The appeal of private targets in international acquisitions

Int. Fin. Markets, Inst. and Money 24 (2013) 198–222

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Journal of International Financial Markets, Institutions & Money j ou rn al ho me pa ge : w w w . e l s e v i e r . c o m / l o c a t e / i n t f i n

The appeal of private targets in international acquisitions Jeff Madura, Jurica Susnjara ∗ Florida Atlantic University, United States

a r t i c l e

i n f o

Article history: Received 1 May 2012 Accepted 18 December 2012 Available online 26 December 2012 JEL classification: G15 G32 G34 Keywords: International Mergers Acquisitions Private Size Development

a b s t r a c t Using a sample of 8000 targets in the US and Western Europe over the 1997–2009 period, we find that private targets receive significantly higher payments from bidders than public targets. We find that the private valuation premium is inversely related to the size of the target. We also find that the private valuation is relatively high when the target has better access to debt in its home country. Also, the private valuation premium is relatively high when the bidder country stock market is more fully developed and relatively low when the bidder has ample access to debt. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Private companies are opaque and their shares are illiquid. Their valuations while they are private reflect a discount compared to similar public firms, which is normally attributed to a lack of liquidity. However, the valuation of a private firm is different than the valuation of a private target that is about to be acquired. Private targets that are acquired will no longer have a liquidity disadvantage when acquired by public firms. Because its value has not been consistently marked to market like public firms, the valuation of a private target could be discounted to reflect the uncertainty surrounding its value. However, the uncertainty surrounding the valuation private target also affords a bidder

∗ Corresponding author at: College of Business, Florida Atlantic University, Boca Raton, FL 33431, United States. Tel.: +1 954 591 5572. E-mail address: [email protected] (J. Susnjara). 1042-4431/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.intfin.2012.12.005

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more freedom to set a payment without constraints that might be imposed by its respective board of directors if the target was public and had a market valuation. Our objective is to assess the valuation of private targets relative to public targets in the global market for corporate control. This topic has been largely ignored in the academic literature, except for studies by Koeplin et al. (2000) and John et al. (2010). Our study is based on a much larger sample (8036 targets, of which 2128 are private) in a more recent (1997–2009) period and our entire focus is on the relative valuations of private targets. The much larger overall sample allows us to use a unique research design in order to compare the relative valuations of private targets versus public targets. Our study is designed to answer 3 broad questions: 1. Do private targets in the global market for corporate control exhibit higher valuations relative to public targets (and therefore exhibit a positive valuation premium) or lower valuations relative to public targets (and therefore exhibit a negative valuation premium)? 2. How does the size of a private target affect the valuation premium of private targets compared to public targets? 3. How do country characteristics influence the valuation premium of private targets compared to public targets? Based on a large sample of global acquisitions, we find that private targets exhibit positive valuation premiums, as measured by relatively higher valuation multiples compared to public targets. These results are intriguing in light of the illiquidity and asymmetric information exhibited by private targets prior to being acquired. Our results suggest that bidder managers can more easily justify paying high prices for private targets, perhaps because there is more ambiguity surrounding their proper valuation. We also find that this private valuation premium (relative to the amount paid for matched public targets) is more pronounced for smaller private targets. Small private targets may attract high offer prices because any degree of overpayment by the bidder for a small private target will not destroy bidder value and may not attract much bidder board scrutiny. Country characteristics can influence the negotiating power of targets and bidders within the global market for corporate control. When the target country has better access to debt (which can discipline the use of free cash flow), the private valuation premium is relatively low. When the bidder country has better access to debt, the private valuation premium is relatively low. However, when the bidder country has easy access to equity (which weakens the discipline over free cash flow), the private valuation premium of the corresponding target is relatively high. Overall, our results suggest that the private valuation premium is influenced by disparate country conditions within the global market for corporate control. The remainder of the paper is organized as follows. Section 2 provides an overview of the relevant literature. Section 3 develops hypotheses about valuations of private targets relative to their public counterparts, drawing from the prior mergers and acquisitions literature. Section 4 discusses the data sources and screens applied to arrive at the final sample. Section 5 explains the methodology used to test the hypotheses. Section 6 presents the results of our analyses. Section 7 summarizes the main findings. 2. Literature review While the issue of valuation and potential misvaluation in mergers has received much attention, most of the focus has been on publicly traded firms. Numerous studies have acknowledged the difficulty in valuation of targets. Hansen (1987) and Travlos (1987) suggest that some bidders may counter the uncertainty surrounding a target’s value by offering stock. Dong et al., 2006 offer a theory to suggest that misvaluation could drive mergers. Moeller et al. (2004) and Chaterjee et al. (2009) suggest that the opinions of target valuations vary, and that the complexity in valuation varies among targets (see Officer et al., 2009; Carlin and Kogan, 2010). The issue of a target’s valuation is especially acute for private targets, since their valuation was not market determined and transparent prior to the acquisition. While bidders pursuing public targets must determine a control premium to apply to the public target’s existing market value, the private

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target does not have an existing market value. Claessens et al. (2002) show that greater ownership concentration increases firm value. Since private companies have greater ownership concentration, they may be valued higher than public firms (meaning that they exhibit a ‘private valuation premium’). Ang and Kohers (2001) argue that if managers with high ownership tend to have greater bargaining power (as suggested by Ghosh and Ruland, 1998), and managers in private companies have bigger ownership stakes than in public companies, then the private company management has greater bargaining power compared to the public company management. However, Wulf (2004) suggests that greater bargaining power does not necessarily result in gains for shareholders. In addition, private company shares are illiquid. Officer (2007) and De Franco et al. (2007) find that private companies in the US exhibit a discount relative to public firms ranging from 17 to 37%, using two different methods of estimating the discount. To the extent the bidders can obtain private targets in the US at a discount (relative to what they would pay for public targets), it might explain why they may benefit from acquiring them. Much of the research on private targets has focused on the bidder perspective. Numerous studies have found that bidders experience favorable abnormal returns when acquiring private targets (see Hansen and Lott, 1996; Chang, 1998; Ang and Kohers (2001); Fuller et al., 2002; Moeller et al., 2004; Faccio et al., 2006; Draper and Paudyal, 2006; Cooney et al., 2009). Rodriguez and Stegemoller (2007) find that some acquisitions of private targets elicit a significant market response for bidders even when the target represents a very small proportion of the bidder value. Cooney et al. (2009) find that the abnormal returns to bidders are more favorable when the corresponding private targets experienced an increase in valuation (prior to a planned IPO by the private target that was ultimately withdrawn). Most studies on private targets do not address the valuation of private targets because these targets do not have market values and their valuations are subject to measurement complications. While the academic research on private target valuation is quite limited in general, it is especially limited when assessed with a global perspective. A small set of global studies on private targets have established a foundation from which future research can be built. A study by Koeplin et al. (2000) assessed US and non-US firms and found a private target discount of around 20% on a small sample of global acquisitions. Their sample consists of 192 firms over the 1984–1998 period. Faccio et al. (2006) assess a sample of 4429 acquisitions by bidders based in Western Europe. They find that Western European bidders experience favorable abnormal returns when acquiring private targets. Their results are similar to those found by other previously cited studies on US acquisitions of private targets, but are from a different perspective. However, this study focuses only on the valuations of bidders and not on valuations of the private targets. A more recent study by John et al. (2010) assesses a sample of 1525 foreign targets (of which 1155 are private) acquired by US firms. They find that bidder returns are generally more favorable for bidders that acquire private firms than bidders that acquire public firms, and that bidder returns are lower when the target country has stronger investor protection. While most of their study is focused on the bidder perspective, using a subsample of 489 targets (of which 153 are private) they find that premiums paid for public targets are higher in high investor protection countries, and that private target valuations are not affected by the investor protection level across countries. Our research is an extension of these three studies. First, we focus completely on the valuation of private targets relative to public benchmarks. Second, we use a more recent database, and one that includes bidders and targets from both US and Western Europe. Consequently, our sample size is significantly larger than those of the previous studies. Third, at least in part due to our larger sample size, we are able to offer a unique method of measuring the valuation of private firms relative to public firms, along with complementary tests of robustness. Fourth, we also give more attention to particular characteristics of the bidder, target, and their respective environments that can influence the valuation of a private target relative to a public benchmark. 3. Hypotheses We develop hypotheses for the magnitude and sign of the private valuation premium, and also for how a private target’s characteristics and bidders’ country characteristics influence the private valuation premium.

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3.1. Price paid for a private target versus public target The private company may be off the radar within the market for corporate control, because it does not have a market valuation or an analyst following, and cannot be closely monitored by prospective bidders. In fact, there may be much uncertainty surrounding its valuation. Thus, the private target’s bargaining power may be more limited because its traits may be perceived as less attractive than public counterparts. 1H0 : The acquisition price for a private company should be relatively low compared to the price paid for a public company (meaning that the private valuation premium is negative). However, a counter argument can be made that serves as our alternative hypothesis. It may be easier for the bidder’s management to convince a board and its shareholders to pay a high acquisition price for a private company that has limited financial data and no transparent market value. Some bidders might want to pay a high price for a target for reasons other than maximizing shareholder value (see Jensen, 1986 or Roll, 1986). Other bidders might want to pay a high price for a target because they believe that the synergies will make the acquisition worthwhile. Regardless of the bidder motive, the limited financial data and ambiguous market value of a private target limits the benchmarking with public company comparables, and therefore may allow bidders to justify paying larger amounts (relative to fundamentals) for private companies than public companies, without resistance from their board. 1HA : The acquisition price for a private company should be relatively high compared to the price paid for a public company (meaning that the private valuation premium is positive).

