Secondary buyouts: Why buy and at what price?

Journal of Corporate Finance 18 (2012) 1306–1325

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Journal of Corporate Finance journal homepage: www.elsevier.com/locate/jcorpfin

Secondary buyouts: Why buy and at what price? Yingdi Wang ⁎ Mihaylo College of Business and Economics, California State University Fullerton, Fullerton, CA 92834, United States

a r t i c l e

i n f o

Article history: Received 16 June 2012 Received in revised form 2 September 2012 Accepted 3 September 2012 Available online 10 September 2012 JEL Classification: G23 G24 G32

Keywords: Private equity Buyouts Exits

a b s t r a c t This paper studies the economic logic and pricing of secondary buyouts, a form of leveraged buyout that has become increasingly popular. I investigate three potential explanations for secondary buyouts: efficiency gains, liquidity-based market timing, and collusion. The results are most consistent with the liquidity-based market timing hypothesis. Specifically, firms are more likely to exit through secondary buyouts when: the equity market is “cold”, the debt market condition is favorable, and the sellers face a high demand for liquidity. While this hypothesis shows a constrained optimal strategy for private equity firms, I do not find any strong efficiency gains for the target firms. Further, my analyses on pricing show that secondary buyouts are priced higher than first-time buyouts due to favorable debt market conditions. Overall, the results are consistent with the notion that secondary buyouts serve no purpose aside from alleviating the financial needs of private equity firms. © 2012 Elsevier B.V. All rights reserved.

1. Introduction A secondary buyout is a leveraged buyout (LBO) where the private equity sponsor, who had previously taken control of a target through an LBO, sells the target firm to another private equity firm or to a financial sponsor, instead of selling it back to the public market. While the general perception of LBOs is that private equity firms target mismanaged public corporations, which they can fix, repackage, and sell back to the public market, secondary buyouts have become increasingly popular in recent years. This type of deal as a fraction of all buyouts, has grown from 13% in the 1980s to 35% in the last five years (Pitchbook). In addition, they are playing an important role in the LBO recovery, as a part of the greater recovery from the recent financial crisis. 1 Over the years, practitioners and scholars have made many arguments in support of LBOs. However, the increase in secondary buyouts is a puzzling phenomenon given the usual arguments about the nature of the benefits of LBOs. LBOs are typically thought to create value through high leverage, high performance pay, and active monitoring of the portfolio companies' management (Jensen (1989), Lehn and Poulsen (1989), Kaplan (1989b), Smith (1990), Baker and Wruck (1991)). Providing that the initial buyout was successful, a second LBO should only be able to prompt minimal additional changes to the target firm's leverage and governance structure. Therefore, the phenomenon of secondary buyouts cannot be explained given the usual arguments about

⁎ Tel.: +1 657 278 2709. E-mail address: [email protected]. 1 According to Dealogic, 24 out of 60 LBO deals exited through secondary buyouts in the first quarter of 2010, and the two biggest LBO deals worldwide were both secondary buyouts. The two deals are: EQT Partners' $3.4 billion acquisition of the German publishing group Springer Science + Business Media from Cinven and Candover, completed on February 2, 2010, and Bridgepoint Capital's sale of Pets at Home, a British pet-shop chain, to Kohlberg Kravis Roberts (KKR) for 955 million pounds ($1.54 billion), completed on January 27, 2010. 0929-1199/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jcorpfin.2012.09.002

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the reasons for buyouts. The increasing number of secondary buyouts implies either that many initial buyouts failed, or that secondary buyouts are driven by a set of reasons that are different from those that are often cited in the buyout literature. Some analysts, moreover, have proposed an alternative explanation: secondary buyouts do not occur for the benefit of improving the target firms, but rather as a way for private equity firms to help each other solve their investing and exiting problems. For example, The Economist cautions “How well investors are being served by secondary buy-outs is less clear.... Once a business has been spruced up by one owner, there should be less value to be created by the next”. 2 These deals are often further portrayed as having high price tags and are pursued at the expense of the target firms. For instance, Simmons Bedding went through five LBOs in less than two decades. Each time the deal got larger, and the debt load got heavier. The company is now in Chapter 11 while various private equity firms have profited $750 million from the five buyouts.3 Given the large volume of secondary buyouts in recent years and the reasons for skepticism about these deals, understanding the logic behind secondary buyouts seems like an issue worth studying. What motivates secondary buyouts? Do these deals still benefit the target companies? How are they priced? Does the fact that the seller generally earns substantial profits on the deal mean that buyers overpay? To answer these questions, I analyze a hand-collected sample of UK firms. There are two main reasons for using a UK sample. First, the regulation that requires all firms, including private ones, in the UK to submit annual financial reports permits me to collect a large sample without the potential bias towards more successful deals. 4 Second, the UK market is, after the U.S., the second most active buyout market in the world, making it an ideal source for buyout data. To understand the economic logic behind secondary buyouts, I group possible explanations into three testable categories: 1) efficiency gains: pure efficiency gains that result from transferring control to another firm that can better utilize the assets, 2) liquidity-based market timing: private equity firms' efforts to “time” market conditions and their liquidity needs, and 3) collusion: collusion on the part of private equity firms to manipulate assets and returns. To evaluate the efficiency gains explanation, I use a three-year event window centered on the buyout year and study the effect of buyouts on the targets' size, operating cash flows, and profitability based on earnings. These three measures are chosen to account for firms' restructuring, their profits excluding financing decisions, and investors' profits, respectively. Efficiency gains can be thought of as the optimal explanation for secondary buyouts and is best rooted in the past literature. Perhaps surprisingly, the evidence supporting efficiency gains is at best mixed. While the buyout has positive effects on firms' operating cash flows, higher profits seem to be achieved through expansions, not by running the firms more efficiently. Once changes in size are taken into account, targets' profitability shows significant drops that are not due to decreasing economies of scale. To better account for firms' organizational structures and changes induced by macroeconomic conditions, I match secondary buyouts with similar first-time buyouts. The results are also mixed. Secondary-buyout targets show a better performance in generating operating cash flows but a worse performance in generating earnings. To examine the collusion motive, I calculate a cross-participation matrix that indicates which pairs of buyout firms tend to trade with one another. The collusion motive for secondary deals would imply that particular pairs of buyout firms trade with one another on multiple deals, so as to help each other exit deals at good prices. However, I do not find any such pattern in the cross-participation matrix. A comparison with 100,000 bootstrapped cross-participation matrices further shows no support for this motive. Given that firms have various ways of colluding, it is necessary to recognize that the lack of a trading pattern does not completely rule out collusion. However, the overall results suggest that collusion is not pervasive in that trading poorly performing companies at above-market prices is not the driving force behind secondary buyouts. Of the three hypotheses proposed above, I find the evidence to be most consistent with liquidity-based market timing motive. The results suggest that the popularity of secondary buyouts is a product of private equity firms trying to time the equity and debt market conditions, while the sellers are under their liquidity constraints. Specifically, I analyze firms' exit decisions and find that secondary buyouts are more likely to occur when the equity market does not perform well. During a “cold” equity market, measured by a low industry IPO volume, secondary buyouts can serve as alternative exits when other exit routes become less attractive. Moreover, a favorable debt market, measured by the size of the high-yield market, indicates a greater ability for private equity firms to borrow and invest in portfolio companies, and it has a positive impact on the likelihood of secondary buyouts. The private equity partnership's demand for liquidity also affects their exit choices. When private equity firms are attempting to raise a new fund, they have incentives to demonstrate their abilities to generate returns. The empirical results document that if the selling firms raised a new fund in the two years following the exit, then the exit is more likely to be through a secondary buyout. Moreover, when sellers need to raise a new fund during a “cold” equity market, they are more likely to choose to exit through a secondary buyout. The time since the original buyout also affects the likelihood of a secondary buyout. Presumably, as private equity firms hold their portfolio firms longer, their desire to exit increases. Empirically, the results suggest that the longer the firm has been held, the more likely it is to exit as a secondary buyout. All these results are consistent with the view that secondary buyouts tend to occur when private equity partnerships have relatively high demand for liquidity.

2

See the article, “Circular logic”, in the February 27, 2010 issue of The Economist. See The New York Times, November 28, 2009. 4 Private U.S. firms are not required to submit annual financial reports. As a result, focusing on U.S. targets would only yield a sample of approximately 30 firms. In addition, due to IPO-related reporting requirements, a U.S. sample would potentially face a selection problem that biases the results toward more successful firms. 3

