Stockholm School of Economics Department of Accounting Department of Finance Master Thesis Spring 2012
Do Parents Matter? Vendor Identity of Private Equity Buyouts and Improvements in Operating Performance
Abstract: In this paper, we investigate the effects of different types of vendors on private equity funds’ ability to enhance the operating performance of their targets. Where previous research has focused on one type of vendor at a time, we take a cross-sectional approach, dividing our sample into five different vendor identities. We use a dataset of 218 buyouts in Scandinavia with exits between 1998 and 2010 and test for differences in EBITDA-margin, ROA and Sales CAGR compared to industry peers. We find no significant difference in abnormal operating performance across different vendor identities when using multi-group variance analysis. However, on weak statistical significance levels, individual tests indicate that buyouts from private vendors show abnormal operating performance compared to other vendors as a group; this also holds for buyouts from multidivisional companies. Moreover, we provide evidence that private equity owned firms in Scandinavia outperform industry peers over the investigated time period.
Keywords: Private equity, buyouts, abnormal operating performance, vendor identity, non- parametric tests
Authors: Victor Adler (21144)♦ and Robin Norberg (21407)♠ Supervisor: Assistant Professor Håkan Thorsell Discussants: Christine Ahlstrand (21179) and Katerina Hanackova (40169) Presentation: May 25, 2012, 10:00-12:00 in room 536
♦ [email protected] ♠ [email protected] i
Acknowledgements
We would like to thank our tutor, Håkan Thorsell, Assistant Professor at the Department of Accounting at the Stockholm School of Economics, for valuable input and comments throughout the writing of this thesis. Furthermore, we would like to thank lecturer Håkan Lyckeborg for assistance with statistical matters. We would also like to give a special acknowledgment to Ole Falk Hansen for greatly facilitating our data collection by sharing the ambitious dataset from his and his colleagues’ MSc Thesis. Finally, we wish to thank all our friends and fellow students that took their time to read through this thesis and provide comments.
Stockholm, May 2012
Victor Adler Robin Norberg
ii
Contents I Introduction ...... 1 Background ...... 1 Purpose ...... 2 Delimitations & definitions ...... 3 Outline ...... 5 II Theoretical and Empirical Foundation ...... 6 Value creation in private equity buyouts ...... 6 Owner identity and room for improvements ...... 9 Vendor identity in buyouts ...... 11 Contribution ...... 14 III Hypotheses ...... 16 IV Method ...... 20 Measuring operating performance ...... 20 Assigning peer groups ...... 24 Testing robustness ...... 26 Model specification ...... 28 V Data ...... 31 Buyout sample and selection criteria ...... 31 Sample distribution ...... 32 VI Empirical Results and Discussion ...... 37 Improvements in operating performance ...... 37 Vendor identity and operating performance ...... 38 Implications ...... 44 VII Conclusion ...... 48 IIX References ...... 49 IX Appendix ...... 54 Appendix A – definition of ratios ...... 54 Appendix B – exchange rates ...... 55 Appendix C – definition of statistical methods employed ...... 55 Appendix D – holding period of buyouts ...... 58 Appendix E – supplementary tables and figures ...... 58 Appendix F – sample firms and peer groups ...... 61
iii
List of tables
Table 2.1 - Selection of studies on value creation in private equity buyouts ...... 9 Table 2.2 - Overview of governance mechanisms in different ownership structures ...... 11 Table 2.3 - Studies on vendor characteristics and post-buyout value creation ...... 14 Table 5.1 - Distribution of sample by country and vendor identity ...... 34 Table 5.2 - Distribution of sample by industry ...... 34 Table 5.3 - Summary statistics for firm characteristics by vendor identity at entry year ...... 36 Table 6.1 - Summary statistics for operating performance, unadjusted and adjusted ...... 37 Table 6.2 - Changes in operating performance for sample, peer groups and difference in medians ...... 38 Table 6.3 - Adjusted changes in operating performance measures grouped by vendor identity ...... 39 Table 6.4 - Ranks and test statistics using the Kruskal-Wallis test ...... 40 Table 6.5 - Mann-Whitney U test comparing groups in relation to the remaining sample population...... 42 Table 6.6 - Summary of hypotheses and results ...... 43 Table d.1 - Holding periods of buyouts in sample ...... 58 Table e.1 - Unadjusted changes in operating performance, measures, grouped by vendor identity ...... 58 Table e.2 - Adjusted changes in operating performance measures, grouped by vendor identity ...... 59 Table e.3 - Peer group changes in operating performance measures, grouped by vendor identity ...... 60 Table f.1 - Sample of buyouts ...... 61
List of figures
Figure 3.1 – Summary of (r) hypotheses ...... 19 Figure 3.2 – Summary of (d) hypotheses ...... 19 Figure 4.1 – Event window definition ...... 23 Figure 5.1 – Sample distribution by year of initial investment and exit year ...... 33
iv
I Introduction In this section we introduce the reader to our research topic. We provide a short background on our research rationale, purpose, delimitations and finally provide an outline for the remaining part of our thesis.
Background 3,510 km west of Chile, in the Pacific Ocean, lies one of the world’s most isolated inhabited islands: Easter Island. It is believed to have been inhabited by humans since somewhere between 700 and 1200 CE when the first Polynesians arrived by boat. By that time, the island was covered in lush vegetation and subtropical moist broadleaf forests. It also had a flourishing wild life with vast seabird colonies at the edges of the island and many species of land birds living in the forests. By the time European settlers arrived in the 18th century however, the island had undergone massive deforestation. It had also seen its’ population soar to around 15,000 and then decline to the 2,000 - 3,000 thought to have inhabited the island when the first European ship visited. As the large palm trees of the subtropical forest had disappeared, 21 species of trees and all species of land birds had become extinct, while the people on the island instead had become dependent on farming.
To this day, experts still argue as to what were the exact causes of this collapse. One theory suggests that the trees were used to build and transport the large stone statues known as Moai that can be found on the island. What is clear however is that the island was overexploited by the original settlers, and if you live at Easter Island today, and you want to build something out of wood, you will have to resort to import.
Moving a couple of centuries forward, and around 15,000 kilometers north-east, to Scandinavia in 2012, there might be a similar development unfolding. In Scandinavia, private equity investments have grown substantially over the last few years. In 2009, the Nordic market amounted to 13% of European private equity and venture capital investments (European Private Equity & Venture Capital Association, 2010). From being almost non-existent twenty years ago, the private equity industry have quite suddenly become a force to be reckoned with in all of the Scandinavian countries.
Although there are many views on the details of how private equity investors create value in their portfolio companies, the general concepts are less debatable. Jensen (1989) argues in his seminal paper that private equity sponsors deploy a model with performance-based compensation, active governance and highly leveraged capital structures as means to increase the performance and value of their targets. As the private equity sector has grown however, so has the competition for potential buyout-targets. In its’ infancy in the US, private equity mainly acquired ill-managed public companies from the stock-market; hence the term buyouts. In that perspective, it is easy to see
1 how the typical private equity processes of active governance, leverage and performance-based compensation can have positive effects on the targets’ operations. Today however, the landscape is different. Leveraged buyouts (LBO’s) occurs from all sorts of vendors as privately held firms, publicly held firms, state owned firm, divisions in large corporations and even private equity owned firms (secondary buyouts) are all subjects to potential LBO’s. Although not delimited to Scandinavia, Strömberg (2008) for example shows that divisional- and secondary- buyouts in particular have grown immensely over the last few years. We find this development interesting as some of these owners have probably already implemented many of the typical private equity modifications. Secondary buyouts are especially intriguing as the target is most likely already highly leveraged, compensation for managers can be expected to be performance based, and the target has probably been actively managed during the holding period of the private equity owner.
We believe that there are certain parallels between the history of Easter Island and the Scandinavian private equity industry. Can it be the case that with the enormous growth of the private equity industry, competition for good targets for acquisition has led to overexploitation? As private equity funds now acquire firms from all sorts of places is that a sign that their original sources have become “deforested”? And if so, are these new sources of “timber” any worse than the original source; the stock market?
Purpose As private equity continues to grow in Scandinavia, and as competition for desirable private equity targets intensifies, there is an increasing need to understand how and where private equity generate value. In this thesis, we will investigate the effects of different types of vendors on the private equity funds’ ability to enhance the operating performance of their targets. To operationalize our purpose, we have split the purpose into two interrelated research questions:
Research question 1: Does the type of previous owner of buyout targets affect subsequent improvements in operating performance?
Research question 2: Is there a significant difference in improvements in operating performance depending on type of previous owner?
2
Delimitations & definitions Our first delimitation concerns the geographic area of our study. We have chosen to limit our study to Scandinavia, i.e. Denmark, Norway and Sweden. This is grounded in two different rationales. Firstly, the private equity industry in these countries has grown to become an important ownership type over the last decade. Secondly, firm specific data on accounting numbers and mergers and acquisitions are, compared to other countries, to a large extent publicly available in Scandinavia. This opens for detailed studies not possible elsewhere.
In this thesis, a leveraged buyout (LBO) is defined as the takeover of a controlling interest in a company, using a high amount of borrowed capital. The terms buyout and leveraged buyout are used interchangeably throughout the rest of the thesis. By using this definition, we will include both LBOs and MBOs1. There are three reasons for including the MBOs. Firstly, databases on buyouts rarely have data on management ownership. Including for example a limit on the stake management has in the target would therefore greatly decrease the number of observations that could be used in the study. Secondly, only a few MBO’s have occurred in Scandinavia in the last few years. For example, Bergström et. al. (2007) use a sample of 73 buyouts between 1993 and 2005, of which only two have management owner stakes of over 50%. Thirdly, as the basic ideas of improved governance, incentive realignment and higher leverage are the same for MBOs and LBOs, it makes theoretical sense to include both. We refer to the firm acquired through the leveraged buyout transaction as the target or target firm and the acquirer as the private equity firm/fund.
When conducting our research, we wish to focus on private equity and exclude venture capital. The reason for this delimitation is that venture capital has different characteristics, and different theoretical justifications, from private equity. More precisely, venture capital is used when a firm, often in a start-up phase, requires financing but has trouble getting debt financing from banks or other financial institutions. Private equity on the other hand, has the objective of acquiring firms, often in a mature phase, and creating value by improving them in different ways. The improvements are often associated with the activities mentioned in the background: active governance, leverage and performance-based compensation. Today, there is no widely accepted way of distinguishing between private equity and venture capital and there might be grey areas and in-
1 LBO = leveraged buyout, MBO = Management buyout, where the management of the firm acquires the company. Often with financial backing from a private investor and/or a bank consortium. 3 between-cases. As an example, The European Venture Capital Association (ECVA) (2010) defines private equity as venture-, growth-, replacement- capital and buyouts – clearly drawing no line between the two investment types. Still, for the purpose of this thesis, such a distinction has to be made; especially as venture capital and private equity, as mentioned above, rests on different theoretical foundations. For some buyouts, press releases and other available information openly state if the financial backing is venture capital or private equity. For most buyouts however, no such information is available. However, the funds themselves generally state if they consider themselves to be venture capital or private equity and we believe this is a good indication of what kind of investments they undertake. For this reason, we have chosen to only include buyouts made by funds that consider themselves as private equity.
To distinguish between the different types of owners that private equity investors can acquire from, we will use the term vendor identity. The different vendor identities will be public, private, government/state, private equity (often called secondary buyout) and multidivisional company (often called divisional buyout). As an example, if a private equity investor acquires a company from the stock market, the vendor identity will be public. We believe these vendor identities cover all categories of owner types acting in the Scandinavian market. Furthermore, these vendor identities are in line with definitions used in Strömberg (2008) where private-to-private, public-to-private, secondary / financial vendor and distressed are used to denote different kinds of buyouts. In this thesis we have added government/state because of the debate in Swedish media concerning private equity funds acquiring government owned firms in particularly health care and education. In government/state, we have also chosen to include cooperative ownership as we believe they have a similar stakeholder approach to ownership. In addition, we have chosen to not include distressed as a vendor identity as we believe the underlying reasons for acquiring firms in distress are different from those in typical private equity investments. Furthermore, as we have chosen to only include buyouts made by funds that consider themselves as private equity, distress focused funds generally fall outside of this category anyway.
As we wish to investigate the effects of different vendor identities on the private equity funds’ ability to affect the operating performance of their targets, there is also a need to define operating performance. As pointed out by Barber and Lyon (1996), there seems to be a considerable variation in different measures employed by researchers to measure operating performance. However, judging from Barber & Lyon (1996) and Lie (2001), accounting measures focusing on profitability, like ROE, ROA, EBIT-/EBITDA-margins etc., seems to be the dominant variables
4 used. As we wish to capture changes in operational performance and not differences in leverage or tax schemes, we will use accounting measures that are not contaminated by such effects.
Outline The rest of this thesis will be structured as follows: We will describe relevant theory and empirical findings on private equity and corporate governance in section II. Based on the theory and empirics in section II, we will lay down the hypotheses that we want to test in section III. In section IV we will describe the method and the different tests that will be used. Section V will cover the sample and data as well as provide descriptive statistics. Empirical results and discussion will follow in section VI after which conclusions will be covered in section VII.
5
II Theoretical and Empirical Foundation In this section, we briefly introduce the reader to theory and empirical findings on value creation by the Private Equity industry on the operational level. Furthermore, theory on corporate governance in different ownership structures will be reviewed.
Value creation in private equity buyouts Private Equity provides equity capital to firms not publically traded. In addition to financing, private equity exercises active ownership of its investments. In a private equity buyout a private equity fund acquires the majority stake in an existing or mature firm (Kaplan & Strömberg, 2008). Strategic innovation and industry expertise from Private Equity have been suggested to be significant in driving value creation (Berg & Gottschalg, 2005; Cressy, et al., 2007). However research has primarily focused on the role of corporate governance mechanisms. Kaplan & Strömberg (2008) categorize these changes as governance, operational and financial engineering. Each of them will be outlined below.
Governance – incentive realignment hypothesis
When private equity acquires a company, the incentive structure is reviewed and changed to align the interests of shareholders and other stakeholders, in particular management (Jensen, 1989). The most prominent mechanism is a significant equity stake for management – thus partly resembling the payoff structure of shareholders. This encourages management to use resources more efficiently and, theoretically, only undertake positive net present value investments. Kaplan (1989) finds that management ownership increases by a factor of four in buyout transactions. While the realignment of incentives has its merits, one downside is the under-diversification of risk for management. Holthausen & Larcker (1996) find that management might become excessively risk-averse with a too large equity share. Since management relies on salary from the company, allocating substantial wealth in the company generates a potentially high volatility of consumption2. In sum however, realigning incentives between shareholders and stakeholders reduces moral hazard and can be a substantial source for value creation even though excess risk- aversion is a potential side-effect.
2 The fundamental theorem of finance appeared first in Ross (1973) and states that the return of an asset i depends on its correlation with consumption. In this case it would mean that management requires a higher return for holding equity in the company where they are employed due to the high correlation with other determinants of wealth (income) for consumption 6
Operational – control hypothesis
Before a buyout, ownership of the target firm may be dispersed; this particularly holds for public- to-private transactions. With dispersed ownership, monitoring and control is a public good which may result in small investors benefiting (“free-riding”) from the work of larger shareholders (Shleifer & Vishny, 1997). With concentrated ownership, the wealth gains of control improve as monitoring costs do not rise with more concentrated ownership. Empirically we can observe that private equity portfolio companies are more actively involved in governance. Acharya & Kehoe (2008) find for example that they have more frequent board meetings, informal contacts, and that they do not refrain from replacing poorly performing CEO’s. In their study they find that two- thirds of the CEO’s are replaced in a four year period. To conclude, private equity aims to solve the monitoring and control issue and engage in active ownership of their portfolio companies.
Financial - free cash flow hypothesis
Leverage creates pressure on managers not to engage in negative NPV projects because they have to make interest and principal payments. Managers possess significant discretion of cash flows and can decide to invest them or pay them as dividends to shareholders. Jensen (1986) specifically addresses mature companies with weak governance structures where management can engage in “empire building” – growing the firm above its optimal size. With increasing leverage however, managers are forced to pay debt holders rather than engage in negative NPV projects. Furthermore, debt may increase firm value through tax advantages (e.g. deductibility of interest payments) and discipline management (a bankruptcy is costly for managers since they lose control and reputation). The major drawback of debt is that it increases the sensitivity to financial distress. Palepu (1990) also suggests that excess leverage can foster short-termism in companies when positive NPV investments are not undertaken.
In a model developed by Axelson et. al (2009), the advantage of debt financing for monitoring in buyouts is further developed. Since equity capital is raised ex ante and pooled there is a significant uncertainty; and accordingly investors demands a premium. Debt is raised ex post and for a specific investment in a buyout target. At this time the public state of the economy is known and included in pricing the debt. The distribution between ex ante and ex post capital is selected to maximize the value of the fund. Furthermore, debt contributed to the monitoring and control of the firm by imposing covenants on the firm. With increasing leverage, the payoff and incentive structure for the debt holder is converging with that of the equity holders.
7
To conclude, leverage is a powerful measure to solve the free cash flow problem and increase firm value. In private equity, the ex post character of debt lowers agency cost of financing. The main side effect of debt is that it increases the sensitivity to financial distress.