3.2. Impact of target size on the private valuation premium Pagano et al. (1998) and others verify that relatively larger private firms are more likely to go public than small private firms. Many private firms are too small to engage in an IPO, and therefore may have a weaker bargaining position when negotiating with a prospective bidder. Poulsen and Stegemoller (2008) show that firms facing financial (liquidity) constraints tend to favor sell-outs to IPOs, making their bargaining position weaker. This implies that bidders may pay lower acquisition prices (relative to similar public targets) for small private targets than large private targets. Also, if small private targets exhibit more asymmetric information than large private targets, they may receive discounted bids from bidders that reflect the greater uncertainty surrounding the target’s value. Lastly, John et al. (2010) found that while all targets’ valuation multiples decrease with size, the decrease in valuation multiples of private targets is of greater magnitude. 2H0 : Small private targets have lower standardized valuations than large private targets. However, small private targets might command higher private valuation premiums than large private targets. When a bidder pursues a small private target that is subject to much asymmetric information, the bidder’s management may more easily justify a high bid without excessive scrutiny by its board that a large private target would draw. In addition, the bidder can more easily afford to pay a high price for a smaller private target than a large private target. Thus, the bidder’s management can convince a board and its shareholders to pay a higher price relative to fundamentals for a small private firm than for a large private firm. Moreover, the potential damage to a bidder from overpayment is limited when the private target is relatively small. The risk that the bidder’s management and board will ultimately become subject to public criticism for overpayment is much higher when purchasing a large private firm rather than a small private firm. Therefore, bidders may be more cautious in their payment for a large private target than a small private target. 2HA : Small private targets have higher standardized valuations than large private targets.

3.3. Impact of target country characteristics on the private valuation premium The target’s country characteristics may affect culture in the global market for corporate control, and therefore could affect the amount paid for private targets versus public targets. We consider the

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following target country characteristics: access to debt, stock market development, IPO activity, and investor protection, as explained next.

3.3.1. Target country’s access to debt A common reason for private targets to accept takeovers is that they have limited access to funding relative to public targets and therefore cannot fully capitalize on growth options. As such, any changes in access to debt funding in their local market should affect them disproportionately. In countries where the access of private targets to debt is restricted, the private targets should be more willing to be acquired, and therefore have less bargaining power. Conversely, in countries where private targets have more access to debt, they may be more capable of pursuing growth options on their own, and are may be more resistant to an acquisition. Therefore, they would have more bargaining power if approached by a bidder. These arguments are in line with Officer’s (2007) availability of liquidity hypothesis, but applied in an international context. 3H0 : Relatively higher private valuation premiums are paid for private targets that are based in countries with better access to debt funding.

3.3.2. Target country’s stock market development A target country’s stock market development may also influence the private valuation premium because it can affect the relative liquidity of the private companies versus their public counterparts. Since a developed home stock market allows a public target better financing opportunities, it will provide it with more bargaining power in an acquisition, at least relative to a private target that does not have access to the same equity market. 4H0 : A private company has a lower valuation relative to its public counterparts within a given country that has a well-developed stock market. However, private companies might be more appealing targets in a more fully developed stock market because their intrinsic value is low due to their relative lack of liquidity. Their liquid public counterparts may even seem relatively overvalued prior to the acquisition when the target’s stock market is fully developed. In addition, private targets in a country with a developed stock market may be more appealing than public targets because public targets cannot cite lack of access to funding as a reason to seek a bidder in a country with a developed stock market. 4HA : A private company has a higher valuation relative to its public counterparts within a given country that has a well-developed stock market.

3.3.3. Target country’s IPO activity Since IPOs can serve as an alternative to private targets to being acquired, we expect that more activity may increase the bargaining position of the private targets. 5H0 : Valuations of private targets are higher in countries with more IPO activity.

3.3.4. Target country’s investor protection Many studies from the law and finance literature such as La Porta et al. (1998), have showed that better investor protection results in less systematic risk, higher firm valuations, dividend yields and P/E ratios than in less protected markets. Since public firms in high investor protection countries should be priced to reflect the better investor protection. John et al. (2010) find that public targets receive higher premiums when they are in countries with stronger investor protection. There is less uncertainty surrounding target valuations in these countries, and bidders may be willing to pay a higher premium as a result. But private targets do not directly benefit from investor protection laws since their shares are not publicly traded. Our hypothesis is: 6H0 : Valuations of private targets should be relatively low as compared to their public counterparts in a country where investor protection is high.

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3.4. Impact of bidder country characteristics on the private valuation premium The private valuation premium in the global market for corporate control may also be influenced by the bidder country’s access to debt, stock market development, IPO activity, and investor protection, as explained next. 3.4.1. Bidder country access to debt funding Greater access to debt funding can result in more prudent bidder behavior. The free cash flow argument (Jensen, 1986) suggests that firms with more leverage in their capital structure will be more judicious in their use of cash. Additionally, greater bidder access to debt should increase the likelihood of debt being used to fund the cash portion of the purchase, and cash purchases tend to result in lower acquisition valuations (see Moeller et al., 2004 and Rossi and Volpin, 2004). While it is still possible for bidders in these cultures to overpay when acquiring private targets because of the opacity of private targets, we expect that bidders will be more cautious. 7H0 : In cultures where bidders have greater access to debt, private targets will have lower relative valuations. 3.4.2. Bidder country’s stock market development Related to the arguments above regarding bidder’s access to debt funding, if the bidder’s stock market is more developed, bidders have easier access to equity. Unlike debt, equity does not constrain the use of free cash flow. Bidders in this culture might be less cautious, and more willing to offer higher prices for private targets if they have easy access to stock financing. 8H0 : A more developed bidder stock market may lead to higher valuation premiums for private targets. 3.4.3. Bidder country’s IPO activity Using the same argument above about stock market development, bidders in a market with much IPO activity may be less cautious and more willing to offer higher prices for private targets if they have easy access to stock financing. 9H0 : A high level of IPO activity in a bidder’s country may lead to higher valuation premiums for private targets. 3.4.4. Bidder country’s investor protection Bidder country investor protection may also have an impact on relative valuations of public and private targets. If better investor protection forces management behavior to be more in line with shareholders’ interests, bidders from countries with better investor protection should be less likely to overpay for private targets, all else equal. 10H0 : Better bidder country investor protection should result in lower valuation premiums for private targets. 4. Data The primary source of data for this study is Securities Data Corporation Platinum Mergers And Acquisitions Database (SDC). We apply sample screening criteria based on the existing literature on mergers and acquisitions: • The sample covers 13 years (1997–2009). • The sample includes successful and unsuccessful bids for at least 50% of target’s equity (SDC Deal Forms ‘Acquisition of Majority Interest’, ‘Acquisition of Assets’ and ‘Merger’), with deal value being at least $1 million. • The bidder and the target are either from the US or Western Europe; for the purpose of this study, Western Europe is defined as the pre-2004 European Union (EU) 15, plus Norway and Switzerland. This encompasses the European countries that were members of either EU or EFTA (European Free

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Table 1 Targets by public status and country/region. Country/region

Total

Private

Public

Austria Belgium Denmark Finland France Germany Greece Iceland Ireland-Rep Italy Luxembourg Netherlands Norway Portugal Spain Sweden Switzerland UK US Civil law Common law Total

24 69 79 44 378 206 45 2 39 92 9 118 133 29 190 133 46 1710 4690 1597 6439 8036

2 32 41 6 107 46 4 0 7 23 0 16 13 4 140 9 5 711 962 448 1680 2128

22 37 38 38 271 160 41 2 32 69 9 102 120 25 50 124 41 999 3728 1149 4759 5908

The table shows the number of targets per country and public status. The sample is from Securities Data Corporation and covers bids for at least 50% of target’s equity from 1997 to 2009 where the bidder and the target are from Western Europe or United States. Utilities, financials, privatizations and serial targets were excluded. Requiring the availability of the Enterprise Value/Target Total Assets ratio for the target and excluding the top and bottom 1% of the bids on that ratio reduces the sample size to 8036 bids, of which 2128 were private.

•

• •

• •

Trade Association) throughout the period we study, and is the pool of countries used by Faccio et al. (2006). Since the primary purpose of the study is to compare acquisitions of public targets to those of private (stand-alone and subsidiary) targets, only public, stand-alone private and subsidiary targets were selected. Acquisitions of utilities (SIC 49XX) and financials (SIC 6XXX) were excluded due to problems with interpreting accounting data of regulated companies. All privatizations (sales of a government controlled entity to a non-government entity) were excluded. These involve potentially drastically different incentives of sellers from what is usually seen in the private sector. Only those targets that were not targets of another bid in the six months prior to the announcement were considered. We require availability of the Enterprise Value/Target Total Assets ratio for the target, since that is the main valuation multiple used to calculate the private valuation premium. The top and bottom 1% of the bids on Enterprise Value/Target Total Assets were excluded to minimize the influence of outliers. The requirement of this ratio reduces the sample to 8036 acquisitions, of which 5908 were public targets, 536 subsidiaries, and 1592 private stand-alone targets.

