Debt Structure, Private Equity Reputation, and Performance in Leveraged Buyouts

Debt Structure, Private Equity Reputation, and Performance in Leveraged Buyouts Chen Liu September 20, 2013 Abstract This paper provides a comprehensive study of deal characteristics and participants involvement in leveraged buyouts (LBOs) and their impact on target firms performance. Using a sample of 501 U.S. LBOs completed between 1986 and 2011, I find that higher industry-adjusted changes in return on assets and return on sales are associated with larger amount of leverage added during the buyout process, tighter LBO loan covenants, and equity contribution of target firms management. LBOs are more likely to exit through an IPO or a sale if they use more bank debt with tighter covenants and are sponsored by private equity firms of high reputation. These relations are robust to credit market conditions and aggregated LBO activities. The evidence suggests that the main source of value creation in LBOs is the reduced agency costs through the disciplining effect of debt, closer monitoring by lenders, and the better aligned management incentives. Private equity firms reputation is also important in ensuring successful deal outcomes. My findings also suggest that the poor performance observed in recent LBOs is a result of less leverage, fewer bank loans, and less restrictive covenants used in these deals. Key Words: leveraged buyout, private equity, debt structure, covenants Queen s School of Business, Queen s University, Kingston, Ontario, Canada. K7L3N6. Email: cliu@business.queensu.ca. I am grateful to my supervisor Lynnette Purda for comments that substantially improved this paper. I also thank Edwin Neave and Wei Wang for helpful comments. I am responsible for all remaining errors.

1. Introduction In a leveraged buyout (LBO), 1 a company is acquired using a relatively small portion of equity and a relatively large portion of outside debt financing. Jensen (1989) argued that the LBO structure of highly leveraged capital structures, active corporate governance, concentrated ownership stakes, and well-aligned managerial incentives make the LBO form superior to widely held public corporations. Early empirical work supported the merits of this structure with papers by Kaplan (1989a) and Smith (1990) finding improvements in performance for firms undergoing an LBO in the 1980s. However, more recent studies by Guo, Hotchkiss, Song (2011) and Cohn, Mills, and Towery (2011) find few performance improvements to target firms of LBOs completed in the 1990s and first half of the 2000s. Therefore, I am motivated to examine how recent LBO deals differ from the earlier ones and whether these differences are responsible for smaller performance enhancements in these later deals. To do this, I seek to identify the primary drivers of LBO success with a particular focus on leverage, LBO debt structure and terms, and the changing role of banks and private equity investors in the resulting firms. I also examine whether performance is related to different credit market and LBO market conditions or premium paid in the buyout transaction. Figure 1 presents the structure of a typical LBO transaction. Equity investors in LBOs are mainly private equity (PE) firms, management of the target firms, or another corporation. Financiers of these transactions include banks, institutional investors, and public debt holders, whereas institutional investors are principally structured investment vehicles and loan participation mutual funds (Miller 2012). Traditionally, banks were heavily involved in financing LBO deals. However, the function of banks have changed significantly as more and more banks move to securitize loans that they previously would have held (Bord and Santos, 2012). In addition, PE firms have becoming more important in sponsoring LBO deals and the 2000s has observed an increase in club deal LBOs, where two or more PE firms conduct a buyout together As a result, I am motivated to begin my examination of LBO performance drivers by investigating how the structure of these deals 1 A management buyout (MBO) is a form of LBO when incumbent management team takes over the firm. This paper includes MBOs in the sample and uses the general term LBO. - 1 -

and the participants involved have changed over time. To undertake this examination, I construct a comprehensive dataset of 501 public-to-private U.S. LBO transactions completed between January 1, 1986 and December 31, 2011 from Capital IQ and the SDC. I require all transactions to have financing details from LPC s Dealscan and pre- and post-buyout financial data from Compustat or Capital IQ, and missing data are filled from SEC filings. This dataset has the following merits. First, it is to my knowledge the most comprehensive U.S. LBO sample with large number of deals that have post-buyout data available. As the target firms become private with the buyout, LBO studies on performance improvement are restricted by data availability. For example, Kaplan (1989a) s sample contains 76 management buyouts between 1980 and 1986 and Guo et al. (2011) s study of LBOs between 1990 and 2006 has 196 observations, 96 of which have post-buyout data available. By hand-collecting financial information of target firms that have publicly traded debt or subsequently file an IPO, I am able to construct a larger LBO sample than those used in previous studies. This large sample allows me to explore the heterogeneity among these LBO deals and generate results with better statistical properties in the cross-sectional analysis. Second, the sample period covers the cyclicality of LBOs starting from its first wave of the late 1980s and early 1990s, the slight recovery and decline in the 1990s, and the most recent boom and bust in the 2000s. This sample period makes this paper one of the first studies that directly compares how LBO deal characteristics, performance, and their relation have changed over time. 2 Moreover, this paper presents the most up-to-date sample that includes LBOs completed by December 31 st, 2011. This allows me to examine LBOs completed during and after the 2007-08 credit crisis, while most other studies use deals completed before the crisis. Using this data, I first measure post-buyout operating performance. Following Kaplan (1989a) and Guo et al. (2011), I calculate the percentage changes in EBITDA and net cash flows scaled by total assets or sales from the last fiscal year before the LBO to the first three years after the buyout completion, adjusted by industry medians. I find that performance change is largely positive for LBOs in the 1980s and 1990s but almost 2 Guo et al. (2011) studies deal characteristics and performance changes by comparing the deal pricing and financing details calculated in their paper with the results presented in Kaplan and Stein (1993). - 2 -