Evidence of returns in private equity
Research studying the performance of private equity funds by scholars and industry associations has, according to Phalippou & Gottschalg (2009), historically overstated returns. Due to the limited availability of public data, in particular in the US, studies has suffered from a strong survivorship bias and a shift towards the best performing funds. Both Kaplan & Schoar (2005) and Phalippou & Gottschalg (2009) report underperformance for private equity owned firms relative to the S&P 500. While gross returns are positive, the net-of-fees and risk-adjusted returns are negative or on par with S&P 500. Phalippou & Zollo (2005) further finds that private equity investments are correlated with the stock market and are exposed to tail risk, corresponding to an increased volatility of investments. Despite this grim evidence of the private equity industry, single funds offer positive returns net-of-fees. Scholars (see e.g. Phalippou & Zollo, 2005) have suggested that past performance can predict future returns and that small and inexperienced funds are the least profitable. Recent research by Cressy et. al (2007) finds that private equity funds with industry specialization performs better and suggests the importance of the general partners in value creation.
Evidence of value creation in private equity
A large share of scholars in private equity study private equity investments in terms of improvements in operational performance (see Cumming et. al (2007)). While deal data suffers from a survivorship and reporting bias, this type of accounting data is publically available in many countries in Europe. While scholars has raised concerns about the quality and reliability of accounting figures (Wu, 1997; Beuselinck & Manigart, 2007) this method is commonly used by scholars and practitioners to evaluate private equity performance. Using accounting data also makes sense as, in practice, private equity transactions are frequently quoted in terms of multiples based on accounting data (e.g. X times EBITDA)
A selection of studies investigating improvements in operating performance is included in table 2.1 below. From these studies it can be concluded that there are significant improvements in operating performance following a buyout. Consistent with Jensen (1989), these changes should be attributable to superior governance mechanisms. Recently, scholars (see e.g. Cressy et. al,
8
2007) has found that industry specialized private equity funds deliver higher returns on average highlighting the skill of target selection (“stock-picking”) as a value driver.
Table 2.1 – Selection of studies on value creation in private equity buyouts Authors Country Findings (Bergström, et al., SWE Sample of 73 buyouts 1998-2006. Significant improvements in operational 2007) performance following buyout (Cressy, et al., 2007) UK Sample of 122 buyouts 1995-2002. Operating profitability 4,5% greater than for peers post-buyout (Harris, et al., 2005) UK Plant-level evidence of MBO’s; Significant productivity increases following transfer of ownership (Desbrieres & FRA Sample of 121 firms; Operating performance exceeding peers both ex ante and Schatt, 2002) ex post buyout. (Kaplan, 1989) US Sample of 48 MBO’s 1980-1986. Evidence on post-buyout operating improvements and value increases (Lichtenberg & US Sample of 1100 plants; significant improvements in total factor productivity in Siegel, 1991) particular in MBO’s (Nikoskelainen & UK Sample of 321 LBO’s 1995-2004. Managerial equity holding closely linked to Wright, 2007) improved operational performance (Smith, 1990) US Sample of 58 MBO’s 1977-1986. Significant increase in operating returns and decrease in working capital
Owner identity and room for improvements Private equity buyouts stem from different types of owners; including private equity (secondary buyouts), private/family owned businesses, public corporations and state-owned/cooperative businesses. In this paper we are interested in the effects of vendor identity on subsequent improvements in operational performance following a buyout. As was discussed above, private equity theoretically generates value through incentive realignment, increased control (active governance) and increased leverage. Two of these measures (control and incentive realignment) are closely connected to corporate governance theory, while the third is more financially oriented. Below, we will discuss the governance mechanisms and financing used by different types of owners as this will have bearing on likely improvements that can be expected after a buyout. We have excluded private equity from this discussion as that was covered above.
Public corporations – Significant research has been conducted studying the relationship between ownership concentration, ownership types, governance, and corporate performance in public corporations. According to Shleifer & Vishny’s (1997) review of the corporate governance literature, a large shareholder is the most powerful governance mechanism. However a significant minority shareholder can also exercise significant power. Overall, recent studies suggest that ownership concentration is beneficial for corporate performance at intermediate levels, while too dispersed or concentrated ownership in public corporations is detrimental for corporate performance (Thomsen & Pedersen, 2000). In companies with dispersed ownership, boards are often large with outside board members. These two factors (dispersed ownership and large
9 boards) have been shown to have a negative impact on performance (Agrawal & Knoeber (1996)). As a group, public corporations should include firms with concentrated as well as dispersed ownership. Accordingly, free-rider problems and other governance weaknesses should be present to some extent in this group. As for financing, public corporations are typically less leveraged than private equity investments (Kaplan & Strömberg, 2008). According to Jensen (1989), this low debt structure is sub-optimal and inefficient in many public corporations – especially in companies with excess funds.
Private/family owned businesses – Privately owned businesses have a significant equity share which can be viewed as an incentive for monitoring and controlling management. Often, the major shareholder takes an active role; either in management or in the board. Anderson et al. (2003) argue that families, as a form of undiversified ownership, can mitigate certain agency problems (as described by Jensen and Meckling, 1976) as the owners generally are more interested in firm survival than other types of shareholders. However, private- and family owned businesses hold less leverage than private equity since the owners’ wealth may not be diversified and hence they may be excessively risk-averse (Agrawal & Nagarjan, 1990).
Subsidiaries/divisions – In a divisional structure, the group headquarters is an intermediary between management and shareholders. This give rise to agency costs as managerial compensation, strategic targets and monitoring/control is upon the discretion of group management. Fama & Jensen (1983) for example, suggest that multidivisional firms suffer from extraordinarily high agency costs due to lack of appropriate control mechanisms. However, during the last decades, multidivisional firms have more clearly defined their businesses; conglomerate structures are less prevalent then during the 1980’s (Davis, et al., 1994). As far as debt financing is concerned, Multidivisional group structures can be expected to use debt primarily to decrease tax burden rather than to incentivize management.
State-owned/cooperative businesses – State-owned firms can either be run for profit or to provide a service for the society. Independent on return requirements or not, it is a widely held belief that government-owned firms are less efficient and profitable than privately owned firms (Dewenter & Malatesta, 2001). Furthermore, in government-owned firms, governance mechanism may focus on stakeholder rather than shareholder value and not prioritize efficient execution and dividends for shareholders; accordingly D’Souza & Megginson (1999) report strong improvements in operational performance following privatization. While scholars suggest different reasons for this, the main paradigm is that state-owned firms forgo profit maximization to focus on political and social objectives. While the heterogeneity of state-owned firms makes
10 generalizations difficult, it is reasonable to believe that they must employ a stakeholder approach when deciding on e.g. remuneration3.
To conclude, different owners have different incentives and targets of monitoring and controlling management. The theory suggests that private equity and privately held businesses have the lowest agency costs through insider governance. Public corporations suffer from outsider governance and free-riding of monitoring and control. Subsidiaries/divisions suffer from the discretion of group management and state-owned/cooperative businesses employ a stakeholder rather than a shareholder approach. Following a buyout, these differences are likely to have an impact on subsequent improvements in operating performance. This will be discussed in the next section.
Table 2.2 – Overview of governance mechanisms in different ownership structures Value Private Equity Private/family Public Subsidiaries State-Owned creation owned corporations /divisions /cooperative mechanisms businesses businesses Remuneration Governance Remuneration based on Remuneration set Remuneration on – Incentive decided by corporate by compensation the discretion of N/A1) realignment corporate board performance (e.g. committee corporate HQ (e.g. owner) shares) Active Often passive Stakeholder Operational Active ownership ownership; Control by the ownership with rather than through frequent limited separation discretion of –Monitoring large boards and shareholder and control board-meetings of ownership and corporate HQ few meetings approach control Leverage not Highly leveraged Less leveraged if Leverage used as a Financial – to pay-out free wealth is not restricted by Financing mechanism to N/A1) cash flows to sufficiently dispersed pay-out free cash structure shareholders diversified ownership flows 1) We could not find enough theory to determine general tendencies within these fields Note: See e.g. Singh et. al (2003); Agrawal & Knoeber (1996) for a theoretical and quantitative review of corporate governance mechanisms in different ownership structures.
Vendor identity in buyouts While research on private equity and buyouts have largely focused on public-to-private transactions and the corporate governance issues in public corporation with dispersed shareholding, public-to-private buyouts only account for 7% of the transactions during 1975- 2006 (Strömberg, 2008). While transaction values are seldom public, Strömberg (2008) estimate
3 In Sweden, the Ministry of Enterprise, Energy and Communication (Näringsdepartementet) distinguish between two-types of government owned businesses; profitability and public interest- businesses (Regeringskansliet, 2012). Hence depending on type of government buyout improvements in operating performance is likely to differ. This distinction is used throughout the Nordic region. 11 that in 2006, public-to-private transactions accounted for 40% of total transaction values. This is a substantial decrease from being almost 70% during the 1980’s4. While the drivers of public-to- private buyouts have been researched thoroughly (Jensen, 1989), other types of buyouts have received less attention.
Of particular interest is divisional buyouts accounting for 31% of number of transactions and 31% of value (Strömberg, 2008). While theory developed by Fama & Jensen (1983) argue of the severe agency problems inherent in multidivisional firms there are few studies examining the impact of a buyout of such a division. This is of particular interest in Scandinavia where buyouts from multidivisional firms has historically represented a large share of total transactions and an even larger share of transaction value5.
Further, secondary buyouts have grown rapidly in recent years. Wang (2011) finds that improvements in operating performance in secondary buyouts are small and suggest that these deals are driven by the condition of capital markets6. The presence and growth of secondary buyouts connects with the Easter Island story recounted in the introduction; is there crowding- out in Private Equity with too few buyout targets available? Do seller characteristics matter for value creation in Buyout targets?
Empirical findings on Vendor identity and performance improvements
The theoretical framework previously discussed suggests that improvements in operating performance should be the highest in buyout targets with weak governance mechanisms. Empirically, we observe that buyouts of public-corporations and divisions constitute a large share of transactions, consistent with theory. Other organizational structures such as state- owned/cooperative should also see improvements following an acquisition driven by a shift to a shareholder rather than stakeholder approach. Overall, the distribution of transactions by vendor identity in Strömberg (2008) is consistent with theory suggesting that value creation should be highest in buyout targets with poor governance mechanisms ex ante. Strömberg (2008) also addresses the need to understand the mechanisms behind different types of buyouts since research has mainly focused on large public-to-private transactions, although these buyouts only
4 Another striking observation from Strömberg (2008) is the rapid growth of LBO transactions, 40% of the number of transactions has taken place after 2004. 5 This will be covered in section V, data. 6 Wang (2011) finds that secondary buyouts are frequent when the equity market is cold (e.g. limited opportunity for an IPO) and the debt market offers cheap financing. 12 represents a small share of the total number of buyouts. Hence, Strömberg (2008) calls for further research to understand if different types of buyouts differ in their ability to create economic value. So far, very few studies have heeded this call.
In his MSc thesis, Jääskeläinen (2011) study abnormal operating performance in 144 private equity buyouts during 2007-2009. He partly studies vendor identity albeit conducts no tests for robustness, nor does he use variance analysis to see if there are significant differences across groups. In his sample he notes however that differences emerge; in particular between secondary- and divisional- buyouts. He concludes that abnormal improvements in EBITDA are the highest for divisional buyouts while secondary buyouts show the fastest abnormal sales growth.
Kreuter et al. (2005) study buyout performance and seller characteristics and find that there are substantial differences between sellers. Privatizations, although a small sample (n=7) generate the highest performance while secondary buyouts generate the lowest. Acquisitions of private/family-owned businesses generate higher performance than public-to-private. This last result somewhat contradicts the idea of corporate governance as the primary value driver. However, Kreuter et al. (2005) look at the internal rate of return (IRR) rather than improvements in operating performance.
Edenholm & Stenlund (2008) find in their MSc Thesis, studying family firms, that Private Equity may not improve operating performance (defined as profitability) in family-owned businesses but may rather drive sales growth to increase firm value.
Meuleman & Goosens (2007) do not find larger improvements in operating performance in divisional buyouts in comparison to buyouts of family-owned businesses. Meuleman et. al (2008) however finds that divisional buyouts show abnormal improvements in operating performance compared other types of buyout. A possible explanation for the differences is that Meuleman & Goosens (2007) study a smaller sample of firms in the Netherlands while Meuleman et. al (2008) look on a larger sample on the UK buyout market.
Nyren & Åsbrink (2010) study the Nordic buyout market and compare primary and secondary buyouts in their BSc thesis. While their results are not statistically significant, they are similar to Wang (2011), suggesting that secondary buyouts are driven by other motives than improving operating performance.
Renneboog et al. (2007) study public-to-private transactions in UK 1997-2003 and finds that the on average 40% premium paid is due to undervaluation of the target firm, potential for incentive
13 realignment and tax benefits of private firms. However they do not find any evidence for the free cash flow hypothesis (as described by Jensen, 1989).
To conclude there are limited research available distinguishing improvements in operating performance on vendor identity. Kreuter et al.’s (2005) cross-sectional study is the only one that use more than two vendor identities and show that there are substantial differences. Other empirical work focus mainly on one type (e.g. secondary-, divisional-) and compare with the total sample. A short summary of the available empirical research is presented below in table 2.3
Table 2.3 – Studies on vendor characteristics and post-buyout value creation Authors Country Findings (Kreuter, et al., 2005) EU1 Study on buyout performance and vendor characteristics, 205 firms. Buyouts in privatizations and of family-owned businesses generate the highest IRR but a higher variability in returns. (Meuleman & NL Study on family firm and divisional buyouts; 167 firms. No difference in Goosens, 2007) value creation depending on type. (Meuleman, et al., UK Study on divisional buyouts; 238 firms. Significant changes in productivity 2008) compared to other types of buyouts (Edenholm & SWE Study on buyouts of family owned businesses; 54 firms. Acquired family Stenlund, 2008) firms underperform peers in operating performance but grow faster e.g. through M&A activity (Nyren & Åsbrink, SWE Study on primary and secondary buyouts; 72 firms. Indications that value 2009) creation is higher in primary buyouts
(Wang, 2010) UK Study on primary and secondary buyouts; 465 primary buyouts and 140 secondary buyouts. Finds that secondary buyouts are driven by conditions of capital markets. (Jääskeläinen, 2011) Scandinavia Study on private equity buyouts during the recession 2007-2009; 144 buyouts, 32 secondary buyouts. Value creation in secondary buyouts mainly through sales growth; limited EBITDA improvements. Divisional buyouts exhibit the largest operational improvements.
(Renneboog, et al., UK Public-to-private study. Undervalued stock, tax-benefits and incentive 2007) realignment explanatory for the average 40% premium paid in public-to- private transactions. 1) Kreuer, Gottschalg and Zollo (2005) study measures value creation in Gross IRR rather than in terms of improvements in operational performance.
Contribution The Private Equity business is, if compared to most other industries, still young. However, it has attracted considerable attention in recent years, especially in Scandinavia. Theories on private equity value creation following buyouts have been studied to relatively wide extent. These theories and associated empirical findings are heavily rooted in corporate governance concepts. Corporate governance is on its own a widely researched subject. For the purpose of this thesis however, the most relevant studies and theories are those comparing different types of owners. Here, theory is suggesting cross-sectional differences in governance mechanisms and by extension, differences in performance following a buyout. So far, however, few studies have
14 connected these two threads, i.e. connected differences in corporate governance between different owner types and private equity value creation. The few studies currently available have focused on one owner type (like for example secondary buyouts). Since private equity value creation, at least theoretically, is so heavily based on corporate governance issues, and owner type has been found to be an important indicator of governance mechanisms used in a firm, we believe there is a need to investigate the effects of different types of vendors on subsequent operating performance improvements in private equity buyouts.
15
III Hypotheses In this section, we formulate the hypotheses we aim to study in our research. The hypotheses are based on the theoretical frameworks and empirical findings reviewed in the theory section.
Ownership type and improvements in operating performance
While the relationship between ownership structure and corporate performance is debated (see Demsetz (1983) for a discussion suggesting an endogenous relationship between ownership structure and firm performance), empirical evidence suggests that it has an impact. Previous research in the private equity area has largely been based on corporate governance theory and we have chosen to follow that path in this thesis. This section will operationalize our research question by defining eleven hypotheses. Below, hypotheses for each type of previous owner (vendor identity) will be discussed and defined. We have chosen to frame our hypotheses in two ways. The first way consists of a ranking of different vendor identities from lowest to highest expected improvements in operating performance. These hypotheses will be denoted (r). Recognizing that much of the value creation in private equity remains a “black box”, and that effects from vendor identities might be small, we have created a second set of hypotheses. These simply determine if we believe a certain vendor identity will be significantly higher (or lower), in terms of improvements in operating performance, than the rest of the population. These hypotheses will be denoted (d). In addition to the (r)- and (d) hypotheses, we have one hypothesis relating the whole sample of private equity owned firms to other firms. This will be described in the end of this section.
Private equity – secondary buyouts
As the business model of private equity is to acquire firms and hold them during a fixed period of time to improve corporate governance, a subsequent acquisition by another private equity fund (so called secondary buyouts) is a peculiar phenomenon; however steadily growing in recent years. The main arguments for secondary buyouts are strategic expertise and need for liquidity. Strategic expertise could for example be that a regional private equity fund divests a portfolio company to a global player with opportunities to expand globally or inject more capital into R&D. The need for liquidity refers to the fixed time of private equity funds meaning that portfolio companies may need to be divested before the governance mechanisms are fully implemented. However, the main mechanisms used by private equity have already been implemented by the previous owner. Because of this, we believe that improvements in operational performance will be the lowest in secondary buyouts; this is consistent with theory and to the limited set of studies available. Hence we hypothesize that:
16
Hypothesis 1 (r): The improvements in operational performance will be lowest for buyouts where vendor identity is Private Equity
Hypothesis 1 (d): The improvements in operational performance will be significantly lower for buyouts where vendor identity is Private Equity than for other types of buyouts
Private/family owned businesses
Private- and family owned businesses is far from a homogeneous group with a multitude of factors (e.g. involvement from owners) contributing to heterogeneity in corporate governance and financing issues. A relevant example is the degree of involvement from the owner ranging from passive to being active in management. However theory suggests that these firms have similar mechanisms as private equity held firms except that they normally do not employ a highly leveraged capital structure. Hence, we hypothesize that:
Hypothesis 2 (r): The improvements in operational performance for buyouts where vendor identity is Private will be larger than when vendor identity is Private equity but smaller than for other vendor identities.