Of 2128 private (private stand-alone or subsidiary) targets, 1166 (55%) were outside the United States, making this a true international finance study. See Table 1 for a breakdown of targets per country. The exact number of targets used in a particular regression depends on the methodology used as well as on the availability of a particular set of independent variables. Investor protection variables were compiled from Transparency International, La Porta et al. (1998) and Djankov et al. (2008). Data on access to debt are from Beck et al. (2001) Financial Structure database, updated through 2009. Stock market capitalization data are also from the World Bank and

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its Metadata database. GDP data are from the International Monetary Fund and its World Economic Outlook database. 5. Methodology 5.1. Measurement of the private valuation premium We use a unique methodology for measuring the private valuation premium in order to avoid potential distortions that could bias the results. We use Enterprise Value/Target Total Assets as our primary valuation multiple for both public and private targets. Enterprise Value is the target’s total equity valuation implied by the price offered by the acquirer for the target. In simple terms, if the acquirer offers to purchase 60% of target’s equity for $6B, the Enterprise Value of the target is $10B. The target total assets are used as a proxy for target fundamentals; this is in line with similar recent studies such as John et al. (2010). Total assets are more readily available than the more conventional measures of accounting fundamentals such as earnings or the book value of equity. Additionally, studies that use earnings or book value of equity face the problem of having to exclude targets where those accounting figures are negative. Furthermore, there are problems inherent in any cross-country analysis of accounting figures. While this study focuses on the targets from US and Western Europe, and while the EU regulators have made a concerted effort through a series of directives to both harmonize accounting standards within the Common Market and to make the accounting figures more value-relevant (and therefore closer to GAAP), differences among countries still persist. Studies by O’Brien (1998), King and Langli (1998), Arce and Mora (2002), and Ballas and Hevas (2005) show that there are systematic differences in valuation multiples across the EU market that involve earnings or equity as denominators. Using total assets as the valuation multiple denominator reduce the accounting problems and makes the multiples more comparable across firms in different countries. We estimate the private valuation premium based on a variant of the Kaplan and Ruback (1995) “comparable industry transaction” method. This method compares private targets to public targets in comparable acquisitions, as opposed to comparing them to all comparable public firms (including non-targeted ones). Kaplan and Ruback (1995) argue that “comparable industry transaction” method results in lower average valuation errors; it has been used in related studies such as Koeplin et al. (2000) and Officer (2007). Another reason for using public targets as a benchmark in this study as opposed to all public firms is that the control premium may vary across countries. A bidder invariably offers more than the pre-announcement market value for the target, which is necessary to acquire control of the target. Therefore, comparing private targets to all public companies instead of only public targets would underestimate the difference in valuation of private and public companies, at least partly by the amount of the perceived control premium. If the private valuation premium is measured as a percentage difference between the valuation multiple of a private and matched public target, it will inherently exaggerate the effect of outliers and skew the results. In particular, using the public target multiple in the denominator leaves (−1, +infinity) as the range of possible outcomes and will bias the results toward finding a positive private valuation premium. Officer (2007) and similar studies address the skewness by eliminating all data with the premium above 1 (100%) if the public target multiple is used as a denominator. Yet, this truncation eliminates deals with the highest private valuation premiums and biases the mean of the remainder of the sample toward the finding of a private discount. Our primary measurement for comparing private to public targets is a natural logarithm of the ratio of a valuation multiple of a private target and the mean valuation multiple of comparable public targets. Logarithms are taken to address the issue of skewness and to further reduce the impact of outliers. We compare each private target’s valuation to the mean valuation of comparable public targets that are: • located in the same country; • classified in the same two-digit SIC code;

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• in the same size category, with total assets no more than 50% smaller or greater than the total assets of the private target; and • within a three-year acquisition announcement window centered around the acquisition announcement date for the private target. 5.2. Cross-sectional analysis of the private valuation premium We conduct a cross-sectional analysis using OLS to test our hypotheses regarding how different bidder, target, and deal characteristics affect the price offered for private targets relative to similar public targets, while controlling for other known offer price determinants. To explain the variation in the private valuation premium (PP), we apply the following model as the primary test of our hypotheses, with control variables not shown here for brevity: PVP = ˛ + ˇ1 TarSize + ˇ2 TarDebtAccess + ˇ3 TarStockDev + ˇ4 TarIPO + ˇ5 TarCPI + ˇ6 TarLegal + ˇ7 BidDebtAccess + ˇ8 BidStkDev + ˇ9 BidIPO + ˇ10 BidCPI + ˇ11 BidLegal + ε where TarSize TarDebtAccess TarStDev TarIPO TarCPI TarLegal BidDebtAccess BidStkDev BidIPO BidCPI BidLegal

log of target total assets target country’s total financial system deposits, scaled by GDP target country’s total stock market capitalization of listed firms, scaled by GDP target country IPO activity, scaled by GDP target country CPI (Corruption Perception Index) dummy equal to 1 if the target country’s legal origin is common law, 0 otherwise bidder country’s total financial system deposits, scaled by GDP bidder country’s total stock market capitalization of listed firms, scaled by GDP bidder country IPO activity, scaled by GDP bidder country CPI (Corruption Perception Index) dummy equal to 1 if the bidder country’s legal origin is common law, 0 otherwise

The target’s pre-announcement stand-alone value is measured by the log of its total assets. Total assets were collected from SDC and originate from the target’s last balance sheet prior to deal announcement. Access to debt funding is measured as the country’s total financial system deposits in the same calendar year as the announcement, scaled by its GDP. Total financial system deposits represent the funding available for borrowers through the country’s financial system. The GDP is scaled to control for the size of the economy, allowing figures to be not only comparable across time but across countries as well. The effect of inflation is also implicitly controlled for, as is the wealth of the country, so that we isolate the effect of the credit markets as much as possible. A country’s stock market development is measured by the combined market capitalization of all public firms in the country in the same calendar year as the announcement, once again scaled by its GDP in the same year. Scaling by GDP achieves standardization and isolates the effect of the stock market development from the overall wealth of the country. To measure a target or bidder country’s IPO activity, we use the IPO dollar volume in both target and bidder country (collected from SDC’s New Issues database and then aggregated by year and country) in the same calendar year as the acquisition announcement, scaled by the GDP of the country in the same year. The investor protection is primarily addressed through two variables. One proxy of investor protection is Transparency International’s annual Corruption Perception Index (CPI), which measures the perceived level of corruption by government officials in a country in a given year. The CPI is on a scale of 1–10, where a higher number means better (lower) corruption. Wu (2005) showed that the level of shareholder protection was a significant determinant of the CPI. Lee and Ng (2009) showed that CPI predicted corporate valuations, and that firms from more (less) corrupt countries trade at significantly lower (higher) market multiples. CPI provides us with a continuous variable to distinguish among civil law countries, which even La Porta et al. (1998) noted have a large degree of variance in the quality

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of investor protection. In addition, CPI varies over time, providing additional value relative to other proxies of investor protection discussed below. As an alternative proxy of investor protection, we use a dummy variable to distinguish target countries that are subject to common law versus civil law, and also distinguish bidder countries that are subject to common law versus civil law. The dummy variable is equal to 1 if the country belongs to the common law tradition (United Kingdom, United States and Ireland in our sample) and 0 if the country belongs to the civil law tradition. The law and finance literature of La Porta et al. (1998) has shown that countries from the common law legal tradition (United Kingdom and its former colonies and territories) have better investor protection than their civil law counterparts. For the purpose of robustness checks of our multivariate results, we use other investor protection proxies suggested by previous literature, either in lieu of or in addition to the two variables described above. La Porta et al. (1998) construct a creditor-rights index and an accounting standards index. Djankov et al. (2008) construct an anti-self-dealing index, and revise an anti-director-rights index first introduced by La Porta et al. (1998). The four variables are on different scales, but in each instant a higher value denotes better investor protection. 5.3. Control variables In addition to the main variables included to test our hypotheses, several control variables are included. Following Fuller et al. (2002), Moeller et al. (2004) and Officer (2007), we control for the distinction between stand-alone private targets and those that are subsidiaries of other corporations. Gertner et al. (1994) and Harris and Raviv (1996) argue that conglomerates constitute potentially efficient internal capital markets for capital-constrained companies. Alternatively, subsidiaries may be less efficient due to cross-subsidization, which could result in a lower sales price. To the extent that subsidiaries and stand-alone targets are viewed differently, we apply a control variable to distinguish between them, and assign a value of 1 if a target is a subsidiary and 0 otherwise. As our measure of target’s financial health, we use the target’s cash and marketable securities scaled by total assets. The alternative proxy of net working capital scaled by total assets used by De Franco et al. (2007) and Officer (2007) in their studies of US domestic acquisitions is not suitable for our analysis because of our cross-country analysis. As a robustness check, we use a ratio of target’s cash and marketable securities scaled by total assets to the matching firm’s cash and marketable securities scaled by total assets, both instead of and in addition to target relative cash holdings. This alternative proxy controls for the matching firm’s liquidity position. In all models this ratio was insignificant, whether used in conjunction with target’s cash holdings or separately, and those results are omitted for brevity. We account for targets in the technology sector with a dummy variable. We account for bidder size, as measured by the log of the bidder total assets. Previous studies confirm that larger bidders are more likely to overpay for targets, all else equal (Moeller et al., 2004). We include a dummy variable for cash-only deals, 1 if cash-only and 0 otherwise; Officer (2007) has found that domestically cash deals lower valuations of private targets relative to similar public targets, while John et al. (2010) found that on a sample of international acquisitions cash deals lower valuations of public targets while they do not affect private targets. We include a dummy variable that is assigned a value of 1 if the deal is cross-border and 0 otherwise. We also include a dummy variable that is assigned a value of 1 if the deal is announced during the dot-com bubble (1996–2000) and 0 otherwise. 6. Results 6.1. Univariate tests of private valuation premium In the overall sample, as seen in univariate tests in Table 2A, the private valuation premium is positive and significant in both mean and median. Since the private valuation premium is measured as the log of the ratio of the valuation multiple of a private target divided by the average valuation multiple of matched public targets, the overall mean of 0.159 represents a 17.3% higher valuation

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Table 2A Private valuation premium, overall and various subsamples. Variable

Overall

Cash Deal

Stock Deal

Cross-border

Domestic

Bubble

Postbubble

Private valuation premium mean (Student’s t) Private valuation premium median (sign)

0.159*** (4.541) 0.181*** (70)

−0.013 (−0.215) 0.036 (3.5)

0.318*** (3.710) 0.237** (16.5)

0.416*** (3.464) 0.443** (13)

0.134*** (3.657) 0.155*** (57)

0.138** (2.326) 0.097* (18.5)

0.172*** (3.948) 0.236*** (51.5)

The table shows private valuation premium means and medians for the 1997–2009 sample of acquisitions. Private valuation premium is calculated as a log of the ratio of the valuation multiple of a private target to the average valuation multiple of comparable public targets, where comparable targets are matched on country, announcement date, total assets and industry. Valuation multiple is target’s valuation from acquisition offer divided by target’s total assets. Mean and median for the overall sample and various subsamples are presented, along with appropriate Student’s t and sign statistics. *, **, and *** denote significance at the .1, .05, and .01 levels, respectively. “Cash Deal” and “Stock Deal” refer to deals with cash and stock being sole methods of payment, respectively. “Bubble” and Postbubble” refer to deals within 1997–2000 and 2001–2009 periods, respectively.

