.95 55,461 0.00 12.72 219.85 EBITDA Multiple 2,432 4.79 11.10 513.32 4,198 -23,353 -38.87 1,770.81 Net Sales 5y growth 2,150 16.27 7.73 56.54 1,756 -56.09 17.71 464.97 R&D 728 0.16 0.11 0.17 1 0.00 2.91 11.68 Cash 2,348 0.20 0.10 0.29 7 -0.01 9.41 196.72 Leverage 2,740 0.27 0.17 0.29 2 0.00 1.40 2.14 Q 2,785 1.74 1.06 2.74 45 0.00 6.03 58.66 Profitability 2,793 -0.05 0.01 0.34 3 -4.12 -4.63 37.44 Loss 2,793 0.31 0.00 0.46 1 0.00 0.82 -1.33 Offer price to EPS 1,901 66.51 24.23 283.35 5,636 0.00 14.27 236.34
Panel B: Deal characteristics
Transaction value 3,042 1,409.54 209.42 4,526 7,2671 0.00 8.14 87.70 Tender 3,042 0.21 0.00 0.40 1.00 0.00 1.45 0.11 Lockup 3,042 0.04 0.00 0.19 1.00 0.00 4.87 21.76 Termination fee 3,042 0.69 1.00 0.46 1.00 0.00 -0.82 -1.32 Number of bidders 3,042 1.05 1.00 0.25 4.00 1.00 5.76 38.41
Panel C: Premium characteristics
Variables correlations
Table IV shows the correlations between the variables used in the multiple regression. Panel A reports the correlations between the different premium measures. The main takeaway here is that the CAR measures are strongly correlated to each other. The correlations between the independent variables are very weak and even close to zero. This suggests that these variables do not really influence each other. In addition, the variance inflation factors of the independent variables are estimated. Table A.II in the Appendix shows that there is no sign of multicollinearity.
TABLE IV
Pairwise correlation matrix
This table represents the pairwise correlations between the variables used in the multiple regression. Panel A shows the correlations between the different premium measures. Panel B shows the correlation between the private equity indicator and control variables.
V.
RESULTS
This study consists of two parts. In the univariate analysis, the differences in means between the two types of buyers is analysed. Next, the impact of the financial crisis on deal premiums is discussed. In the second part, the results of the multiple regression are investigated to find out whether private equity buyers pay a lower premium. Finally, two robustness checks are presented.
Univariate analysis
This part of the research is performed on the both the full dataset (Table V) and the period before-, after- and during the financial crisis (Table B.I and B.II in the Appendix). As described in the methodology section, the Jarque-Bera test is used to verify the normality of the variables. Since this test indicates normality for multiple variables, both parametric- and non-parametric tests are performed. Table V below shows the outcome of the univariate analysis using the Welch’s t-test. Table D.I in the Appendix reports the results when the differences in means are analysed using the Mann-Whitney test. The results of the Mann-Whitney test do not greatly vary from the estimates of the Welch’s t-test, implying that the first part of the research has some robustness.
Private equity buyers vs. strategic buyer
TABLE V
Univariate analysis: private equity- vs. strategic buyers
This table shows the results of the univariate analysis between private equity buyers and strategic buyers. The sample contains 3042 US-based deals during the period 1996-2017. The selected deals consist of 291 private equity deals and 2751 strategic deals. Panel A, B, and C report the mean, standard deviation, and number of observations of respectively the target characteristics, deal characteristics, and premiums. Enterprise value, Equity value, Total Assets, Total Debt, and Transaction value are in millions of US dollars. The EBITDA Multiple is defined as the target firm’s enterprise value divided by their EBITDA. The Net Sales 5y growth is the growth, in percentage terms, of net sales over the 5 years preceding the SDC announcement. R&D is defined as the target’s research and development expenses for the 12 months prior to the announcement over the book value of assets. Cash is defined as the target’s cash holdings over the value of total assets. Leverage is defined as total debt over total assets. Q is defined as the market value of the firm over the book value of assets. Profitability is defined as net income over total assets. Loss is an indicator variable equal to one if profitability is negative and zero otherwise. Offer price to EPS is the ratio of the offer price to Earnings Per Share for the 12 months prior to the announcement. Transaction value is in millions of US dollars and is calculated by subtracting liabilities from the transaction value and by adding net debt. Tender, lockup, and termination fee are indicator variables equal to one (and zero otherwise) if the deal respectively is a tender offer, involves a bidder lockup provision, or includes a target termination fee. The number of bidder is defined as the number of entities (including the acquirer) bidding for the target firm. The 4 week premium is the first type of premium measure and is defined as the offer price to target closing stock price 4 weeks prior to the announcement date, expressed as a percentage. CAR11, CAR5, and CAR3 are the second type of premium measure and are defined as respectively the 11-day, 5-day, and 3-day cumulative abnormal return (in percentage points) calculated using the market model. The difference in means is analysed by performing a Welch’s t-test. The Mann-Whitney test is reported in Table D.I in the Appendix. ***, **, * represent significance at the 1%, 5%, and 10% levels respectively.