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Because exits are only observable after an exit has been achieved, one natural concern is the sample selection problem. I use a Heckman (1979) sample selection model to address this issue. None of the estimates changes meaningfully so their implications remain the same. The above results are also robust with respect to alternative measures of equity and debt market conditions. To analyze deal pricing, I use the “comparable industry transaction method” described in Kaplan and Ruback (1995) and compare estimated enterprise value and enterprise multiples of secondary buyouts to those of first-time buyouts. This comparable industry transaction approach is essentially a difference-in-difference method that relies on estimated discounts/ premiums from companies in similar industries and involved in similar transactions. The results show that secondary buyouts are associated with a 19% higher enterprise value and an approximately 14% higher enterprise multiple, compared to first-time buyouts. However, higher deal prices are driven by other factors, most notably the debt market condition, for which the size of the high-yield market is used as a proxy. These results are consistent with the idea that more secondary buyouts occur when the market conditions are favorable. My findings add to the growing literature on private equity in law and finance. Prior studies emphasize the value creation and tax benefits that have resulted from public-to-private buyouts (buyouts of public corporations) (Jensen (1986), Baker and Wruck (1991), Kaplan (1989a), Smith (1990)). However, the composition of buyouts has changed over the last twenty years, and public-to-private deals only accounted for 6.7% of all LBOs between 1980 and 2007 (Strömberg (2008)). As a result, previous findings are no longer applicable to the current state of buyouts. Guo et al. (2011) show that less value is created in recent deals; results in this paper help to explain why. In addition, secondary buyouts are priced highly due to a favorable debt market, so without substantial value creation, the high prices paid on these deals warrant further concerns. My paper also contributes to the literature on market timing. Studies have found that entrepreneurs often choose IPOs within a window of opportunity (Pagano et al. (1998), Lerner (1994), Lucas and McDonald (1990)). Takeovers can also be related to managerial timing of market overvaluations of their firms (Shleifer and Vishny (2003), Rhodes-Kropf et al. (2005)), and a firm's capital structure may be a result of the management's effort to time the equity market (Huang and Ritter (2009), Baker and Wurgler (2002), Hovakimian et al. (2004)). Like entrepreneurs and managers, private equity firms also take advantage of favorable IPO market conditions in reverse LBO transactions (Cao (2011)). In this paper, I provide evidence showing that private equity firms “time” their exit decisions to capital markets while being constrained by their liquidity needs. There are currently three other papers on secondary buyouts. Achleitner and Figge (2011), Sousa and Jenkinson (2012), and Bonini (2010) all examine the operating performance, returns, and financial structure of secondary buyouts to various degrees. This paper was independently done at roughly the same time as these papers. Although analyzing the same phenomenon, this research is different from the listed papers in several regards. First, unlike the other papers, the firm-specific data used in this paper is hand collected from the full set of UK firms. Databases, such as Amadeus, provide incomplete coverage. 5 More importantly, this hand-collected dataset allows me to differentiate between subsidiary changes surrounding a buyout, rather than relying on post-buyout consolidated accounting numbers. This step is important because post-buyout consolidated financial statements can contain irrelevant information, and therefore yield inaccurate comparisons (see Appendix A for details). Second, this paper also provides a more comprehensive analysis of firms' exit strategies. In addition to linking secondary buyouts to the debt market, I provide evidence showing that the equity market performance and sellers' liquidity needs all play a role in firms' exit decisions. Finally, using a (more precise) difference-in-difference method, I show that secondary buyouts' higher prices are in fact driven by debt market conditions. The remainder of this paper is organized as follows. Section 2 discusses the three possible motives in detail. Section 3 describes the data and summary statistics. Section 4 evaluates the validity of each motive. Section 5 examines pricing of secondary deals, and Section 6 concludes. 2. Potential motivations for secondary buyouts There are many possible reasons for firms to transfer assets. Based on their nature and implications, I group the possible reasons into three general categories. In this section, I describe in more detail the three categories and related literature. 2.1. Efficiency gains LBOs are often argued to create value due to high leverage, improved governance structures, and operational engineering (Jensen (1989), Lehn and Poulsen (1989), Kaplan (1989b), Smith (1990), Baker and Montgomery (1994), Baker and Wruck (1991), Acharya et al. (2010)), and the value created through buyouts is evident in the target firms' operating performance gains after the buyout (Jensen (1989), Kaplan (1989b), Smith (1990), Baker and Wruck (1991)). However, LBOs are not without criticisms. For example, Phalippou (2009) and Perry and Williams (1994)) both argue that this value creation is wealth transferred from other stakeholders, and Guo et al. (2011) show that operating performance gains have decreased in recent LBO deals. Even if there are efficiency gains in the original buyout, there is no reason why there could not also be efficiency gains in the secondary one as well. If the private equity firm exited early before the full benefit could be captured, then a second buyout can complete the efficiency gains started in the original buyout. Alternatively, it is possible that buyout firms have different skills so each could provide a different type of value; for example, the first buyout firm could specialize in cutting costs or modernizing 5 For example, out of the 140 firms for which I was able to collect accounting information, only approximately 40 firms appear in Amadeus. Consequently, information obtained from databases may result in a bias.

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production, while the second buyout firm could be particularly good at expanding operations. Efficiency gains as a motive for secondary buyouts then implies an increase in firms' operating performance after the buyout. 2.2. Liquidity-based market timing Alternatively, it is possible that secondary buyouts are not carried out for the purpose of continued improvement of the target firms. Rather, they are by-products of capital market conditions and private equity firms' incentives to exit deals quickly. As exit options, IPOs and sales to strategic buyers are the most obvious strategies, but they are not always attractive options. A “hot” equity market makes it more attractive for entrepreneurs and managers to issue equities and carry out takeovers (Baker and Wurgler (2002), Pagano et al. (1998), Lerner (1994), Lucas and McDonald (1990), Shleifer and Vishny (1986) and Rhodes-Kropf et al. (2005)). The equity market conditions also render the same incentives for private equity firms. Research has shown that private equity firms time reversed LBOs to the IPO market (Cao (2011)); venture firms' exit strategies can also be linked to the equity market performance (Giot and Schwienbacher (2007)). When the equity market is “cold”, private equity firms have less incentives to exit through IPOs and sales to strategic buyers. During this time, secondary deals can serve as alternatives, as the private equity acquirers' purchasing power largely rests on their abilities to borrow. Moreover, they are prone to the over-investment problem when the debt market condition is favorable (Axelson et al. (2009, in press)). Ideally, private equity firms should hold the portfolio companies until the maximum payoff could be achieved. However, because funds have a finite life, funds must exit eventually. Therefore, private equity firms generally look at exit options soon after acquiring a portfolio firm. In addition, much of a private equity partnerships' compensation from a particular deal comes from its effect on the partnership's future fundraising, so they have incentives to exit quickly so as to be able to start fundraising quickly (see Chung et al. (in press)). When the demand for liquidity increases, the incentives to exit also increase. Such a situation can arise when the portfolio company has been held for a long time or when the sellers need to raise a new fund, in which case exiting can be an important indication of the private equity firms' abilities. As a result, private equity firms' exit choices are also constrained by their liquidity needs. 2.3. Collusion The third possibility, the collusion motive, implies that secondary buyouts are a result of collusion on the part of private equity firms, as a number of factors in this market seem to create a favorable environment for trading assets. For example, the industry is opaque and lacks regulation, and portfolio companies are being traded among a limited number of private equity firms. The primary concerns about collusion are: First, private equity firms are trading bad assets among each other; second, private equity firms are exchanging assets at above-market prices so as to artificially boost returns. As a result, secondary buyouts can be pursued at the expense of both the investors and the target firms. The higher the purchase price, the less profit the acquirer's investors would receive upon the sale of the portfolio company. More importantly, if secondary buyouts are used as means of exiting poorly performing portfolio companies, buyouts of such firms would ultimately harm the target companies. If prices are higher at each transaction point, it is very likely that the firm's debt burden would correspondingly increase. Not all firms can sustain the amount of debt incurred in an LBO transaction. Without improvements in operations, going through multiple LBOs could push the poorly performing firm closer to financial distress. As previously mentioned, the five buyouts of Simmons Bedding is one such case. Each buyout had a higher purchase price, added more debt to the firm, and failed to improve the operating performance. The company eventually filed for Chapter 11. Therefore, it is worthwhile to investigate the possibility of collusion. 3. Data and sample statistics I use a sample of UK buyout targets for two main reasons. First, accounting data is needed to examine firm characteristics. Unlike the U.S. firms, all firms in the UK are required to submit annual financial reports. Second, the UK has the second most active LBO market in the world, after the U.S., and the UK market is still growing in size, making it an ideal source for studying secondary buyouts. Because the sample is comprised of UK firms, I use Zephyr (published by Bureau van Dijk), instead of the Security Data Corporation's (SDC) Mergers and Acquisitions database, to compile a list of LBOs completed in the UK between 1997 and 2008. According to a report by LexisNexis, Zephyr has better coverage of deals in Europe, as well as of smaller deals. Secondary LBOs are identified as cases where the buyer and the seller are both private equity firms. I further restrict the sample to completed cases where a majority of shares were acquired. This yields a total of 485 secondary buyouts. 6 With the basic set of secondary buyouts identified, accounting data are hand-collected from the target firms' annual financial reports. One key issue with using UK data is that many firms in the UK are not required to report consolidated financial statements, which are essential to evaluating the performance of a buyout. Of the 485 secondary buyouts occurring during the same time period, 140 have consolidated financial statements. Similar steps are used to collect accounting information for first-time buyouts. Of the 1053 UK first-time buyouts between 1997 and 2008, I am able to gather a sample of 465 first-time buyouts with consolidated financial statements. I describe in detail the steps used for data collection in Appendix A. 6

As a verification of Zephyr's classification, all transactions are later cross-checked from the targets' annual financial reports.

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Table 1 Secondary LBO activities in the UK. Panel A: LBO activities across time Year

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Total

Number of transactions

Percentage

All LBOs

Secondary LBOs

243 269 427 374 413 382 475 477 436 470 541 401 4908

10 20 45 44 25 25 28 53 43 65 83 44 485

4.1% 7.4% 10.5% 11.8% 6.1% 6.5% 5.9% 11.1% 9.9% 13.8% 15.3% 11.0% 9.9%

Panel B: Industry distribution Fama–French industry

Consumer nondurables Consumer durables Manufacturing Energy HiTec Telecom Shops, wholesale, retail, and some services Health Utilities Others Non-classified Total

All LBOs

All secondary LBOs

Secondary (final sample)

N

Percentage

N

Percentage

N

Percentage

849 143 918 39 476 54 672 113 29 1551 64 4908

17.3% 2.9% 18.7% 0.8% 9.7% 1.1% 13.7% 2.3% 0.6% 31.6% 1.3% 100.0%

45 15 81 5 45 7 64 23 5 151 44 485

9.3% 3.1% 16.7% 1.0% 9.3% 1.4% 13.2% 4.7% 1.0% 31.1% 9.1% 100.0%

17 4 26 1 11 5 30 7 2 36 1 140

12.1% 2.9% 18.6% 0.7% 7.9% 3.6% 21.4% 5.0% 1.4% 25.7% 0.7% 100.0%

The table shows the distribution of buyouts in the UK. Panel A reports the number of LBOs completed in the UK between 1997 and 2008. All LBOs represents the total number of completed LBOs, where the acquirer sought more than 50% of the target firm's shares. Secondary LBOs is the total number of secondary buyouts identified from Zephyr. Percentage shows the number of secondary buyouts as a percentage of all LBOs in a year. Panel B reports the industry distribution of all LBOs, all secondary buyouts, and secondary buyouts with consolidated financial statements (Column Secondary (final sample)). Industries are categorized according to the Fama–French 10 industry classification, based on the target firm's primary business. The Others industry includes firms which operate in mines, construction, building material, transportation, hotels, bus services, entertainment, and finance. The Non-classified group contains firms without a Fama–French 10 industry classification.