Hypothesis 2 (d): The improvements in operational performance will be significantly lower for buyouts where vendor identity is Private than for other types of buyouts
State-owned and cooperative businesses
While there are profit-making state-owned and cooperative businesses, a buyout transaction will shift a stakeholder approach towards a shareholder approach with a stronger focus on profitability. Theoretically, the effects of incentive realignment, active governance and increased leverage should be large – as these mechanisms are used to very limited extent in government owned organizations. Hence, we should expect large operational improvements following a buyout.
Hypothesis 3 (r): The improvement in operational performance will be the largest for buyouts where vendor identity is Government/State.
Hypothesis 3 (d): The improvements in operational performance will be significantly higher for buyouts where vendor identity is Government/State than for other types of buyouts
17
Subsidiaries – divisional buyouts
While early criticized by scholars (Jensen & Meckling, 1976; Fama & Jensen, 1983) for its inherent agency problems, the divisional organization has seen its decline since the 1980’s with an increasing focus on core competencies and divestment of non-core business units. With a shift in governance from group management to shareholders, operating performance should improve significantly. Empirically, divested business units are often less prioritized parts of the sellers business with limited monitoring and access to internal capital markets (Cuman, 2011). The latter argument suggests that there may be positive NPV projects available that before lacked funding. Hence we hypothesize that:
Hypothesis 4 (r): The improvements in operational performance for buyouts where vendor identity is Multidivisional company will be smaller than when vendor identity is Government/State but larger than for other vendor identities.
Hypothesis 4 (d): The improvements in operational performance will be significantly higher for buyouts where vendor identity is Multidivisional Company than for other types of buyouts
Public corporations
While representing only a small share of transactions, the public corporation has brought the most attention from scholars and practitioners due to their size and the inherent agency problems. While Jensen (1989) predicted the decline of the public corporation, the mere threat of a takeover seems to have disciplined management in many public firms. However, theory and empirical evidence still suggests that agency problems arise in public corporations, especially when ownership is dispersed.
Hypothesis 5 (r): The improvements in operational performance for buyouts where vendor identity is Public will be smaller than when vendor identity is Government or Multidivisional company, but larger than for other vendor identities.
Hypothesis 5 (d): The improvements in operational performance will be significantly higher for buyouts where vendor identity is Public than for other types of buyouts
18
Figure 3.1 – Summary of (r)-hypotheses
Largest 1. Government /State (Hypothesis 3) Relative 2. Multidivisional company (Hypothesis 4) Improv- ements 3. Public (Hypothesis 5) in operating 4. Private (Hypothesis 2) performance 5. Private equity (Hypothesis 1) Smallest
Figure 3.2 – Summary of (d)-hypotheses
Vendor identity Improvements in operating performance in relation to other types of buyouts
1. Government/State (Hypothesis 3) Higher 2. Multidivisional company (Hypothesis 4) Higher 3. Public (Hypothesis 5) Higher 4. Private (Hypothesis 2) Lower 5. Private equity (Hypothesis 1) Lower
All target firms
At last, to be line in theory and previous empirical findings, the improvements in operating performance of private equity investments should exceed that of industry peers. Hence our last hypothesis is that:
Hypothesis 6: The improvements in operating performance should be significantly higher for the sample of buyouts compared to industry peers.
19
IV Method In this section we present and motivate the method used in our thesis. Firstly, we present the operating statistics used to measure operating performance. Secondly, we discuss the peer group method and thirdly, we define our model specification.
Existing literature on performance in private equity apply different methods to measure value creation. We perceive a dichotomy has emerged between studies based on internal rate of return (IRR) and accounting-based operating performance measures. While the first measures the actual returns to investors, it is inherently suffering from a sample selection bias since data is provided on a voluntarily basis (Phalippou & Gottschalg, 2009). Studying operating performance, instead of returns, as an approximation for value creation has become the prevalent method; particularly attractive in the Nordic region where data is available for all limited liability companies7.
In the influential paper of Barber & Lyon (1996), they divide performance measures into three parts: (1) measuring operating performance, (2) choice of benchmark and (3) selecting the appropriate test statistics. Each of them will be elaborated below8.
Measuring operating performance When studying operating performance it should be recognized that focus is on firm value rather than equity value since we study operating metrics such as revenues and margins (Phalippou & Gottschalg, 2009) and hence our focus is on the second equation below:
=
퐸푞푢푖푡푦 푣푎푙푢푒 푉푎푙푢푎푡푖표푛 푚푢푙푡푖푝푙푒 ∗ 푅푒푣푒푛푢푒푠 ∗ 푀푎푟푔푖푛 − 푁푒푡 푑푒푏푡 =
Our reasoning for퐹푖푟푚 not푣푎푙푢푒 studying푉푎푙푢 equity푎푡푖표푛 value푚푢푙푡푖푝푙푒 is twofold∗ 푅푒푣푒푛푢푒푠. Firstly∗ 푀푎푟푔푖푛, the databases commonly used to acquire accounting data do not allow us to decompose between interest- and non-interest- bearing liabilities. Secondly, as previously discussed, private equity investors are paid a multiple on operating performance according to Kaplan & Strömberg (2008). This is also supported in interviews of private equity fund managers made by Norman & Riboe (2011) in their BSc Thesis: “A dollar saved below the EBITDA is worth a dollar. A dollar saved above that line is worth ten”. They further note that negotiating loan terms and setting up tax structures has become increasingly
7 Kaplan (1989) was one of the first scholars to measure value creation in Private Equity using operating performance measures. As only companies that are public has to post SEC filings in the US the article suffers from a selection bias as data is provided on a voluntarily basis. this is a general drawback of research on US buyouts. 8 The method in Barber & Lyon has been developed and tested by Lie (2001) 20 commoditized, consequently private equity value creation is achieved mainly through operational improvements.
Recent studies (Bergström et. al, 2007; Vinten, 2007) has to a large extent employed the method developed by Barber & Lyon (1996). In their paper they first suggest operating income measures in favor of earnings since they are not obscured by special items, tax, and changes in capital structure. Secondly, to increase comparability, these measures should be scaled by the operating assets during the same time period. The prevalent measures used in the aforementioned studies is EBITDA-margin growth, changes in return on assets (ROA), growth in sales and changes in return on invested capital (ROIC). We have chosen to define the first three measures as our operating statistics (OPS), while excluding ROIC. Below follows a discussion on each measure9.
EBITDA – we study margin growth of EBITDA as the main measure of corporate performance. The reason is that EBITDA is the closest available approximation to available cash flow available in published accounts in the countries surveyed. Further we enhance comparability with previous studies within the area often surveying changes in EBITDA. By relating the EBITDA to sales and thereby creating a margin, we increase comparability across observations.
ROA – we study changes in this measure to study the improvements in profitability of a company in relation to its asset base. This measure accounts for improvements in e.g. working capital efficiency and/or reduction in tangible assets. ROA is also the main measure suggested by Barber & Lyon (1996), supported in works by Leslie & Oyer (2009) and Cao & Lerner (2006). In this thesis, we define ROA as EBITt / Total Assetst, recognizing that a more appropriate measure uses previous year’s asset base (t-1) or an average asset base over the year (t) and includes financial income. However, such a definition would force us to exclude a number of observations due to lack of data – especially for the buyouts that occurred a long time ago. We consider this to be detrimental to our purpose and hence we choose the simplified version of ROA described above.
Revenue / Sales – we study revenue growth10 since growth ultimately expands the absolute level of EBITDA ceteris paribus. Further we have noted that when divesting a portfolio company private equity often quote the sales growth in press releases; possibly to provide tangible evidence
9 A careful definition of each of the operating statistics used (OPS) is included in Appendix A 10 We make no distinction between revenue and sales in this thesis, and the two terms are used interchangeably 21 on value creation11. We measure sales growth by using an annualized average growth measure during the holding period, CAGR.
ROIC - In contrast to Bergström et. al (2007), we have decided not to study this measure. Following the definition of ROIC in Appendix A we observe two difficulties; measuring the size of invested capital and adjusted tax rate12. While Bergström et. al (2007) recognize that ROIC would “theoretically give the most neutral cross industry comparison of operating profitability, taking into account both profit margins and capital efficiency”, they recognize that measuring invested capital is difficult and yields excess dispersion in the data. Emtemark & Olsson (2012) assumes in their MSc thesis an adjusted tax-rate of 30% when calculating ROIC, we believe this is an over simplification of the problem. To conclude, while theoretically appealing, we consider ROIC difficult to implement given availability of data.
Abnormal operating performance
We define abnormal operating performance as:
= X (X ) (1) 푒푥푖푡 푒푥푖푡 In (1) we obtain the abnormal퐴푂푃푖 operating푖 − 퐸performance푖 by subtracting expected operating performance from the actual operating performance. The implicit counterfactual is an assigned peer group of other firms in the same industry and should be interpreted as the abnormal operating performance if the firm had not been acquired by a private equity fund. Since we study percentage changes rather than levels, a peer group methodology is feasible. The intuition behind this method, described below, is that we control for the impact of market timing attributable to factors affecting the industry, this could be macro- and micro- factors affecting growth and profitability of the specific industry. Hence we subtract for performance resulting from a buy-and- hold strategy and measure improvements from executing operational improvements within the buyout company.
Since we measure change the specification becomes:
= 푒푥푖푡 푒푥푖푡 (2) X푖 X푖 푖 푒푛푡푟푦 푒푛푡푟푦 퐴푂푃 �X푖 � − 퐸 �X푖 �
11 Joint research by Deloitte and the Boston Consulting Group (Marsden, 2008) highlights revenue growth as the “new” driver of value creation in private equity buyouts 12 e.g. excluding the benefit of the tax shield arising from interest deductability 22
We obtain our three operating statistics (OPS), Change in EBITDA-/ROA, and Sales CAGR, by subtracting the median change in the peer group, and then changes in the sample and across vendor types are checked for robustness. In (2) revenue is computed as a cumulative annual growth rate to increase comparability across time periods. The methods used for assigning peer groups and testing robustness are elaborated below. First however, we define our event window.
Event window
We define the entry year (or buyout year) as the reporting year in which the target firm was acquired. In other words, the accounting numbers that are used for each target firm as entry numbers are the first available with the private equity fund as the new owner. That means that regardless of if a firm was acquired in, for example March or December 2004, the entry year will be 2004. The exit year is defined as the last full reporting year during which the private equity fund was the owner of the target firm. This means that if a target firm was divested in 2007, regardless of what month the transaction took place, the exit year will be 2006. With this definition we capture only those changes in operating performance occurring while the private equity fund is the owner. We also avoid problems arising from organizational restructurings at the time of entry or exit. If, for example, a private equity fund sells a firm to a large international multidivisional company in 2005, and the target firm is immediately integrated within the new organization, accounting numbers for 2005 are likely to give, at best, a distorted image of the state the company was in at the time of exit; it is probably more likely that there will not exist any accounting data at all for 2005. Hence, using data from year-end 2004 is less biased. The same reasoning can be applied for entry year definition. As private equity firms often conduct major changes to the organizational structure at the time of acquisition, using data from when the firm has already been acquired will be less distorted than data from before it was acquired. We believe this is a sound and conservative method as we reduce potential accounting biases, we reduce sample selection bias by not imposing a too strict requirement on data availability, and finally operational improvements are likely to occur during the latter stages of ownership and consequently our method is conservative and likely to underestimate the effects. We believe this is preferable to inducing additional accounting and sample selection biases.
Figure 4.1 – Event window definition Event window: 20X1-20X4 Accounting Data used Entry Exit
Reporting year for target firm 20X0 20X1 20X2 20X3 20X4 20X5 20X6
23
Exchange rates
As we study not only changes in operating measures, but also levels, we have to converted the data into euro (EUR). This also simplifies assignment of peer groups that are also derived in EUR. Exchange rates have been derived from OANDA13 and for years preceding 1999 the European currency unit (ECU) has been used. The exchanges rates can be found in Appendix B. We recognize that changes in exchange rates affects our operating statistics (OPS). However the exchange rates has been fairly stable during the period. In the case of Danish Kroner (DKK) the currency has been pegged to the EUR through ERM II during the period (Danmarks Nationalbank, 2012).
Assigning peer groups To evaluate the economic and statistical significance of pre- to post- buyout changes in operating performances, our operating statistics have to include an adjustment by some benchmark. Guo et. Al (2009) concludes that industry median adjusted performance provides the most direct comparison to previous research within private equity and operational improvements. Median peer performance adjustment is therefore the method that we will use in this thesis. An alternative measure suggested by Nohel & Tarhan (1998) includes industry-, pre-buyout performance and market-to-book assets ratio for assigning a matching peer group. This method controls for extreme pre-event performance; hence it increases precision and statistical significance (Lie, 2001). However, we deem this method to not be computationally viable. The reason is twofold: Firstly, market-to-book ratios are only available for public firms – representing only a fraction of our sample. Secondly, while Lie (2001) argues that control groups should be based on firms with similar levels of performance this would yield a too small control group given the Scandinavian context of our thesis. Another method possibly applicable suggested by Rosenbaum & Rubin (1983) is the propensity score. This method measures predicted probability of group membership (peer vs. target company) for observed characteristics. However while seen as an advancement to simple matching; biases remain since it only controls for observed variables (Shadish, 2002). Finally we believe a too complicated peer method would produce a non- satisfactory number of peer-companies.
13 OANDA corporation is a supplier of currency conversion, FX trading and FX information. Headquarted in Toronto, Canada 24
In accordance with previous studies in Scandinavia (Bergström et. Al, 2007; Vinten ,2007; Jääskeläinen, 2011) we use the NACE14 classification scheme developed by the EU commission to assign peer groups. While Bergström et. al (2007) look at the first four NACE digits we have decided to look on the first two digits. This is supported by Barber & Lyon (1996) who conclude that a more specific industry classification15 does not produce more accurate estimates. Moreover, we have put two more restrictions in place; geographic location and asset size.
The geographic distribution of peer companies is restricted to European countries16. Previous studies have defined their benchmark differently; restricting their sample nationally (Bergström et. al, 2007) or regionally (Jääskeläinen, 2011; Vinten, 2007). While theoretically appealing, these scholars have noted that this approach often results in a non-satisfactory number of peers and consequently have been forced to expand their geographic reach and/or industry definition. We believe our approach has merits while acknowledging its disadvantages. Firstly, our method permits using peer groups with homogeneous asset size, as recommended by e.g Barber & Lyon (1996). While the peer group should perhaps ideally be matched on past-performance, using this method at least captures the size dimension17. Secondly, a large share of our sample consists of manufacturing firms competing on global markets; hence it is reasonable to study their European peers. The main disadvantage in our approach is that nationally bounded industries, most notably service intense industries (e.g. Healthcare) may not be comparable to a set of European peers18. However we argue that our approach is consistent and that employing a tailor-made peer methodology for each peer would become arbitrary and in-transparent.
Following the industry and geographic restriction we derive a sample of twenty peers for each buyout based on total asset size in the pre-event (i.e. pre-buyout) year. The peer group is then held constant throughout the period as suggested by Barber & Lyon (1996). When deriving NACE classification for a consolidated group, we must manually correct for group 7415 – Management Activities of Holding companies and other cases where the firms business is not reflected
14 NACE = Nomenclature Generale des Activites Economiques dans l´Union Europeenne 15 e.g. beyond a two digits SIC code. The NACE system is European Union equivalent to the US SIC (Standard Industrial Classification) 16 Including EU member states and EFTA (e.g. Switzerland, Norway) 17 Contemporary finance theory and related empirical evidence suggest that small firms outperform larger firms (e.g. Fama & French, 1992). Matching peers by asset size controls for size. 18 A relevant example in our sample is Carema and Attendo active in the heavily regulated healthcare sector. Growth in the private care, public pay healthcare sector can be seens as driven mainly by national regulation rather than global/regional trends. 25 in the NACE classification. This have been done by using the NACE-code of the main subsidiary. We have then assigned the specific firm a NACE on the two-digit level. This once again rationalizes our choice of industry granularity since determining a four-digit NACE for missing/misspecified firms would be difficult. Finally, our accounting measures for the defined operating statistics are derived from a median of the peer group to exclude outliers.
Peer group matching was conducted using the Orbis database. In addition, we were handed a dataset from the MSc Thesis by Gulliksen et. al (2008) which included peer matching for many observations collected with the same method as ours and taken from the same database as Orbis’19. The data contribution from Gulliksen et. al (2008) was particularly valuable as Orbis only includes data from 2002 onwards20. Finally we excluded private equity owned firms from the peer groups as well as unconsolidated data for subsidiaries owned by the target company.
Testing robustness To deal with causal inference we should account for the sample selection bias and survivorship bias21. The latter occurs in our sample as well as in the matching process and is twofold. Firstly, companies that went bankrupt during the period are not included in the sample, nor in the peer group; hence our results could potentially be biased upwards. Secondly, the issue of mean- reversion in accrual based accounting (Barber & Lyon, 1996) could bias our results. Following the J-curve effect22 in Private Equity buyouts our results could be biased upwards; this would imply that the operating statistics are overestimated in the last year of ownership23.