Private equity buyer Strategic buyer Difference
in means
Mean St. Dev. No. Mean St. Dev. No.
Panel A: Target characteristics
Enterprise value 1,289.76 3,455.50 275 1,812.71 5,864.72 2,677 -522.95 * Equity value 1,017.31 2,605.53 277 1,418.98 4,589.62 2,686 -401.67 *
Total Assets 995.57 2,914.72 284 1,650.64 6,282.32 2,513 -655.07 Total Debt 434.16 1,189.18 228 635.93 2,605.49 2,077 -201.77 EBITDA Multiple 12.95 109.55 257 3.82 541.50 2,175 9.12 Net Sales 5y growth 14.74 114.30 238 16.46 44.43 1,912 -1.73 R&D 0.12 0.10 78 0.16 0.18 650 -0.05 ** Cash 0.15 0.17 284 0.21 0.30 2,064 -0.06 *** Leverage 0.35 0.28 227 0.26 0.29 2,513 0.10 Q 1.41 1.37 272 1.78 2.85 2,513 -0.36 Profitability -0.02 0.21 280 -0.06 0.35 2,513 0.04 Loss 0.36 0.48 280 0.31 0.46 2,513 0.06 ***
Offer price to EPS 60.93 217.22 176 67.08 289.29 1,725 -6.15
Panel B: Deal characteristics
Given that Tobin’s Q also proxies for overvaluation, this result is reverse from the expectation that private equity buyers often look for undervalued firms (Jensen, 1989). Lastly, consistent with prior literature, private equity buyers seem to acquire more targets with negative profitability. However, caution should be used in the interpretation of this last result. The mean profitability between the two types of buyers is not significantly different. Moreover, Table D.I in the Appendix shows that when using the Mann-Whitney test, the difference in ‘’loss’’ is only significant at the 10% level.
Panel B of Table V reports the deal characteristics of the two buyers. Again, using the full dataset there are significant differences in characteristics. First, strategic buyers are more involved in tender offers than private equity buyers. As shown by Huang and Walkling (1987) tender offers are associated with, on average, higher premiums. Next, Table V shows that targets pursued by strategic buyers are more likely to use defensive tactics. In fact, in the total sample only 2 targets of a private equity buyer used the lockup option. These results suggest that private equity buyers are able to negotiate better deal terms. As stated in the literature review earlier, these two defensive tactics (lockup option and termination fee) are associated with higher deal premiums (Burch, 2001; Officer, 2003).
Panel C of Table V reports two different measures of premiums. The direct premium paid by private equity buyers is significantly lower. A reason for this could be that the premiums are determined by both the different- and target characteristics. So, in order to find out whether private equity buyers pay a smaller premium, the premium measures need to be controlled for target- and deal characteristics that, according to prior literature, influence the premium.
Financial crisis
Panel C of Table B.II in the Appendix shows that during the crisis the premium is significantly higher than before- and after the crisis. As emphasized by Fralich and Papadopoulos (2017) this suggests that buyers tend to believe that the target is only temporarily undervalued, and that after the crisis the target value would return back to normal.