Table 2B Private valuation premium, regional subsamples. Variable

Target Common

Target Civil

Target US

Target non-US

Bidder Common

Bidder Civil

Bidder US

Bidder non-US

Private valuation premium mean (Student’s t) Private valuation premium median (sign)

0.164***

0.073

0.174***

0.122*

0.163***

0.108

0.200***

0.047

(4.560) 0.189***

(0.454) −0.014

(4.247) 0.181***

(1.787) 0.186*

(4.512) 0.180***

(0.732) 0.263

(4.830) 0.188***

(−0.708) 0.126

(70)

(0)

(55)

(15)

(67.5)

(2.5)

(59.5)

(10.5)

The table shows private valuation premium means and medians for the 1997–2009 sample of acquisitions. Private valuation premium is calculated as a log of the ratio of the valuation multiple of a private target to the average valuation multiple of comparable public targets, where comparable targets are matched on country, announcement date, total assets and industry. Valuation multiple is target’s valuation from acquisition offer divided by target’s total assets. Mean and median for the overall sample and various subsamples are presented, along with appropriate Student’s t and sign statistics. *, **, and *** denote significance at the .1, .05, and .01 levels, respectively. “Common” and “Civil” refer to countries from common law and civil law legal traditions, respectively.

multiple on average for private targets. The evidence suggests that private targets receive higher valuations relative to comparable public targets. We believe these results are attributed to bidders more easily justifying a high payment for a target with limited financial data and no transparent market value, without being subjected to constraints that can be imposed by benchmarking to comparable public companies. Our finding of a private premium generally differs from the results of Koeplin et al. (2000), Officer (2007) and John et al. (2010). While both our and the Koeplin et al. (2000) studies are international in scope, there is very little overlap in terms of the sample: our study starts in 1997, while theirs ends in 1998. Also, our study contains over 8000 deals, compared to 192 deals in the Koeplin et al. (2000) study. The John et al. (2010) sample years (1984–2005) overlap with our study slightly more, but their sampling methodology results in only 489 targets (153 private) with valuation data. They do find evidence of a private premium, but only among low-investor-protection targets. The Officer (2007) study was purely domestic in nature, and, as stated above, its truncation method biased the results toward finding the private discount. Tables 2A and 2B show results from measuring the private valuation premium across subsamples categorized by characteristics. The evidence strongly supports hypothesis 1HA of a private valuation premium across subsamples. One exception within the univariate results in Table 2A, is the subsample in which the method of payment is cash-only, as the valuations of private targets are statistically indistinguishable from valuations of public targets.

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Table 3A Private valuation premium (additionally matching on cash-only), subsamples. Variable

Overall

Cash Deal

Stock Deal

Cross-border

Domestic

Bubble

Postbubble

Private valuation premium mean (Student’s t) Private valuation premium median (sign)

0.228***

0.149**

0.305***

0.501***

0.202***

0.201***

0.244***

(5.869) 0.257***

(2.406) 0.197**

(3.273) 0.385**

(3.565) 0.538***

(5.022) 0.220***

(3.247) 0.174**

(4.920) 0.287***

(80)

(20)

(16.5)

(14.5)

(65.5)

(22)

(58)

The table shows private valuation premium means and medians for the 1997–2009 sample of acquisitions. Private valuation premium is calculated as a log of the ratio of the valuation multiple of a private target to the average valuation multiple of comparable public targets, where comparable targets are matched on country, announcement date, total assets, industry and whether payment method is 100% cash. Valuation multiple is target’s valuation from acquisition offer divided by target’s total assets. Mean and median for the overall sample and various subsamples are presented, along with appropriate Student’s t and sign statistics. *, **, and *** denote significance at the .1, .05, and .01 levels, respectively. “Cash Deal” and “Stock Deal” refer to deals with cash and stock being sole methods of payment, respectively. “Bubble” and Postbubble” refer to deals within 1997–2000 and 2001–2009 periods, respectively. Table 3B Private valuation premium (additionally matching on cash-only), subsamples. Variable

Target Common

Target Civil

Target US

Target non-US

Bidder Common

Bidder Civil

Bidder US

Bidder non-US

Private valuation premium mean (Student’s t) Private valuation premium median (sign)

0.233***

0.124

0.234***

0.207**

0.235***

0.115

0.255***

0.140*

(5.882) 0.257***

(0.654) 0.195

(5.298) 0.255***

(2.566) 0.262**

(5.935) 0.255***

(0.670) 0.263

(5.686) 0.261***

(1.825) 0.235**

(79)

(1)

(62)

(18)

(77.5)

(2.5)

(63)

(17)

The table shows private valuation premium means and medians for the 1997–2009 sample of acquisitions. Private valuation premium is calculated as a log of the ratio of the valuation multiple of a private target to the average valuation multiple of comparable public targets, where comparable targets are matched on country, announcement date, total assets, industry and whether payment method is 100% cash. Valuation multiple is target’s valuation from acquisition offer divided by target’s total assets. Mean and median for the overall sample and various subsamples are presented, along with appropriate Student’s t and sign statistics. *, **, and *** denote significance at the .1, .05, and .01 levels, respectively. “Common” and “Civil” refer to countries from common law and civil law legal traditions, respectively.

6.2. Robustness test of valuation premium To examine the possibility that cash-only offers for private targets are matched with offers for public targets involving stock, or vice versa, we add cash-only payments to our criteria for matching private targets to comparable public targets. We then repeat the univariate tests for significance on the newly constructed log of the ratio of valuation multiples, private targets relative to comparable public ones. As seen in Table 3A, once cash-only deals for private targets are matched with cash-only deals for public targets, the private target premium relative to comparable public targets extends to this subsample as well. Notice that the results for the other subsamples in Tables 3A and 3B are consistent with those of Tables 2A and 2B. Thus, the results hold even when revising the criteria used to find matching public targets for each private target. 6.3. Alternative robustness test of private valuation premium To further test the robustness of our finding of a private valuation premium, we examine the overall difference in valuation multiples between private and public targets, without matching on target characteristics. While this method is not as theoretically sound without the matching, it allows for a larger sample. We find that the mean difference of private target minus public target valuation multiples is .585, which is significant at the .01 level (t-statistic = 22.24). We check the possibility that

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Table 4A Private valuation premium, top vs. bottom third by various variables. Variable

Bottom third

Target total assets Target country variables Target debt access Target stock market development Target IPO activity Target CPI Bidder country variables Bidder debt access Bidder stock market development Bidder IPO activity Bidder CPI

0.154**

Top third −0.029

Bottom third − top third 0.183***

0.157*** −0.068 0.105* 0.201***

0.121* 0.321*** 0.335*** 0.175***

0.037 −0.389*** −0.230*** 0.035

−0.090 0.171*** 0.143** 0.142**

0.292*** 0.045 0.317*** 0.220***

−0.383*** 0.125 −0.174*** −0.078

The table shows private valuation premium means by different subsamples. The subsamples are top and bottom thirds of the overall sample by different variables. Private valuation premium is calculated as a log of the ratio of the valuation multiple of a private target to the average valuation multiple of comparable public targets, where comparable targets are matched on country, announcement date, total assets and industry. Valuation multiple is target’s valuation from acquisition offer divided by target’s total assets. t-Test for the significance of the difference assumes unequal variances (assuming equal variances yields identical significance levels). *, **, and *** denote significance at the .1, .05, and .01 levels, respectively. “Debt access” is measured as the country’s total financial system deposits in the same calendar year as the announcement, scaled by its GDP. A country’s stock market development is the market capitalization of all public firms in the country in the same calendar year as the announcement, scaled by its GDP. A country’s IPO activity is the IPO dollar volume in the same calendar year as the acquisition announcement, scaled by the GDP. CPI is Transparency International’s annual Corruption Perception Index; higher number denotes better (lower) corruption in a country.

Table 4B Private valuation premium, common vs civil law. Variable

Common

Civil

Common − Civil

Target legal origin Bidder legal origin

0.164*** 0.163***

0.073 0.108

0.091 0.055

The table shows private valuation premium means by different subsamples. Private valuation premium is calculated as a log of the ratio of the valuation multiple of a private target to the average valuation multiple of comparable public targets, where comparable targets are matched on country, announcement date, total assets and industry. Valuation multiple is target’s valuation from acquisition offer divided by target’s total assets. t-Test for the significance of the difference assumes unequal variances (assuming equal variances yields identical significance levels). *, **, and *** denote significance at the .1, .05, and .01 levels, respectively. “Common” and “Civil” refer to countries from common law and civil law legal traditions, respectively.

our matching technique consistently matches private targets with slightly larger public targets. This would cause a bias because public firms tend to be larger, and larger targets receive lower valuation multiples. We find (results omitted for brevity) that private targets are on average 7.3% larger in terms of total assets than the public targets against which they are matched in our method. Thus, if a bias was caused by our matching method, it would have been toward finding a private discount, so our results are not caused by the matching technique.