Issues in peer group design
The peer group design could infer that we under-/over- estimate the true operating impact of private equity ownership if our industry adjustment is imprecise (or contain omitted variables). Recent studies (Vinten, 2007; Bergström et. al, 2007; Guo et al, 2009) treats this issue of causal inference depending on peer group differently but reach similar conclusions. Vinten (2007) study
19 Gulliksen et. al (2008) used Amadeus, whereas we use Orbis. Orbis and Amadeus are both services provided by Bureau van Dijk Electronic Publishing; a company headquartered in Brussels, Belgium 20 For a further description of this dataset, how it was used, and how it was adjusted to fit our study, see the data section below. 21 The process of sample selection is further described in part V, Data 22 The J-curve effect refers to the phenomenom that buyout firms initially underperform, this is followed by rapid improvements in returns and operating performance. See Grabenwaerter & Weidig (2005)for a review 23 Accounting manipulation could for example be done by late recognition of revenues generated in t-1 or by capitalizing costs. See Cumming & Walz (2010) for a review on Private Equity and accounting disclosure. 26 the Danish private equity market between 1999-2004 and tests the impact of different matching methodologies. He concludes that a larger peer group produces the most significant results in his dataset. He also concludes that matching by asset size is important since operational improvements seem to be the largest in buyouts of small firms. Bergström et. al (2007) define their peer group as the twenty largest Swedish firms in the industry. The underlying assumption is that mature firms reflect the steady state of the industry. While theoretically appealing we believe that neglecting the size dimension would bias our results24.
Finally it must be recognized that regardless of peer group design there is no perfect counterfactual for our sample of buyout targets since firms are heterogeneous and may have different sales and product mix. However, controlling for industry, geography and asset size will nevertheless make it possible to draw certain conclusions.
Acquisitions and divestitures
Management skills in M&A could be the driving factor behind improved operational performance and hence bias our estimate of operational improvements. Only if the buyout companies pursue the same M&A strategy as their peers would we have an unbiased estimate of the operating impact of private equity ownership. Guo et. al (2009) concludes that in their sample of 94 US buyouts, 50% percent of the target companies conduct significance acquisitions post- buyout.
While Kaplan (1989) controls for add-ons in 5 of 48 buyouts in his sample, most research leaves this issue untouched for two reasons. Firstly, data availability is scarce and secondly, as pointed out by Bergström et. al (2007), acquiring businesses can be seen as a substitute to in-house growth. The critical issue is how our operating statistics are affected. In case of the EBITDA-margin and ROA, the cost structure most likely differs between firms; consequently over-/under- estimating operational improvements by private equity. As most previous studies that we are aware of, we find it difficult to control for acquisitions by studying changes in EBITDA margin and ROA, and consequently decide not to do so. We appreciate the weakness of this method, but by including Sales CAGR as a measure of operating performance, we can at least indirectly observe tendencies of M&A activity.
24 e.g. Potentially over-estimating improvements in operating performance resulting from private equity ownership 27
Goodwill
Goodwill recognition arises when the buyouts targets assets are revalued at market value. The effect on our OPS is normally a downward bias in our ROA measure. However we have chosen not to adjust for goodwill for two reasons. Firstly, our consolidated accounts for the buyout targets and peers are not detailed enough to decompose goodwill from other intangible assets. Secondly, goodwill contribute to operating performance since it (at least theoretically) should represent an economic asset25.
Model specification Previous studies (Bergström et. al, 2007; Jääskeläinen, 2011) investigating operating performance in private equity has mainly applied two statistical methods; variance analysis and estimating explanatory models using linear regression. The first method is used to compare the means or medians of different groups, either by using a T-test or a Wilcoxon signed rank test. The second method estimates abnormal performance by studying a selection of factors. For example, changes in personnel costs, working capital and leverage are used in the aforementioned studies26. However we only employ the first method, variance analysis, for two reasons. Firstly, data on factors of interest are scarce and would further reduce our sample. Secondly, our focus is to measure differences in the operating statistics between groups (in this case vendor identities).
In analyzing differences between groups, the t-test assumes normal distribution of the variables and homoscedasticity; i.e. that each groups variance is approximately equal. Bergström et. al (2007) applies the t-test; in line with Barber & Lyon (1996) they argue that controlling for extreme values the assumption of normality is justified. However Barber & Lyon (1996) argue that the statistical power of a non-parametric test is always higher when studying operating measures. Also when looking at previous studies, and our sample (see data section), the OPS are far from normally distributed with the data being skewed with fat tails. The skewed distribution of our sample is probably attributable to the nature of private equity; buyout targets are often recapitalized and/or put on more leverage hence increasing the risk. Hence, we use the Wilcoxon signed-rank test to compare if the difference in OPS between the sample and peers is significantly
25 Lys et. al (2012) investigates the relationship between accounting goodwill and predicting operating performance in a sample of 2123 merger transactions in the US between 2002-2006. They conclude that goodwill often reflects an excess premium paid for the firm rather than an economic asset. 26 In Bergström et. al (2007) changes in incentives, measured as management ownership in the company is also studied. 28 different from zero27. We also use the test for the different vendor identities, testing if the peer adjusted OPS are different from zero for each group.
The Wilcoxon test compares medians of matched samples; by comparing medians, no assumption on the sample distribution is necessary. Significance levels are reported at 1%, 5%, 10% and 15% to estimate the statistical power of our estimates. The Wilcoxon test and the other statistical methods employed are further explained in Appendix C.
Multi-group variance analysis
When comparing our OPS across groups (vendor identity) we would also like to employ a multi- group version of a t-test or a Wilcoxon signed-rank test to test our (r)-hypotheses. We may either use ANOVA for a parametric test or Kruskal-Wallis for a non-parametric. The ANOVA method relies on three assumptions; normally distributed variables, homoscedasticity and similar numbers of observations in each group (Hamilton, 2009). While ANOVA is considered a robust method even when the assumptions are violated, overriding combinations of assumptions becomes problematic. Analogous to the decision of using a non-parametric test for comparing the sample to peers we believe we don’t meet the assumptions necessary to apply ANOVA. Our sample is not normally distributed and the number of observations in each group differs widely. In this case an alternative rank-based non-parametric method could be used instead of the ANOVA. We use the Kruskal-Wallis test to compare medians across groups. Similar to the Wilcoxon-signed rank test, this method requires weaker assumptions about measurements, distribution and spread (Hamilton, 2009).
In the Kruskal-Wallis test, the sampling distribution converges towards a chi-square (χ2) when the number of groups exceed three and the number of observations per group exceed five. A limitation with the test is that if we cannot reject our null hypothesis, that there is no significant difference across groups, it does not mean we can accept it. This is a drawback of the limited assumptions necessary to conduct the test. The only assumptions in Kruskal-Wallis is that observations should be independent and drawn from the population by random sampling. Further, similar number of observations across groups, although not necessary for conducting the test, increases robustness. The Kruskal-Wallis test is further explained in Appendix C.
27 Also used by Kaplan (1989) and Guo et. al (2009) 29
Further methods employed
When applying multi-group variance analysis in our sample we recognize that obtaining sufficient significance levels may be difficult; mainly due to the nature of non-parametric test, since they require few assumptions obtaining significance is more difficult. Our results are however also likely to be heterogeneous, beyond vendor identity. While we adjust changes in operating performance to obtain the abnormal level, the performance of a firm is likely to emanate from a multitude of sources beyond vendor identity. Recognizing the relatively small size of our groups (vendor types) in combination with the aforementioned difficulties, we may conduct another statistical test to draw conclusions from our sample. We apply the Mann-Whitney U-test to compare changes in one group with the remaining sample population (the set of (d)-hypotheses). The test is non-parametric and similar to the Wilcoxon-signed rank test but for independent samples with unequal sample size (Fay & Proschan, 2010). The test assigns ranks and compare medians between two groups and assumes that the observations are independently distributed. In small samples, a U statistic is calculated by adding up and comparing ranks between groups. For larger samples, U is approximately normally distributed and we use a standardized Z statistic. This method is more thoroughly covered in Appendix C. It is important to recognize that the interpretation from the Mann-Whitney U test is different from the Kruskal-Wallis test. Here we compare differences to the remaining population rather than the cross-sectional approach applied with Kruskal-Wallis. Given the heterogeneity and size of our sample we believe we can reach significant and interesting results with this method, however being less conclusive about the cross-sectional differences.
To sum up, we believe that our choice of methods are based on sound economic intuition and statistical assumptions. However, we recognize that obtaining statistical significance is likely to be more difficult in a non-parametric test since no assumptions are made on the sample distribution. The statistical methods we use answer three questions; firstly, the Wilcoxon-signed rank test answers if there is a difference in abnormal operating performance between buyouts and peers, as well as giving an indication of how buyouts from individual vendor identities perform in relation to peers. Secondly, the Kruskal-Wallis test answers if there are differences between vendor types. Thirdly, the Mann-Whitney U test answers if there are differences between vendor types and the remaining population. The latter approach is used since we recognize the difficulty in obtaining significant conclusions with a non-parametric test in a small sample.
30
V Data In this section our empirical strategy is discussed in connection with a general overview of the data. Sample distribution and descriptive statistics are presented and briefly discussed.
Buyout sample and selection criteria In their Master’s thesis, Gulliksen et. al. (2008) gathered data on a total of 349 buyouts between 1994 and 2008. We have been fortunate to be given the permission to use this database in our thesis and it forms the foundation for our dataset. The database is, as far as we know, the largest database available on private equity deals in Scandinavia. Compared with other research on the private equity industry in Scandinavia (see eg. Jääskeläinen (2011), Bergström et. al. (2007)), the sample is at least twice as large. As such, it is likely to cover a large majority of the deals during this time period and it is regarded by Gulliksen et. al. (2008) as a highly representative sample.
In their aim to cover as many deals as possible, Gulliksen et. al. (2008) used a number of sources to identify the buyouts. The private equity and venture capital associations for each of the Scandinavian countries28 were used to identify the private equity funds. The funds’ webpages were then used as the main source to identify buyouts. However, since the reporting of buyouts on the webpage is voluntary, Zephyr, Mergermarket, Menon and press releases were used for cross-checking to minimize survivorship bias. Accounting information was gathered from Amadeus (Bureau Van Dijk), Ravn Foretaksinformasjon, Affärsdata and NN Markedsdata.
Although the database is large, both in terms of deals and financial information, it does not include information about previous owners (vendor identities) of the target firms. Using Factiva, Affärsdata and internet searches, we went through each deal in the database and added, where information was available, vendor name and vendor identity. In the process, several deals were identified where the private equity fund only made a minority investment – not acquiring control in the target firm. To adhere to our definition of a buyout, these deals were excluded from the dataset. In addition, when reading through press releases, we identified a few acquisitions which were unmistakably Venture Capital-investments29; which were consequently also excluded.
In the original dataset, there is a lack of financial information for a number of target firms. Gulliksen et. al. (2008) attribute these missing values to financial information unavailability in the
28 There are three major Private Equity associations in Scandinavia: Norwegian-, Swedish- and Danish Venture Capital Association (NVCA, SVCA and DVCA) 29 Either because it was stated in the press release that it was a Venture capital investment or because of the reasons stated for the investment in the press release (growth capital, seed capital etc.). 31 databases they have used for all years prior to 1997. However, there are still a number of firms without full financial information where the buyout took place after 1997. Reasons for this could be add-on acquisitions, name changes, internationalization of headquarters etc. We have used the Orbis database, which has the same owner, Bureau Van Dijk, as Amadeus that Gulliksen et. al. (2008) used, to fill in missing values. Still, financial information could not be found for all firms.
In addition to the database supplied by Gulliksen et. al. (2008), we have added 35 deals with exits between 2008 and 2010 where we could determine the vendor identity for the entry year. To find these deals, we have used the same search criteria as Gulliksen et. al. (2008). That is, we searched the webpages of all the private equity firms included in the Gulliksen et. al (2008) database and used Zephyr for cross-checking. When retrieving financial data from Orbis, ten companies displayed lack of financial information to the extent that we had to exclude them from the dataset, resulting in a total of 25 deals added to the original database.
Sample distribution In total, after adding 25 deals and adjusting the Gulliksen et. al (2008) database for venture capital- and minority investments, as well as lack of financial information, our dataset comprises 218 buyouts with full or partial financial information for the period 1996 to 2010 (for a list of all the buyouts, see Appendix F). The distribution over time can be seen below in figure 5.1. Entries are most common around the turn of the millennium, whereas exits are most common between 2005 and 2007. This could be seen as an indication of the 5 to 7 year holding periods often associated with Private Equity. Furthermore, we observe that the maximum holding period in our sample is 11 years, the minimum 1 year, and the average is 4,2 years (see Appendix D). We can observe a sharp decrease in number of transactions for both of the economic downturns in the time period, i.e. 2001/2002 and 2008/2009, indicating that private equity deals are affected by economic conditions.
32
Figure 5.1 – Sample distribution by year of initial investment and exit year
60
50
40 deals 30 of
No. 20
10
0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Entry year Exit year From the 218 buyouts in our sample, 62% are targets with Swedish headquarters, 21% with Norwegian headquarters and 17% with Danish headquarters (see Table 5.1 below). This means that the sample is slightly skewed, with a larger proportion of Swedish companies compared to previous studies by Gulliksen et. al. (2008) and Jääskelaäinen (2011), where Sweden has represented around 56% and 48%30 respectively. A possible explanation for this can be our somewhat stricter definition of private equity compared to, at least Gulliksen et. al. (2008), which means that we have excluded a number of deals that we have considered as venture capital. As the private equity market generally is seen as more mature in Sweden than the other two countries, and the size of the deals if often larger – the excluded venture capital buyouts have probably been comparatively more numerous in Denmark and Norway.
Another observation is that, although the total sample is large in comparison to previous studies on this subject, the number of observations are limited for buyouts where the vendor is public (23 obs.) or private equity (21 obs.); and particularly when it is Government/State (6 obs.) (see table 5.1). Interesting to note is perhaps also that the most common vendor identity is multidivisional company (94 obs.). This probably reflect the trend to focus on core competencies within large multidivisional companies that have been present the last few decades. It is perhaps not likely that this development will be able to continue for very long in the future as there are not an unlimited amount of non-core divisions to be divested. This could be worth noticing as it might decrease generalizability of our results over time.
30 In Jääskeläinen’s (2011) study, Swedish companies accounted for 58 transactions out of a total of 144. However, 22 of these where Finnish. 33
Table 5.1 – Distribution of sample by country and vendor identity
Vendor Identity Sweden Norway Denmark Total
Public 14 (6%) 4 (2%) 5 (2%) 23 (11%)
Private 35 (16%) 23 (11%) 16 (7%) 74 (34%)
Government/State 4 (2%) 1 (0%) 1 (0%) 6 (3%)
Private Equity 15 (7%) 3 (1%) 3 (1%) 21 (10%)
Multidivisional company 68 (31%) 15 (7%) 11 (5%) 94 (43%) Total 137 (62%) 46 (21%) 36 (17%) 218 (100%)
Out of the 21 sections in the official NACE codes, 16 are represented in our sample – demonstrating that private equity seems to invest in all kinds of different industries. However, manufacturing is by far the most common industry for the target firms in our sample, constituting 35% of the total number of target firms (see table 5.2.); and the five largest industries in terms of number of target firms, constitute almost 83% of the sample.
Table 5.2 – Distribution of sample by industry NACE Section No. of observations % of total Manufacturing 77 35% Wholesale and retail trade 40 18% Professional, scientific and technical activities 31 14% Information and communication 17 8% Transportation and storage 15 7% Construction 7 3% Administrative and support service activities 7 3% Human health and social work activities 7 3% Financial and insurance activities 3 1% Real estate activities 3 1% Arts, entertainment and recreation 3 1% Mining and Quarrying 2 1% Electricity, gas, steam and air conditioning supply 2 1% Water supply; sewerage, waste management and remediation activities 2 1% Agriculture, Forestry and Fishing 1 0% Accommodation and food service activities 1 0% Total 218 100%
When looking at vendor identity and differences in size, both if measured as total assets or revenue at entry year, it is difficult to draw any general conclusions. However, buyouts from public, government/state or multidivisional companies seems to involve larger target firms than those acquired from private or private equity vendors (table 5.3). Another observation in relation to vendor identity is that buyouts from private owners or multidivisional companies display the highest variations in most of the firm characteristics. Especially EBITDA-margin and ROA have considerably higher standard deviations for these two vendor identities (table 5.3). For the total 34 sample, table 5.3 shows that the differences between means and medians are substantial for a number of variables – particularly for Revenue and Total Assets. This is an indication that the distribution of these figures is likely to be skewed, giving evidence, as mentioned in the method section, that non-parametric tests are to be preferred in the analysis.
All firm characteristics that are specified in absolute terms, for all vendor identities, have higher means than medians, meaning that our sample seems to include a lot of smaller deals and a few disproportionally large ones. The large differences between high and low values for most of the firm characteristics show that private equity investments cover target firms of all shapes and sizes. However, there might be differences between private equity firms in terms of which kind of deals they specialize in.