Multiple regression analysis
premium is regressed on the private equity dummy and the control variables. The different premium measures are estimated in separate models.
Table VI
Multiple regression: explaining deal premiums
This table shows the results of the multiple OLS regression with two different deal premium measures as dependent variable. The sample contains 3042 US-based deals during the period 1996-2017. The selected deals consist of 291 private equity deals and 2751 strategic deals. Columns [1] and [2] show results from the first premium measure (the 4 week direct premium in percentage terms) regressed on the private equity dummy, target- and deal characteristics. Columns [3], [4], [5], and [6] show results from the second premium measure (cumulative abnormal returns for 3 different event windows, in percentage terms) regressed on the private equity dummy, target- and deal characteristics. Target- and deal characteristics are retrieved from the Thomson ONE database. The CAR premium is calculated using stock- and market returns retrieved from Datastream. Total assets (ln) is the natural logarithm of the book value of total assets. Profitability is net income to total assets. Leverage is the book value of debt to total assets. Tobin’s Q is the market value divided by the book value of assets. Tender, termination fee, and lockup are dummy variables which equal one if the deal involves respectively a tender offer, termination fee, lockup option. Competition is a dummy variable which equals one if there are more than one bidder. ***, **, * represent significance at the 1%, 5%, and 10% levels respectively. The White robust standard errors are shown in parentheses.
4 week premium CAR3 CAR5 CAR11
[1] [2] [3] [4] [5] [6] Constant 56.19 *** 51.39 *** 30.05 *** 33.27 *** 31.87 *** 30.05 *** (5.05) (8.67) (2.09) (2.17) (1.80) (3.26) Independent variable Private equity -12.13 *** -15.78 *** -3.13 * -3.30 ** -4.50 *** -3.26 ** (3.71) (4.12) (1.66) (1.78) (1.37) (1.58) Target characteristics Total assets (ln) -4.16 *** -4.51 *** -1.53 *** -1.79 *** -1.39 *** -1.32 *** (0.76) (0.79) (0.29) (0.30) (0.26) (0.28) Profitability -12.98 * -10.42 1.40 1.59 0.49 1.82 (7.27) (7.33) (1.83) (1.90) (1.57) (1.60) Leverage 20.98 * 22.16 * 1.69 1.77 1.90 3.57 ** (11.44) (12.04) (1.92) (2.02) (1.76) (1.82) Q 1.31 ** 1.58 ** 0.40 0.33 -0.01 -0.29 (0.67) (0.72) (0.20) (0.22) (0.18) (0.20) Deal characteristics Competition 34.67 * 36.11 * -5.01 ** -4.27 ** -5.46 *** -5.15 *** (16.97) (16.65) (2.07) (2.16) (1.80) (1.81) Tender 9.98 *** 6.86 * 4.54 *** 4.78 *** 5.13 *** 3.93 *** (3.85) (3.44) (1.18) (1.21) (1.06) (1.11) Lockup 13.97 14.48 -0.31 0.24 0.41 1.73 (11.92) (11.33) (2.62) (2.63) (2.09) (2.07) Termination fee 2.54 2.35 4.26 *** 3.59 *** 3.18 *** 1.75 * (3.19) (3.39) (1.11) (1.14) (0.98) (1.05)
Industry dummies Yes Yes
Year dummies Yes Yes
The first two columns [1], use the direct 4-week premium as dependent variable. In columns [3], [4], [5], and [6] the premium is defined as respectively the CAR3, CAR5 and CAR11. The control variables are added to strengthen the robustness of the model. Next, an indicator variable for acquisitions by private equity buyers is included. So, if target- and deal characteristics can explain the difference in deal premium, this private equity indicator variable should turn out to be insignificantly different from zero in the regression.