insignificant for the deals in the 2000s. For example, during the period of 1986-1993, the median industry-adjusted percentage increases in net cash flow to sales are significant at 32.7%, 28.2%, and 31.5% in the first three years after the buyout. Between 1994 and 2011, these increases are still significant, but by a lesser extent at 18.5%, 13.7%, and 29.8%. However, from 2002 to 2011, only the increase in the first year after the buyout is significant at 13.3%, while changes in the second and third years become insignificant. I next examine how LBO deal characteristics have varied over time that may be responsible for the decreasing performance based on Jensen (1989). I find three important changes. First, LBOs in the 1990s and 2000s do not borrow as much as the ones in the 1980s. For the deals in the late 1980s, leverage increased by a median of 45% to a post-buyout debt-to-asset ratio of 74% in the first full year after the buyout completion. However, the median leverage increase was only 22% and post-buyout leverage was 57% for deals in the 2000s. Second, there is a structural change in the composition of the LBO debt. The proportion of bank debt in total LBO debt has decreased from a median of 85% in the late 1980s to a median of 34% in the 2000s. In the meanwhile, institutional investors have become more important in the LBO market with institutional loans financing a median of 63% of total LBO debt in the 2000s. In addition, covenants within the LBO loans have become less restrictive. Third, PE firms have become more important players in LBO transactions. The proportion of deals sponsored by PE firms increased from 68% in the late 1980s to 96% in the second half of the 2000s. In addition, there are more club deals in recent years, leading to mega LBOs with large transaction values between 2005 and 2007. Having documented a decline in post-buyout operating performance and a shift in deal structure and participants, the second part of the paper seeks to identify what aspects of a deal s structure and the role of participants are associated with its performance. Possible drivers of performance that I consider are (1) change in leverage, (2) monitoring by lenders, (3) involvement of PE firms, and (4) better aligned management incentives. I control for pre-buyout characteristics of target firms, credit market and LBO market conditions, LBO loan spread for each deal, and the premium paid for the target firm, and other deal characteristics in examining these performance drivers. - 3 -

Regression results show that target firms have better performance when leverage is increased by a larger amount through the buyout, LBO loan covenants are more restrictive, and when managers of the target firms contribute equity and participate in the buyout. PE reputation, however, is not related with changes in operating performance. In addition, I do not find evidence that links performance to credit market conditions, LBO loan spreads, or the buyout price. Overall, these results suggest that the main source of value creation is the reduced agency costs in the post-buyout firms through the discipline effect of debt, closer monitoring by lenders, and the better aligned management incentives. These results help us to understand the observed reduced performance enhancement in the more recent LBOs as they use less leverage and less restrictive loan covenant, which are important drivers for performance improvement. Another way to examine LBO success is to look at the outcome of each deal whether it goes bankrupt, exits through an IPO or a sale to financial or strategic buyer. Using an IPO and a sale as indicator of LBO success, I find that LBOs are more likely to succeed if they use more bank debt and tighter covenants, experience no CEO change, and are sponsored by highly reputable PE firms. LBOs are more likely to fail if the buyers are subsidiary of banks that are also financiers of the deals. These results are consistent with the lenders monitoring and PE firms reputation as sources of value creation in LBOs. I also find that LBOs completed during the time when interest rates are lower than their historical average are less likely to succeed, providing some evidence for the market timing behavior of LBO buyers that they overinvest in unprofitable deals under times of favorable credit market conditions. Contributions of this paper are as follows. First, this is one of the first large sample LBO studies with a sample period that covers the entire cyclicality of the LBO history. Second, this paper contributes to the literature on value creation of LBOs by examining the primary drivers of performance improvement and successful deal outcomes. To the best of my knowledge, this is the first paper that studies the effects of detailed LBO financing structure and its contractual features, and PE reputation on post-buyout operation performance. Results of this paper will further our understanding of when and how an LBO may be successfully employed to improve firm performance. By doing so, it facilitates our - 4 -

understanding as to why recent LBOs seem to be less successful than previous transactions. Third, this paper contributes to the literature of private equity reputation by being one of the first studies that investigate how PE reputation affects performance. The finding that, controlling for target and deal characteristics, PE reputation is not related to operating performance in the first three years after the buyout but is important in ensuring successful deal outcomes provides some indirect evidence that PE firms create value through later stage of LBOs. Findings of this paper will motivate future studies in investigating when and how PE firms create value in LBOs. Forth, this paper contributes to the literature on debt structure and debt contracting in the setting of LBOs. This paper finds that loan covenants are important drivers of operating performance and instrumental to ensure successful outcomes. This result has important implications for practitioners as well as policy makers that they should focus on covenant to reduce risks and to improve performance of target firms. The proportion of bank debt is also important for LBOs to exit through an IPO or a sale, suggesting that composition of LBO debt needs to be carefully structured. The rest of the paper proceeds as follows. Section 2 reviews related literature and develops hypotheses. Section 3 describes the sample and provides evidence on post-buyout operating performance. Section 4 presents the changing characteristics and participants of LBO deals over time. Section 5 examines the drivers of post-buyout performance. Section 6 conducts robustness analyses. Section 7 concludes. 2. Literature review and hypotheses 2.1 Measuring value creation in LBOs Previous studies have examined value creation in LBOs in two ways: returns to LBO investors and post-buyout performance improvement in LBO target firms. In the first approach, value creation is measured as the returns to invested debt and equity capital from the time of buyout to a subsequent IPO, sale of the firm, or bankruptcy. Studies on LBO deal level returns suggest significant value creation through LBOs as evidenced by positive returns to investors. For example, Kaplan (1989a) estimates a median market-adjusted - 5 -