35
Table 5.3 – Summary statistics for firm characteristics by vendor identity at entry year
Firm Characteristic (tEUR) Mean Std Dev Median p5 p95
Public Revenue 226 531 444 490 66 535 144 90 781 491 Total Assets 200 884 279 699 70 752 11 400 782 288 EBITDA-margin (%) 6,88% 7,32% 6,17% -6,46% 23,06% ROA (%) 5,76% 9,70% 3,62% -9,60% 25,61% Private Revenue 63 419 213 900 19 307 515 157 362 Total Assets 41 084 64 616 16 801 948 212 468 EBITDA-margin (%) 7,66% 20,17% 6,97% -22,79% 47,18% ROA (%) 7,09% 21,27% 6,07% -18,77% 45,29% Government/State Revenue 198 069 119 422 116 641 100 836 332 218 Total Assets 144 617 63 247 123 511 94 969 249 417 EBITDA-margin (%) 9,65% 6,88% 7,97% 3,55% 21,39% ROA (%) 7,35% 2,79% 7,49% 4,49% 11,48% Private Equity Revenue 92 151 114 686 54 931 1 307 384 497 Total Assets 65 751 77 538 40 169 111 327 241 479 EBITDA-margin (%) 9,38% 7,95% 8,61% -0,64% 25,96% ROA (%) 12,38% 15,67% 8,80% -12,39% 52,57% Multidivisional company Revenue 129 040 222 123 63 484 2 056 643 468 Total Assets 146 975 348 044 44 769 2 009 445 675 EBITDA-margin (%) 3,75% 23,42% 7,36% -19,91% 23,21% ROA (%) -4,60% 57,34% 4,00% -109,54% 35,93%
Total Revenue 115 725 245 696 43 510 1 735 353 687 Total Assets 111 490 258 996 35 626 1 590 378 953 EBITDA-margin (%) 6,13% 19,58% 7,15% -15,93% 25,96% ROA (%) 2,34% 40,74% 5,27% -20,05% 40,13%
The table reports selected firm characteristics at entry year for different vendor identities. Revenue and Total Assets are displayed in tEUR whereas EBITDA-Margin and ROA are displayed in percentage.
36
VI Empirical Results and Discussion
In this section, we present the results of our study. The hypotheses generated will be tested and analyzed followed by a discussion on our results and implications.
Improvements in operating performance When looking at the distribution of OPS (EBITDA-margin, ROA and Sales CAGR) in our sample, it is clear that there are large differences between high and low values of all three measures, both for adjusted and unadjusted figures (see table 6.1). Furthermore, standard deviations are high, indicating that although median changes are positive for all OPS, the risk of significant deterioration of operating performance during the holding period is, both in absolute terms and in relation to industry peers, high.
Table 6.1 – Summary statistics for operating performance, unadjusted and adjusted
Firm Characteristic Mean Std Dev Median p5 p95
Sample (unadjusted) Δ EBITDA -2,91% 75,36% 0,91% -17,32% 21,57% Δ ROA 7,18% 42,19% 1,79% -17,91% 39,79% Sales CAGR 25,07% 60,68% 11,75% -16,58% 98,69% Adjusted for peers Δ EBITDA -3,98% 78,19% 1,64% -20,85% 24,53% Δ ROA 7,60% 42,02% 1,68% -18,62% 41,40% Sales CAGR 30,21% 56,02% 18,81% -19,46% 113,47%
The table reports selected operating statistics in percentage points. Unadjusted measures report absolute differences in OPSs whereas adjusted measures report differences in relation to industry peers for each OPS.
When examining absolute improvements in operating performance (table 6.2), we observe that for all our OPS, changes are positive and significantly different from zero. When comparing and adjusting for peer group performance, improvements are even greater – with high significant levels. The median increase of the EBITDA-margin during the holding period, when adjusted for the peer groups, is 1,24 percentage points. For ROA and Sales CAGR, these numbers are 2,10 and 18,04 respectively (see table 6.2). It is notable that peer group performance over this period has been rather poor. For all three OPSs, peer group medians are negative.
EBITDA-margin improvements have been small (median difference of 1,24% compared to peers) for the target firms, and ROA improvements have been slightly better (median difference of 1,79% compared to peers). This could be an indication that Private Equity firms work with both margin improvements and capital efficiency in their target firms.
37
Sales CAGR displays the largest difference between our sample target companies and peer groups. One potential explanation for this could be add-on acquisitions during the holding period; something we have not adjusted for. Arguably, add-on acquisitions could be more common in private equity owned companies than in other companies; which is also suggested by Jääskeläinen (2011). 11,75% in annual growth as sample median (not adjusted for industry peers) should probably be regarded as a high number – difficult to reach organically in the type of mature companies often associated with private equity buyouts. We consider it therefore to be likely that this number reflect high M&A activity within the target companies. The high peer adjusted sales CAGR in our sample contradicts the findings in Bergström et. al (2007) where sales growth in private equity owned companies in Sweden was not significantly different from that of industry peers, However, our findings are in line with the results in Gulliksen et. al (2008) and Jääskeläinen (2011).
Table 6.2 – Changes in operating performance for sample, peer groups and difference in medians
OPS Sample median Peer group median Difference
Δ EBITDA margin 0,91%*** -0,34%*** 1,24%****
Δ ROA 1,79%**** -0,31%*** 2,10%****
Sales CAGR 11,75%**** -6,29%**** 18,04%**** The table reports sample median for the EBITDA-margin and ROA changes in percentage points. Sales CAGR is reported in annual percentage growth over the period. Peer group median refers to the peer groups assigned for each buyout target. Difference is calculated as sample median deducted by peer group median; this is denoted abnormal changes in operating performance in this paper. Significance levels are based on a two-tailed Wilcoxon signed-rank test. *, **, ***, and **** denote levels that are significantly different from zero at 15%, 10%, 5%, and 1% levels, respectively.
To conclude, we find support for hypothesis 6 (The improvements in operating performance should be significantly higher for the sample of buyouts compared to industry peers) as all three OPSs shows significant improvements compared to industry peers – although with high standard deviations. This finding is in line with previous research in Scandinavia and elsewhere.
Vendor identity and operating performance When dividing the sample by vendor identity, we can observe that for all vendor identities, and for both adjusted and unadjusted measures, standard deviations are high for all OPSs and improvements range from very negative to very positive regardless of previous owner type. For a full table of summary statistics for the three OPSs by vendor identity, please see Appendix E, table e.1 and e.2.
Looking at table 6.3, we can observe that EBITDA-margin improvements in relation to industry peers (adjusted changes) are positive and significant (median) for Multidivisional company, Public and
38
Private Equity vendor identities. Buyouts made from Private owners show positive median improvements, although not significantly different from zero. Government/State show negative adjusted EBITDA-changes but not significantly different from zero. At a first glance, this seems to somewhat contradict our hypotheses. Government/State surprisingly display the worst development whereas Public, Private Equity and Multidivisional Company vendors seem to perform more in line with expectations. Private vendors show a big difference between mean and median values for adjusted EBITDA-margin improvements, indicating a very skewed distribution.
For changes in ROA, table 6.3 shows that the only vendor identity which yields positive peer adjusted improvements significantly different from zero is Multidivisional Company. Again, Government/State has the lowest median changes in peer adjusted ROA, followed by Private and Public. Private Equity has the second highest median, which is not in line with our hypotheses.
Peer adjusted Sales CAGR is positive and significant for all vendor identities and Public and Private buyouts display higher median growth than the other three groups. In general, peer adjusted Sales CAGR is high across all groups with no vendor identity showing less than 13% annual growth in relation to industry peers.
Table 6.3 – Adjusted changes in operating performance measures grouped by vendor identity
OPS Public Private Government/ Private Equity Multidivisional State company 2,88%** 1,03% -1,88% 1,70%*** 2,76%**** Δ EBITDA margin (3,31) (-21,99%) (-3,34%) (3,08%) (5,83%)
1,68% 1,57% 0,40% 2,16% 2,29%**** Δ ROA (4,95%) (1,52%) (-5,96%) (1,34%) (14,71%)
24,12%**** 24,27%**** 17,00%*** 13,33%**** 15,68%**** Sales CAGR (20,15%) (47,48%) (17,27%) (17,10%) (24,47%) The table reports adjusted changes in the selected operating statistics grouped by buyout type for the holding period. All ratios have been adjusted by subtracting the median of the assigned peer group. Numbers in parentheses denote mean in percent whereas numbers not in parentheses denote medians. Changes in EBITDA margin and ROA is calculated as the difference in the adjusted ratios between the year before exit (t-1) and the entry year (t=0). Changes in sales is calculated as an annual compounded growth rate between the aforementioned years. Significance levels have been calculated for medians and are based on two-tailed Wilcoxon signed-rank test. *, **, ***, and **** denote levels that are significantly different from zero at 15%, 10%, 5%, and 1% levels, respectively.
Although several of the OPSs are significantly different from zero and although we can observe differences in medians between the different vendor identities, we have yet to establish if there is a statistically significant difference between the groups. As mentioned in the method section, we do this by employing the Kruskal-Wallis test for the different vendor identities for each OPS. The Kruskal-Wallis test, with post-hoc multiple comparisons, allows us to test our set of (r)-
39 hypotheses. As can be seen from the results of the Kruskal-Wallis tests in table 6.4, none of the vendor identities are significantly different from one another, this holds for all our OPS31. There could be numerous reasons why we do not find any significant difference between the vendor identities. For example, as mentioned in the hypotheses section, value creation in private equity remains an endogenous process where governance issues are only one part of the puzzle. In addition, vendor identity does not fully capture differences and “quality” of governance. Furthermore, knowing that the sample displays considerable standard deviation, and only minor differences in medians between the groups, lack of statistical significance is perhaps not surprising. Another reason could be that power in Kruskal-Wallis tests increases if the number of observations are similar across the different groups. In our case, the power of the test is probably negatively affected as N for Multidivisional company is as high as 81 (87 for Sales CAGR) and N for Government/State is as low as 5.
Table 6.4 –Ranks and test statistics using the Kruskal-Wallis test
OPS N Mean rank χ2 df. Asymp. Sig. Δ EBITDA margin Public 21 98 Private 61 81 Government/State 5 63 Private Equity 21 90 Multidivisional 81 98 Total 189 5,407 4 0,2480 Δ ROA Public 21 87 Private 54 82 Government/State 5 71 Private Equity 20 75 Multidivisional 81 91 Total 181 2,557 4 0,6345 Sales CAGR Public 21 95 Private 60 110 Government/State 5 88 Private Equity 20 86 Multidivisional 87 92 Total 193 4,925 4 0,2951
The table reports Kruskal-Wallis test results on the adjusted OPSs using vendor type as grouping variable. Mean rank denotes the magnitude of changes in the relevant variable for each vendor type. Asymptotic significance denotes the significance level in percent. *, **, ***,**** denote two-tailed levels where the null hypothesis can be rejected (i.e. that any populations are equal) at 15%, 10%, 5%, and 1% levels, respectively.
31 Note that this does not mean that all vendor identities are the same in terms of operating improvements. It only means that the null hypothesis that all groups are the same cannot be rejected 40
As the Kruskal-Wallis test does not display acceptable significant levels we believe that there is no need to continue with post-hoc multiple comparisons across the different vendor identities to establish a ranking.
To test our set of (d)-hypotheses, we employ the Mann-Whitney U-test to the different vendor identities. I.e. we test for each vendor identity if it is different from the rest of the sample in terms of improvements in operating performance. The results can be seen below in table 6.5. As can be expected following the results in the Kruskal-Wallis test, most of the Mann-Whitney U- tests do not yield significant results; leaving us with statistically weak results. However, for Private and Multidivisional companies, significance levels are acceptable for certain OPSs (see table 6.5). These findings will be described below.
When vendor identity is Private, EBITDA-improvements in relation to industry peers is lower than for the rest of the sample population, with a two-tailed significance level of 10,8%. This lends some support to hypothesis 2(d) and is in line with our expectations that private owners often have high management ownership (incentive realignment) as well as active ownership and control. Hence, private equity improvements in these areas following a buyout from a private vendor should be minor and yield only negligible results. For Sales CAGR however, the opposite is true, i.e. Sales CAGR in relation to industry peers is higher for buyouts from private vendors than for the rest of the sample. Assuming many of the private owners are families, the results are in line with the findings in Edenholm & Stenlund (2008) who find significantly higher sales growth following a buyout from family-owned firms compared to other buyouts. Our results indicate that for privately owned companies, private equity buyouts increase possibilities for growth, perhaps because new capital is injected that was not available before. In essence, this theory would however be more in line with typical venture capital strategies than with private equity strategies. Another possible explanation is that private owners, often families, are potentially excessively risk-averse; as was pointed by for example Agrawal & Nagarjan (1990). Risk-aversion is not compatible with aggressive buy-and-build strategies focusing on sales growth. For this reason, the type of add-on acquisitions often associated with private equity could have more effect, or in other words lead to higher growth, for buyouts from private vendors than for buyouts from other vendor identities as growth has not been an area of focus prior to the buyout.
For buyouts from Multidivisional companies, the Mann-Whitney U-test yields significant results for changes in EBITDA in relation industry peers. At a two-tailed significance level of 11,3%, we can
41 determine that improvements in EBITDA-margin is higher for buyouts from Multidivisional companies than for the rest of our sample. This result lends support, albeit with a weak significance level, to hypothesis 4(d.). Theoretically, the results are in line with the Fama & Jensen’s (1983) suggestion that multidivisional firms suffer from high agency costs due to lack of control mechanisms. A private equity buyout, with increased control and active ownership, would therefore be expected to yield good results with profitability measures such as EBITDA-margin.
Table 6.5 – Mann-Whitney U test comparing groups in relation to the remaining sample population
OPS N Mean Rank Z Asymp. Sig.
Public Δ EBITDA margin 21 100(94) 0,470 0,6386 Δ ROA 21 90(91) -0,053 0,9576 Sales CAGR 21 95(97) -0,178 0,8588 Private Δ EBITDA margin 61 86(99) -1,607 0,1081* Δ ROA 54 87(93) -0,726 0,4681 Sales CAGR 60 110(91) 2,147 0,0318*** Government/State Δ EBITDA margin 5 65(96) -1,226 0,2201 Δ ROA 5 77(91) -0,623 0,5331 Sales CAGR 5 88(97) -0,365 0,7151 Private Equity Δ EBITDA margin 21 96(95) 0,051 0,9595 Δ ROA 20 85(92) -0,548 0,5840 Sales CAGR 20 86(98) -0,960 0,3372 Multidivisional company Δ EBITDA margin 81 102(90) 1,585 0,1129* Δ ROA 81 96(87) 1,252 0,2104 Sales CAGR 87 92(101) -1,181 0,2376
The table reports Mann-Whitney U test results on the adjusted OPSs comparing each vendor type to the remaining sample population. Mean rank for the group is shown with the mean rank for the remaining population in parentheses. Z-Values are positive when the tested vendor identity has higher ranks than the rest of the sample and negative when ranks are lower than the rest of the population. *, **, ***,**** denote two-tailed levels where the null hypothesis can be rejected (i.e. that the two populations are equal) at 15%, 10%, 5%, and 1% levels, respectively.
42
A summary of our results in terms of how they relate to our eleven hypotheses is shown below in table 6.6. We generally find no or weak support for most of our hypotheses with the exception of hypothesis 6, where results are clearly indicating abnormal performance for private equity owned companies in relation to industry peers.
Table 6.6 – Summary of hypotheses and results
Hypotheses Support Hypothesis 1 ( ): The improvements in operational performance will be lowest for buyouts where vendor No identity is Private Equity 풓 ( ): The improvements in operational performance will be significantly lower for buyouts where No vendor identity is Private Equity than for other types of buyouts Hypothesis풅 2 ( ): The improvements in operational performance for buyouts where vendor identity is Private No will be larger than when vendor identity is Private equity but smaller than for other vendor 풓 identities. ( ): The improvements in operational performance will be significantly lower for buyouts where No3/Yes1 vendor identity is Private than for other types of buyouts Hypothesis풅 3 ( ): The improvement in operational performance will be the largest for buyouts where vendor No identity is Government/State. (풓): The improvements in operational performance will be significantly higher for buyouts where No vendor identity is Government/State than for other types of buyouts Hypothesis풅 4 ( ): The improvements in operational performance for buyouts where vendor identity is No Multidivisional company will be smaller than when vendor identity is Government but larger than 풓 for other vendor identities. ( ): The improvements in operational performance will be significantly higher for buyouts where Yes1 vendor identity is Multidivisional company than for other types of buyouts Hypothesis풅 5 ( ): The improvements in operational performance for buyouts where vendor identity is Public No will be smaller than when vendor identity is Government or Multidivisional company, but larger 풓 than for other vendor identities. ( ): The improvements in operational performance will be significantly higher for buyouts where No vendor identity is Public than for other types of buyouts Hypothesis풅 6 ( ): The improvements in operating performance should be significantly higher for the sample of Yes1,2,3 buyouts compared to industry peers. (r)풅 and (d) denote the group of hypotheses, (r) for hypotheses based on rankings and (d) for hypotheses based on difference between a certain vendor identity and the rest of the sample. Numbers denote significant operating statistic(s) supporting the hypothesis; 1) Δ EBITDA margin 2) Δ ROA 3) Sales CAGR.
43
Implications This MSc thesis contributes to the growing literature of operating performance in private equity buyouts. Responding to the call from Strömberg (2008) for more research to understand different types of buyouts we make a contribution notwithstanding our less robust results. This section will discuss the implications of our research consecutively: We discuss generalizability followed by a discussion on our contribution previous research. Finally we will address areas for future research.
External and internal validity limits the generalizability of our results. Since we restrict our sample to the Scandinavian countries during 1996-2010 our results are mainly valid for that time period and geography. However by calculating peer adjusted OPS, the time dimension (e.g. business cycles) should be subtracted. However, the peer methodology is only an approximation, ideally we would like to control for growth potential to reduce the impact of “stock picking”, i.e. find successful firms. For public firms this could be done by looking at the book-to-market ratio. However, an equivalent metric is not available for private firms. In sum, external validity of our findings is difficult to assess since value creation in private equity is likely to depend on a complex set of factors.