Columns [1] and [2] use the first type of premium measure (4 week direct premium) as dependent variable. Both columns show a significantly negative private equity indicator variable, implying that private equity buyers pay a lower deal premium than strategic buyers, (b = -12.13) significant at the 1% level. So, everything else constant, private equity buyers pay a deal premium lower by 12.13% of the market value of the target 4 weeks before the announcement date. This private equity premium discount increases once industry- and year dummies are included (b = -15.78). The observant reader will notice that the private equity indicator variables for [1] and [2] are of the same magnitude as the difference in 4 week premium measures reported in Table VI. Hence, controlling for target- and deal characteristics does not seem to reduce the average premium difference.
Columns [3], [4], [5], and [6] use the second type of premium measure (cumulative abnormal returns) as dependent variable. Again, the private equity indicator variable is negative and significant over three different event windows. This implies that target shareholders gain less when a private equity buyer acquires the target firm, suggesting that they receive a significantly lower deal premium. The private equity indicator is negative and is significant at the at the 10%, 5%, 1%, 5% respectively (b = - 3.13; b = - 3.30; b = - 4.50; b = -3.26). The private equity deal premium discount seems to increase as the event window increase. As discussed in the methodology section, the length of the event window influences the estimation of abnormal returns. Narrow windows (-1 to +1) have the advantage of being less dependent on the model used to calculate the expected ‘normal’ returns (Fama, 1991). Whereas longer event windows (-5 to +5) better capture prior information leakage and the probability of success of a specific deal. (Bargeron et al.; 2008, Schwert; 1996; 2000).
profitability and the deal premium in column [1] (b = - 12.98) significant at the 10% level, is puzzling and is different from prior literature.10 However, the significance of this effect disappears once the year- and industry dummies are included. As for the deal characteristics, deals that involve more than one a bidder, and deals that involve a tender offer tend to get a higher deal premium. Surprisingly, the termination fee is only significant when regressed on the CAR premium measure. It could be the case that the breakup fee is signed after the announcement date but before the end of the different event windows.
Robustness checks
To see whether these findings are robust to deviations from the standard model, multiple robustness checks are performed. The first robustness check is to split the 22-year into three different samples. The results are presented in table C.I in the Appendix. As discussed in the literature review, the financial crisis made it more difficult to correctly valuate the target (Latham and Braun, 2011). Columns [3] and [4] of table C.I show that when the regression is performed during the crisis period, the independent variables exhibit a higher volatility (reflected by higher standard errors). The higher volatility and low sample size makes it difficult to determine whether there was a private equity discount during the crisis. Henceforth this period is ignored in this first robustness check. The main point of this robustness check is to verify whether the private equity discount persists in the period before- and after the crisis. The results show that there are no large differences between the OLS regression on the full-sample. This implies that multiple regression presented in table VI have some robustness concerning the different periods.
The error terms in the 6 regressions presented in table VI show signs of non-normality. A common practice is to perform the regression on a trimmed or winsorized dataset. In this study the extreme points on the dataset contain important information, therefore the dataset is not trimmed but winsorized. The results of this analysis are presented in table C.II in the Appendix. Apart from the difference in the significance of the profitability variable, no large differences are observed. Even though the error terms in the winsorized regressions are still not normally distributed, their skewness and kurtosis are closer to normality. This gives an indication that non-normality of the error terms has no influence on the regressions.
VI. CONCLUSION
By using a unique dataset and two different premium measures, this study examines if private equity buyers pay a lower deal premium.
The first part of the research shows that private equity buyers pursue different targets than the ones pursued by strategic buyers. In line with prior literature, the univariate analysis shows that private equity buyers pursue targets that are smaller, have lower R&D expenditure, and hold less cash relative to the targets bought by strategic buyers. Next, the univariate analysis shows that private equity buyers offer different deal terms; private equity buyers make less tender offers, and are involved in less deals that include lockup options and termination fees. This suggests that private equity buyers are able to negotiate superior deal terms.
In the second part of this study the private equity indicator variable and the control variables are regressed on the two premium measures. As expected, the multiple regressions show that private equity buyers pay a smaller premium than strategic buyers. The 4-week direct premium paid by strategic buyers is 15.78% higher than that of strategic buyers. Next, the abnormal returns to target shareholders (CAR) over an 11-day event window are lower when the target is acquired by a private equity buyer (3.26%). This confirms that private equity buyers pay a lower deal premium. These findings are robust when the regressions are performed on (i) different time periods and (ii) a winsorized sample.