6.4. Univariate tests of valuation multiples for key independent variables Next, we focus on the variation in the valuation multiples among private targets. We divide the sample of private targets into subsamples by different levels of the key independent variables, and compare the relative valuation multiples between the highest and lowest levels of those independent variables. We divide each subsample into thirds, and measure the private valuation premium for the top and bottom categories. Results are shown in Table 4A. A statistically significant difference in private valuation premium between top and bottom thirds of the sample by a particular variable indicates that the variable is significantly related to the private valuation premium.

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6.4.1. Effect of target size We examine whether the private valuation premium is conditioned on target size (as measured by total assets), by assessing the different subsamples categorized by target size. As shown in Table 4A, the mean private valuation is positive and significant for the subsample representing the bottom third, but is not significant for the subsample representing the top third. Furthermore, the difference in relative valuation multiples between the bottom and top thirds of the private targets is statistically significant at the .01 level. These results explicitly confirm the implication of John et al. (2010) and support hypothesis 2HA that private firms exhibit a higher valuation premium (relative to their public counterparts) when they are smaller. 6.4.2. Effect of target country access to debt When isolating the target country debt access variable, the mean private valuation premium is positive and significant for the subsamples representing the bottom third and the top third for this variable. However, the difference in mean private valuation premiums between the subsamples representing the top and bottom thirds is not significant. 6.4.3. Effect of target country stock market development When isolating the target country stock market development, the private valuation premium is not significant in the subsample representing the bottom third. However, it is positive and significant in the subsample representing the top third. In addition, the difference in mean private valuation premiums between subsamples is significant, which supports our hypothesis 4HA that the private valuation premium is higher in target countries that have a relatively high stock market development. 6.4.4. Effect of target country IPO activity When isolating the IPO activity in the target country, the private valuation premium is positive and significant in the subsamples representing both the bottom and the top third. The difference in mean private valuation premiums between subsamples is significant, which supports our hypothesis 5H0 that the private valuation premium is higher in target countries that have a relatively active IPO market. It is important to note that while Officer (2007) hypothesized this result he found no evidence of it domestically. 6.4.5. Effect of target country investor protection When isolating the Corruption Perception Index (CPI) in the target country, the private valuation premium is positive and significant for the subsamples representing the bottom third and the top third for this variable. However, the difference in mean private valuation premiums between the subsamples representing the top and bottom thirds is not significant. Thus, univariate tests do not offer evidence that target country investor protection affects the valuations of private and public targets differently. This is in contrast to John et al. (2010) who find that better investor protection helps public targets more. 6.4.6. Effect of bidder country debt access We also consider characteristics of the bidder country. When isolating the bidder country debt access, the private valuation premium is not significant in the subsample representing the bottom third. However, it is positive and significant in the subsample representing the top third. In addition, the difference in mean private valuation premiums between subsamples is significant. This implies that, without controlling for additional variables, the private valuation premium is higher in bidder countries that have a relatively high access to debt. 6.4.7. Effect of bidder country stock market development When isolating the bidder country stock market development, the private valuation premium is positive and significant in the subsample representing the bottom third, while not significant in the subsample representing the top third. The difference in mean private valuation premiums between subsamples is not significant.

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6.4.8. Effect of bidder country IPO activity When isolating the IPO activity in the bidder country, the private valuation premium is positive and significant in the subsamples representing both the bottom and the top third. The difference in mean private valuation premiums between subsamples is significant, which supports our hypothesis 9H0 that bidders are less cautious and the private valuation premium is higher in bidder countries that have a relatively active IPO market. 6.4.9. Effect of bidder country investor protection When isolating the Corruption Perception Index (CPI) in the bidder country, the private valuation premium is positive and significant for the subsamples representing the bottom third and the top third for this variable. However, the difference in mean private valuation premiums between the subsamples representing the top and bottom thirds is not significant. Thus, univariate tests do not offer evidence that bidder country investor protection affects the valuations of private and public targets differently. Since the legal origin is measured as a binary variable, that variable is not included in Table 4A. However, it is shown in Table 4B. We find that the difference in private valuation premiums between common and civil law targets is not significant, and also find no difference in private valuation premiums between common and civil law bidders. 6.5. Multivariate analysis We begin our multivariate analysis with a focus on the impact of private versus public status on valuation multiple of a target. This is a stronger version of the univartiate tests, because it simultaneously controls for other variables. We regress the valuation multiples of all targets on a series of explanatory variables, including a dummy variable for target’s private versus public status. Results are shown in Tables 5A–5D. We not only assess the total sample of targets, but also apply the multivariate model to many different subsamples that are described at the top of each column. Table 5A provides results for the entire sample using the different investor protection variables in our regression models one at a time. Tables 5B–5D focus on models with our two main investor protection variables, CPI and the common law dummy (“legal”), used concurrently.1 Table 5B provides results for the entire sample along with subsamples of cross-border acquisitions and domestic acquisitions (see column headings). Table 5C provides results from applying the multivariate model to the bubble period, post-bubble period, and for subsamples classified by cash versus stock method of acquisition payment. Table 5D discloses results from applying the multivariate model to subsamples of targets in civil law countries and in common law countries. All models are strongly significant based on the F-value per model, and the adjusted R-square statistic ranges from 21 to 40% among the multivariate models. Our main focus is on the coefficient of the dummy variable (TarPrivate) for target being private, because this addresses our first hypothesis about the private valuation premium. The coefficient is positive and significant across all subsamples and all combinations of investor protection variables, implying a higher valuation for private targets as compared to public targets. The coefficient ranges between 0.350 and 0.679 over various subsamples and independent variable combinations, implying that for every $1M in target’s total assets a private target would be valued at between $350K and $679 more than a comparable public target. These results are consistent with our univariate results, and offer additional support for our hypothesis 1HA that private targets exhibit a valuation premium in comparison to public targets. Our results generally differ from those of John et al. (2010), who focus on the bidder perspective but have a portion of their analysis directed toward targets in a manner similar to our Tables 5A–5D. They regress target valuations on a series of explanatory variables, including a dummy for the target public status. John et al. (2010) do find evidence of a private premium, but only among the low-investor-protection countries. Their results indicate no significant difference in valuations of private and public targets among high-investor-protection countries.

1 Models with other combinations of investor protection variables yielded no qualitative differences, and are omitted for brevity.

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Table 5A OLS regression results, valuation multiple as the dependent variable, corporate governance variables one at a time. Variable Intercept TarPrivate TarSize Target country variables TarDebtAcess TarStockDev TarIPO TarCPI TarLegal TarADRI TarASDI TarCredRigIn TarAcctStdIn Bidder country variables BidDebtAccess BidStockDev BidIPO BidCPI BidLegal BidADRI BidASDI BidCredRigIn BidAcctStdIn Control variables TargetSub TargetCash TargetTech BidderSize CashDeal Cross-border StockBubble Observations (used) R-square (adjusted) F-value

0.513*** 0.455*** −0.189***

0.163* 0.460*** −0.188***

0.383*** 0.458*** −0.191***

0.232** 0.460*** −0.190***

0.116 0.450*** −0.191***

0.947*** 0.458*** −0.191***

−0.262*** 0.216*** 0.012** 0.006

−0.258*** 0.184*** 0.013**

−0.167** 0.229*** 0.012**

−0.246*** 0.246*** 0.011**

−0.280** 0.244*** 0.012*

−0.280*** 0.245*** 0.010

0.073 −0.039 −0.002 −0.004 −0.001 −0.083 0.185*** −0.004 −0.060**

−0.140* 0.169*** −0.005

0.040 0.119** −0.004

−0.083 0.174*** −0.006

0.221* 0.080 −0.004

−0.051 0.209*** −0.005

−0.038 −0.072** −0.232** −0.077*** −0.012*** −0.197*** 0.464*** 0.291*** 0.113*** −0.270*** 0.142*** 0.113*** 8036 (4143) 0.300 (0.297) 103.93***

−0.197*** 0.460*** 0.294*** 0.114*** −0.268*** 0.133*** 0.108*** 8036 (4148) 0.299 (0.296) 103.82***

−0.195*** 0.459*** 0.279*** 0.113*** −0.273*** 0.133*** 0.132*** 8036 (4148) 0.302 (0.299) 105.15***

−0.196*** 0.467*** 0.290*** 0.113*** −0.275*** 0.106*** 0.112*** 8036 (4148) 0.300 (0.297) 104.24***

−0.197*** 0.466*** 0.282*** 0.112*** −0.276*** 0.141*** 0.147*** 8036 (4141) 0.301 (0.299) 104.69***

−0.196*** 0.463*** 0.283*** 0.113*** −0.277*** 0.125*** 0.102*** 8036 (4128) 0.300 (0.297) 103.52***

The table reports results of cross-sectional OLS regressions. Dependent variable is the log of the target’s valuation multiple (target’s valuation from acquisition offer divided by target’s total assets). “TarPrivate” is 1 if a target is unlisted, 0 if public. Size is a company’s total assets. “DebtAccess” is the country’s total financial system deposits in the same calendar year, scaled by its GDP. “StockDev” is the market capitalization of all public firms in the country in the same calendar year, scaled by its GDP. “IPO” is the IPO dollar volume in the country in the same calendar year, scaled by the GDP. CPI is Transparency International’s annual Corruption Perception Index; higher number denotes better (lower) corruption in a country. “Legal” is 1 if a country belongs to common law tradition, 0 otherwise. “ADRI” and “ASDI” are Anti-Director Rights Index and Anti-Self-Dealing Index, respectively; both are from Djankov et al. (2008). “CredRigIn” and “AcctStdIn” are Creditor Rights Index and Accounting Standards Index, respectively; both are from La Porta et al. (1998). “TargetSub” is 1 if target is a subsidiary, 0 otherwise. “TargetCash” is target’s cash and marketable securities scaled by total assets. “TargetTech” is 1 if the target is a high-technology company, 0 otherwise. “CashDeal” is 1 if the payment is 100% cash, 0 otherwise. “Cross-border” is 1 if the target and bidder country are not the same, 0 if they are. “StockBubble” is 1 if the deal was announced 1997–2000, 0 otherwise. *, **, and *** denote significance at the .1, .05, and .01 levels, respectively, using White’s (1980) standard errors. “Observations” is the number of targets in a subsample; “used” is the number of targets with all of the independent variable data available.