return of 28% (mean 42%) for investors in 25 MBOs in the 1980s that went public after an average of 2.7 years. Also, Guo et al. (2011) finds a median market- and risk-adjusted return to pre-buyout capital of 68.7% (mean 94.7%) for a sample of 70 LBOs completed from 1990 to 2006. On the LBO fund level, literature provides mixed evidence. Kaplan and Schoar (2005) investigate returns for 160 LBO funds between 1980 and 2001 and find that the median fund underperformed stock market index, generating only 80% of the return on the S&P 500. Higson and Stucke (2012) find that the buyout funds in their sample have significantly outperformed the S&P 500, with funds liquidated in the period 1980-2000 generating excess returns of on average 4.5% per year. The different results of these two studies are mainly due to sample selections as both studies find large heterogeneity in returns across funds. Higson and Stucke (2012) find just over 60% of the funds in their sample outperform the S&P500 and Kaplan and Schoar (2005) show that for the subset of funds that have been around for at least five years, the median performance exceeds the S&P500 by 50% (mean 80%). They also show that this performance is persistent and suggest that different LBO sponsors may have different skills in managing their portfolio companies. The heterogeneity in fund returns and performance persistence motivate me to examine characteristics of PE firms to determine whether and how they are associated with the performance of target firms. The second way to examine value creation in LBOs is to focus on the post-buyout operating performance of target firms. 3 Kaplan (1989a) studies 48 management buyouts from 1980 to 1986 and finds that industry-adjusted ratios of EBITDA to sales increased by 21.3%, cash flow to sales increased by 28.3%, and capital expenditure to sales decreased by 25.6% during a three-year period following the buyout. Smith (1990) reports a significant increase in operating cash flow per employee and per dollar of operating assets from the year prior to the buyout to one year post-buyout for 58 MBOs between 1977 and 1986. Lichtenberg and Siegel (1990) study 193 LBOs between 1981 and 1986 with a total of 1,132 plants and show that plant total factor productivity increases more than the 3 Some studies, for example Guo et al. (2011), use changes in operating perofrmance as an explanation for returns to investors. - 6 -

industry average in the years following a buyout. In contrast to the significant performance enhancement documented in the early studies, using this approach, the evidence of performance improvement is weaker for more recent LBOs. Guo et al. (2011) find a median of only 2.25% industry-adjusted increase in operating margins and a 12.54% decrease in cash flow margins for 94 LBOs completed between 1990 and 2006. Cohn et al. (2011) also find little evidence of performance enhancement using corporate tax return data for 317 LBOs from 1995 to 2007. This paper follows the second approach and examines post-buyout operating performance for the following reasons. First, this paper defines value creation as the real effects of buyouts, such as increased efficiency and reduced costs, rather than purely financial returns. 4 For this purpose, operating performance is a cleaner measure of value creation compared with the returns to investors. This is because returns to investors do not necessarily reflect the real value creation as they are usually calculated upon the exit of the deal, therefore depending on market conditions and investors market timing ability. Second, one of the goals of this paper is to construct a comprehensive and up-to-date LBO database that includes deals completed during and after the 2007-09 credit crunch. Most of these deals have not reached their outcome yet so no returns to investors are available for these deals. However, I can still examine the value creation and its drivers through operating performance change. One problem with measuring performance using cash flow variables from target firms financial statements, as mentioned in Cumming, Siegel, and Wright (2007), is that they are in general subject to managerial manipulation. However, as all the performance ratios in the paper are industry-adjusted, and assuming all firms in the same industry are subject to managerial manipulation in the similar ways, I expect the effect from manipulation to be small although the incentives for LBOs to show improved performance are probably greater than the average firms. 4 According to KKR founder Henry Kravis, private equity firms create value in LBOs over the long-term as managers, not merely as financial engineers. Kravis said that We only make money because we improve the operations of the newly acquired company. Source: Merger Talk - LBO firms rush to exits with quick flips. Reuters News, December 30, 2004. - 7 -

2.2 How LBOs create value: hypotheses In this subsection, I develop hypotheses on the sources of post-buyout performance changes of LBO target firms. I hypothesize that performance is positively influenced by the disciplining effect of increased leverage, better monitoring by lenders, active involvement of PE firms, and better aligned management incentives. 2.2.1 The disciplining and monitoring effect of debt The first key ingredient in a buyout transaction is leverage. Jensen (1986, p325) states that many of the benefits in going-private and leveraged buyout transactions seem to be due to the control function of debt. Leverage creates pressure on managers not to waste money, because they must make interest and principal payments. This pressure reduces the free cash flow problem described in Jensen (1986) where entrenched managers dissipate the free cash flows and overinvest in negative-npv projects. Also, the increased risk of financial distress associated with higher leverage motivates managers to operate the firm efficiently and to increase profit. Therefore, I expect that target firms that have increased their leverage by a greater amount through the buyout have better performance. Hypothesis 1.1(Debt Disciplining Hypothesis): Firms with higher level of leverage increase have better post-buyout performance. In addition to the disciplining effect of debt, I examine whether the monitoring by lenders lead to better performance of target firms. At the center of this examination is the conflict of interest between shareholders and bondholders that has negative impact on the value of the firm s outstanding debt as well as the total value of the firm (Bradley and Roberts, 2004). Lenders monitoring on managers behavior can help to mitigate these conflicts and reduce the attendant agency costs. To study the monitoring effect, I first examine the proportion of bank debt in total LBO loans. This is because banks are generally thought to have more incentives and comparative advantages in monitoring borrowers (Diamond 1984, 1993; Park 2000). Therefore, traditional thinking suggests that LBOs that are funded with a larger proportion of bank debt to have better performance as these deals are more closely monitored by banks. - 8 -