Despite the attempt to conduct a widely encompassing study on the difference in deal premiums, there are still some limitations. First, since private equity buyers are not obliged to disclose as many deal information as strategic buyers, important data-points are lost in the build-up of the database. Subsequently, the sample size of private equity deals is only limited. As mentioned before, the ratio between the two types of buyers is similar to prior studies. But, the inclusion of more private equity deals would have increased the reliability of the main findings. Second, using hand-collected information on the selling process, Fidrmuc et al. (2012) suggest that the target’s decision concerning whether to sell the firm in a formal auction, controlled sale, or a private negotiation affects the deal premium. Unfortunately, there is very little data on the selling process. Especially for private equity buyers, the Thomson ONE database does not report whether a firm is sold in a formal auction, controlled sale or a private negotiation.
why is it that private equity buyers still win highly competitive deals? It could be that unobservable target characteristics explain why some targets are acquired by private equity buyers. An interesting direction for further research is to further analyse the selling process that evolves prior to the announcement of the first bid. According to Fidrmuc et al. (2012) the M&A competition analysed in the literature so far only reflects the tip of the iceberg of actual competition.
VII. REFERENCES
Alexandridis, G., Petmezas, D., & Travlos, N. G. (2010). Gains from mergers and acquisitions around the world: New evidence. Financial Management, 39(4), 1671-1695.
Alexandridis, G., Mavrovitis, C. F., & Travlos, N. G. (2012). How have M&As changed? Evidence from the sixth merger wave. The European Journal of Finance, 18(8), 663 688.
Alexandridis, G., Fuller, K. P., Terhaar, L., & Travlos, N. G. (2013). Deal size, acquisition premia and shareholder gains. Journal of Corporate Finance, 20, 1-13.
Barber, F., & Goold, M. (2007). The strategic secret of private equity. Harvard Business Review, 85(9), 53.
Bargeron, L. L., Schlingemann, F. P., Stulz, R. M., & Zutter, C. J. (2008). Why do private acquirers pay so little compared to public acquirers?. Journal of Financial Economics, 89(3), 375-390.
Boone, A. L., & Mulherin, J. H. (2011). Do private equity consortiums facilitate collusion in takeover bidding?. Journal of Corporate Finance, 17(5), 1475-1495.
Bradley, M., Desai, A., & Kim, E. H. (1988). Synergistic gains from corporate acquisitions and their division between the stockholders of target and acquiring firms. Journal of financial Economics, 21(1), 3-40.
Brooks, G.T. (2010). Selling Your Business to a Strategic Buyer or Private Equity Group Which is Better? Retrieved May 04. 2018, from https://www.allbusiness.com/selling your-business-to-a-strategic-buyer-or-private-equity-group-which-is-better-14908901 1.html.
Burch, T. R. (2001). Locking out rival bidders: the use of lockup options in corporate mergers. Journal of Financial Economics, 60(1), 103-141.
Campa, J. M., & Hernando, I. (2004). Shareholder value creation in European M&As. European financial management, 10(1), 47-81.
Chatterjee, S. (1986). Types of synergy and economic value: The impact of acquisitions on merging and rival firms. Strategic management journal, 7(2), 119-139.
Dittmar, A., Li, D., & Nain, A. (2012). It pays to follow the leader: Acquiring targets picked by private equity. Journal of Financial and Quantitative Analysis, 47(5), 901-931.
Dong, M., Hirshleifer, D., Richardson, S., & Teoh, S. H. (2006). Does investor misevaluation drive the takeover market?. The Journal of Finance, 61(2), 725-762.
Eckbo, B. E. (2009). Bidding strategies and takeover premiums: A review. Journal of Corporate Finance, 15(1), 149-178.
Eisenbarth, I., & Meckl, R. (2014). Optimizing the Timing of M&A Decisions—An Analysis of Pro-and Anticyclical M&A Behavior in Germany. American Journal of Industrial and Business Management, 4(09), 545.