To improve internal validity we have tried to reduce confounding effects, this is discussed more in depth in the method section. While we identified several confounding factors, data availability made it difficult to control for all; the results should be interpreted with this in mind. However a stricter requirement on data would inevitably cause a sample selection bias. Another issue is the fact that we conduct a large number of significance tests. Bland & Altman (1995) concludes that: “one must beware of attaching too much importance to a lone significant result among a mass of non-significant ones” and concludes that an increased number of hypotheses raises the demand on significance levels to control for the Family-wise error rate (FWER)32. The method most widely adapted for adjusting significance levels is the Bonferroni correction; (Hochberg & Benjamini, 1995; Abdi, 2007) where the significance level is divided by the number of hypotheses; dramatically raising the demand on significance levels. We refrain from using this method for three reasons: Firstly, controlling for the FWER is mainly done in experimental sciences (e.g. clinical trials in medicine)
32 i.e. If we test 20 hypotheses using 5% as significance level the probability that none will be significant is 0,9520=0,36 and then 1-0,36=0,64 that at least one is significant (See Bland & Altman (1995) for a review) 44 and rarely in finance and economics when observational data is used33. Secondly, our hypotheses are defined ex-post and based on theory rather than defined ex-ante to obtain significance. In addition, we are conservative and apply non-parametric test in contrast to some previous scholars (e.g. Bergström et. al, 2007). Thirdly,, there is a lively debate among statisticians whether to use FWER corrections or not (See e.g. O´Keefe 2003).
We believe our thesis makes contributions that apply to: I) Research on the Scandinavian private equity industry, II) Research on value creation in private equity buyouts, and III) practitioners
Our results build on existing literature on the Scandinavian private equity industry. In line with previous research (e.g. Bergström et. al, 2007) we find evidence on value creation in private equity-owned firms. From our literature review we believe our dataset is amongst the largest to date in Scandinavia, with regards to number of buyouts and time period34. However, our research is primarily concerned with the cross-section of value creation for vendor identity. While Jääskeläinen (2011) finds indications that there should be differences, we find it hard to establish statistical significance since variation is large within different vendor identities.
In addition, our results contributes to understanding value creation in private equity. In line with Strömberg (2008), we find that buyouts stem from a variety of sources. Research has primarily been concerned with public-to-private buyouts, only representing a fraction of both ours, and Strömberg’s (2008), sample. We make two findings consistent with theory; firstly divisional buyouts from multidivisional firms show the largest improvements in operating performance. This is in line with theory (e.g. Kaplan & Strömberg, 2008) which define corporate governance mechanism as the main role for private equity. Secondly, buyouts from private owners show little improvements in operating performance, except for sales growth. Overall we find indications that private buyouts elicit venture capital characteristics with investment in early stage businesses and hence a higher risk, confirmed by the variability in performance within this group35. In addition, buyouts from private owners seem to occur in industries with high risk; peer group changes in
33 We conducted a brief review of methodology searching major databases in the SSE library using the keywords “False discovery rate”, “Bonferroni”, “Correction”, “Multiple comparisons” and “Family-wise error rate”; the major share of applied work was in disciplines other than that of finance and economics 34 We build our dataset from Gulliksen et. al (2008) which includes 349 buyouts, their sample of buyouts is reduced significantly when using our definition of private equity 35 See e.g. Sahlman (1990) for a review of venture capital and the difference from leveraged buyouts (private equity) 45 operating performance for private buyouts show the highest variability as shown in Appendix E, table e.3.
The abnormal sales growth in our sample raises the question: Is corporate governance mechanisms the main tool for value creation in private equity? It is clear that much of the observed “value creation” must be through acquisitions, particularly in private buyouts. Looking at secondary buyouts from private equity, we would expect small improvements from theory. However, in our sample we cannot conclude that these buyouts are significantly different from the remaining population.. Further, variability in operating performance in our sample is very high. While partly explained by private equity overinvestment in good times it may be that that increasing the operational risk is a common feature in private equity36. To conclude, while our use of vendor identities is an approximation for quality of corporate governance pre-buyout our research shows that value creation is heterogeneous, due to a multitude to factors and remains an endogenous process.
This thesis has reached three main findings of interest for practitioners working within, or in close connection to the private equity industry. Firstly, our thesis finds support for value creation in private equity buyouts, however we do not study the allocation of wealth between investors and fund managers. Secondly, we find indications that buyouts from multidivisional companies may be attractive. Thirdly, our results suggest that buyouts from private owners exhibit strong sales growth while adjusted improvements in EBITDA- and ROA- is moderate or negative. This last result is of particular importance for private equity fund investors; while private equity firms claim to invest in firms at a mature phase many of the buyouts studied exhibit venture capital characteristics. Consequently the operational risk inherent in private equity may be understated.
From the results of our study, we have identified three areas for future research. Firstly, we believe that further research in vendor identity in private equity buyouts could yield more robust results, the industry remains young with a large share of total deals in recent years. Expanding the dataset across time and geography may strengthen our results. Secondly, further research on value creation in secondary buyouts should be considered. Representing an ever increasing share or buyouts, they still lack much theoretical support37. Research on industry expertise and strategic
36 See e.g. Axelsson et. al (2009) for a review of private equity overinvestment and operational risk 37The increasing share of secondary buyouts is discussed by e.g. Achleitner & Figge (2012), and shown empirically in Strömberg (2008) 46 innovation by Berg & Gottschalg (2005) indicate that value creation in buyouts is beyond corporate governance, but research and empirical evidence is scarce. Thirdly,, research on measuring and evaluating quality of corporate governance mechanisms would benefit research on firm performance and corporate governance in general, and the role of corporate governance mechanisms employed in private equity buyouts in particular.
47
VII Conclusion In this section, we briefly summarize the contribution, results and implications of our thesis.
In this thesis, we have investigated the effects of different types of vendors on private equity funds’ ability to enhance the operating performance of their targets. We have sought to fill a gap in previous research where only a sparse amount of studies have focused on vendor identity, often limited to studying one type of vendor; most commonly private equity (secondary buyouts)
Using data on 218 buyouts in Scandinavia with exits between 1998 and 2010, we tested for peer adjusted improvements in operating performance for five different vendor identities using EBITDA-margin, ROA and Sales CAGR as proxies. In contrast to our hypotheses, we could not find significant differences in adjusted operating performance across the different vendor identities when employing variance analysis in the form of a Kruskal-Wallis test. However, when comparing each vendor identity to the remaining sample as a group, we found weak evidence of abnormal operating performance for buyouts from multinational companies and private vendors. Particularly, buyouts from private vendors showed lower EBITDA-margin improvements than the rest of the sample, as expected, but higher Sales CAGR, which was not expected. Buyouts from multidivisional companies on the other hand, displayed higher EBITDA-margin improvements than the rest of the sample in line with theory. Given the high number of non- significant results in our study however, there are weak indications that vendor identity might affect subsequent improvements in operating performance. Possibly, governance theories, relating value creation to incentive realignment and increased control, only provides limited explanation to differences in operating performance. Another possible explanation is that different owner types are not a satisfactory proxy for differences in governance. Regardless of which, future research is needed to be able to better understand value creation in private equity.
In addition to investigating the effect of vendor identity, we also tested for improvements in operating performance in buyouts in general. We found evidence that during the holding period, private equity owned firms perform better in terms of EBITDA-margin, ROA and Sales CAGR than industry peers. This is in line with previous research and our results contribute to the growing empirical evidence of value creation in private equity.
In general, it seems as if Private equity remains to be value creating, despite the increased competition for buyout targets. Neither do the new sources of buyouts, like multidivisional firms, other private equity firms, and government seem to affect the funds’ ability to create value in their targets to any large extent. In contrast to the inhabitants of Easter Island, “deforestation” is perhaps not much of a problem to private equity firms; not yet, at least. 48
IIX References In this section we include the references cited in our thesis. Citations are done using the Harvard System of Referencing. Newspaper articles have been accessed using Factiva.
Abdi, H., 2007. The Bonferroni and Šidák Corrections for Multiple Comparisons . In: Encyclopedia of Measurement and Statistics. Thousands Oaks: Sage.
Acharya, V. & Kehoe, C., 2008. Corporate Governance and Value Creation: Evidence from Private Equity. SSRN Working Paper Series.
Achleitner, A & Figge, C.,2012. Private Equity Lemons? Evidence of Value Creation in Secondary buyouts. European Financial Management, [Forthcoming].
Agrawal, A. & Knoeber, C. R., 1996. Firm Performance and Mechanisms to Control Agency Problems between Managers and Shareholders. Journal of Finance and Quantitative analysis, 31(3), pp. 377-397.
Agrawal, A. & Nagarjan, N., 1990. Corporate structure, agency costs, and ownership control: the case of all-equity firms. Journal of Finance, Volume 45, pp. 1325-1331.
Anderson, R., Mansi, S. & Reeb, D., 2003. Founding family ownership and the agency costs of debt. Journal of Financial Economics, Volume 68, pp. 263-285.
Axelson, U., Strömberg, P. & Weisbach, M. P., 2009. Why are Buyouts Levered? The Financial Structure of Private Equity Funds. Journal of Finance, 64(4), pp. 1549-1582.
Barber, B. M. & Lyon, J. D., 1996. Detecting abnormal operating performance: The empirical power and specification of test statistics. Journal of Financial Economics, Volume 41, pp. 359-399.
Benjamini, Y. & Hochberg, Y., 1995. Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society (Series B: Methodological), 57(1), pp. 289-300.
Berg, A. & Gottschalg, O., 2005. Understanding Value Generation in Buyouts. Journal of Restructuring Finance, 2(1), pp. 1-49.
Bergström, C., Grubb, M. & Jonsson, M., 2007. The Operating Impact of Buyouts in Sweden: A Study of Value Creation. The Journal of Private Equity, 11(1), pp. 22-39.
Beuselinck, C. & Manigart, S., 2007. Financial Reporting Quality in Private Equity Backed Companies: The Impact of Ownership Concentration. Small Business Economics, 29 (3), pp. 261- 274.
Bland, M. J. & Altman, D. G., 1995. Multiple significance tests: the Bonferroni method. British Medical Journal, Volume 310, p. 170.
Cao, J. & Lerner, J., 2006. The Performance of Reverse Leveraged Buyouts. Stockholm, Swedish Institute for Financial Research Conference on The Economics of the Private Equity Market.
49
Cressy, M., Munari, F. & Malipiero, F., 2007. Playing to their strengths? Evidence that specialization in Private Equity Confers Competitive Advantage. Journal of Corporate Finance, 13(1), pp. 647-669.
Cuman, D., 2011. Value Creation in Divisional Buyouts. Saarbrücken: LAP Lambert Academic Publishing.
Cumming, D., Siegel, D. S. & Wright, M., 2007. Private Equity, Leveraged Buyouts and Governance. Journal of Corporate Finance, 13(1), pp. 439-460.
Cumming, D. & Walz, U., 2010. Private Equity Returns and Disclosure Around the World. Journal of International Business Studies, 41(4), pp. 727-754.
Danmarks Nationalbank, 2012. Foreign-exchange policy / ERM II. [Online] Available at: http://www.nationalbanken.dk/DNUK/MonetaryPolicy.nsf/side/Exchange_Rate_Mechanism_ _ERM_II!OpenDocument [Accessed 10 04 2012].
Davis, G. F., Diekmann, K. A. & Tinsley, C. H., 1994. The Decline and Fall of the Conglomerate Firm in the 1980s: The Deinstitutionalization of an Organizational Form. American Sociological Review, Issue 59, pp. 547-570.
Demsetz, H., 1983. The structure of ownership and the theory of the firm. Journal of Law and Economics, 26(2), pp. 375-390.
Desbrieres, A. & Schatt, P., 2002. The impacts of LBOs on the performance of acquired firms: the French case. Journal of Business Finance and Accounting, 29(1), pp. 695-729.
Dewenter, K. L. & Malatesta, P. H., 2001. State-Owned and Privately Owned Firms: An Empirical Analysis of Profitability, Leverage, and Labor Intensity. The American Economic Review , 91(1), pp. 320-334.
D'Souza, J. & Megginson, W. L., 1999. The Financial and Operating Performance of Privatized Firms during the 1990s. The Journal of Finance, 54(4), pp. 1397-1438.
Edenholm, F. & Stenlund, D., 2008. Family Firm Buyouts in Scandinavia - Impacts on Operational Performance. Master Thesis, Stockholm School of Economics.
Emtemark, P. & Olsson, P., 2012. Club Deals vs. Sole private equity Sponsor Deals: A Study of Operating Performance on the Swedish Buyout Market. Master Thesis, Stockholm School of Economics, pp. 1-55.
European Private Equity & Venture Capital Association, 2010. EVCA 2010 Nordic Report, Brussels: EVCA.
Fama, E. F. & French, K. R., 1992. The Cross-Section of Expected Stock Returns. Journal of Finance, 47(2), pp. 427-465.
50
Fama, E. F. & Jensen, M. C., 1983. Separation of Ownership and Control. Journal of Law and Economics, 26(2), pp. 301-325.
Fay, M. P. & Proschan, M. A., 2010. Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Statistical Surveys, 4(1), pp. 1-39.
Grabenwarter, U. & Weidig, T., 2005. Exposed to the J-Curve: Understanding and Managing Private Equity Fund Investments. 1st ed. London: Euromoney books.
Gulliksen, A. V., Falk Hansen, O. & Audestad Wara, K., 2008. The Operating Performance of Scandinavian Private Equity Companies. Master Thesis, Norwegian School of Economics and Business Administration, pp. 1-95.
Guo, S., Hotchkiss, E. & Song, W., 2009. Do Buyouts (Still) Create Value?. Journal of Finance, 66(2), pp. 479-517.
Hamilton, L. C., 2009. Statistics with Stata. 2nd ed. Belmont: Brooks/Cole.
Harris, R., Siegel, D. & Wright, M., 2005. Assessing the Impact of Management Buyouts on Economic Efficiency: Plant Level Evidence from the United Kingdom. Review of Economics and Statistics, 87(1), pp. 148-153.
Holthausen, R. W. & Larcker, F. D., 1996. The Financial Performance of Reverse Leveraged Buyouts. 42(3), pp. 293-32.
Jensen, M. C., 1986. Agency Costs of Free Cash Flow, Agency Costs and Takeovers. The American Economic Review, 76(2), pp. 323-329.
Jensen, M. C., 1989. Eclipse of the Public Corporation. Harvard Business Review, Issue September- October, pp. 1-31.
Jensen, M. C. & Meckling, W. H., 1976. Theory of the Firm, Managerial Behavior, Agency Cost and Ownership Structure. Journal of Financial Economics, 3(4), pp. 305-360.
Jääskeläinen, J., 2011. Operational Performance of Nordic Private Equity Backed Buyouts in the Recession of 2007-2009. Master Thesis, Aalto University, pp. 1-80.
Kaplan, S., 1989. The Effects of Management Buyouts on Operating Performance and Value. Journal of Financial Economics, 24(2), pp. 217-254.
Kaplan, S. & Schoar, A., 2005. Private Equity Performance: Returns, Persistence and Capital Flows. The Journal of Finance, 55(4), pp. 1791-1823.
Kaplan, S. & Strömberg, P., 2008. Leveraged Buyouts and Private Equity. Journal of Economic Perspectives, 22(4), pp. 1-27.
Koller, T., Goedhart, M. & Wessels, D., 2005. Valuation: Measuring and Managing the Value of Companies. 4th ed. London: Wiley Finance.
51
Kreuter, B., Gottschalg, O. & Zollo, M., 2005. Truths and Myths about Determinants of Buyout Performance. Working paper of the INSEAD-Wharton Alliance, pp. 1-10.
Leslie, P. & Oyer, P., 2009. Managerial Incentives and Value Creation: Evidence from Private Equity. Rock Center for Corporate Governance Working Paper, 21(1), pp. 1-42.
Lichtenberg, F. R. & Siegel, D., 1991. The Effects of Leveraged Buyouts on Productivity and Related Aspects of Firm Behavior. NBER Working Paper Series, Issue #3022, pp. 1-55.
Lie, E., 2001. Detecting Abnormal Operating Performance: Revisited. Financial Management, 30(2), pp. 77-91.
Lys, T., Vincent, L. & Yehuda, N., 2012. The Nature and Implications of Acquisition Goodwill. Working paper. Northwester University, Issue March 12, 2012, pp. 1-54.
Marsden, R., 2008. Keys to Growing Shareholder Value in Private Equity, UK: Deloitte, The Boston Consulting Group.
Meuleman, M., Amess, K., Wright, M. & Scholes, L., 2008. Agency, Strategic Entrepreneurship, and the Performance of Private Equity-Backed Buyouts. Entrepreneurship Theory and Practice, 33(1), pp. 213-239.
Meuleman, M. & Goosens, L., 2007. The Change in Ownership After a Buyout: Impact on Performance. Working Paper at Ghent University, Volume #07-484, pp. 1-20.
Nikoskelainen, E. & Wright, M., 2007. The Impact of Corporate Governance Mechanisms on Value Increases in Leveraged Buyouts. Journal of Corporate Finance, 13(4), pp. 511-537.
Nohel, T. & Tarhan, V., 1998. Share Repurchases and Firm Performance: New Evidence on the Agency Costs of Free Cash Flow. Journal of Financial Economics, 49(1), pp. 187-222.
Norman, C. & Riboe, R., 2011. Does a Local Advantage Exist in Private Equity?. Bachelor Thesis, Stockholm School of Economics, pp. 1-35.
Nyren, G. & Åsbrink, A., 2009. Differences in Operating Performance: A Study of Primary and Secondary Buyouts. Bachelor Thesis, Stockholm School of Economics.