Non-firm-specific variables are obtained from various sources. Industry IPO volume is collected from SDC. Information on leveraged loans is from Thompson Reuters' Datastream. I construct buyout sponsors' information, such as fundraising activities, from Preqin, a private equity database that reports 9523 funds and covers about 70% of all buyouts and venture capital raised by private equity firms. The accounting information of private firms, which I use to adjust for industry performance, is collected from Amadeus. For an overview of the sample, Table 1 presents statistics on transaction numbers and industry distribution. In Panel A, LBO transaction numbers are listed by year. As we can see from this panel, the number of LBOs in the UK has grown from 243 in 1997 to 401 in 2008, and buyouts exhibit a procyclical pattern. Panel B of this table shows the Fama–French 10 industry distribution of all LBOs, all secondary LBOs, and the 140 secondary LBOs in the final sample (Secondary (Final sample)). 7 Firms that do not have a Fama–French 10 industry classification are included in the row labeled “Non-classified”. Similar to the distribution of all secondary LBOs, the final sample is concentrated in manufacturing, shops, wholesale, retail, and the Others category. Since the final sample is restricted to firms with consolidated statements, it is possible that the sample is not representative of the market. To check for this possibility, Table 2 describes deal outcomes and duration for all sample firms. Similar exits and durations of firms in the final and initial sample should mitigate the concern over an unrepresentative sample. Deal outcomes are identified as of February, 2010. First-time LBOs are further divided to private-to-public and public-to-public deals. As Panel A indicates, a majority of the deals have not achieved an exit. The high percentages are consistent with the findings in Strömberg (2008). Most of the firms that have exited were sold to strategic buyers, followed by secondary buyouts. IPOs only account for a small fraction of exits. This result holds for both secondary and first-time buyouts. Panel B presents the median duration of each deal type in number of months. Most firms, regardless of secondary or first-time LBOs, are being held for more than 30 months

7

I use the Fama–French 10 industry instead of the Fama–French 48 industry classification because some private firms only have two-digit SIC codes.

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Table 2 Deal outcomes and duration. Outcome

Secondary LBOs

First-time LBOs

All

Final sample

All

Public-to-private

Private-to-private

Panel A: Deal outcomes IPO Sold to strategic buyer Secondary buyout Sold to management Bankruptcy No exit Total

3 (0.6%) 45 (9.3%) 37 (7.6%) 10 (2.1%) 3 (0.6%) 387 (79.8%) 485 (100.0%)

2 (1.4%) 28 (20.0%) 22 (15.7%) 2 (1.4%) 0 (0.0%) 86 (61.4%) 140 (100.0%)

3 (0.7%) 87 (18.7%) 47 (10.1%) 9 (1.9%) 3 (0.7%) 316 (68.0%) 465 (100.0%)

2 (1.7%) 26 (21.7%) 25 (20.8%) 6 (5%) 1 (0.8%) 60 (50.0%) 120 (100.0%)

1 (0.3%) 61 (17.7%) 22 (6.4%) 3 (0.9%) 2 (0.6%) 256 (74.2%) 345 (100.0%)

Panel B: Duration IPO Sold to strategic buyer Secondary buyout Sold to management Bankruptcy No exit Total

22 37 33 48 39 – 35

23 34 34 44 – – 32.5

29 35 33 53 39 – 37

35 39 32 51 39 – 39

12 32 37 57 41.5 – 32

The table reports deal outcomes and duration as of February 2010. Under Column Secondary LBOs, All indicates the full sample of secondary buyouts, whereas Final sample only reports deal outcomes of firms in the final sample with consolidated financial statements. First-time LBOs are further divided to public-to-private and private-to-private deals. Panel A shows the number and percentage of each deal outcome. Panel B shows the median months to outcome. “–” indicates that no exit has been observed within the sample time period.

before an exit is achieved. Overall, Tables 1 and 2 show that the industry distribution, exits, and deal durations of the final sample all resemble those of the entire UK market. 4. Possible explanations for secondary buyouts In this section, I examine the three possible motives for secondary buyouts: 1) efficiency gains, 2) liquidity-based market timing, and 3) collusion. 4.1. The efficiency gains motive To test for efficiency gains, I examine year-by-year performance changes within a three-year event window centered on the buyout year. The three-year window is chosen to minimize potential noise from using a longer event window, while still allowing enough time to examine the trend going into the buyout. Moreover, compared to Ordinary Least Squares (OLS), which is highly sensitive to the end points, this method focuses on more detailed actual changes induced by the buyout. Due to consolidated reporting, irrelevant units may be included in firms' post-buyout financial statements. To account for this issue, I divide the sample into two categories: Same subsidiaries and Different subsidiaries, based on whether the portfolio company's subsidiaries changed from one full fiscal year prior to the buyout to one full year after it. The Different subsidiaries group is excluded from all subsequent analyses of the firms' operating performance changes (see Appendix A for details). If secondary buyouts were driven by efficiency gains, the target firms should exhibit post-buyout operating performance gains. Table 3 reports percentage changes in the targets' size, operating cash flows, and profitability. Due to the sample size, only median percentage changes are examined. To control for industry-wide effects, industry-adjusted medians are computed by subtracting the corresponding medians of the Fama–French 10 industry. Two-tailed Wilcoxon signed-rank tests are used to assess whether the percentage changes are significantly different from zero. 1, 2, and 3 each represent one, two, and three full fiscal years prior to or after the buyout, indicated by ‘−’ and ‘+’ signs. To mitigate the impact of accounting writeups of assets from mergers and acquisitions, fixed assets and sales, instead of total assets, are used as proxies for size. As the table indicates, target firms follow an upward trend going into the buyout. Firms' size, operating cash flows, and profitability all increase prior to the buyout. After the buyout, earnings before interest, taxes, depreciation and amortization (EBITDA) continues to grow at a rate of 16.3%, 13.9%, and 19.3%, at one, two, and three years after the buyout, respectively. Industry-adjusted EBITDA exhibits the biggest increase of 16.5% at year + 1. The number declines to 8.5% in the third year after the buyout. Nevertheless, the buyout seems to have a positive impact on the firms' cash flows. Increases in profits can be achieved through running the firm more efficiently or through expansions, which provide artificial boosts to a firm's sales. Prior literature shows that buyouts have a positive impact on firms' efficiency. However, changes in firms' size and profitability shown here are inconsistent with improved efficiency. After the buyout, sales and fixed assets show significant growth rates, while profitability drops significantly. More specifically, industry-adjusted changes in EBITDA/fixed assets, EBITDA/sales, earnings/sales (profit margin), and ROA all show significant decreases in the second and third year after the

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Table 3 The effect of secondary buyouts on the target firms' operating performance. Panel A: All secondary-buyout targets Same subsidiaries (N = 94) from year i to year j

A. Size measures Fixed assets Median change Industry-adjusted change Sales Median change Industry-adjusted change B.1 Operating cash flow EBITDA Median change Industry-adjusted change EBITDA/sales Median change Industry-adjusted change EBITDA/fixed assets Median change Industry-adjusted change B.2 Profitability ratios Earnings/sales Median change Industry-adjusted change ROA Median change Industry-adjusted change

−3 to −2

−2 to −1

At year −1 5.4%*** 5.5%*** At year −1 15.4%*** 17.1%***

13.50 1.7%*** 2%*** 30.09 11.4%*** 12.2%***

At year −1 14.2%* 14.3%* At year −1 0.4% 0.3% At year −1 3.7%* 4.6%

4.13 12.7%*** 12.5%*** 0.13 0.7% 0.3% 0.34 5.2%*** 4.7%**

At year −1 0.9%* 0.8% At year −1 1.1%*** 1.4%**

0.07 0.9%*** 0.6% 0.09 1.5%*** 0.6%

−1 to +1

−1 to +2

−1 to +3

87.9%*** 93.3%***

96.1%*** 97.8%***

113%*** 114.8%***

12.0%*** 13.0%***

19.7%*** 20.4%***

36.9%*** 38.2%***

16.3%*** 16.5%***

13.9% 11.5%

19.3%*** 8.4%

1.2%* 0.5%

−0.4% −1.5%***

−0.6% −1.5%**

−12.4%*** −22.2%***

−15.3%*** −42.3%***

10.4%*** −28.3%***

−2.9%*** −4.1%

−0.6%*** −1.9%***

−1.6%*** −3.2%***

−5.3%*** −7.9%***

−6.7%*** −8.2%***

−3.8%*** −10.4%***

Panel B: Large vs. small firms Small (N = 47) from year i to year j −3 to −2 A. Size measures Fixed assets Median change Industry-adjusted change Sales Median change Industry-adjusted change B.1 Operating cash flow EBITDA Median change Industry-adjusted change EBITDA/Sales Median change Industry-adjusted change EBITDA/fixed assets Median change Industry-adjusted change B.2 Profitability ratios Earnings/sales Median change Industry-adjusted change ROA Median change Industry-adjusted change

−2 to −1

Large (N = 47) from year i to year j

−1 to +1

−1 to +2

−1 to +3

−3 to −2

At year −1 6.77 7.6%* 0.9% 8.1%* 1.9%*

92.8%*** 94.5%***

96.1%*** 97.8%***

113.0%*** 114.8%***

At year −1 16.73 25.6%*** 12.8%*** 26.4%*** 15.0%***

9.5%*** 11.1%***

14.8%*** 15.9%***

At year −1 1.85 26.6% 4.9% 30.1% 5.1%

20.6%** 21.2%**

At year −1 0.1 0.2% 0.7% −0.2% −0.1%

−2 to −1

−1 to +1

−1 to +2

−1 to +3

At year −1 48.83 3.4%** 2.1%*** 3.4%** 2.1%***

79.0%*** 79.3%***

94.3%*** 94.3%***

116.9%*** 112.8%***

28.4%*** 27.0%***

At year −1 71.82 12.6%*** 8.5%*** 13.0%*** 8.6%***

14.6%*** 14.6%***

26.7%*** 27.4%***

41.1%*** 42.5%***

18.8% 19.1%

−0.3% −10.8%

At year −1 10.96 13.5% 12.8%*** 13.6% 12.9%***

15.6%*** 15.3%***

16.3% −1.1%

41.2%** 10.1%

1.5%*** 1.1%**

−0.2% −0.7%

−0.6% −1.5%*

At year −1 0.16 0.7% 0.7% 0.8% 0.4%

0.8% −0.1%

−1.3%* −5.4%**

−2.4%* −1.7%

At year −1 0.5 3.7% 6.3%*** 6.0% 5.8%**

−27.2%*** −31.6***

−24.7*** −44.7%***

−20.5%*** −35.8%***

At year −1 0.26 3.6% 4.1%*** 3.7% −0.4%

−7.0%*** −15.3%***

−14.8%*** −39.4%***

−6.1%*** −20.9%***

At year −1 0.06 0.7% 0.6%** 0.2% 0.2%

−1.8%*** −2.2%***

−4.4%*** −7.2%***

−4.1%*** −6.7%***

At year −1 0.08 1.2%* 1.3%*** 1.1% 0.9%

−5.5%*** −5.9%***

−9.2%*** −13.1%***

−6.4%*** −9.7%***

At year −1 0.12 1.1% 1.8%** 1.5% 0.7%

−7.1%*** −7.9%***

−9.9%*** −14.0%***

−10.7%*** −15.8%***

At year −1 0.08 1.1%** 1.3%*** 1.1%* 0.5%

−3.1%*** −7.4%***

−5.0%*** −14.2%***

−4.9%*** −17.1%***

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Table 4 Differences in operating performance changes between secondary and first-time buyouts.