Fama, E. F. (1991). Efficient capital markets: II. The journal of finance, 46(5), 1575-1617. Fidrmuc, J. P., Roosenboom, P., Paap, R., & Teunissen, T. (2012). One size does not fit all:
Fralich, R., & Papadopoulos, A. (2017). The financial crisis, acquisition premiums and the moderating effect of CEO power. Long Range Planning.
Flanagan, D. J., & O'Shaughnessy, K. C. (2003). Core-related acquisitions, multiple bidders and tender offer premiums. Journal of Business Research, 56(8), 573-585.
Gaughan, P. A. (2009). M&As in troubling times. Journal of Corporate Accounting & Finance, 20(2), 45-50.
Gorbenko, A. S., & Malenko, A. (2014). Strategic and financial bidders in takeover auctions. The Journal of Finance, 69(6), 2513-2555.
Hayward, M. L., & Hambrick, D. C. (1997). Explaining the premiums paid for large acquisitions: Evidence of CEO hubris. Administrative Science Quarterly, 103-127. Huang, Y. S., & Walkling, R. A. (1987). Target abnormal returns associated with acquisition
announcements: Payment, acquisition form, and managerial resistance. Journal of Financial Economics, 19(2), 329-349.
Kaplan, S., & Schoar, A. (2003). Private equity performance: Returns, persistence and capital (No. w9807). National Bureau of Economic Research.
Jensen, M. C. (1989). Eclipse of the public corporation. Or Jensen, Michael. 1989. “Eclipse of the Public Corporation.” Harvard Business Review, 67(5): 61–74.
Kengelbach, J., & Roos, A. W. (2011). Riding the Next Wave in M & A: Where are the Opportunities to Create Value?. Boston Consulting Group, Incorporated.
Latham, S., & Braun, M. (2011). Economic recessions, strategy, and performance: a synthesis. Journal of Strategy and Management, 4(2), 96-115.
Loderer, C., & Martin, K. (1990). Corporate acquisitions by listed firms: The experience of a comprehensive sample. Financial management, 17-33.
Lubatkin, M., & O'Neill, H. M. (1987). Merger strategies and capital market risk. Academy of Management Journal, 30(4), 665-684.
MacKinlay, A. C. (1997). Event studies in economics and finance. Journal of economic literature, 35(1), 13-39.
Malmendier, U., & Tate, G. (2005). CEO overconfidence and corporate investment. The journal of finance, 60(6), 2661-2700.
Martos-Vila, M., Rhodes-Kropf, M., & Harford, J. (2013). Financial vs. strategic buyers (No. w19378). National Bureau of Economic Research.
Moeller, S. B., Schlingemann, F. P., & Stulz, R. M. (2004). Firm size and the gains from acquisitions. Journal of financial economics, 73(2), 201-228.
Motis, J. (2007). Mergers and Acquisitions Motives. Toulouse School of Economics EHESS (GREMAQ) and University of Crete. Retrieved from economics.soc.uoc. gr/wpa/docs/paper2mottis. pdf.
Offenberg, D., & Pirinsky, C. (2015). How do acquirers choose between mergers and tender offers?. Journal of Financial Economics, 116(2), 331-348.
Officer, M. S. (2003). Termination fees in mergers and acquisitions. Journal of Financial economics, 69(3), 431-467.
Palepu, K. G. (1986). Predicting takeover targets: A methodological and empirical analysis. Journal of accounting and economics, 8(1), 3-35.
Preqin Private Equity Online (2017). Private Equity & Venture Capital Spotlight March 2017. Retrieved May 13. 2018 from http://docs.preqin.com/newsletter/pe/Preqin-Private Equity-and -Venture-Capital-Spotlight-March-2017.pdf
Raad, E. (2012). Why Do Acquiring Firms Pay High Premiums To Takeover Target Shareholders: An Empirical Study. Journal of Applied Business Research, 28(4), 725. Ryngaert, M., & Netter, J. M. (1988). Shareholder wealth effects of the Ohio antitakeover
Safieddine, A., & Titman, S. (1999). Leverage and corporate performance: Evidence from unsuccessful takeovers. The Journal of Finance, 54(2), 547-580.