Another way to examine the monitoring effect is through the investigation of LBO loan covenants. Chava and Roberts (2008) suggest that covenants increase firm value in two ways. First, covenants that monitor and control managers behavior mitigate the reduction in firm value from the conflicts of interest between shareholders and debtholders and managers acting on behalf of shareholders to expropriate bondholders wealth. Specifically, covenants can restrict borrowers use of cash flows and require them to repay with proceeds of excess cash flow, asset sale, or debt and equity issuance. Based on Miller (2012), in a typical syndicated loan contract, 100% of net proceeds from asset sales and debt issuance and 50% to 75% of excess cash flow are required to prepay the loans. These requirements mitigate the free cash flow problems described in Jensen (1986). In addition, covenants specify the maximum level of different categories of debt to be used by the borrowing firms, therefore reducing the risk of post-buyout financial distress. Second, covenants define the circumstances under which creditors are permitted to intervene in management. This threat of transfer of control rights from borrowers to creditors serves as a discipline mechanism for managers. In particular, covenants enforce minimum financial performance measures against the borrowers, therefore motivating managers of the LBO target firms to increase revenue. Besides covenants, the maturity structures of LBO loans are also important. When LBOs are financed with short-term loans, the incentive effects of debt described by Jensen (1986) tend to be stronger. In particular, a shorter maturity increases required debt service payments, thus increasing the incentives for mangers to work harder to generate cash and avoid wasting resources in the earlier stages of the LBOs. In combination, these LBO loan characteristics form Hypothesis 1.2. Hypothesis 1.2 (Lenders Monitoring Hypothesis): LBOs with more bank loans, tighter loan covenants, and shorter loan maturity are associated with better post-buyout performance. 2.2.2 Private equity involvement Another possible source of value creation in LBOs may be the involvement of PE firms. As equity investors in LBOs, PE firms are incentivized to actively engage in the - 9 -

target firms management. Also, general partners (GPs) of PE funds are paid a management fee of 2% on the fund s capital and receive a carried interest of 20% of the profits above a certain benchmark realized by the fund. Therefore, GPs have incentives to closely monitor their portfolio firms. As described by KKR s founder Henry Kravis, PE firms generally aren t board members who show up once a month... Most of us in the industry live with these companies on a day-to-day basis. 5 However, it is hard to directly observe PE firms involvement in management, as target firms become private after the buyouts and therefore not required to disclose corporate governance information. 6 As a result, I use PE firms reputation as a proxy for their experience and skills to manage the target firms, where reputation of each PE firm is calculated as its years of experience or its market share based on all buyout deals it sponsored in history or in the past 36 months. The idea to use PE firms past experience as a proxy for their current skills is based on Kaplan and Schoar (2005) s finding of performance persistence of PE funds. Specifically, they find that performance of PE funds persists over time and that larger and older funds perform better than the new ones. The observed performance persistence can be attributed to PE firms experience and skills in selecting, restructuring, and monitoring target firms. Better-performing PE firms gain experience through their experiential learning from previous deals and PEs with lower returns cannot get funds from investors and fail to exist. In addition, Axelson, Stromberg, and Weisbach (2009) argue that highly reputable PE firms are less susceptible to risk shifting as they have incentives to pursue relatively more conservative investment strategies in order to maintain their reputation. Therefore, using PE firms reputations as an indicator of their ability and skills, I hypothesize that LBOs sponsored by highly reputable PE firms perform better. Hypothesis 2.1 (Private Equity Reputation Hypothesis): LBO deals sponsored by PE 5 Source: Merger Talk - LBO firms rush to exits with quick flips. Reuters News, December 30, 2004. 6 Some studies look at the board composition of target firms using the Dash dataset that s only available for U.K. firms. For example, Cornelli and Karakas (2011) examine the board structure for 88 U.K. LBOs from 1998 to 2003 and find significant changes in board size and composition when a firm goes private. Board size generally decreases and the presence of outside directors is drastically reduced, as they are replaced by individuals employed by the LBO sponsors. - 10 -

firms of high reputation experience higher performance improvement. A recent trend in LBOs is club deals, where two or more PE firms pool their assets to acquire target firms and manage them collectively. Club deals can be beneficial as each PE firm may bring different expertise to target firms. For example, when KKR teamed up with Bain Capital and Vornado Realty Trust to acquire Toys "R" Us, the New York Times stated that it was clear what each firm brought to the table. Kohlberg Kravis has a good reputation in the retail business, Bain has a good record doing turnarounds, and Vornado clearly knows real estate. 7 However, as the number of PE firms in the club gets larger, it is harder to make timely operational and management decisions. For example, Jeffrey Walker, a managing partner of CCMP Capital, argued that it was difficult to manage an LBO that has more than two or three investors. 8 Also, based on the experience of the venture capital industry, which is closely similar to the private equity industry, the ideal size of the consortium is two PE firms. To sum up, anecdotal evidence suggests that club deals may improve performance through each PE firm offering valuable management advice. However, as the size of the club gets larger, this benefit may decrease. Hypothesis 2.2: Club Deals perform better than LBOs sponsored by a single PE firm. However, this advantage tends to decrease as the number of PE firms participating in a deal gets larger. Recent studies in LBOs have investigated the relationship between banks and PE firms and how it affects returns to PE investors at the exit of LBOs. Fang, Ivashina, and Lerner (2012) find that LBOs sponsored by PE firms that are subsidiaries of banks (the bank-affiliated deals) exhibit worse equity returns if the deal is completed during the peaks of the credit market. Ivashina and Kovner (2012) find that bank relationship formed through repeated interactions between banks and PE firms lead to more favorable loan terms and higher equity return to the PE firms. Empirical results on the effect of banking relation on returns to PE firms are mixed, depending on the nature of the relation and the motivation behind it. I follow Fang et al. (2012) and examine bank-affiliated deals, as there may be some distinct features of these deals. First, as banks are in general less willing to 7 Source: Do Too Many Cooks Spoil the Takeover Deal, the New York Times, April 3, 2005 8 Source: Buyout Veterans Have Questions about Club Deals, Dow Jones Newswires, January 24, 2007. - 11 -