O'Keefe, D. J., 2003. Colloquy: Should Familywise Alpha Be Adjusted?. Human Communication Research, 29(3), pp. 431-447.
Palepu, K. G., 1990. Consequences of Leveraged Buyouts. Journal of Financial Economics, 27(1), pp. 247-262.
Phalippou, L. & Gottschalg, O., 2009. The Performance of Private Equity Funds. Review of Financial Studies, 4(22), pp. 1-50.
Phalippou, L. & Zollo, M., 2005. Performance of private equity Funds: Another puzzle?. Working Paper, INSEAD.
52
Regeringskansliet, 2012. SOU 2012:14 - Ekonomiskt värde och samhällsnytta (Betänkande av ägarutredningen), Stockholm: Sveriges riksdag.
Renneboog, L., Simons, T. & Wright, M., 2007. Why do Public Firms go Private in the UK? The Impact of Private Equity Investors, Incentive Realignment and Undervaluation. Journal of Corporate Finance, 13(4), pp. 591-628.
Rosenbaum, P. R. & Rubin, D. B., 1983. The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70(1), pp. 41-55.
Ross, S., 1973. The Economic Theory of Agency: The Principal’s Problem. American Economic Review, Volume 63, pp. 134-139.
Sahlman, W. A., 1990. The Structure and Governance of Venture Capital Organizations. Journal of Financial Economics, 27(1), pp. 473-521.
Shadish, W. R., 2002. Revisiting Field Experimentation: Field Notes for the Future. Psychological Methods, 7(1), pp. 3-18.
Sheskin, D. J., 2011. Handbook of Parametric and Nonparametric Statistical Procedures. 5th ed. London: Chapman & Hall.
Shleifer, A. & Vishny, R. M., 1997. A Survey of Corporate Governance. The Journal of Finance, 52(2), pp. 737-783.
Smith, A. J., 1990. Corporate Ownership Structure and Performance - The Case of Management Buyouts. Journal of Financial Economics, 27(1), pp. 143-164.
Strömberg, P., 2008. The new demography of private equity. Globalization of Alternative Investment Working Papers Volume 1, The Global Economic Impact of Private Equity Report 2008, pp. 3-26.
Thomsen, S. & Pedersen, T., 2000. Ownership Structure and Economic Performance in the Largest European Companies. Strategic Management Journal, 21(6), pp. 689-705.
Wang, Y., 2011. Secondary Buyouts: Why Buy and at What Price?. Working Paper, California State University, pp. 1-63.
Vinten, F., 2007. The Performance of Private Equity Buyout Fund Owned Firms. Working Paper. Copenhagen Business School, pp. 1-62.
Wu, T. W., 1997. Management Buyouts and Earnings Management. Journal of Accounting, Auditing and Finance, 12(1), pp. 373-389.
53
IX Appendix In this section additional data from our study is presented to uncover data, definitions and other material relevant to our thesis.
Appendix A – definition of ratios Ratio Definition Sales/total The quantity of business a company conducts during the financial turnover year. A measure of whether the company is expanding/contracting its businesses. Derived from consolidated accounts EBITDA Earnings before interest, taxes, depreciation and amortization EBITDA margin EBITDA margin =
퐸퐵퐼푇퐷퐴푡
푇표푡푎푙 푡푢푟푛표푣푒푟푡 EBIT Earnings before interest and taxes. Excludes income and expenditure from non-recurring or discontinued activities. Key advantage is that EBIT excludes the effect of different capital structures and tax effects. Total assets The total of current and long-term assets. Used to assess the size of the company. Book values e.g. accounting figures are used to calculate the ratio
ROA Return on Assets =
퐸퐵퐼푇푡
푇표푡푎푙 푎푠푠푒푡푠푡 ROIC Return on invested capital. Used to calculate the companies efficiency at allocating capital under control for profitable investments
( ) ROIC =
where WC=퐸퐵퐼푇 working∗ 1−휏 capital and = . 푊퐶+퐹푖푥푒푑 푎푠푠푒푡푠
EBIT *(1- ) is a simplification of 휏the NOPLAT푇푎푥 푟푎푡푒 measure (Net operating profit less adjusted taxes) where we have that: 휏 =
푁푂푃퐿퐴푇 where Invested capital = Fixed assets+Current assets-Short term 퐼푛푣푒푠푡푒푑 퐶푎푝푖푡푎푙 payables푅푂퐼퐶
Source: (Koller, et al., 2005)
54
Appendix B – exchange rates Year DKK/EUR NOK/EUR SEK/EUR 2008 0,1342 0,1007 0,0910 2007 0,1341 0,1254 0,1060 2006 0,1341 0,1213 0,1082 2005 0,1340 0,1244 0,1062 2004 0,1344 0,1211 0,1108 2003 0,1343 0,1187 0,1100 2002 0,1346 0,1371 0,1091 2001 0,1338 0,1248 0,1074 2000 0,1339 0,1209 0,1128 1999 0,1343 0,1237 0,1168 1998 0,1339 0,1123 0,1053 1997 0,1329 0,1235 0,1147
1996 0,1353 0,1250 0,1168 Source: Oanda.com (Accessed 2011-04-02)
Appendix C – definition of statistical methods employed We employ non-parametric tests for testing robustness of our data and to compute levels of significance. While their specification differs these tests have common features;
• They lack parameters i.e. no assumptions are made about the data e.g. normality, homogeneity of variances etc. However they assume that variables are independently distributed • They work on the principle of ranking data, each observation is assigned a rank and analysis is carried out on the ranks rather than the actual values. The null hypothesis is accepted if there is no significant difference in the sum of ranks between groups. • They study the center of the population (the median if different from the mean) • Our hypothesis becomes:
o H : = o H0 : 휇1 휇2 • For K groupsA 휇1 in≠ Kruskal휇2 -Wallis we have that: o H : = = [… ] = o H0 : 휇1 휇2 for at least휇 one푘 set of i and j A 푖 푗 The specifications are휇 defined≠ 휇 in (Sheskin, 2011) and test statistics are computed using the Stata 12 IC software from StataCorp.
55
Wilcoxon-signed rank test
We conduct the Wilcoxon-signed rank test with the four step methodology denoted below. The sampling distribution approaches the normal distribution when the sample size becomes large. This test assumes matched pairs (in our case sample matched by peer groups)
1. Calculate the difference between each pair of observations; take sign into account. 2. Rank order the differences giving the lowest rank to the smallest difference 3. Define T as the sum of ranks for differences with the less frequent sign 4. Calculate the standard deviation and Z-score and compare with a normal distribution table to obtain the significance level
( + 1)(2 + 1) = 24 푁 푁 푁 푇 � 휎 ( + 1) 4 = 푁 푁 �푇 − � 푍 푇 Mann-Whitney U test – for large samples 휎
We conduct the Mann-Whitney test with the three step methodology denoted below. The sampling distribution approaches the normal distribution when the sample size becomes large (N>30). The test is analogous to the Wilcoxon-signed rank test however does not require matched pairs.
1. Rank data for the total two groups; find the sum of ranks for the smaller sample and denote it T ( ) 2. Define U as: = + -T where is the number of scores in the smaller 푁퐴 푁퐴+1 sample and 푈 is the푁퐴 equivalent푁퐵 2for the larger. 푁퐴
3. Calculate the푁 퐵standard deviation and Z-score and compare with a normal distribution table to obtain the significance level
( + + 1) = 12 푁퐴푁퐵 푁퐴 푁퐵 휎푈 � ( ) 2 = 푁퐴푁퐵 푈 − 푍 푈 휎
56
Kruskal-Wallis test
The test is similar to the Mann-Whitney U test but for K independent samples (K>2). Hence we rank the data for the total K groups and then define as the sum of the ranks for group i. If the sample is sufficiently large (N(for each group) 5) the푅푖 test statistic is approximately chi-squared with K-1 degrees of freedom. We define the Kruskal≥ -Wallis test statistic as:
2 = 3( + 1) χ with K-1 degrees of freedom ( ) 2 12 푘 푅푖 퐻 푛 푛+1 ∑푖=1 푛푖 − 푛 → 1. Apply the same methodology as before for ranking but now for K groups 2. Compute the Kruskal-Wallis statistic H 3. Define a decision rule for rejecting the null hypothesis 2 >χ ( , 1) where is the significance level
퐻 훼 퐾 − 훼
57
Appendix D – holding period of buyouts Table d.1 – holding periods of buyouts in sample Mean Std.Dev Median p5 p95 Holding period 4,2 2,1 3 1 7 Insignificant differences in holding periods resulting from vendor type (< ); conseqeuently only numbers for total sample presented. ∓ퟏ 풚풆풂풓
Appendix E – supplementary tables and figures Table e.1 – Unadjusted changes in operating performance measures, grouped by vendor identity
Vendor identity N Mean Std.Dev Median p5 p95
Public Δ EBITDA margin 21 1,58% 8,38% 0,18% -7,10% 19,46% Δ ROA 21 3,19% 15,77% 0,69% -12,19% 38,26% Sales CAGR 21 18,22% 31,73% 13,92% -5,07% 57,34% Private Δ EBITDA margin 61 -18,29% 129,47% 0,38% -30,34% 24,90% Δ ROA 54 1,17% 26,17% 0,87% -19,56% 32,86% Sales CAGR 60 42,27% 98,24% 15,35% -12,96% 152,04% Government/State Δ EBITDA margin 5 -2,57% 7,65% 0,80% -14,95% 4,41% Δ ROA 5 -6,73% 20,17% 0,86% -42,68% 4,39% Sales CAGR 5 6,55% 11,45% 12,83% -12,38% 14,97% Private Equity Δ EBITDA margin 21 1,78% 5,66% 0,90% -5,04% 13,35% Δ ROA 20 1,03% 9,44% 1,92% -16,39% 16,95% Sales CAGR 20 13,02% 18,18% 10,92% -15,29% 44,60%
Multidivisional company Δ EBITDA margin 81 6,27% 21,67% 2,40% -14,73% 26,54% Δ ROA 81 14,60% 57,81% 4,05% -28,16% 78,01% Sales CAGR 87 18,69% 31,20% 7,81% -17,66% 86,59% The table reports unadjusted changes in operating performance grouped by buyout type for the holding period. Changes in EBITDA margin and ROA is calculated as the difference in the ratio between the year before exit (t-1) and the entry year (t=0). Changes in sales are calculated as an annual compounded growth rate between the aforementioned years. All results are reported in percentage points rounded to the second decimal.
58
Table e.2 – Adjusted changes in operating performance measures, grouped by vendor identity Vendor identity N Mean Std.Dev Median p5 p95
Public Δ EBITDA margin 21 3,31% 9,14% 2,88% -8,19% 16,63% Δ ROA 21 4,95% 15,49% 1,68% -10,77% 41,40% Sales CAGR 21 20,15% 47,52% 24,12% -34,29 60,07% Private Δ EBITDA margin 61 -21,99% 134,34% 1,03% -55,97% 24,27% Δ ROA 54 1,52% 24,37% 1,57% -19,96% 35,99% Sales CAGR 60 47,48% 84,17% 24,27% -9,25% 167,65% Government/State Δ EBITDA margin 5 -3,34% 7,92% -1,88% -16,20% 4,50% Δ ROA 5 -5,96% 22,70% 0,40% -45,54% 12,10% Sales CAGR 5 17,27% 10,34% 17,00% 2,28% 30,93% Private Equity Δ EBITDA margin 21 3,08% 8,06% 1,70% -3,97% 16,39% Δ ROA 20 1,34% 9,49% 2,16% -15,48% 17,49% Sales CAGR 20 17,10% 19,30% 13,33% -14,97% 51,04%
Multidivisional company Δ EBITDA margin 81 5,83% 20,52% 2,76% -11,75% 28,34% Δ ROA 81 14,71% 58,07% 2,29% -23,80% 79,70% Sales CAGR 87 24,47% 34,90% 15,68% -19,46% 110,48% The table reports adjusted changes in operating performance grouped by buyout type for the holding period. All ratios have been adjusted by subtracting the median of the assigned peer group. Changes in EBITDA margin and ROA is calculated as the difference in the ratio between the year before exit (t-1) and the entry year (t=0). Changes in sales are calculated as an annual compounded growth rate between the aforementioned years. All results are reported in percentage points rounded to the second decimal.
59
Table e.3 – Peer group changes in operating performance measures, grouped by vendor identity
Vendor identity N Mean Std.Dev Median p5 p95
Public Δ EBITDA margin 21 -1,73% 4,75% -0,17% -9,54% 2,83% Δ ROA 21 -1,77% 3,41% -1,25% -5,14% 0,56% Sales CAGR 21 -1,94% 27,93% -3,36% -29,91% 4,82% Private Δ EBITDA margin 61 3,70% 42,11% -1,15% -8,89% 20,29% Δ ROA 54 -0,35% 4,72% -0,35% -8,89% 5,87% Sales CAGR 60 -5,21% 20,56% -7,13% -22,97% 10,20% Government/State Δ EBITDA margin 5 0,77% 1,21% 0,27% -0,23% 2,67% Δ ROA 5 -0,77% 4,12% -0,21% -7,71% 2,87% Sales CAGR 5 -10,72% 7,91% -14,66% -17,44% -0,35% Private Equity Δ EBITDA margin 21 -1,30% 4,46% -0,36% -4,94% 3,37% Δ ROA 20 -0,31% 2,59% -0,48% -5,60% 3,60% Sales CAGR 20 -4,08% 15,66% -6,42% -17,80% 32,40%
Multidivisional company Δ EBITDA margin 81 0,44% 6,61% -0,29% -6,09% 9,33% Δ ROA 81 -0,11% 3,86% -0,09% -5,50% 4,38% Sales CAGR 87 -5,78% 16,08% -5,27% -27,40% 6,74% The table reports unadjusted changes in operating performance grouped by buyout type for the holding period. Changes in EBITDA margin and ROA is calculated as the difference in the ratio between the year before exit (t-1) and the entry year (t=0). Changes in sales are calculated as an annual compounded growth rate between the aforementioned years. All results are reported in percentage points rounded to the second decimal.