Schwert, G. W. (1996). Markup pricing in mergers and acquisitions. Journal of Financial economics, 41(2), 153-192.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The journal of finance, 19(3), 425-442.
Shawver, T. J. (2002). Determinants of bank merger premiums. Bank Accounting and Finance, (6), 26-29.
Shleifer, A., & Vishny, R. W. (1992). Liquidation values and debt capacity: A market equilibrium approach. The Journal of Finance, 47(4), 1343-1366.
Steger, U., & Kummer, C. (2007). Why Merger and Acquisition (M & A) Waves Reoccur: The Vicious Circle from Pressure to Failure. Lausanne, Switzerland: IMD.
Vild, J., & Zeisberger, C. (2014). Strategic buyers vs. private equity buyers in an investment process.
Walkling, R. A., & Edmister, R. O. (1985). Determinants of tender offer premiums. Financial Analysts Journal, 41(1), 27-37.
Williams, E. (2007). PE vs. strategic purchaser: Understand the difference, Boston Business Journal.
APPENDIX A
Table A.I presents the skewness, kurtosis and the p-value associated with the Jarque-Bera test. Skewness measures the shape of the distribution and its symmetry. Kurtosis refers to the degree of peak in the distribution. More peak than normal implies that a distribution has fatter tails. A perfect normal distribution is reflected by a skewness of zero and a kurtosis of three. Clearly, the 4 week premium and CAR11 do not follow a normal distribution. The Jarque-Bera test (based on Skewness and Kurtosis) tests for normality of the data. The null hypothesis for the test is that the data is normally distributed. Based on the reported p-values the data may not be normally distributed. However, non-normally distributed seems not to be an issue as the sample is considerably large.
Table A.I Jarque-Bera test
Skewness Kurtosis Probability
4 Week Premium 14.05 361.39 0 CAR11 0.78 237.87 0
CAR5 0.95 3.34 0
CAR3 0.89 3.15 0
Table A.II reports the estimates of the variance inflation factors (VIFs) for the independent variables used in the multiple regression analysis. The VIFs are estimated using a regular OLS model, estimated without industry- and year dummies. Columns [1], [2], [3], and [4] show the estimates with respectively the 4 week premium, CAR11, CAR5, and CAR3 as dependent variable. There is evidence of multicollinearity table if the largest VIF is greater than 10 and the average of all the VIFS is shows that there are no signs of multicollinearity. For further discussion on this see Chatterjee (2012)
APPENDIX B
Table B.I
Univariate analysis using different time periods: private equity- vs. strategic buyers
This table shows the results of the univariate analysis between private equity buyers and strategic buyers for three different time periods. Columns [1], [2], and [3] represent respectively the pre-crisis period (1996-2007), the crisis period (2008-2010), and the post-crisis period (2011-2016). The sample contains 3042 US-based deals during the period 1996-2017. The selected deals consist of 291 private equity deals and 2751 strategic deals. Panel A, B, and C report the mean, standard deviation, and number of observations of respectively the target characteristics, deal characteristics, and premiums. Due to space constraints the description of the variables can be found in Table B.II.