take risks than other investors, I expect that bank-affiliated PEs choose to sponsor deals with lower risks, therefore generating lower returns. Second, bank-affiliated deals provide parent banks with cross-selling opportunities (such as potential M&A advisory work, cash-management services, etc.) that increase their fee income. As a result, buyout decisions may not be based on PE s expectation on efficiency improvement of target firms, but only to take advantage of these cross-selling opportunities. Hypothesis 2.3: Bank-affiliated LBOs perform worse than stand-alone deals. 2.2.3 Management participation When incumbent managers of target firms participate in a buyout, they become the equity investors and their incentives are well-aligned with other shareholders. As a result, agency costs are minimized. Hypothesis 3.1 (Management Participation Hypothesis): Management-participated LBOs tend to have better performance due to better-aligned management incentives that reduce agency costs. Some studies look at management turnover and consider it as a way to measure PE firms control over target firms. Gong and Wu (2011) find that 51% of incumbent CEOs are replaced within two years of the LBO announcement. Acharya and Kohoe (2008) find that for U.K. LBOs, one third of the CEOs are replaced within the first 100 days and two-thirds are replaced over a four-year period. However, management turnover can be a noisy measure. First, it may not be clear that the management change is due to PE firms unless it is explicitly indicated in the proxy statement. Second, even it is confirmed that PE firms replace the CEO or CFO, management change may not necessarily indicate increased control from the PE firms. On the one hand, management turnover can be consistent with replacing bad managers by the good ones. On the other hand, the same managers running the company before and after the buyout may indicate low pre-buyout agency problem and therefore there is no need to replace managers. As a result, I expect the effect of management turnover on performance to be ambiguous. Hypothesis 3.2 (Management Turnover Hypothesis): The effect of management change on performance is ambiguous. - 12 -

3. Data and post-buyout performance 3.1. The Buyout Sample LBO sample of this paper is constructed from the Standard and Poor s Capital IQ and the Securities Data Company s (SDC) U.S. Mergers and Acquisitions Database. I manually combine LBO deals from the above two sources and eliminate duplicate observations. Compared with the SDC dataset, Capital IQ has an advantage as it allows me to link the LBO transaction details to target firms financials and information on LBO buyers (private equity firms, management teams, or another corporation). However, one possible problem with Capital IQ is its limited coverage of earlier deals. Stromberg (2008) compares the 1980s LBOs from Capital IQ and other data sources and estimates that Capital IQ covers between 70% and 85% of the LBO sample for this period. As one of the goals of this paper is to compare deal characteristics of LBOs during the period of 1986 to 2011, I also collect LBO transactions from SDC which has better coverage of deals in the late 1980s and early 1990s. Each LBO transaction in my sample meets the following criteria: (1) the transaction is flagged as an LBO, MBO, or secondary LBO and completed between January 1, 1986 9 and December 31, 2011; (2) the target is a publicly traded U.S. company; and (3) the transaction value is $10 million or larger. The minimum deal value of $10 million is lower than that in some other studies, such as Kaplan (1989a) and Guo et al. (2011). It is chosen to avoid biasing against earlier time periods when small deals were more common. This initial screening yields a total of 1,586 LBO transactions. To reconstruct the financial structure of each deal accurately, I require that all transactions have financing details available from Reuter s LPC Dealscan loan database. 10 I match the Dealscan data with the buyout sample by borrower names and time of the transactions. I then reconstruct the financial structure for each deal using the tranche level data of LBO loans from Dealscan and the mezzanine debt from Capital IQ. This reduces the sample to 885 observations. In 9 My sample starts from 1986 because the loan information from Dealscan starts from 1986. 10 Restructuring LBO deals across databases requires matching by names of target firms. Target firms mostly appear under their old names in SDC, Dealscan, and Compustat, while Capital IQ uses only the most recent names. I keep track of all name changes using a text search in Company Tearsheet of CapitalIQ. I also use the Wall Street Journal for name changes if the Tearsheet is ambiguous. - 13 -

addition, I require that all target firms have pre- and post- buyout financial information from COMPUSTAT or Capital IQ, 11 and missing data are filled from SEC filings. This drops 384 transactions, the majority of which are the buyouts in the 1980s, as Capital IQ mainly provides financial statement information for the 1990s and the 2000s. My final sample consists of 501 LBO transactions. Figure 2 presents the total number of LBO deals (left y-axis, solid line) and the inflation-adjusted 12 total transaction values (right y-axis, bar) by LBO effective year. 13 The first LBO boom occurred in the late 1980s, with a total transaction value increasing from $24 billion in 1987 to a peak of $81 billion in 1989. The largest deal during the time was KKR s buyout of RJR Nabisco in 1989 with a transaction value of $39 billion. This first wave of LBOs ended with the recession in 1990-1991 when the high yield bond market collapsed. The buyout market started to resume in 1996 but crashed with the bursting of the tech bubble in 2001. In the mid-2000s, LBOs reappeared in a third buyout boom. Total transaction value increased sharply from $5.4 billion in 2002 to $65 billion in 2005 and reached a historical high of $273 billion in 2007. The years 2006 and 2007 observed the surge of the mega-buyouts, including the acquisitions of TXU ($42 billion, later renamed as Energy Future Holdings Corp ), HCA Holdings, Inc ($33 billion), Kinder Morgan ($28 billion), and First Data ($27 billion). Table 1 breaks down the sample by industry grouping based on SIC codes. Target firms are from eight broad industries but are concentrated in the manufacturing sector with approximately 44.5% of the sample coming from this sector. Firms in the service industry and the wholesale and retail industry are the next biggest grouping. In the late 1980s and early 1990s, almost 50% of the buyout transactions were from the manufacturing sector. Since the year 1997, relatively more firms come from the service and the wholesale/retail industries. Overall, the sample shows an increased industry scope for LBOs over time. 11 Capital IQ provides financials for private companies that belong to one of the following categories: (1). Private companies with publicly traded debt, (2). M.A. targets filing financials in 8-K/A SEC forms; (3). D&B Financials; (4). U.S. bank subsidiaries filing with various regulatory bodies in the U.S., such as FFIEC, CUA, OTS. My sample contains samples of cases (1) and (2). 12 In the rest of the paper, unless specified, all values are inflation adjusted with 2005 as the base year. 13 LBO effective year is the year an LBO is complete. This is shown as the deal closed date or effective date in Capital IQ and SDC. - 14 -