It is possible that our unique results are due to our use of a different sample. Unlike John et al. (2010), we consider Western European acquirers and US targets. We also restrict non-US targets to Western Europe. This, as well as requiring a slightly different set of independent variable data, results in a usable sample of around 4000 targets (1200 private) in most regressions, while John et al. (2010) examine target valuations on a sample of 489 targets (153 private). In order to make our study more directly comparable to John et al. (2010), as well as to further test the robustness of our findings, we attempted to replicate their methodology and sampling as much as possible. We repeated our regression analysis with the valuation multiple as the dependent variable and a dummy variable for target’s private versus public status as the main independent variable. We

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Table 5B OLS regression results, valuation multiple as the dependent variable.

Variable Intercept TarPrivate TarSize Target country variables TarDebtAcess TarStockDev TarIPO TarCPI TarLegal Bidder country variables BidDebtAccess BidStockDev BidIPO BidCPI BidLegal Control variables TargetSub TargetCash TargetTech BidderSize CashDeal Cross-border StockBubble Observations (used) R-square (adjusted) F-value

All targets

Cross-border

Domestic

0.503*** 0.457*** −0.187***

−0.355 0.534*** −0.156***

0.655*** 0.446*** −0.194***

−0.260*** 0.161** 0.013** 0.016 0.092

−0.134 0.070 0.015** 0.076* 0.118

−0.392*** 0.372*** 0.010 −0.069** 0.059

−0.090 0.214*** −0.005 −0.069** −0.055

0.096 0.118 −0.003 −0.035 −0.036

−0.199*** 0.463*** 0.292*** 0.112*** −0.258*** 0.138*** 0.113*** 8036 (4143) 0.299 (0.296) 92.72***

−0.371*** 0.077 0.328*** 0.107*** −0.056

−0.152** 0.510*** 0.279*** 0.114*** −0.297***

0.061 1315 (646) 0.271 (0.250) 12.92***

0.115*** 6721 (3502) 0.309 (0.306) 120***

The table reports results of cross-sectional OLS regressions. Dependent variable is the log of the target’s valuation multiple (target’s valuation from acquisition offer divided by target’s total assets). “TarPrivate” is equal to 1 if a target is unlisted, 0 if public. Size is a company’s total assets. “DebtAccess” is the country’s total financial system deposits in the same calendar year, scaled by its GDP. “StockDev” is the market capitalization of all public firms in the country in the same calendar year, scaled by its GDP. “IPO” is the IPO dollar volume in the country in the same calendar year, scaled by the GDP. CPI is Transparency International’s annual Corruption Perception Index; higher number denotes better (lower) corruption in a country. “Legal” is 1 if a country belongs to common law tradition, 0 otherwise. “TargetSub” is 1 if target is a subsidiary, 0 otherwise. “TargetCash” is target’s cash and marketable securities scaled by total assets. “TargetTech” is 1 if the target is a high-technology company, 0 otherwise. “CashDeal” is 1 if the payment is 100% cash, 0 otherwise. “Cross-border” is 1 if the target and bidder country are not the same, 0 if they are. “StockBubble” is 1 if the deal was announced 1997–2000, 0 otherwise. *, **, and *** denote significance at the .1, .05, and .01 levels, respectively, using White’s (1980) standard errors. “Observations” is the number of targets in a subsample; “used” is the number of targets with all of the independent variable data available.

restricted country-level variables to only a dummy variable for high or low investor protection (based on either anti-director-rights or anti-self-dealing index). We limited the sample to only US acquirers and foreign (western European) targets. Even with all those changes, our finding of a statistically significant private premium remains (results omitted for brevity). While the main focus of Tables 5A–5D is on the coefficient of the target public status dummy, among the controls are variables important for our hypotheses. We find that the TarSize is negative and significant in all models, suggesting that smaller targets overall receive higher valuation multiples. Regarding the country characteristics that are addressed in our hypotheses, the target valuation multiples are higher when the target country debt access is low, stock market development is high, and IPO activity is high. In addition, the target valuation multiple is high when the bidder country stock market development is high. Regarding the control variables, target valuation multiples tend to be relatively high when target cash holdings are large, when targets are in high-tech industries, and when bidders involved in the acquisitions are large. In terms of deal characteristics, target valuations are relatively low for cash-only deals and high for cross-border deals and deals during the stock market bubble of 1997–2000.

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Table 5C OLS regression results, valuation multiple as the dependent variable.

Variable Intercept TarPrivate TarSize Target country variables TarDebtACCESS TarStockDev TarIPO TarCPI TarLegal Bidder country variables BidDebtAccess BidStockDev BidIPO BidCPI BidLegal Control variables TargetSub TargetCash TargetTech BidderSize CashDeal Cross-border StockBubble Observations (used) R-square (adjusted) F-value

Stock-bubble

Post-bubble

Cash-only

Stock-only

0.607* 0.449*** −0.164***

0.643*** 0.403*** −0.201***

0.447* 0.351*** −0.155***

−0.141 0.679*** −0.248***

−0.502** 0.167 0.014** 0.070 −0.006

−0.217** 0.192** 0.007 −0.031 0.164**

−0.212** −0.132 0.012 0.024 0.328***

−0.730*** 0.513** 0.016 0.106 −0.196

−0.153 0.172 −0.007 −0.112** −0.010

−0.162* 0.219*** 0.015 −0.033 −0.095

−0.014 0.226*** 0.008 −0.080** −0.074

0.513* 0.070 −0.030* −0.108 0.047

−0.418*** 0.945*** 0.443*** 0.114*** −0.437*** 0.144*

−0.147* 0.203** 0.166*** 0.110*** −0.153*** 0.177***

−0.113 0.155 0.160*** 0.101***

−0.418** 0.620*** 0.316*** 0.151***

2989 (1674) 0.372 (0.365) 54.38***

5047 (2469) 0.275 (0.270) 51.65***

0.226*** −0.141** 4097 (1452) 0.220 (0.210) 22.42***

0.199 0.429*** 1256 (1005) 0.420 (0.401) 39.63***

The table reports results of cross-sectional OLS regressions. Dependent variable is the log of the target’s valuation multiple (target’s valuation from acquisition offer divided by target’s total assets). “TarPrivate” is equal to 1 if a target is unlisted, 0 if public. Size is a company’s total assets. “DebtAccess” is the country’s total financial system deposits in the same calendar year, scaled by its GDP. “StockDev” is the market capitalization of all public firms in the country in the same calendar year, scaled by its GDP. “IPO” is the IPO dollar volume in the country in the same calendar year, scaled by the GDP. CPI is Transparency International’s annual Corruption Perception Index; higher number denotes better (lower) corruption in a country. “Legal” is 1 if a country belongs to common law tradition, 0 otherwise. “TargetSub” is 1 if target is a subsidiary, 0 otherwise. “TargetCash” is target’s cash and marketable securities scaled by total assets. “TargetTech” is 1 if the target is a high-technology company, 0 otherwise. “CashDeal” is 1 if the payment is 100% cash, 0 otherwise. “Cross-border” is 1 if the target and bidder country are not the same, 0 if they are. “StockBubble” is 1 if the deal was announced 1997–2000, 0 otherwise. *, **, and *** denote significance at the .1, .05, and .01 levels, respectively, using White’s (1980) standard errors. “Observations” is the number of targets in a subsample; “used” is the number of targets with all of the independent variable data available.

6.6. Multivariate analysis, matched comparable transactions Our primary technique for cross-sectional analysis of the private valuation premium involves matching private and public targets on four dimensions: country, size, announcement date and industry. We regress the private valuation premium (measured as log of the ratio of a valuation multiple of a private target to those of matched public targets) on a series of explanatory variables, with results disclosed in Tables 6A–6C. As was the case with Tables 5A–5D, Table 6A provides results using different investor protection variables in our regression models separately. The coefficients’ sign and significance on various explanatory variables are largely indifferent to the choice of the investor protection variable(s) used. However, CPI and common law dummy are the only investor protection variables that, used either separately or in conjunction with each other, do not result in serious multicollinearity. We therefore again focus in Tables 6B and 6C on models with our two main investor protection variables, CPI and the common law dummy (“legal”), used concurrently.2 In addition to solving the multicollinearity problem, and as discussed earlier, using CPI allows for a

2

As with Tables 5A–5D, models with other combinations of investor protection variables are omitted for brevity.

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Table 5D OLS regression results, valuation multiple as the dependent variable.