A. Size measures Fixed assets Median change Industry-adjusted change Sales Median change Industry-adjusted change B.1 Operating cash flow EBITDA Median change Industry-adjusted change EBITDA/sales Median change Industry-adjusted change EBITDA/fixed assets Median change Industry-adjusted change B.2 Profitability Ratios Earnings/sales Median change Industry-adjusted change ROA Median change Industry-adjusted change Number of secondary buyout targets Number of first-time buyout targets

Secondary vs. matched first-time (2) From year i to year j

Secondary vs. all first-time From year i to year j

Secondary vs. matched first-time (1) From year i to year j

−1 to +1

−2 to +2

−1 to +3

−1 to +1

−2 to +2

−1 to +3

−1 to +1

−2 to +2

−1 to +3

20.2% 27.2%

23.1% 33.4%**

52.9%** 60.4%**

31.5% 37.7%*

54.6%* 54.6%***

71.0%*** 70.0%***

−11.3% −6.7%

8.8% 13.2%

17.9% 18.8%

−0.6% −2.2%

−4.8% 1.4%

20.7%*** 21.6%***

−5.7% −7.1%

−2.3% 0.8%

13.4% 7.2%

−0.2% −2.7%

−9.2% −11.7%

17.7% 11.8%

23.8%*** 23.8%***

9.9% 5.3%

43.2%*** 31.9%*

11.4% 9.4%

34.9% 20.1%

36% 20.1%

−9.8% −11.8%

1.3% −1.0%

29.3% 18.9%

1.8%*** 2.7%***

0.3% 0.3%

1.2% 3.2%

0.3% 2.9%**

0.7% 1.7%

0.4% 4%

0.9% 1.6%*

0.6% 2.2%

2.4%* 4.5%**

17.1%*** 32.1%***

12.1%* −0.9%

22.9%** 29.6%*

14.6%* 31.4%***

20.5%*** −1.7%

10.7%* 18.7%*

20.5%*** 25.5%***

21.9%*** 16.5%**

31.7%*** 75.3%***

−0.2% 1.3%

−0.5%*** 0.8%*

−0.5%** 1.9%

−0.6% 1.2%

−0.1%** −1.0%*

−1.0%* 2.0%

−0.5% −0.7%

−0.9%** −0.4%**

−1.0%** 0.2%

−0.3% 2.0%** 94 465

−1.7% 0.9%

−0.7% 1.3%

−1.7% 3.6%** 59 59

−1.1% 6.7%

−1.4 0.3%

2.0%** −2.9% 73 185

3.6%** −3.3%**

−2.6% −2.8%

The table shows the differences in median changes in operating performance for secondary (Same subsidiaries) and first-time buyouts. Differences are computed as the changes for secondary targets from year i to year j minus the changes for first-time targets from corresponding years. A positive number indicates that the change for secondary targets is bigger than it is for first-time targets. Secondary vs. all first-time presents the difference between secondary buyouts and the full sample of first-time buyouts, which include public-to-private and private-to-private buyouts. Secondary vs. matched first-time (1) shows results from matched secondary buyouts and first-time private-to-private buyouts based on the target firm's Fama–French 10 industry classification and total assets at one year prior to the buyout. This match results in 35 first-time buyouts being matched with multiple secondary buyouts. For those multiple matches, firms closest in size are chosen, resulting in 59 unique firms being matched. Since private-to-private targets are much smaller firms, the rest of secondary buyout target firms are not rematched. Secondary vs. matched first-time (2) presents matched results based on the year of buyout completion and the targets' industry classification. Wilcoxon rank-sum test is performed for the median difference between secondary and first-time buyouts. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

buyout. Also worth noting in this panel is the volatility in cash flows and profitability that cannot be observed from OLS regressions. Because targets show a decline in profitability when scaled by size, the lack of efficiency gains could be contributing to firms reaching decreasing economies of scale. Therefore, I divide the firms into “small” and “large” firms based on the median value of total assets at one year before the buyout. Panel B of Table 3 presents the results. As this panel shows, decreasing economies of scale is not the cause of the decline in profitability. Both small and large firms show significant drops in profitability. Overall, Table 3 shows that firms' operations follow an upward trend going into the buyout. After the buyout, firms' operating profit increases but not relative to the size of the firm. In other words, although profits go up after the buyout, the target firms' efficiency does not.

Notes to Table 3: The table presents the raw and industry-adjusted median percentage changes in operating performance from year i to year j for secondary-buyout targets with consolidated financial statements. Industry-adjusted percentage change is computed as the change for the buyout target minus the median value for a sample of private firms in the same industry in the UK for a given fiscal year. Whether firms operate in the same industry is determined according to the Fama–French 10 industry classification. Years 1, 2, and 3 each represent the first, second, and third full fiscal year before (indicated by “−”) or after (indicated by “+”) the buyout year. At year − 1 indicates the median value at one full fiscal year prior to the buyout year. The final sample of 140 secondary-buyout targets is further divided to Same subsidiaries and Different subsidiaries based on subsidiary changes within a one-year window centered on the buyout year. The Different subsidiaries group is excluded from all subsequent analyses on the target firms' operating performance (details provided in Appendix I). Panel A shows results for firms whose subsidiaries did not change from one full year before the buyout year to one full year after it. Panel B shows the percentage changes for small and large firms within the Same subsidiaries group. The median value of total assets from one full year prior to the buyout is used as the cutoff point for Large and Small. Twotailed Wilcoxon signed-rank tests are performed to test whether the percentage changes are significantly different from zero. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

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One potential explanation for the lack of efficiency gains is that the performance benchmark is inappropriate, since buyout targets' organizational structure and debt load are very different from those of their industry peers. For a better benchmark, I also examine the differences in operating performance changes between secondary and first-time buyouts. Table 4 presents the results. Differences are computed as the median percentage changes in secondary targets from year i to year j minus the median percentage changes in corresponding first-time targets. Therefore, a positive number indicates a better performance of secondary targets, and a negative number implies the opposite. The results are again mixed. When comparing with all first-time buyouts, differences in both the raw percentage change and industry-adjusted change show that secondary buyouts generate higher EBITDA, both in levels and as a fraction of the size of the firm. However, changes in the two profitability ratios do not show significant differences between the two groups. First-time buyouts include both public-to-private and private-to-private buyouts. To account for the differences in the incentive structure and the buyout restructuring process between public and private firms, 8 I further match targets of secondary buyouts to private-to-private buyouts based on: 1) the Fama–French 10 industry and size before the buyout (results presented in Column Secondary vs. Matched first-time (1)) to control for industry and firm size differences, and 2) the Fama–French 10 industry and buyout year (results presented in Column Secondary vs. Matched first-time (2)). The second set of criteria is designed to account for macroeconomic conditions and time trend, as there are more secondary buyouts in recent years. The two columns show that there are no significant differences in performance changes for firms matched on industry and size. For firms matched on industry and buyout year, changes in EBITDA also do not differ. However, secondary-buyout targets show significantly better performance in EBITDA/fixed assets, while profit margin and return on assets (ROA) show that first-time buyouts are more profitable. 9 Overall, the results presented in this section show mixed evidence in support of the efficiency gains motivation. Secondarybuyout targets have higher profits after the buyouts. However, these target firms show significant drops of profitability ratios and EBITDA when scaled by the size of the firm. In light of the literature's emphasis on value creation, these results suggest that secondary buyouts do not improve the efficiency of the target companies.

4.2. The liquidity-based market timing motive In this section, I turn to the second hypothesis: liquidity-based market timing. To test for this hypothesis, I analyze the probability of firms exiting through secondary buyouts, in comparison to IPOs and sales to strategic buyers when market conditions vary and when the sellers' liquidity needs change. Probit models are used to predict the probabilities. The dependent variable equals one if the firm exited through a secondary buyout, and zero if it went IPO or was sold to a strategic buyer. 10 Industry IPO volume in the UK in the exit year is used as a proxy for equity market conditions. The higher the industry IPO volume, the better the equity market. If private equity firms are timing the market, this variable should be negatively associated with the probability of exiting through a secondary buyout. To measure the condition of the debt market, I use the size of the high-yield market in the year of exit, and this variable should be positively correlated with the probability of exiting through a secondary buyout. Other control variables include firm and industry characteristics. Year dummies are added to all regressions. Marginal effects and robust standard errors clustered by industry are reported in Table 5. The results show evidence in support of secondary buyouts varying with capital market conditions. A “hotter” equity market is significantly negatively correlated with firms exiting through secondary buyouts. More specifically, the estimated marginal effect shows that an increase in one unit of industry IPO volume significantly decreases the likelihood of firms choosing secondary buyouts by 12.9%. The debt market condition, on the other hand, is significantly positively associated with the probability of firms exiting through secondary buyouts. This is consistent with the notion that greater borrowing ability is correlated with a higher number of secondary buyouts. Regression estimates also show that size, measured by post-buyout fixed assets, is significantly related to firms' exit choices, with bigger firms being more likely to exit through secondary buyouts. The lack of significance of targets' profitability, measured by three-year EBITDA growth, also provides meaningful interpretations in that it suggests that firms are not exiting worseperforming target companies through secondary buyouts. The obvious problem in using probit estimation is the issue of selection, since exit routes can only be identified if the firm has exited. Therefore, I follow Heckman (1979) and use a Heckman selection model to correct for this potential problem. In the first stage, I take the full sample of buyouts and predict which firms are more likely to exit, using a proxy for sellers' reputation, firms' EBITDA/sales before the buyout, and firms' total assets before the buyout as a measure of the economic significance of the buyout. 8 Part of the gains from public-to-private buyouts comes from mitigating the agency problems in public corporations. Private firms, on the other hand, do not face the same agency problems in public corporations. Moreover, unlike public-to-private buyouts, private firms experience substantial growth after the buyout (Chung (2009)). 9 A lower profit margin and ROA could be attributed to secondary buyouts having higher leverage. To mitigate the impact of leverage, I also measured the two profitability ratios by (earnings + interest paid)/sales and (earnings + interest paid)/fixed assets. Although the results show smaller differences, secondary buyouts still significantly underperform matched private-to-private buyouts. 10 I identify a total of 303 exits. Because a comparison of operating performance changes is no longer needed, the 45 secondary-buyout targets with different post-buyout subsidiaries are added back into the sample, and the 140 secondary buyouts are all exit routes chosen by the previous private equity owner. Out of first-time LBOs, 163 firms have achieved complete exits.