I next examine how buyout price have varied over time. Following Kaplan and Stein (1993), I measure the buyout price, referred to as total capital, as the sum of the market value paid for the target firm s equity, the value of the firm s outstanding debt, and the fees paid in the transaction, minus any cash removed from the firm to finance the buyout. Column (1) of Table 2 presents the sample medians of total capital by LBO effective year. For the full sample of 501 transactions, the median buyout price is $ 620.97 million. There is a trend towards larger deals in later years, significant at 1% level based on the nonparametric trend test. The median capital for LBOs in the 1990s is $382 million. It increased to $1,956 million in 2007 due to the mega club deals and reached $2,223 million in 2011 after the LBO markets recovered from the crisis. I describe the buyout price relative to fundamentals using two primary measures of cash flows: EBITDA and the net cash flow (NCF) in the last full year prior to the buyout completion, where NCF is calculated as EBITDA minus capital expenditures. Columns (2) and (3) present the median ratios of EBITDA and NCF to total capital by LBO effective year. The nonparametric trend test shows that both ratios have been decreasing significantly at 5% level, suggesting a trend of increased buyout price over time. The decline in buyout cash flow to price ratios may not necessarily reflect anything unique to the buyout market. To control for overall market trends and macroeconomic factors, I follow Kaplan and Stein (1993) and calculate the market-adjusted measures that subtract the earnings to price ratio of the S&P 500 14 from the buyout ratios of EBITDA and NCF to total capital for the quarter in which the deal was priced. Columns (4) and (5) show that market-adjusted measures still exhibit a significant downward trend, indicating an increase in buyout prices relative to fundamentals over time. Another way to look at the extent of a buyout-specific trend in prices is to examine the premium paid in the buyout transactions, calculated as the percentage difference between the price paid for a firm s equity and the firm s stock price 1 month before the first announcement of buyout. 15 Column (7) presents the annual median premiums paid to 14 The S&P 500 earnings data are downloaded from the S&P Index Data Platforms. 15 I also calculate the premium using stock 1 day and 1 week before the buyout announcement, the results are robust. - 15 -

shareholders. The median premiums show no significant time trend over the entire sample periods. The difference between the trend of buyout premium and that of market-adjusted ratios of buyout price to fundamentals may be due to the missing data in calculating buyout premium, as some of the stock prices are missing for the sample firms. As a result, the rest of this paper will use price relative to fundamentals instead of buyout premiums. 3.2. Evidence on post-buyout operating cash flows In this subsection, I calculate measures of post-buyout operating performance and examine how they have changed over time. 3.2.1 Methodology To document the post-buyout operating performance, I use the operating income as measured by EBITDA and net cash flow (NCF). Operating income measures the cash generated from buyout firms operations before depreciation, interest, or taxes, and the calculations do not include gains or losses from sales of divisions or assets. NCF is the primary component of the numerator in a net present value analysis to value a buyout company. A permanent increase in NCF, therefore, should lead to an increase in value. EBITDA and NCF are scaled by sales and average assets for each fiscal year. Performance change is calculated as the percentage changes of these cash flow measures in the first three full years after the year of LBO completion (year +1, +2, and +3) compared to the last fiscal year before the buyout completion (year -1). In order to evaluate the economic and statistical significance of pre- to post-buyout changes in performance, I follow Kaplan (1989a) and Guo et al. (2011) and calculate the industry-adjusted performance measures. The industry-adjusted change equals the percentage change in the cash flow variables for the target firms minus the median percentage change over the relevant period for all Compustat firms in the same industry. Firms in the same industry as the target firms are those that have the same four-digit SIC code. Comparisons are made at the three-digit level and then at the two-digit level if fewer than five industry matches are found. - 16 -

3.2.2. Evidence on changes in operating performance Table 3 summarizes the medians of unadjusted and market-adjusted percentage changes in operating performance for the last full year prior to completion of the buyout year (year -2 to year -1) and from year -1 to up to 3 years after the buyout completion. Panel A shows the median changes over the entire sample period and Panel B presents the medians in each LBO wave and their time trend. As shown in Panel A, industry-adjusted percentage increases in EBITDA to sales are significant at 7.0%, 6.9%, and 8.9% in years +1, +2, and +3 relative to year -1. Changes in NCF to sales adjusted by industry medians are also positive and significant, with medians of 18.5%, 13.9%, and 19.6% in years +1, +2, and +3. In contrast to the increased cash flow variables that are scaled by sales, there are 12.2% and 9.8% significant decreases in EBITDA over average assets in year +1 and +2 relative to year -1. The median changes become insignificant in year +3. The significant decrease in the first year after buyout can be explained by the fact that most LBO firms write up the book value of their inventories at the time of the buyout (Kaplan, 1989b). According to Kaplan (1989b), this paper inventory write-up is expensed in the first full year after the buyout. As a result, cost of goods sold is artificially high and the measured change in operating income as a ratio of average assets during the period underestimates the true change. Also, as argued by Kaplan (1989b), for most of the buyouts, buyout accounting leads to a change (usually an increase) in the book value of the assets, representing the difference between the market value of equity and the book value. This may also lead to an underestimates of operating improvement or an overestimate of performance declining. The industry-adjusted changes in the net cash flow as a proportion of average assets decreased by 5.7% in the first year after the buyout (significant at 10%), the median changes then become insignificant in year +2 and +3. Overall, the evidence in operating performance for the whole sample period suggests industry-adjusted performance improvement after the buyouts. According to Kaplan (1989a) who finds a significant increase in operating performance for the management buyout in the 1980s and Guo et al. (2011) that documents little performance improvement for LBOs in the 1990s and early 2000s, there may be a - 17 -