Variable Intercept TarPrivate TarSize Target country variables TarDebtAccess TarStockDev TarIPO TarCPI TarLegal Bidder country variables BidDebtAccess BidStockDev BidIPO BidCPI BidLegal Control variables TargetSub TargetCash TargetTech BidderSize CashDeal Cross-border StockBubble Observations (used) R-square (adjusted) F-value

Target civil

Target common

Bidder civil

Bidder common

0.038 0.350*** −0.154***

1.866*** 0.459*** −0.196***

−0.106 0.427*** −0.129***

1.785*** 0.437*** −0.200***

−0.163 0.052 0.011 0.107**

0.031 0.127 0.040*** −0.197***

−0.255*** 0.055 0.016** 0.052 0.153

−0.252* 0.159 0.009 0.027 0.048

0.208 0.149 −0.006 −0.084* −0.045

−0.197 0.207* −0.006 −0.045 −0.086

0.112 0.104 −0.008 −0.020

0.140 0.239* 0.028** −0.290***

−0.298* 0.148 0.198** 0.057*** −0.162** 0.204** 0.090 1597 (532) 0.264 (0.238) 10.24***

−0.164** 0.471*** 0.289*** 0.120*** −0.288*** 0.111* 0.116*** 6439 (3611) 0.299 (0.295) 85.02***

−0.308* 0.172 0.264*** 0.065*** −0.077 0.203** 0.021 1666 (611) 0.242 (0.219) 10.53***

−0.152** 0.477*** 0.286*** 0.118*** −0.299*** 0.098 0.123*** 6370 (3532) 0.305 (0.301) 85.64***

The table reports results of cross-sectional OLS regressions. Dependent variable is the log of the target’s valuation multiple (target’s valuation from acquisition offer divided by target’s total assets). “TarPrivate” is equal to 1 if a target is unlisted, 0 if public. Size is a company’s total assets. “DebtAccess” is the country’s total financial system deposits in the same calendar year, scaled by its GDP. “StockDev” is the market capitalization of all public firms in the country in the same calendar year, scaled by its GDP. “IPO” is the IPO dollar volume in the country in the same calendar year, scaled by the GDP. CPI is Transparency International’s annual Corruption Perception Index; higher number denotes better (lower) corruption in a country. “Legal” is 1 if a country belongs to common law tradition, 0 otherwise. “TargetSub” is 1 if target is a subsidiary, 0 otherwise. “TargetCash” is target’s cash and marketable securities scaled by total assets. “TargetTech” is 1 if the target is a high-technology company, 0 otherwise. “CashDeal” is 1 if the payment is 100% cash, 0 otherwise. “Cross-border” is 1 if the target and bidder country are not the same, 0 if they are. “StockBubble” is 1 if the deal was announced 1997–2000, 0 otherwise. *, **, and *** denote significance at the .1, .05, and .01 levels, respectively, using White’s (1980) standard errors. “Observations” is the number of targets in a subsample; “used” is the number of targets with all of the independent variable data available. “Common” and “Civil” refer to common law and civil law legal traditions.

time-variant measure of investor protection, while the legal system variable allows for continuity with the law and finance literature of La Porta et al. (1998). A positive coefficient on an explanatory variable suggests that an increase in that variable results in a larger valuation of the private target relative to the matching public target. 6.6.1. Effect of target size Our multivariate analysis of the effect of target size on the private valuation premium confirms our univariate findings. The coefficient of TarSize in Tables 6A–6C is negative and significant in the overall sample of private targets, suggesting that smaller private targets exhibit larger valuation premiums than larger private targets. This confirms John et al. (2010) and supports hypothesis 2HA that private firms exhibit a higher valuation premium (relative to their public counterparts) when they are smaller. Our explanation for this finding is that the bidder’s board is less likely to scrutinize management’s acquisitions of a smaller target, as any mistakes in overvaluing a small private target will likely cause less damage than mistakes from overvaluing a larger private target. The bigger the target, the bigger the scrutiny over the payment made by the bidder and the lower the private valuation premium.

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Table 6A OLS regression results, private valuation premium as the dependent variable, additional corporate governance variables one at a time. Variable Intercept TarSize Target country variables TarDebtAccess TarStockDev TarIPO TarCPI TarLegal TarADRI TarASDI TarCredRigIn TarAcctStdIn Bidder country variables BidDebtAccess BidStockDev BidIPO BidCPI BidLegal BidADRI BidASDI BidCredRigIn BidAcctStdIn Control variables TargetSub TargetCash TargetTech BidderSize CashDeal Cross-border StockBubble Observations (used) R-square (adjusted) F-value

−1.904 −0.151***

−1.985*** −0.148***

−1.797*** −0.148***

−1.942*** −0.148***

−1.605*** −0.148***

−1.711 −0.150***

0.763** 0.166 0.018 0.119

0.696** 0.129 0.034

0.879a 0.172 0.046

0.821a, * 0.281 0.039

0.683a 0.155 0.042

0.981a, ** 0.481* 0.032

0.066 −0.097a −0.516 −0.020a −0.036 −1.144** 0.925*** −0.028 −0.091

−1.013*** 0.803*** −0.038

−1.325 *** 0.922*** −0.051 a,

−1.335*** 0.820*** −0.049

−1.507 *** 1.014*** −0.052 a,

−1.282*** 0.637*** −0.038

0.358 0.138a 0.990* 0.128a 0.035** −0.075 0.832*** 0.015 0.226*** −0.413*** 0.106 −0.260** 1018 (666) 0.212 (0.193) 10.94***

−0.073 0.843*** 0.025 0.226*** −0.407*** 0.315 −0.246** 1018 (667) 0.213 (0.193) 10.98***

−0.069 0.841*** 0.018 0.223*** −0.412*** 0.250 −0.265** 1018 (667) 0.211 (0.191) 10.85***

−0.065 0.847*** 0.020 0.225*** −0.405*** 0.316 −0.265** 1018 (667) 0.213 (0.193) 10.97***

−0.061 0.834*** 0.024 0.225*** −0.415*** 0.228 −0.304** 1018 (667) 0.212 (0.192) 10.92***

−0.062 0.842*** 0.014 0.224*** −0.416*** 0.258 −0.253** 1018 (666) 0.214 (0.194) 11.04***

The table reports results of cross-sectional OLS regressions. Dependent variable is the private valuation premium, calculated as a log of the ratio of the valuation multiple of a private target to the average valuation multiple of comparable public targets; comparable targets are matched on country, total assets, announcement date and industry. Valuation multiple is target’s valuation from acquisition offer divided by target’s total assets. “Size” is a company’s total assets. “DebtAccess” is the country’s total financial system deposits in the same calendar year, scaled by its GDP. “StockDev” is the market capitalization of all public firms in the country in the same calendar year, scaled by its GDP. “IPO” is the IPO dollar volume in the country in the same calendar year, scaled by the GDP. CPI is Transparency International’s annual Corruption Perception Index; higher number denotes better (lower) corruption in a country. “Legal” is 1 if a country belongs to common law tradition, 0 otherwise. “ADRI” and “ASDI” are Anti-Director Rights Index and Anti-Self-Dealing Index, respectively; both are from Djankov et al. (2008). “CredRigIn” and “AcctStdIn” are Creditor Rights Index and Accounting Standards Index, respectively; both are from La Porta et al. (1998). “TargetSub” is 1 if target is a subsidiary, 0 otherwise. “TargetCash” is target’s cash and marketable securities scaled by total assets. “TargetTech” is 1 if the target is a high-technology company, 0 otherwise. “CashDeal” is 1 if the payment is 100% cash, 0 otherwise. “Cross-border” is 1 if the target and bidder country are not the same, 0 if they are. “StockBubble” is 1 if the deal was announced 1997–2000, 0 otherwise. *, **, and *** denote significance at the .1, .05, and .01 levels, respectively, using White’s (1980) standard errors. “a ” denotes coefficients with a Variance Inflation Factor above 10. “Observations” is the number of targets in a subsample; “used” is the number of targets with all of the independent variable data available.

An exception to the finding above is the cash-only subsample. When cash is the exclusive method of payment, Table 6C suggests that the target’s larger size does not reduce private targets’ valuations more than the valuations of public targets. This result does not change if the matching includes the payment method and cash-only offers for private targets are matched against cash-only public targets (results omitted for brevity). It is possible that using cash as the sole payment method introduces a higher overall level of board and management scrutiny. Overall, Tables 6A–6C show that cash-only acquisitions result in significantly lower private valuation premiums (this result holds when the payment method is added to matching criteria).

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Table 6B OLS regression results, private valuation premium as the dependent variable.