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Table 5 The effect of market conditions on firms' exit choices. Probit

Heckman selection

(1) Industry IPO volume HY market size Log (firm assets after buyout) EBITDA growth Industry sales growth

(2)

(3)

1.617*** (0.158) 0.084*** (0.012) −0.002 (0.001) −0.100 (0.138)

−0.119*** (0.025) 1.443*** (0.099) 0.073*** (0.013) −0.002 (0.001) −0.049 (0.120)

−0.129*** (0.026)

0.075*** (0.016) −0.002 (0.001) −0.057 (0.126)

(Exiting)

Seller reputation Log (firm assets before buyout) Pre-buyout EBITDA/sales ρ Year dummies Cluster by industry Number of observations Pseudo R2

Yes Yes 303 0.181

Yes Yes 303 0.128

Yes Yes 303 0.186

(Secondary exit) −0.113*** (0.026) 0.186*** (0.057) 0.066** (0.028) −0.001 (0.001) −0.323** (0.155)

0.281* (0.156) 0.319*** (0.052) −0.455 (0.362) 0.071 (0.284) Yes Yes 605

The table shows results from probit regressions and Heckman's selection model of firms' exit choices. In the probit model, the dependent variable equals one if the exit is a secondary buyout, and zero if the exit is an IPO or a sale to a strategic buyer. Industry IPO volume is the log of industry IPO volume in the UK in the year of exit. HY market size is the logarithm of the high-yield market issuance in the year of exit. Log (firm assets after the buyout) measures the logarithm of fixed assets three years after the buyout. EBITDA growth is the growth in EBITDA as of three years after the buyout. Industry sales growth is the average sales growth for all firms in the same Fama–French 10 industry. Marginal effects are reported for probit regressions. In the Heckman's selection model, stage 1 predicts the probability of a firm exiting (Exiting), and stage 2 predicts the probability of exiting through a secondary buyout. Seller'sPE reputation is an indicator variable that equals one if the seller is one of the 50 largest private equity firms in the world, and zero otherwise. Log (firm assets before buyout) is the logarithm of fixed assets at one full fiscal year before the buyout. Pre-buyout EBITDA/sales is EBITDA/sales at one full fiscal year before the buyout. Coefficients are reported. ρ reports the estimated coefficient and standard error for the correlation of the error terms for the selection and treatment equation. Hence, a ρ that is significantly different from zero would indicate a selection problem. All regressions include year dummies. Robust standard errors clustered by industry are in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

In the second stage, I include the inverse Mills' ratio generated from the first stage and estimate the probability of firms exiting through secondary buyouts only for the sample of firms with observed exits. The estimated ρ, which is the correlation between the error term for the selection and the treatment equation, is not significantly different from zero. This indicates that the concern about sample selection does not impose a problem here. Moreover, the results do not change meaningfully. Therefore, the interpretations remain the same: firms' are more likely to exit through secondary buyouts when the equity market is “cold” and when the debt market performs well. The above regressions test individual effects of the equity and debt market. However, if the market timing argument holds, one would further expect to observe the interaction of the debt and equity market having an impact on firms' exit decisions. Such an impact would not be correctly captured by adding an interaction term between the equity and debt market in regressions. The reason is that different combinations of debt and equity market performances could have opposite predictions on the likelihood of firms exiting through secondary deals. Therefore, I create a matrix showing the percentage of firms exiting through secondary buyouts under four combinations of “hot” and “cold” capital markets Table 6. A debt market is characterized as “hot” if the size of the high-yield market in the exit year exceeds the median high-yield market size for the entire sample period. An equity market is Table 6 Exiting through secondary buyouts in different market conditions. Debt market

Equity market

Hot Cold

Hot

Cold

60.0% 77.6%

32.1% 53.4%

The table shows a matrix representing the percentage of firms that exit through secondary buyouts under different combinations of hot and cold debt and equity markets. If HY market size (defined in Table 5) in an exit year exceeds the median high-yield market size for the entire sample period, the debt market is classified as Hot, and Cold otherwise. A Hot (cold) equity market is determined by whether the logarithm of industry IPO volume in the UK in theexit year exceeds (is below) the corresponding median industry IPO volume for the entire sample period. The percentages are calculated as the number of secondary buyouts exiting under each condition divided by the total number of firms that exited under the same condition.

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categorized as “hot” if the industry IPO-volume in the exit year is higher than its corresponding industry's median IPO volume for the sample period. In the matrix, the cases that matter the most are when the debt and equity market display different conditions, and the results are consistent with the market timing argument. Compared to IPOs and sales to strategic buyers, the most secondary buyouts occur when the equity market is “cold” but the debt market is “hot”. 77.6% of all exits under this combination are secondary buyouts. In contrast, only 32.1% of exits are secondary buyouts when the debt market is cold but the equity market is hot, indicating that IPOs and sales to strategic buyers are more attractive exits under this condition. In pursuing a value-maximizing strategy, the private equity firm should hold the portfolio company until the maximum payoff can be achieved, whether it be sales to the public market, strategic buyers, or other private equity firms. However, there are factors that constrain their maximization. One such factor is their liquidity needs. When there is a higher demand for liquidity, firms would face a higher exit pressure, and this could impact their likelihood of exiting through a secondary buyout. As discussed in Section 2, private equity firms' liquidity demand changes when they need to raise a new fund. To test for this case, I construct an indicator variable based on the seller's fundraising activities within two years of exiting the portfolio company, since two years is the typical fundraising period. Fundraising equals one if the selling private equity firm raised a new fund within two years of exiting the target firm, and zero otherwise. Table 7 reports the results. Both the probit and the Heckman's selection model show evidence supporting the link between fundraising and firms' exit decisions. Raising a new fund significantly increases the probability of exiting through secondary buyouts. With a 22.7% increase, the result is both statistically and economically significant. I also find evidence showing that fundraising during a “cold” equity market influences the probability of exiting through secondary buyouts. Fundraising * Industry IPO volume is an interaction term between Industry IPO volume and Fundraising. When the equity market does not perform well but the private equity firm needs to raise a new fund, the probability of exiting through a secondary buyout increases significantly by 6.2%. The estimated results of other control variables are similar to those reported in Table 5. Another situation that can change private equity firms' liquidity demand, and therefore influence their exit decisions, is when the portfolio company has been held for a long time. Log (holding period) measures the number of months that the portfolio company has been held by a private equity firm. As this variable can be correlated with other factors, such as firm's restructuring efforts, the interpretation of it is limited. However, it is still useful to examine Log (holding period) because presumably, the longer the firm has been held, the closer the fund is to its finite life. Therefore, private equity firm's desire to exit also increases. Estimated results are presented in Table 8. Although the magnitude is small, holding period is also significantly positively correlated with the probability of exiting through secondary buyouts. Overall, the above results on fundraising and holding period

Table 7 The effect of fundraising on firms' exit choices. Probit

Fundraising Industry IPO volume HY market size Log (firm assets after buyout) EBITDA growth Industry sales growth

Heckman Selection

(1)

(2)

0.227*** (0.044) −0.112*** (0.022) 1.221*** (0.088) 0.053*** (0.014) −0.001 (0.001) −0.084 (0.109)

0.587*** (0.197) −0.074* (0.039) 0.995*** (0.082) 0.052*** (0.014) −0.001 (0.001) −0.095 (0.108) −0.100* (0.056)

Fundraising * industry IPO volume Seller reputation Log (firm assets before buyout) Pre-buyout EBITDA/sales ρ Year dummies Cluster by industry Number of observations Pseudo R2

Yes Yes 303 0.235

Yes Yes 303 0.246

(Exiting)

(Secondary exit) 0.396*** (0.117) −0.079** (0.036) 0.172** (0.071) 0.062*** (0.021) −0.000 (0.001) −0.337** (0.158) −0.062* (0.034)

0.291* (0.166) 0.317*** (0.051) −0.474 (0.324) 0.157 (0.192) Yes Yes 605

The table shows the effect of sellers' fundraising activities on firms' decisions to exit through secondary buyouts. Results from both probit regressions (Probit) and Heckman's selection model (Heckman selection) are reported. Fundraising is an indicator variable that equals one if the seller raised a new fund within two years of exiting the portfolio company, and zero otherwise. Fundraising * industry IPO volume is the interaction term between the fundraising variable and the equity market measure. All other variables are defined in Table 5. Year dummies are included in all regressions. Robust standard errors clustered by industry are in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Y. Wang / Journal of Corporate Finance 18 (2012) 1306–1325

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Table 8 The effect of holding period on firms' exit decisions. Probit

Log (holding period) Industry IPO volume HY market size Log (firm assets after buyout) EBITDA growth Industry sales growth

Heckman selection

(1)

(2)

0.104*** (0.040) −0.122*** (0.025) 1.478*** (0.110) 0.077*** (0.015) −0.002 (0.001) −0.051 (0.123)