[1] Pre-crisis [2] Crisis [3] Post-crisis
Panel A: Target characteristics
Mean St. Dev. No. Mean St. Dev. No. Mean St. Dev. No. Enterprise value 1,417.07 5,044.05 1,934 1,962.52 7,179.24 171 2,516.06 6,588.18 847 Equity value 1,058.58 3,682.09 1,940 1,737.48 6,663.71 173 2,045.82 5,310.95 850 Total Assets 1,423.54 6,518.29 1,876 1,514.71 5,310.49 165 1,997.76 4,771.22 756 Total Debt 513.34 2,398.53 1,594 605.04 1,797.15 121 895.48 2,856.97 590 EBITDA Multiple 1.65 621.84 1,569 -14.06 250.11 154 15.81 186.08 709 Net Sales 5y growth 19.13 67.75 1,352 21.39 41.14 126 9.56 24.76 672
R&D 0.15 0.17 354 0.21 0.24 83 0.15 0.14 291 Cash 0.17 0.22 1,565 0.29 0.27 151 0.24 0.41 632 Leverage 0.28 0.30 1,856 0.24 0.28 157 0.23 0.26 727 Q 1.57 2.36 1,865 1.96 2.84 164 2.12 3.45 756 Profitability -0.06 0.35 1,876 -0.13 0.44 165 -0.02 0.25 752 Loss 0.29 0.45 1,876 0.47 0.50 165 0.33 0.47 752
Offer price to EPS 56.14 222.45 1,325 98.10 608.87 85 89.02 336.28 491
Panel B: Deal characteristics
Table B.II
Univariate analysis using different time periods: private equity- vs. strategic buyers
This table shows the results of the univariate analysis between private equity buyers and strategic buyers. The definitions of Columns [1], [2], and [3] are given in table B.I reported above. The sample contains 3042 US-based deals during the period 1996-2017. The selected deals consist of 291 private equity deals and 2751 strategic deals. Panel A, B, and C report the mean, standard deviation, and number of observations of respectively the target characteristics, deal characteristics, and premiums. Target- and deal characteristics are retrieved from the Thomson ONE database. The CAR premium is calculated using stock- and market returns retrieved from Datastream. Enterprise value, Equity value, Total Assets, Total Debt, and Transaction value are in millions of US dollars. The EBITDA Multiple is defined as the target firm’s enterprise value divided by their EBITDA. The Net Sales 5y growth is the growth, in percentage terms, of net sales over the 5 years preceding the SDC announcement. R&D is defined as the target’s research and development expenses for the 12 months prior to the announcement over the book value of assets. Cash is defined as the target’s cash holdings over the value of total assets. Leverage is defined as total debt over total assets. Q is defined as the market value of the firm over the book value of assets. Profitability is defined as net income over total assets. Loss is an indicator variable equal to one if profitability is negative and zero otherwise. Offer price to EPS is the ratio of the offer price to Earnings Per Share for the 12 months prior to the announcement. Transaction value is in millions of US dollars and is calculated by subtracting liabilities from the transaction value and by adding net debt. Tender, lockup, and termination fee are indicator variables equal to one (and zero otherwise) if the deal respectively is a tender offer, involves a bidder lockup provision, or includes a target termination fee. The number of bidder is defined as the number of entities (including the acquirer) bidding for the target firm. The 4 week premium is the first type of premium measure and is defined as the offer price to target closing stock price 4 weeks prior to the announcement date, expressed as a percentage. CAR11, CAR5, and CAR3 are the second type of premium measure and are defined as respectively the 11-day, 5-day, and 3-day cumulative abnormal return (in percentage points) calculated using the market model. The difference in means is analysed by Welch’s t-test.
[2] - [1] [2] - [3] [3] - [1]
Panel A: Target characteristics
Difference p-value Difference p-value Difference p-value Enterprise value 545.46 0.33 -553.54 0.35 1,098.99 0.00 Equity value 678.90 0.19 -308.35 0.57 987.25 0.00 Total Assets 91.17 0.84 -483.05 0.28 574.21 0.01 Total Debt 91.70 0.60 -290.44 0.15 382.14 0.00 EBITDA Multiple -15.72 0.54 -29.88 0.16 14.16 0.41 Net Sales 5y growth 2.25 0.58 11.83 0.00 -9.58 0.00 R&D 0.07 0.02 0.06 0.03 0.00 0.76 Cash 0.12 0.00 0.05 0.10 0.07 0.00 Leverage -0.05 0.05 0.01 0.68 -0.06 0.00 Q 0.39 0.09 -0.16 0.54 0.55 0.00 Profitability -0.07 0.06 -0.10 0.00 0.04 0.00 Loss 0.18 0.00 0.13 0.00 0.05 0.02
Offer price to EPS 41.96 0.53 9.08 0.89 32.88 0.04
Panel B: Deal characteristics