Variable Intercept TarSize Target country variables TarDebtAccess TarStockDev TarIPO TarCPI TarLegal Bidder country variables BidDebtAccess BidStockDev BidIPO BidCPI BidLegal Control variables TargetSub TargetCash TargetTech BidderSize CashDeal Cross-border StockBubble Observations (used) R-square (adjusted) F-value

All targets

Domestic

Target common law

Bidder common law

−1.965 −0.150***

−2.167 −0.152***

−2.270* −0.170***

−2.006 −0.170***

0.811** 0.235 0.018 0.038 −0.070

−0.351 1.154*** −0.019 0.032 0.146

0.722a, ** 0.290a 0.022 0.043

0.003a −0.075a 0.036a 0.354a 0.288a

−1.108a, *** 0.843a −0.023 −0.044 0.459

−0.345a 1.192a −0.036a −0.371a

−0.057 0.752*** 0.024 0.249*** −0.398*** 0.309 −0.286** 966 (651) 0.228 (0.207) 11.01***

−0.043 0.773*** 0.024 0.249*** −0.397*** 0.290 −0.274** 951 (644) 0.225 (0.204) 10.68***

−1.140*** 0.776*** −0.026 −0.033 0.364 −0.068 0.836*** 0.019 0.227*** −0.411*** 0.243 −0.247** 1018 (666) 0.214 (0.192) 9.8***

−0.028 0.810*** 0.027 0.227*** −0.404*** −0.252* 926 (624) 0.198 (0.182) 12.53***

The table reports results of cross-sectional OLS regressions. Dependent variable is the private valuation premium, calculated as a log of the ratio of the valuation multiple of a private target to the average valuation multiple of comparable public targets; comparable targets are matched on country, total assets, announcement date and industry. Valuation multiple is target’s valuation from acquisition offer divided by target’s total assets. “Size” is a company’s total assets. “DebtAccess” is the country’s total financial system deposits in the same calendar year, scaled by its GDP. “StockDev” is the market capitalization of all public firms in the country in the same calendar year, scaled by its GDP. “IPO” is the IPO dollar volume in the country in the same calendar year, scaled by the GDP. CPI is Transparency International’s annual Corruption Perception Index; higher number denotes better (lower) corruption in a country. “Legal” is 1 if a country belongs to common law tradition, 0 otherwise. “TargetSub” is 1 if target is a subsidiary, 0 otherwise. “TargetCash” is target’s cash and marketable securities scaled by total assets. “TargetTech” is 1 if the target is a high-technology company, 0 otherwise. “CashDeal” is 1 if the payment is 100% cash, 0 otherwise. “Cross-border” is 1 if the target and bidder country are not the same, 0 if they are. “StockBubble” is 1 if the deal was announced 1997–2000, 0 otherwise. *, **, and *** denote significance at the .1, .05, and .01 levels, respectively, using White’s (1980) standard errors. “a ” denotes coefficients with a Variance Inflation Factor above 10. “Observations” is the number of targets in a subsample; “used” is the number of targets with all of the independent variable data available.

6.6.2. Effect of target country debt access Tables 6A–6C show that, on aggregate, greater target country access to debt funding results in higher valuations of private targets relative to comparable public targets. These results support our hypothesis 3H0 as well as Officer’s (2007) availability of liquidity hypothesis that the expected access to debt is more beneficial to private targets that do not have the luxury of selling stock to the general public if in need of cash. If debt as a source of cash is more readily accessible, it improves the bargaining position of private targets disproportionately. We also find that this result does not hold for the stockonly subsample (Table 6C). If the private target is willing to accept bidder stock as a payment method, its desire to obtain liquidity is less important in its decision to be acquired. 6.6.3. Effect of target country stock market development There is limited evidence that target country’s stock market development has an effect on valuations of private targets relative to comparable public targets. Any effect seems confined to the stock-only subsample. Table 6C, shows that among stock-only acquisitions, a more developed target stock market results in higher private valuation premiums (valuations of private targets relative to public ones), consistent with our hypothesis 4HA . Better home stock market development may improve the

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Table 6C OLS regression results, private valuation premium as the dependent variable.

Variable Intercept TarSize Target country variables TarDebtAccess TarStockDev TarIPO TarCPI TarLegal Bidder country variables BidDebtAccess BidStockDev BidIPO BidCPI BidLegal Control variables TargetSub TargetCash TargetTech BidderSize CashDeal Cross-border StockBubble Observations (used) R-square (adjusted) F-value

Stock-bubble

Post-bubble

Cash-only

Stock only

−3.043 −0.232***

−0.787 −0.093**

−0.419 0.008

−4.830 −0.183***

−1.435a −6.157a, * 0.162** 1.947a, ** P/C

1.113*** −0.532 0.094 −0.036 0.350

1.791*** −1.338* 0.174 −0.741* 0.238

−0.740a 4.988a, ** 0.076a 0.674a −3.190a, ***

4.570* 6.271a, * −0.043 −1.873a, * −0.824a

−1.276*** 0.855*** −0.121 −0.058 0.351

−1.683*** 0.907*** −0.143 0.452 1.794***

−0.545a −3.606a −0.085a 0.010a 1.101a

−0.073 0.945*** 0.013 0.256*** −0.774*** P/C

−0.074 0.707*** 0.033 0.204*** −0.326*** −0.008

−0.116 1.422*** −0.140 0.152***

−0.050 0.347 0.232 0.276***

373 (253) 0.321 (0.278) 7.47***

645 (413) 0.176 (0.14) 4.94***

0.305 −0.540* 319 (147) 0.320 (0.230) 3.57***

−0.422a −0.197 217 (168) 0.300 (0.221) 3.79***

The table reports results of cross-sectional OLS regressions. Dependent variable is the private valuation premium, calculated as a log of the ratio of the valuation multiple of a private target to the average valuation multiple of comparable public targets; comparable targets are matched on country, total assets, announcement date and industry. Valuation multiple is target’s valuation from acquisition offer divided by target’s total assets. “Size” is a company’s total assets. “DebtAccess” is the country’s total financial system deposits in the same calendar year, scaled by its GDP. “StockDev” is the market capitalization of all public firms in the country in the same calendar year, scaled by its GDP. “IPO” is the IPO dollar volume in the country in the same calendar year, scaled by the GDP. CPI is Transparency International’s annual Corruption Perception Index; higher number denotes better (lower) corruption in a country. “Legal” is 1 if a country belongs to common law tradition, 0 otherwise. “TargetSub” is 1 if target is a subsidiary, 0 otherwise. “TargetCash” is target’s cash and marketable securities scaled by total assets. “TargetTech” is 1 if the target is a high-technology company, 0 otherwise. “CashDeal” is 1 if the payment is 100% cash, 0 otherwise. “Cross-border” is 1 if the target and bidder country are not the same, 0 if they are. “StockBubble” is 1 if the deal was announced 1997–2000, 0 otherwise. *, **, and *** denote significance at the .1, .05, and .01 levels, respectively, using White’s (1980) standard errors. “a ” denotes coefficients with a Variance Inflation Factor above 10. “P/C” denote variables being excluded from a particular model due to perfect collinearity. “Observations” is the number of targets in a subsample; “used” is the number of targets with all of the independent variable data available.

bargaining positions and therefore valuations of all targets willing to accept bidder stock. These higher valuations may, however, be more pronounced for private targets if the acquirer considers public targets from such markets to be overvalued. 6.6.4. Effect of target country IPO activity The variable representing target country IPO activity is insignificant for the entire sample and most of the subsamples; this is similar to Officer’s (2007) findings. It is only during the 1997–2000 stock market bubble that the greater IPO activity in the target country, as another source of liquidity for a private target, helps private targets and results in higher private valuation premiums. 6.6.5. Effect of target country investor protection The target country Corruption Perception Index is negative and significant in one model. The distinction between target country’s legal origin appears significant only for stock-only deals (Table 6C), where being in a common law country benefits public targets at the expense of their private

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counterparts. Overall, in contrast to the findings of John et al. (2010), evidence of a relationship between target’s investor protection and the private valuation premium is very limited. 6.6.6. Effect of bidder country debt access Tables 6A–6C offer evidence that the bidder country’s access to debt funding reduces valuations of private valuation premiums, consistent with hypothesis 7H0 . When bidders have greater access to debt, they may exercise more prudent bidder behavior (Jensen, 1986), which results in less pronounced overvaluation of private targets. 6.6.7. Effect of bidder country stock market development There is modest evidence that greater bidder stock market development benefits private targets more than public ones, in line with our hypothesis 8H0 . More developed bidder stock markets can lead to overall higher valuation multiples, regardless of target’s public status. There are several possible reasons for this, including that bidders find it easier to raise cash when their stock markets are more developed, or that bidder management and their boards feel more confident in the management’s abilities when bidder stock markets are doing well. 6.6.8. Effect of bidder country investor protection The bidder country corruption level (Bidder CPI score) is not significant. Bidder country belonging to the common law tradition results in higher private premiums only in cash-only deals (Table 6C). In general, we do not find strong evidence that the private valuation premium is influenced by the bidder country’s investor protection. 6.7. Multivariate analysis, robustness checks To test the robustness of our results, we revise our matching process of identifying a public target matched firm by also requiring that offers for private targets with cash as the sole payment method are matched against similar offers for public targets. Similarly, offers for private targets that involve stock (exclusively or in conjunction with cash) are also matched with corresponding offers for public targets. While these results are omitted for brevity, all of the findings above, both in terms of sign and significance, remain unchanged when this modified matching method is used. Tables 6A–6C contain fewer subsamples than Tables 5A–5D. This is due to the fact that once the matching technique was employed the cross-border, target civil law and bidder civil law subsamples of private targets did not contain enough degrees of freedom for meaningful multivariate analysis. Also, four of the subsamples (bidder common law, target common law, bubble, stock-only offers) have serious multicollinearity problems among country variables, with Variance Inflation Factors exceeding 10. Those are also the subsamples with the most pronounced deviations in coefficients and significance from the overall sample; as multicollinearity tends to suppress t-values and statistical significance, this provides a possible explanation for those deviations. 7. Summary and conclusions We study a sample of over 8000 acquisitions in the US and Western Europe. We find that private targets receive relatively higher valuation multiples than comparable public targets. This finding consistently holds for univariate analyses, and for multivariate analyses, regardless of the specific model applied and sample assessed. The private valuation premium is intriguing in light of the illiquidity and asymmetric information exhibited by private targets. Our result supports our hypothesis that a bidder can more easily justify paying a high multiple for target in which there is a lack of transparency. We find that the private valuation premium is inversely related to the size of the target. Bidders may be able to more easily justify a relatively high payment for small targets with more asymmetric information, and overpayment for small targets will not destroy bidder value. We find that the target’s country characteristics influence the valuations of private versus public targets in the global market for corporate control. The private valuation premium is higher when firms in the target country have greater access to debt, and therefore have more negotiating power. We also find that the bidder

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