fundamental change in the performance of LBOs during the sample periods. Therefore, I divide the sample period into three sub-periods, 1986-1993, 1994-2001, and 2002-2011, based on the cyclicality of the LBO market presented by Figure 2. Panel B of Table 3 shows the median changes of performance in each sub-period and the time trend. The nonparametric trend test results show that there is less performance improvement in the more recent deals for all four measures. For example, during the period of 1986-1993, the industry-adjusted percentage increases in NCF to sales are significant at 32.7%, 28.2%, and 31.5% in the first three years after the buyout. The increases between 1994 and 2011 are still significant, but by a lesser extent at 18.5%, 13.7%, and 29.8%. From 2002 to 2011, only the increase in the first year after buyout is significant at 13.3% and the changes in years +2 and +3 are insignificant. To conclude, results in this section show some evidence of performance improvement after the buyout. However, this is mainly driven by LBOs in the early years, as there is a significant trend of decreased post-buyout performance improvement in the more recent deals. 4. LBO deal characteristics and participants involvement Having documented a decreasing trend of improvements to operating performance, this section studies factors that are expected to be important drivers for performance based on the debt disciplining, lenders monitoring, private equity reputation, and management participation hypotheses developed in section 2.2. Specifically, I focus on the pre- and post-buyout leverage and its change, the composition of LBO debt and its contractual features, the reputation and bank affiliation of private equity firms, and the trend in club deals. 4.1. Leverage, debt structure, and contractual features The debt disciplining hypothesis and the lenders monitoring hypothesis state that firms that have larger amount of leverage added during the LBOs and are more closely monitored by lenders will experience more improvement to operating performance. Having - 18 -

documented a decreasing trend of performance improvement, I now examine whether and how leverage, bank debt proportion, loan covenants and maturity structure have changed over time. 4.1.1. Leverage Panel A of Table 4 reports the capital structure change from the buyout. Columns (1)-(3) show the sample median of the pre- and post-buyout leverage and leverage changes, calculated using the financial data from Compustat and Capital IQ. Prior to the buyout, firms have a median leverage ratio of 31.92%. Leverage increased significantly to a sample median of 64.35% after buyout, with a median percentage increase in leverage of 30.55%. Column (4) shows the median leverage change of all Compustat firms. Comparing the LBO sample and the Compustat population, I find that the leverage increase is unique to LBO firms. Over time, both the leverage change and the post-buyout leverage ratio have been decreasing. Column (5) shows that equity ratio has a significant increasing trend over time. The decreasing trend of leverage change, in combination with the declined post-buyout performance, provides some preliminary evidence for the debt disciplining hypothesis. 4.1.2. LBO debt structure I next study the composition of LBO debt and its contractual features, using tranche (or facility ) level data constructed from Dealscan and Capital IQ. LBO debt is syndicated through different tranche. According to Miller (2012), revolving credit facility and amortizing term loan (term A loan, or TLa) are usually packaged together and syndicated to banks. 16 The term B, C, and D loans are structured specifically for institutional investors, such as structured investment vehicles, loan participation mutual funds, hedge funds, pension funds, insurance companies, and other proprietary investors (Miller, 2012). Therefore, I consider the revolving credit facility and the term A loan tranche in a loan package as bank debt and term B, C, and D loans as institutional debt. I also consider notes 16 A revolving credit facility allows borrowers to draw down, repay, and re-borrow capital over time. A term loan is an installment loan that requires a complete withdrawal of funds at inception. - 19 -

that are sold to institutional investors as institutional debt. The bridge loan tranche from Dealscan and the mezzanine debt from Capital IQ that are subordinated to bank and institutional debt are considered the junior debt. 17 Panel B of Table 4 presents the structure of LBO debt over the years. For each category of debt, I calculate the ratio of the amount of debt to total LBO debt and the percentage of LBO deals that use this type of debt. Columns (1) and (2) demonstrate the use of revolving credit facilities and term A loans in financing LBO debt. The ratio of revolving credit facilities to total LBO debt has decreased over time, from 61% in 1986 to 13.7% in 2011. The ratio of term A loans to total LBO debt has also declined, from its peak of 76.9% in 1993 to 1.2% in 2011. Although most LBOs continue to use revolving credit facilities, the proportion of LBOs that use A-term loans significantly declined from 47.1% in 1993 to 5.7% in 2011. As a result, the ratio of bank debt to total LBO debt and the percentage of LBO deals that use bank debt have decreased significantly, as shown in Column (3). All of these time trends are consistent with the declining importance of banks in financing LBO deals. With the decrease of bank debt, there has been an increasing use of institutional debt, as presented in Column (4). The proportion of institutional debt has increased from 1.2% in 1989 to 58.4% in 2011 and the nonparametric trend test shows a significant increase in the proportion at 1% level. Since 1998, the proportion of institutional debt in total LBO debt has exceeded that of bank debt, suggesting that institutional investors have become more important in the LBO market. The last column of Panel A demonstrates the use of junior debt. The use of junior debt is mainly concentrated in the late 1980s and the 2000s. The large proportion of junior debt in the total LBO debt in the late 1980s corresponded to the use of high yield junk bond whereas since 2002 the use of mezzanine debt in LBO financing has increased dramatically. In summary, analysis of this subsection shows that the importance of banks as financiers of LBO transactions has been decreasing and that institution debt has become 17 I follow Shivdasani and Wang (2011) and record bridge loans as high-yield bond/note and mezzanine finance. Mezzanine debt is a committed financing with individually negotiated terms. All or part of the interest expense of the mezzanine debt is often in the form of additional securities rather than cash. Investors of the mezzanine debt are typically insurance companies and the mezzanine funds. - 20 -