0.095*** (0.030) −0.113*** (0.022) 1.263*** (0.091) 0.056*** (0.015) −0.001 (0.001) −0.086 (0.111) 0.223*** (0.045)

Fundraising Seller reputation Log (firm assets before buyout) Pre-buyout EBITDA/sales ρ Year dummies Cluster by industry Number of observations Pseudo R2

Yes Yes 303 0.205

Yes Yes 303 0.253

(Exiting)

(Secondary exit) 0.090*** (0.028) −0.102*** (0.028) 0.169** (0.068) 0.060** (0.024) −0.000 (0.001) −0.324** (0.155) 0.174*** (0.066)

0.288* (0.168) 0.318*** (0.051) −0.455 (0.320) −0.769*** (0.213) Yes Yes 605

The table shows the effect of holding period on firms' exit decisions. Results from both probit regressions and Heckman's selection model are reported. Log (holding period) is the logarithm of the number of months that a firm was held before exiting. It is used as a rough proxy for a firm's need to exit. The longer the holding period, the more likely the fund is closer to its end, and therefore, the higher the exit pressure. All other variables are defined in Tables 5 and 7. Year dummies are included in all regressions. Robust standard errors clustered by industry are in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

are consistent with the view that secondary buyouts tend to occur when private equity partnerships have relatively high demand for liquidity. To further verify the robustness of the results on market timing, I use alternative measures to proxy for market conditions (Table 9). Previous studies have found that the industry market-to-book ratio can determine equity market activities. Therefore, I use the industry market-to-book ratio as an alternative measure of the equity market condition. A higher market-to-book ratio would indicate a “hotter” equity market. To proxy for the debt market condition, I follow Axelson et al. (in press) and use the spread between interest rates of high-yield loans and the six month LIBOR rate. Consistent with the findings so far, results presented in Table 9 show that the probability of exiting through secondary buyouts vary with the equity and debt market conditions. The overall evidence in this section shows a strong support for the liquidity-based market timing hypothesis. Firms are more likely to exit through secondary buyouts, as opposed to IPOs and sales to strategic buyers, when: the equity market is “cold”, the debt market shows a favorable condition, and when sellers need to exit their investments.

4.3. The collusion motive The last motivation I examine is collusion on the part of private equity firms, as the secondary LBO market seems to offer a favorable environment for firms to trade assets among each other. If collusion is pervasive, we should observe active secondary-buyout sponsors trading more often with each other. To test this hypothesis, I investigate the buyout patterns of active players in the secondary LBO market. Table 10 provides a cross-participation matrix that clearly maps out trade patterns, if any are present among the thirteen most active acquirers in this market. 11 The vertical axis lists private equity firms as buyers, and the horizontal line lists the same firms as sellers. A dash ‘–’ in this matrix indicates that no deal was carried out between the two parties. “0”, on the other hand, means that one private equity firm did not acquire any target from the other party, but that at least one transaction occurred in the other direction. For example, Bridgepoint Capital did not acquire any portfolio company from Bank of Scotland, while Bank of Scotland acquired one company from Bridgepoint Capital.

11

The identity and number of transactions by the top sellers and acquirers are provided in Appendix B.

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Table 9 Firms' exit choices using alternative measures for market conditions. Heckman selection 1 (Exiting)

Heckman selection 2 (Secondary exit)

(Exiting)

−0.008*** (0.002) 0.090*** (0.013) 0.072*** (0.025) −0.001* (0.001) −0.019 (0.091)

Industry market-to-book Leveraged loan's spread Log (firm assets after buyout) EBITDA growth Industry sales growth Fundraising Log (holding period) Seller reputation Log (firm assets before buyout) Pre-buyout EBITDA/sales ρ Year dummies Cluster by industry Number of observations

0.293 (0.247) 0.322*** (0.047) −0.411 (0.341) −0.010 (0.217) Yes Yes 605

(Secondary exit) −0.010*** (0.002) 0.081*** (0.014) 0.064*** (0.016) −0.001* (0.001) −0.050 (0.094) 0.190*** (0.037) 0.066*** (0.025)

0.296 (0.251) 0.321*** (0.047) −0.422 (0.329) −0.772*** (0.097) Yes Yes 605

The table reports estimated results for Heckman's selection model using alternative measures of market conditions. All specifications and variables, except Industry market-to-book and Leveraged loan's spread, are defined in previous tables. Industry market-to-book is the average of market-to-book ratios for a given industry in the year of exit. Leveraged loan's spread is the difference between the high-yield rate minus the six-month LIBOR rate in the year of exit. All regressions include year dummies. Robust standard errors clustered by industry are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

If active acquirers of secondary buyouts trade more often with each other, this matrix should be somewhat symmetrical. However, there does not appear to be any discernible trade pattern among the top acquirers in the secondary-buyout market. Specifically, the total number of two-way deals, where two private equity firms have acquired at least one target from each other, is only six. To statistically test whether the number of two-way deals is significant, I compare the cross-participation matrix in Table 10 with a bootstrapped sample of 100,000 cross-participation matrices, generated using the actual number of deals carried out. Each bootstrapped matrix resembles the matrix in Table 10. The idea is to generate a distribution for two-way deals, under the assumption that private equity firms choose trading partners at random, and then compare the number of two-way deals shown in Table 10 with this random distribution. Fig. 1 shows the histogram of the number of two-way deals from bootstrapped results. The x-axis represents the number of two-way deals, and the y-axis is the number of bootstrap samples. The vertical line in the middle is where the actual matrix in Table 10 falls. Clearly, the observed data are not inconsistent with the assumption that private equity firms choose trading partners at random. The above results show that active acquirers in the secondary-buyout market are not likely to trade more frequently with each other than with other players in this market. 12As private equity firms have other ways of paying each other off besides trading assets, it is possible that collusion occurs in a way that is not captured by trading patterns alone. However, the concern investigated here is that firms are trading poorly performing portfolio companies, pushing those companies closer to financial distress, or trading assets at above-the-market prices. Results from this paper show that both cases are unlikely. 5. Pricing of secondary buyouts Section 4 has established that secondary buyouts are driven by the liquidity-based market timing motive. However, all three hypotheses have implications on the pricing of secondary buyouts, which has also generated some criticisms. In this section, I use the pricing of first-time buyouts to assess how secondary buyouts are priced. Two measures are used to evaluate pricing. The first measure is the logarithm of enterprise value (log EV), which is estimated by deal value excluding assumed liabilities plus total

12 To rule out the possibility that these active buyers are trading with each other outside of the UK market, I map the same cross-participation matrix using worldwide buyouts that are sponsored by the same private equity firms. The results do not show any trade pattern either (results untabulated).

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debt minus cash. To account for firms' fundamentals, the second pricing measure is an enterprise multiple (EV multiple). Since enterprise value/sales and enterprise value/EBITDA each is noisy by itself, the multiple here is computed as the average of the two. Panel A of Table 11 shows the summary statistics of the two pricing measures for both the secondary and the first-time LBOs. Wilcoxon rank-sum tests are performed to assess whether the median values are significantly different, and the results show that compared to first-time buyouts, secondary buyouts have higher enterprise value as well as enterprise multiple. To better examine deal pricing, I use the “comparable industry transaction method” described in Kaplan and Ruback (1995). Essentially, this method is a difference-in-difference estimation that uses multiples calculated from portfolios of firms in the same industry involved in similar transactions. According to Kaplan and Ruback (1995), this approach provides a more accurate estimation compared to OLS. Specifically, for every firm in the sample, I form a matching portfolio of non-buyout acquisitions in the same Fama–French 10 industry in the UK and compute the average Log EV(EV multiple) for that portfolio. Then I calculate the percent difference between Log EV (EV multiple) for each deal in my sample and the average of Log EV (EV Multiple) of the matching portfolio. Panel B of Table 11 presents the regression results from portfolios formed based on deals that were announced within three years of the announcement dates of LBOs in my sample. Secondary is the variable of most interest. It is an indicator variable that equals one if the buyout is a secondary buyout, and zero if the buyout is a first-time buyout. Similar to what the univariate results indicate, secondary buyouts are associated with higher values. The enterprise value is 19% higher and significant at the 10% level. The enterprise multiple is also 14.4% higher, suggesting that the higher enterprise value is not due to better firm fundamentals. However, this higher pricing is driven by other factors, most notably the debt market condition. A larger high-yield market is associated with 13.5% higher enterprise value, and 32.1% higher enterprise multiple. This finding is consistent with those in Axelson et al. (in press), showing that favorable debt-market conditions can lead to higher deal pricing. Furthermore, the results are consistent with the idea that there are more secondary buyouts when the debt market conditions are favorable. Other factors that are associated with a higher enterprise value but not enterprise multiple include firm size and the acquirer's reputation, measured by the acquirer's ranking in the PEI 50 index published by Private Equity International Magazine. Since a better reputation allows private equity firms to obtain favorable financing (Demiroglu and James (2010)), this variable is used to control for the possibility that higher pricing could be driving by acquirers' financing abilities. The results show that more reputable buyers pay roughly 4.5% more in enterprise value. However, they also target better firms. Once firms' fundamentals are taken into account by using EV multiple, PE buyer reputation is no longer significantly different from zero. To eliminate the possibility that results are driven by information not available at the time of buyout, Pane C of Table 11 shows results using portfolios formed based on a three-year window preceding the deal announcement. A similar conclusion can be reached: secondary buyouts are priced higher than first-time buyouts. However, pricing premiums are driven by better debt market conditions that allow private equity firms to borrow. The fact that there are more secondary buyouts during favorable debt markets reinforces the hypothesis that private equity firms time the performance of the capital markets. Furthermore, the results are consistent with private equity firms overpaying deals when the debt market performs well. Since these purchases also do not show any strong value creation, the deal prices cast doubt on the returns that buyers investors would receive. 6. Conclusion In recent years, buyouts of public firms, as a percentage of all buyouts, have dropped to 6.7%, while secondary buyouts have become increasingly popular, a phenomenon that has raised suspicions about the benefits of such deals. In this paper, I attempt to shed light on the nature of secondary buyouts by 1) providing evidence on the economic logic behind them. To this extent, I investigate three potential explanations: efficiency gains, liquidity-based market timing, and collusion 2) documenting the pricing of secondary LBOs. To overcome the data problem that has limited previous studies on buyouts, I use a hand-collected dataset that contains 465 first-time buyouts with consolidated financial statements, and 485 secondary buyouts out of which 140 have consolidate financial statements. Using this dataset, I do not find any support for the collusion motive. The most active acquirers in this market do no exhibit any discernible trade patterns. However, the efficiency gains explanation is also at best mixed. Targets show higher operating cash flows, but the firms are not run more efficiently. Given the value creation process emphasized by previous literature, the lack of efficiency gains is perhaps a surprising result. My overall analyses support the notion that secondary buyouts are driven by the liquidity-based market timing motive. Specifically, I find that firms are more likely to exit through secondary buyouts: when the equity market condition is “cold”, measured by industry IPO volume; when the debt market condition is favorable, suggesting buyers' greater abilities to borrow, and when private equity firms' liquidity demand changes, in which case they face a higher pressure to exit. These findings are robust to the sample selection problem and alternative measures of market conditions. On the pricing of secondary LBOs, results show that secondary buyouts have higher deal prices compared to first-time buyouts. Although the pricing premium still persists after firm fundamentals are taken into account, higher deal prices are driven by better debt market conditions. This is consistent with the idea that there are more secondary buyouts when the debt market shows a favorable condition. To summarize, the composition of buyouts has changed over the years. In this paper, I show that secondary buyouts are not driven by factors that are not typically cited in the buyout literature. While liquidity-based market timing can be an optimal strategy for private equity firms under certain constraints, there is no real benefit to the target firms. The prices are higher due to