60
Appendix F – sample firms and peer groups Table f.1 – Sample of buyouts Sample firm Country Entry Exit PE Fund Vendor Vendor type NACE year year
Aalborg Industries Denmark 2000 2005 Axcel Axcel Secondary 2830 AB Previa Sweden 2004 2007 Segulah Capio Ab Divisional 8621 AB Ångpanneföreningen Sweden 2000 2001 AB Traction De-Listed from OMX Public-to-private 7112 Acando Consulting Sweden 1999 2003 Accent Finnveden Divisional 6201 Acando Frontec Sweden 2000 2003 Accent De-Listed from OMX Public-to-private 7022 ACO Hud Sweden 2003 2004 Altor Oriflame Cosmetics Divisional 4645 Adixen Sensistor AB Sweden 2000 2006 Litorina Kapital Claes Nylander/Management Private-to-private 3320 Adveta Sweden 1997 2001 Segulah Securum AB Divisional 8042 Ahlsell Sverige AB Sweden 1999 2005 Nordic Capital Trelleborg Divisional 5154 Aleris AB Sweden 2005 2010 EQT ISS Divisional 8622 Alfa Laval AB Sweden 2000 2005 Industri Kapital Tetra Laval Divisional 3559 Alimak Hek Sweden 2001 2006 3i Sweden Management Private-to-private 2822 Annas pepparkakor Sweden 2004 2008 Accent Equity Björn Mattson Private-to-private 8730
Antiximex Sweden 2001 2005 Nordic Capital Servicemaster Co Divisional 1072 APL ASA Norway 2003 2005 Hitech Vision Statoil Divisional 7470 Arca Systems International AB Sweden 1998 2005 Industri Kapital Gambro Divisional 7487 Arexis Sweden 2000 2005 Industrifondene Management Private-to-private 5416 Atea Holding AB Sweden 2001 2006 3i Sweden WM-Data Divisional 7219 Attendo AB Sweden 2004 2006 Bridgepoint Gustav Douglas & MS Private-to-private 7022 Aura Industrier AB Sweden 2000 2006 Bridgepoint Procuritas Secondary 8730 Avitec AB Sweden 1999 2007 3i Sweden De-Listed from OMX Public-to-private 4643 Av-teknik event engineering Sweden 2007 2008 Amplico capital Roberth Fredriksson (founder) Private-to-private 3162 Axenti Holding AB Sweden 1999 2004 Procuritas Finnveden invest Divisional 7739 B2 Bredband Sweden 1999 2005 The Carlyle Group KF Divisional 7410 Ballingslöv international Sweden 1998 2002 EQT Electrolux Divisional 4899 Bekaert Handling Group Denmark 1997 2005 Axcel Fam. Ingvartsen Private-to-private 3102 BERGSALA AB Sweden 2001 2003 Amplico Capital AB Lars Jarham Private-to-private 2410 Bewator AB Sweden 2002 2005 EQT Mellby gård Divisional 5147 Biovitrum AB Sweden 2001 2006 Nordic Capital Pharmacia Divisional 4674 Bluegarden Norway 2003 2007 Ratos Norska Posten Divisional 4646 BMH Marine AB Sweden 2003 2006 Catella Investments Babcock International Group Divisional 6311 Bodilsen Denmark 2006 2009 EQT Bodilsen Holding Divisional 7112 Bravida Sverige AB Sweden 1999 2006 Procuritas De-Listed from OMX Public-to-private 3109 C More Group AB Sweden 2003 2005 Nordic Capital Canal+ Divisional 4531 Callenberg Group AB Sweden 2001 2007 Segulah Expanda AB Divisional 5911 Carema Sweden 1996 2005 3i Sweden Orkla ASA Secondary 7415 Carema Sweden 2005 2010 3i Orkla and Savén Family Private-to-private 8810 Carmeda AB Sweden 2003 2005 HealthCap Norsk Hydro Divisional 7310 Carpark Sweden 2001 2006 Bridgepoint UBS AG Secondary 5221 CC System Sweden 2001 2005 Priveq Investment Employees Private-to-private 6202 Cefar Medical AB Sweden 2003 2006 Accent Mats Döring Private-to-private 2110 Cerbo Group AB Sweden 2003 2007 Vision Capital Ltd Morgan Grenfell PE Secondary 3089 Cermaq ASA Norway 1999 2005 Norgesinvestor Norska staten Government 1571 Cochlear Bone Anchored Sol. Sweden 1999 2005 CapMan Medivir Divisional 2660 Coffee Cup AB Sweden 1999 2006 Smaforetagsinvest Antagligen grundarna Private-to-private 5530 Collett Pharma (Axellus AS) Norway 2004 2005 Ferd Nycomed Divisional 4638 Com Hem Ab Sweden 2003 2005 EQT Telia Divisional 6110 Computas AS Norway 2003 2005 Eqvitec TurnIT Divisional 7221 Contex Holding As Denmark 1999 2007 EQT De-Listed Public-to-private 8690 Coor Service Management Sweden 2004 2007 3i Sweden Skanska Divisional 8110 Cramo Sweden 1996 1999 Industri Kapital Securum Government 3531 CyberCity AS Denmark 2000 2005 Advent Founder Private-to-private 6420 Dahl International Sweden 1999 2004 EQT De-listed from OMX Public-to-private 7010 Dalum Papir AS Denmark 1999 2007 LD Equity Stora Enso Divisional 1712 Damcos Sweden 2004 2006 3i Sweden Danfoss AS Divisional 2912 Dangard Telekom AS Denmark 2006 2007 Nordic Capital Several minority owners Private-to-private 4652
61
Sample firm Country Entry Exit PE Fund Vendor Vendor type NACE Dansk Droge (Axellus) Denmark 2002 2006 Polaris Moller Hansen family Private-to-private 1089 Disa holding Denmark 2005 2008 Procuritas A P Möller Divisional 2891 Dometic International sweden 2001 2005 EQT Elektrolux Divisional 2751 DT Group AS Denmark 2003 2006 CVC Capital De-Listed Public-to-private 5153 Duni AB Sweden 2001 2008 EQT Bonnier group Divisional 1722 Dynal Biotech Norway 2001 2005 Ratos Private Minority owners Private-to-private 4778 Dynapac AB Sweden 2005 2007 Altor Metso minerals Divisional 2952 Dyno Nobel Holding AS Norway 2000 2005 Industri Kapital De-listed Public-to-private 2461 Education & Entertainment Sweden 2000 2004 Segulah Private owner Private-to-private 4690 Eldon Sweden 2001 2006 EQT De-listed from OMX Public-to-private 5143 Elektrokoppar Sverige Sweden 1997 2007 Industri Kapital ABB Divisional 2744 ELFA Sweden 2006 2008 Industri Kapital Jensen Family Private-to-private 4652 Elitfönster Sweden 1999 2004 Triton Skanska Divisional 4673 Eltel Networks TE AB Sweden 2004 2007 Industri Kapital Capman Secondary 4521 Envac Centralsug Sweden 2001 2005 3i, Ratos ICA Handlarnas AB Divisional 4329 Epax (Pronova Biocare) Norway 2005 2007 Ferd Norsk Hydro Divisional 2059 eTRAVELi Norway 2007 2010 Norvestor Founders Private-to-private 7911 Euroflorist Sweden 2004 2007 Accent Equity Nordico Secondary 4622 Euroskilt AS Norway 2005 2007 Verdane DnB Nor & Four seasons venture Secondary 4711 F GROUP AS Denmark 1997 2006 Industri Kapital Thorn EMI Divisional 5245 Fastighets AB Tornet Sweden 2003 2005 Ratos Unilever Divisional 6820 Findus sweden 2000 2006 EQT Nestlé Divisional 1085 FlexLink sweden 1997 2005 EQT SKF Divisional 4669 Frigoscandia Distribution Sweden 2002 2005 Triton Prologis Trust Divisional 5210 Frösunda Sweden 2007 2010 Polaris Praktikertjänst Government 8810 Gant Sweden 2003 2006 3i Sweden Founders & L capital Private-to-private 5142 GCE Holding Sweden 2004 2005 Triton Charter Divisional 4669 GET AS Norway 2006 2007 Candover Liberty global Divisional 6110 Global Garden Products S.p.A. Sweden 2003 2007 AAC Capital UBS capital Secondary 2830 Global Refund AB Sweden 1999 2007 Apax Partners Cendant Divisional 6920 Grycksbo Sweden 2006 2008 Accent Equity Stora Enso Divisional 1712 Guide Konsult AB Sweden 2001 2006 Nordic Capital Framfab Divisional 7414 Haglöfs Sweden 2001 2010 Ratos Part of Atle (on OMX) Public-to-private 4649 Heimstaden Norway 2003 2005 Reiten & Co Founders Private-to-private 7020 Helly Hansen ASA Norway 1997 2006 Investcorp SA Aker RGI & Orkla Divisional 4642 HemoCue Sweden 1999 2007 EQT SKF Divisional 2651 Hemtex Sweden 2004 2007 Priveq Investment The Hemtex stores Private-to-private 4753 HMS Industrial Networks Sweden 2004 2007 Segulah SEB Secondary 2620 Hägglunds Drives Sweden 2001 2008 Ratos Part of Atle (on OMX) Public-to-private 2812 Icopal Denmark 2000 2007 Axcel De-Listed Public-to-private 2399 Ilva AS Denmark 2003 2007 Advent Linde family Private-to-private 4759 Inflight Service AB Sweden 2005 2010 Capman Vasatornet Invest / Ajeje BV Secondary 4799 INR Sweden 2007 2010 Accent Equity Founders Private-to-private 2319 Intelecom Norway 2008 2009 Norvestor De-listed Public-to-private 6190 Intrum Justitia AB Sweden 1998 2005 Industri Kapital De-listed Public-to-private 6619 Isaberg rapid Sweden 2007 2009 Segulah Industrivärden Divisional 2573 IVT Industrier Sweden 2002 2004 ABN Amro 3i group Secondary 2825 Jarowskij Enterprises AB Sweden 2002 2004 Amplico Capital AB Founders Private-to-private 9211 JH Tidbeck Sweden 1999 2004 Procuritas Finnveden Divisional 2593 Jotul AS Norway 2004 2006 Accent De-listed Public-to-private 2752 KappAhl Sweden 2004 2006 Accent KF Government 4771 Kid interiör Norway 2005 2009 Industri Kapital Gundersen Family Private-to-private 4751 Kilroy Travel International Denmark 1998 2005 Axcel Hyy group Private-to-private 7912 Kirudan AS Denmark 2006 2007 LD Equity Founder family Private-to-private 2110 Kongsberg Automotive AS Norway 1999 2001 Industri Kapital De-listed Public-to-private 3430 Kosan Crisplant Denmark 2004 2007 Segulah FKI Divisional 3320 Kronans Droghandel Sweden 2001 2002 3i, Ratos Part of Conglomorate Divisional 4773 Kwintet AB Denmark 1999 2005 Axcel De-listed Public-to-private 4642 LEKOLAR AB Sweden 2004 2007 Procuritas BRIO Divisional 5147 Lindab AB sweden 2001 2006 Ratos De-listed Public-to-private 7010
62
Sample firm Country Entry Exit PE Fund Vendor Vendor type NACE Louis-Poulsen Denmark 1999 2005 Polaris De-listed Public-to-private 4669 MacGregor AB Sweden 1998 2005 Industri Kapital Incentive / Gambro Divisional 4669 Modul-System Sweden 1999 2002 Segulah Harry Andersson Private-to-private 6202 Moss Maritime AS Norway 2000 2001 Reiten & Co Kvaerner Divisional 7420 Multicom Security Sweden 2001 2005 Industri Kapital Telia Divisional 6120 Myresjöhus AB Sweden 2005 2007 Industri Kapital Skanska Divisional 1623 Mölnlycke Healthcare AB Sweden 1997 2005 Nordic Capital SCA och Tamro Divisional 5146 Nederman Sweden 1999 2007 EQT Charter plc Divisional 2899 Nexus Marine Sweden 2006 2008 Amplico capital Silva AB Divisional 4649 NOBIA AB Sweden 1996 2002 Industri Kapital Stora Enso Divisional 3613 Nopco Paper Technology AS Norway 1999 2007 Nordic Capital HENKEL KGAA Divisional 2059 Norfoods Sweden 2000 2004 Segulah Hexagon Divisional 4632 Norse cutting & abandonment Norway 2005 2008 Hitec Vision Founders Private-to-private 0910 Novadan Denmark 2000 2004 Polaris DDMF Divisional 2041 Novasol-dansommer Denmark 2000 2002 Polaris Fredrik Heegard Private-to-private 6831 NVS Installation Sweden 2002 2006 Segulah NCC Divisional 4322 NVS Installation Sweden 2006 2008 Triton Segulah Secondary 4322 Nybron AB Sweden 2000 2005 Nordic Capital Skanska Divisional 7490
Närkes Elektriska Sweden 2006 2010 Segulah Delisted from OMX Public-to-private 4321 O Malmkvist AB Sweden 1999 2004 Procuritas Finnveden Divisional 2814 Ordning & Reda Sweden 1996 2003 Segulah Founder family Private-to-private 4762 Oriflame Cosmetics AB Sweden 1999 2004 Industri Kapital De-listede Public-to-private 5145 Orrefors Kosta Boda Sweden 1996 1998 EQT De-listed Public-to-private 4759 Paroc Panel System OY Sweden 1999 2003 Industri Kapital Partek Divisional 2682 Phadia AB Sweden 2004 2006 PPM Capital Pharmacia/Pfizer Divisional 2059 Pharmadule Emtunga AB Sweden 2000 2003 IDI AB Founder Private-to-private 2511 Plantasjen Norway 2001 2006 EQT Founder Private-to-private 4776 Plastal Sweden Sweden 2001 2004 Gilde SAPA Divisional 2932 Plymovent AB Sweden 2000 2006 Litorina Kapital Founder Private-to-private 2923 Powel Norway 1996 2007 Norvestor Founders Private-to-private 7221 Q-MATIC AB Sweden 2004 2007 Litorina Kapital, 3i Founders Private-to-private 2790 Quality-laboratories Sweden sweden 2000 2004 Ratos Ericsson, Norske veritas Divisional 6201 Rahbekfisk AS Norway 1999 2005 Gilde Albert fisher group Divisional 1520 Rationel Vinduer Denmark 2000 2004 Axcel Calkas Divisional 1623 Reslink Norway 2003 2006 Verdane Telenor Divisional 2829 Revus Energy ASA Norway 2003 2005 Hitech Vision Founders Private-to-private 1110 RGS 90 AS Denmark 2002 2006 CAPMAN Management Private-to-private 162 Sabro Refrigeration Denmark 1996 1999 EQT Laurizen Holding Private-to-private 4322 Sandå Måleri AB Sweden 1998 2007 Procuritas Henrik Schmidt Private-to-private 4544 Sats Holding AB Sweden 2002 2006 Nordic Capital Founders Private-to-private 9304 SBL Vaccin Denmark 2004 2006 3i Sweden Chiron Vaccines Divisional 2120 Scandinavian Beverage Group Norway 2000 2004 CVC Founders Private-to-private 5134 Scandinavian Garment Service Denmark 1998 2001 Procuritas Management Private-to-private 6340 Scandpower Petroleum Tech Norway 2003 2006 Hitech Vision Scandpower Divisional 7221 Scanvan AS Denmark 1998 2003 Procuritas Management Private-to-private 6024 Scribona Sweden 1999 2005 Norvestor Bure Equity AB Secondary 4221 Semantix Sweden 2006 2009 Accent Equity Lantz international Divisional 7430 Semper Sweden 2003 2006 Triton Arla Foods Divisional 1086 Sense EDM AS Norway 2005 2007 Selvaag Invest Founders Private-to-private 3011 Sicom AS Norway 2000 2007 Verdane Capital Founders Private-to-private 7221 Silva Sweden AB Sweden 2003 2004 Amplico Capital AB Tillander Family Private-to-private 3320 Skiinfo Norway 1999 2003 Northzone Management Private-to-private 6201 Sonans Norway 1999 2007 Norvestor Management Private-to-private 8042 Sonion AS Denmark 2000 2007 Nordic Capital Unknown financial investors Secondary 2611 Spaencom AS Norway 2001 2007 ATP Private Equity Ipsen family Private-to-private 2661 Spring Consulting AS Norway 2003 2006 Verdane Management Private-to-private 6201 Stenqvist Sweden 1999 2003 EQT Duni Divisional 1721 Stenqvist Sweden 2003 2008 Triton EQT Secondary 1721 Sterling Airlines AS Denmark 2005 2006 FL Group hf Maersk Air Divisional 6210 Struers Denmark 1998 2001 EQT Roper Industries Divisional 2841
63
Sample firm Country Entry Exit PE Fund Vendor Vendor type NACE Superfos (Pharma Division) Norway 1999 2005 Industri Kapital Superfos Divisional 2522 SWE-DISH Satellite Systems Sweden 2002 2007 3i / Litorina Founders Private-to-private 7420 Swedish Tool AB Sweden 2000 2006 AB Traction Management Private-to-private 5154 Sven-Axel Svensson Bijouterier Sweden 2000 2004 Accent Johnsson family Private-to-private 4778 Svenska Fönster Sweden 2000 2004 Axcel Singapore Telecom Divisional 1623 Synerco AB Sweden 2003 2007 CAPMAN Serco Group Divisional 7022 Synnøve Finden Norway 1996 2007 Norvestor Dag Swanstrøm Private-to-private 1551
SYSteam AB Sweden 1999 2006 Bure Equity AB EDB Ergogroup AS Divisional 7222 TA Teleadress Information Sweden 2001 2005 Industri Kapital Telia AB Divisional 7486 TAC Sweden 1998 2003 EQT Incentive AB / Gambro Divisional 2825 TDC SONG Norway 1996 2001 Reiten & Co Proffice Divisional 6421 TeamTec AS Norway 2003 2005 Verdane Four Seasons Venture Secondary 2821 Telelogic AB Sweden 1998 2001 Ratos Shareholders Public-to-private 5829 THALAMUS NETWORKS Sweden 2002 2007 AB Traction Stillström family Private-to-private 7222 Thermia Värme AB Sweden 1996 2004 Industrifondene Arbustum Invest Secondary 2923 THERMIA VÄRME AB Sweden 2004 2005 Procuritas Industrifonden Secondary 2825 Thule Sweden 1999 2004 EQT Incentive Divisional 2932 Thygesen Textile Group AS Denmark 1998 2006 Axcel Thygesen family Private-to-private 8211 TICKET TRAVEL GROUP Sweden 2002 2003 AB Traction Dag Tveterås Private-to-private 7415 Tradex sweden 2000 2006 EQT Management Private-to-private 2640 Trolltech Norway 2000 2006 Northzone Nokia Divisional 4120 TusenFryd AS Norway 2003 2007 Verdane Bonheur ASA Private-to-private 9321 Tvilum-Scanbirk Denmark 1996 2000 Axcel Management Private-to-private 3614 Tytex Group AS Denmark 1998 2006 Axcel Thygesen family Private-to-private 7487 Utfors Sweden 1999 2003 Norvestor Management Private-to-private 4791 Valinge Innovation AB Sweden 2003 2006 Nordstjernan AB Perván family Private-to-private 7420 Vasby Centrum Denmark 2001 2004 Doughty Hanson Väsbyhem AB Government 6820 Webcenter Unique Norway 1997 2001 Norvestor Mefjorden AS Private-to-private 7221 Wedins Sweden 2000 2003 Accent Kooperativa förbundet Government 4772 WELLTEC AS Denmark 2005 2007 Eqvitec Management Private-to-private 910 Wermland Paper Sweden 2003 2007 Procuritas Petersson family Private-to-private 1712 Wernersson ost Sweden 2004 2007 Accent Equity Magnus och Christer Pehrson Private-to-private 4633 West Fish Aarsæther AS Norway 2003 2005 Verdane Möller Investor AS Secondary 4666 Veststar As Norway 2005 2007 Marin forvaltning Austevoll AS Private-to-private 1020 Vest-Wood (Jeld-Wen) Denmark 2002 2005 Polaris Axcel Secondary 1623 VetXX AS Denmark 2004 2007 Montagu PE LEO Pharma Divisional 2442 VIA TRAVEL GROUP ASA Norway 2003 2005 Norgesinvestor Management Private-to-private 6330 Victor Hasselblad Sweden 1996 2003 Cinven Incentive AB Divisional 2670 Volden Group AS Norway 2005 2006 Origo Kapital AS Management Private-to-private 4533 Wonderland Norway 2004 2006 Altaria Foinco AS Private-to-private 3103 Voss of Norway AS Norway 2003 2006 Verdane Management Private-to-private 1107 VSM GROUP AB Sweden 1997 2005 Industri Kapital Electrolux Divisional 2894 The table reports buyout sample included in the thesis. Sample firm denoted by firm name or legal entity; financial figures are for the GUO. Entry- and exit- year as reported in press- releases or in M&A databases and refers to financial year. Vendor as reported in press releases and/or official records acquired from Orbis and similar databases. Vendor type assessed on the discretion of the authors. NACE classification as reported in official records and if not available determined by NACE of subsidiary holdings or on the discretion of the authors.
64