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Table 10 Transactions by main secondary-buyout sponsors. 3i Group 3i Bank of Scotland Lloyds TSB Barclays Close Brothers Graphite Bridgepoint Blackstone Duke Street Alchemy Apax Gresham HgCapital ABN Amro Electra Phoenix Hermes Legal & General PPM Candover Carlyle Group Charterhouse

4 1 3 1 1 1 1 – 1 – 2 1 – 1 1 – 1 0 – 1 1

Bank of Scotland

Lloyds TSB Bank

Barclays Private Equity

Close Brothers

Graphite Capital

Bridgepoint Capital

Blackstone Group

Duke Street Capital

Alchemy Partners

0

0 1

1 1 1

0 – 1 –

0 1 – – 1

1 1 1 – 1 1

0 – – – – 0 –

– – – – 0 – 0 –

0 – – – – 1 1 – –

0 0 – 0 0 – – – – 0 – – – – – – – – 1 –

1 0 – 0 – – – – 0 0 – – – – – 1 – 1 –

– – – – – – – 2 0 1 1 – – – – 1 0 –

0 0 – 1 – – – 1 – – – 0 – – – – –

0 1 – – – – – – 1 1 – – – 1 – –

– 1 – – 0 – – – – – – – 1 – –

– – – – – – 0 – – 0 – – – –

– – – – – 1 0 – – – – – –

– – – 1 – – – 1 1 0 – –

The table shows trade patterns, if any, by the thirteen most active buyers and sellers in the secondary LBO market. The cross-participation matrix shows buyers on the x-axis and sellers on the y-axis. It highlights the number of times the most active secondary-buyout acquirers have undertaken deals with one another. A dash ‘–’ indicates that no deal was carried out between the two private equity firms. In contrast, a zero represents that only one-way deals were carried out.

Fig. 1. Bootstrapped results for collusion. The figure shows the distribution of randomly generated two-way deals. Based on the actual number of deals, bootstrapping was performed to generate 100,000 matrices that resemble the matrix in Table 10. Then the number of two-way deals from the 100,000 randomly generated matrices is counted to produce this histogram. A two-way deal is defined as a deal where two private equity firms acquired at least one portfolio company from each other. The x-axis represents the number of two-way deals, and the y-axis is the number of bootstrap samples. 6 is the actual number of two-way deals from the cross-participation matrix in Table 10.

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Table 10 Transactions by main secondary-buyout sponsors. Apax partners

Gresham

HgCapital

ABN Amro

Electra Partners

Phoenix Private Equity

Hermes Private Equity

Legal & General Ventures

PPM Ventures

Candover Partners

Carlyle Group

Charterhouse Capital

– – – – – – – – – –

1 1 1 0 – – 1 – – – –

0 – 1 1 0 – – – – – – –

– – – 0 – – – – – 0 0 – –

0 – – 0 – 0 – 1 0 – 0 – – –

0 – – – – 0 – – 1 – – – – – –

– – – – 1 – – – – – – 0 1 – – –

0 – – – – – – 1 – 1 – – – – 0 1 –

1 – 0 – – – – – – 0 – – – – – – – –

– – – 0 – 0 0 – – 2 – – – – – 0 – – –

0 0 1 1 – – – – – – – – – – – – – – – –

0 – – – – – – – – – – – – – – 0 – – – – –

– – 1 1 – – – – – – –

– – – – 1 – – – – –

– – – 0 – – – – –

– – – – – – – –

– – 1 – – – –

– 0 – 1 – 1

– – – – –

– – – –

– – –

– –

–

the debt market, but these deals do not show any strong value creation. Overall, secondary buyouts are expensive, but they serve no purpose aside from alleviating the financial needs of private equity firms. Acknowledgment I am grateful for the helpful guidance from my advisor, Michael Weisbach, and my committee members, Bernadette Minton and Berk Sensoy. I would like to thank Zahi Ben-David, Daniel Cavagnaro, Sergey Chernenko, Ji-Woong Chung, Kewei Hou, Paul Laux, Jongha Lim, Anil K. Makhija, Micah Officer, Ludovic Phalippou, René M. Stulz, Ingrid Werner, the referees of this paper, and the seminar and conference participants at the 2011 FMA Annual Meeting, the Ohio State University, Ohio University, California State University Northridge, California State University Fullerton, and Loyola Marymount University for their helpful comments. I would also like to thank Ji-Woong Chung for his valuable assistance with the data. All errors are my own. Appendix A. Sample construction Because the sample consists of UK firms, I use Zephyr to identify the list of secondary buyouts. Zephyr is a database which provides information on worldwide mergers and acquisitions, private equity deals, and IPOs. There are two advantages of using Zephyr. First, Zephyr covers many middle-market buyouts and deals with smaller values than those in Thomson Financial or Mergerstat (LexisNexis). Its coverage is particularly extensive for European deals, and no minimum deal value is required to be included in the database. Zephyr's coverage starts in 1997. As of December 2008, information on over 600,000 transactions was covered. Second, unlike SDC, which only provides Sedol and Cusip numbers, Zephyr provides the target firm's unique Bureau van Dijk number, which U.K. firms need in order to register. To construct the sample, I start with completed LBO transactions in which a more than 50% stake was acquired between 1997 and 2008 (4908 deals). A buyout is classified as a secondary buyout if (and only if) the buyer and the seller are both identified as private equity firms (490 deals). This classification does not consider any minority stakes sold or cases where a firm is indirectly sold to another private equity firm as part of a bankruptcy procedure. Zephyr's classification of completed transactions and majority stakes is later cross-checked in firms' financial statements. Incorrectly categorized secondary buyouts are dropped from the sample (5 deals). The complete list of secondary buyouts contains 485 deals. I then use these firms' Bureau van Dijk registration numbers to collect annual filings. In a typical LBO, a shell company is created as the target firm's parent, and all proceeds from the target's subsidiaries are then consolidated under the new parent company after the buyout. Therefore, from the target firms' annual filings, I identify all the immediate and ultimate holding companies, both before and after the buyout. Finally, consolidated financial statements are collected using the parent companies' names. Any name change is then traced to ensure a correct parent-subsidiary matching. Firms without consolidated statements are dropped from the sample. These include cases where the parent companies are incorporated outside of the UK and very small

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Table 11 Determinants of deal pricing. Panel A: Summary statistics All

Log EV EV multiple

Secondary LBOs (1)

First-time LBOs (2)

Diff (1–2)

N

Mean

Median

Std dev.

N

Mean

Median

N

Mean

Median

405 389

3.44 7.79

3.36 4.82

1.68 14.29

121 114

4.11 8.79

4.13 5.02

284 275

3.16 7.32

3.03 4.61

EV difference

Panel B: Three-year window centered on deal announcement Secondary

(2)

(1)

(2)

0.190* (0.101)

0.752 (0.964) 0.205*** (0.015) 0.045** (0.022) 0.135** (0.053) −0.063 (0.088) −2.337*** (0.588) Yes 405 0.671

0.144* (0.076)

1.796 (1.225) 0.018 (0.014) −0.040 (0.050) 0.321*** (0.088) −0.148 (0.109) −5.076*** (0.974) Yes 389 0.071

PE buyer reputation High-yield market size Secondary * high-yield market size

Cluster by year Number of observations Adjusted R2 Panel C: Three-year window preceding deal announcement Secondary

−0.197** (0.081) Yes 405 0.036

0.202** (0.096)

Log (total assets) PE buyer reputation High-yield market size Secondary * high-yield market size Intercept Cluster by year Number of observations Adjusted R2

EV multiple difference

(1)

Log (total assets)

Intercept

1.10*** 0.41**

−0.186** (0.085) Yes 405 0.038

0.839 (0.920) 0.203*** (0.014) 0.081** (0.039) 0.192*** (0.062) −0.070 (0.085) −2.978*** (0.688) Yes 405 0.658

−1.378*** (0.087) Yes 389 0.016

0.178* (0.103)

−0.174** (0.079) Yes 389 0.033

0.898 (0.909) 0.208*** (0.014) 0.031 (0.025) 0.122** (0.055) −0.077 (0.084) −2.179*** (0.605) Yes 389 0.679

The table presents results on how secondary buyouts are priced against first-time buyouts. Panel A shows the number of observations, mean, median, and standard deviation of the two pricing measures: the logarithm of enterprise value (Log EV) and enterprise multiple (EV multiple) computed by averaging enterprise value/sales and enterprise value/EBITDA. Many firms in the sample do not provide deal value. Therefore, the number of observations is reduced to 405. Moreover, negative enterprise multiples as a result of negative EBITDA at one year before the buyout do not provide a meaningful comparison. Therefore, these firms are removed, resulting in a further loss of 26 observations. Two tailed Wilcoxon rank-sum tests are performed to determine whether the median values are significantly different. Panels B and C show results from OLS regressions where the dependent variables are acquisition discounts/premiums for buyouts based on Log EV and EV multiple. The acquisition discount/premium is estimated as the percent difference between Log EV (EV multiple) for an LBO and the average Log EV (EV multiple) for a matched portfolio of non-buyout acquisitions of private firms in the same industry in the UK. Whether firms are in the same industry is determined by the Fama–French 10 industry classification. Matched firms are allowed to re-enter portfolios. Log EV Difference and EV Multiple Difference each reports results using the two pricing measures as the dependent variable, respectively. Secondary is an indicator variable that equals one if the buyout is a secondary buyout, and zero if the buyout is a first-time buyout. Log (Total assets) is the log of total assets at one full fiscal year prior to thebuyout. PE buyer reputation is an indicator variable that equals one if the private equity acquirer belongs to the PEI 50 index, which lists the world's 50 largest private equity firms, and zero otherwise. High-yield market size at the time of buyout is used to proxy for debt market conditions, and Secondary*high-yield market size is an interaction term between Secondary and High-yield market size. In Panel B, each matched portfolio contains deals that are announced within a three-year window centered on the announcement dates of LBOs in my sample. In Panel C, the matching window is a three-year window immediately preceding the announcement dates of LBOs in my sample. All results are clustered by year. Robust standard errors are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

companies that are not required to submit consolidated financial statements. Similar steps are followed to collect information for first-time buyouts. The final sample consists of 140 secondary buyouts and 465 first-time buyouts, including both public-to-private and private-to-private deals. For a more comprehensive view of the target firms, Table A.12 shows summary statistics of

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pre-buyout characteristics for both first-time and secondary buyout targets. Firm characteristics are divided to size, profits and profitability, and debt level. Compared to first-time LBOs, targets of secondary buyouts are larger firms with higher operating cash flows. Having gone through a buyout, targets of secondary LBOs also carry more debt, evident in higher levels of total debt and total debt/total assets. Consolidated financial statements need to be used to collect all relevant information. However, in some cases, using consolidated accounting numbers may yield inaccurate comparisons. For example, the targets could be add-on acquisitions for the buyers' exiting portfolio companies. Post-buyout consolidated accounting numbers would then contain other portfolio companies' information. Consequently, it would be incorrect to directly compare accounting data gathered from the parent companies' consolidated financial reports. To account for such issues, my secondary-buyout sample is divided to Same subsidiaries and Different subsidiaries based on whether the target firm's subsidiaries changed within a one-year window centered on the buyout year. A total number of 46 firms experienced subsidiary changes within this window. These firms are included in the Different subsidiaries group and are removed from analyses shown in Tables 3 and 4. These firms are added back to the sample in all other tests. For completeness, Table A.13 presents percentage changes for the Different subsidiaries group. As

Table A.12 Sample summary statistics. All

Size Total assets (£m) Fixed assets (£m) Sales (£m) Profits and profitability EBITDA (£m) EBITDA/sales assets EBITDA/fixed assets ROA Debt level Pre-buyout total debt (£m) Pre-buyout total debt/total assets

Secondary LBOs (1)

First-time LBOs (2)

Diff (1–2)

N

Mean

Median

Std Dev.

N

Mean

Median

N

Mean

Median

605 605 605

87.82 54.66 92.06

11.88 3.58 22.08

372.41 268.5 338.55

140 140 140

97.85 60.49 97.22

30.29 13.72 35.2

465 465 465

84.57 53.03 90.03

8.24 2.34 16.85

22.05*** 11.38*** 18.35***

605 605 605 605

10.26 −0.31 1.35 0.07

1.79 0.1 0.45 0.09

31.68 9.86 5.53 0.27

140 140 140 140

13.26 −1.68 0.7 0.06

4.52 0.13 0.32 0.08

465 465 465 465

9.38 0.1 1.54 0.07

1.32 0.09 0.49 0.09

3.2*** 0.04*** −0.17*** −0.01

605 605

42.78 0.29

1.43 0.18

435.69 0.46

140 140

39.39 0.56

8.69 0.43

465 465

43.05 0.27

1.29 0.16

7.40*** 0.27***

The table contains the number of observations, mean, median, and standard deviation of pre-buyout target characteristics. Firm size, profitability, and debt level are reported. All variables are measured at one full fiscal year before the buyout. Column Diff (1–2) represents the difference in median values between secondary and first-time buyouts. Wilcoxon rank-sum tests are performed to assess whether median values are significantly different. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Table A.13 Operating performance of target firms in the “Different subsidiaries” sample. Different subsidiaries(N = 46) From year i to year j

A. Size measures Fixed assets Median change Industry-adjusted change Sales Median change Industry-adjusted change B.1 Operating cash flow EBITDA Median change Industry-adjusted change EBITDA/sales Median change Industry-adjusted change EBITDA/fixed assets Median change Industry-adjusted change B.2 Profitability ratios Earnings/sales Median change Industry-adjusted change

−3 to −2

−2 to −1

At year −1 3.2% 3.2% At year −1 6.5%** 6.6%***

24.87 0.2% 0.2% 59.23 6.7%*** 6.9%***

At year −1 −3.7% −3.7% At year −1 −1.2%*** −1.3%*** At year −1 −0.034** −0.036*

8.21 −2.6%* −2.6%* 0.14 −0.3% −0.7% 0.21 −1% −4.5%

At year −1 0.2% −0.1%

7% −0.5% −0.9%

−1 to +1

−1 to +2

−1 to +3

103.5%*** 104.0%***

120.4%*** 123.2%***

151.6%*** 149.2%***

21.8%*** 26.0%***

48.8%*** 49.1%***

85.8%*** 84.6%***

−0.7% −0.7%

−7.3% −7.3%

−9.7% −42.9%

0.4%* −0.3%

−1.1% −3.8%

−1.0% −2.6%

−4.3% −11.5%**

−7.7%* −30.3%***

−4.8% −28.0%**

−2.3% −6.3%**

−4.2%** −5.6%***

−3.6% −5.5% (continued on next page)

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Table A.13 (continued) Different subsidiaries(N = 46) From year i to year j

ROA Median change Industry-adjusted change

−3 to −2

−2 to −1

−1 to +1

−1 to +2

−1 to +3

At year −1 0.1% −0.8%

6% −0.4% −1.6%

−2.9% −5.2%**

−4.2%** −10.1%***

−3.3% −11.6%

For completeness, the table shows the median percentage changes and the industry-adjusted median percentage changes in operating performance from year i to year j for secondary-buyout targets in the “Different subsidiaries” sample. The full sample of 140 firms is divided to “Same subsidiaries” and “Different subsidiaries” based on subsidiary changes in a one-year window centered on the buyout year. The “Different subsidiaries” group contains firms whose undertaking subsidiaries changed during the one-year event window and consequently are excluded from Tables 3 and 4. All variables are defined in Table 3. Two-tailed Wilcoxon signed-rank tests are performed to test whether the percentage changes are significantly different from zero. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

the table indicates, the percentage changes in fixed assets and sales are much larger than the numbers presented in Table 3. Moreover, size-scaled operating cash flows and profitability ratios all show much larger declines compared to the results reported in Table 3. Overall, including this group of firms in efficiency gains analyses would show unrepresentative operating performance changes. Appendix B. Most active sellers and acquirers For a more comprehensive picture of the active buyers and sellers in the secondary buyout market, Table B.14 lists the top 27 sellers and acquirers of secondary LBOs. A glance at the top sellers' list tells us that the secondary-buyout market is not dominated

Table B.14 Transactions by the most active secondary-buyout sellers and acquirers. Top sellers

Top acquirers

Rank

PEI50

Name

# Of deals

Rank

PEI50

Name

# Of deals

1 2 2 4 5 5 7 7 7 7 7 7 7 7 7 16 16 16 16 16 21 21 21 21 21 21 21

12 48 – 27 – – – 26 – – – – – – – – – – – 5 – – – – – – –

3i Group Barclays Private Equity Electra Partners Bridgepoint Capital Gresham Royal Bank of Scotland Alchemy Partners Cinven Group ECI Partners Graphite Capital JP Morgan Lloyds Development Capital Montagu Private Equity NatWest Equity Partners HSBC Private Equity Close Brothers Private Equity Aberdeen Murray Johnstone Private Equity NVM Private Equity Phoenix Equity Partners CVC Capital Partners Citicorp Capital Investors Dunedin Capital Partners HgCapital Investor Group PPM Capital Rutland Rutland Partners JP Morgan

36 8 8 6 5 5 4 4 4 4 4 4 4 4 4 3 3 3 3 3 2 2 2 2 2 2 2

1 2 3 4 5 6 6 6 9 10 11 11 11 11 11 16 16 16 16 20 20 20 20 20 20 26 26

12 – – 48 – 27 – – – – – 10 – – – – – – – 9 – – – – – 1 30

3i Group Bank of Scotland Lloyds TSB Bank Barclays Private Equity Graphite Capital Bridgepoint Capital Close Brothers Private Equity Duke Street Capital Gresham Electra Partners ABN Amro Blackstone Group HgCapital Phoenix Equity Partners Royal Bank of Scotland Alchemy Partners Hermes Private Equity Legal & General Ventures PPM Ventures Apax Parners Candover Partners Dunedin Capital Partners Exponent Private Equity ISIS Equity Partners Mercury Private Equity Carlye Group Charterhouse Capital

32 24 22 20 12 10 10 10 9 8 7 7 7 7 7 6 6 6 6 5 5 5 5 5 5 4 4

The table shows the number of transactions sponsored by active sellers and acquirers of secondary buyouts. To save space, only the 27 most active sellers and buyers are reported. PEI50 indicates a firm's ranking in the PEI 50 index, published by Private Equity International Magazine (PEI). A dash ‘–’ implies that the firm is not one of the 50 largest private equity firms in the world.

by a few sellers. With the exception of 3i Group, who exited 36 deals through secondary LBOs, most private equity firms only exited two or three deals through secondary buyouts. Sellers not in the top 27 tend to be smaller private equity firms who only sold one or two portfolio companies during the sample period of 1997 to 2008 (not shown in this table). Acquirers' activities, on the other hand, are much more dominated by a few private equity firms: the total number of secondary buyouts sponsored by the top five acquirers exceeds 100.

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