more important in financing LBOs. These trends may provide some explanations for the decreasing performance of post-buyout firms as bank monitoring has been considered as instrumental in reducing agency costs of debt and therefore creating value in LBOs (Diamond 1984, 1993; Park 2000). This relation will be tested in the multivariate analysis in the next section. 4.1.3. LBO loan spread, maturity, and covenants I next examine the spread, maturity, and covenants of bank debt verses institutional debt. Columns (1)-(3) of Table 4 Panel C present medians of the all-in-drawn interest spread of bank debt, institutional debt, and their differences. All-in-drawn spreads (over 6-month London Interbank Offered Rate (LIBOR) for each tranche are from Dealscan and include both the interest cost and fees associated with borrowing. Column (1) shows a significant increasing trend over time for spreads of bank debt. Institutional debt spreads are higher than bank debt spreads but the institutional-bank difference reported in Column (3) narrows with the increased usage of institutional debt in LBO financing. This is consistent with Miller s (2012) argument that the spread difference between institutional loans and bank loans narrows when the institutional demand for syndicated loans is high. I expect that the maturity structure of LBO loans is related to performance as shorter maturity increase required debt service payment in the earlier stage of the LBOs and therefore increasing the incentives for managers to work hard to generate cash and to avoid wasting resources. Columns (4)-(6) show the median maturity (in months). There is no significant change in the maturity of bank debt. However, the maturity of institutional debt has decreased throughout the sample period, from 120 months in 1992 to 79 months in 2011, suggesting better monitoring of institutional investors in the more recent LBOs. I also examine the covenants of LBO debt as they provide specific requirements and restrictions on management behavior that reduce the agency costs associated with the conflict of interests between shareholders and debt holders. Covenant information is obtained from Dealscan. To measure the tightness of covenant restrictions, I use the covenant intensity index developed by Bradley and Roberts (2004). This index indicates whether each loan package contains the following six specific covenants asset sale sweep, - 21 -

debt issuance sweep, equity issuance sweep, financial covenant, dividend covenant, and secured debt covenant and counts the number these covenants in each package. The first three covenants, also referred to as the sweeps, specify the percentage of net proceeds from an asset sale, debt issuance, or equity issuance that the borrowers must use to pay down any outstanding debt. Financial covenant enforces minimum financial performance measures that the borrowers must maintain. Bradley and Roberts (2004) define a binary variable that is equal to 1 if the loan contract contains more than two restrictions on financial ratios and zero otherwise. Dividend covenant restricts the ability of the borrowers to distribute cash to shareholders and secured debt covenant requires the debt to be secured. The covenant intensity index counts the number of covenants included in each loan package and the index ranges from 0 through 6. Covenants are unique to packages, so that every tranche in a package is covered by all of the covenants. If an LBO uses multiple loan packages, I use the index of the most covenant-heavy package as the covenant index of the deal. Column (7) presents the median covenant intensity index each year for loans with non-missing covenant information. The number in the bracket shows the proportion of LBOs with non-missing loan covenant information of total LBOs each year. As information on covenants is fairly limited prior to 1994, loans syndicated before 1994 only have secured debt covenant reported. During the period of 1995-2011, LBO loans have a median of 5 covenants based on the measure of Bradley and Roberts (2004) and there is a significant decrease in covenant tightness. In 2002, the median number of covenants was 6; however, the number dropped to 1 for LBO loans syndicated in the late 2000s. The Bradley and Roberts (2004) s index only considers the presence of financial covenants. It does not take into account the number or different types of financial covenants used in a debt contract. However, there is large variation in the use of financial covenants in my sample and the number of financial covenants included in the LBO loan packages ranges from 0 through 6. Therefore, I modify the index by including the number of financial covenants used in each package. Specifically, the modified covenant intensity index is the sum of (1) number of financial covenants (up to 6), (2) number of sweeps (asset sales sweep, debt issue sweep, equity issue sweep, excess cash flow sweep, insurance - 22 -

proceeds sweep 18 ), (3) dividend covenant (0/1 variable), and (4) secured debt covenant (0/1 variable). 19 The modified index is presented by Column (8). LBO loans constructed between 1998 and 2002 used more covenants, with a median of 8-10 covenants. The number of covenants decreased in the second half of the 2000s with only 1 or 2 covenants for LBO loans in 2010 and 2011. Column (9) shows the proportion of LBO loans with no financial maintenance covenants (the covenant-lite loans). 20 There is a general trend towards more covenant-lite loans over time. In summary, results from Columns (7)-(9) show declining tightness of covenant restrictions of LBO loans, suggesting weaker monitoring by lenders that may lead to worse post-buyout performance. 4.2. Private Equity in LBOs 4.2.1 Private Equity Involvement The private equity reputation hypothesis on LBO value creation states that LBOs sponsored by PE firms of high reputation tend to perform better as these PEs have better skills to improve performance and to reduce risks of target firms (Kaplan and Schoar, 2005; Axelson, et al., 2009). Therefore, I examine the changing characteristics of PE firms that are expected to be instrumental to LBO success. To identify PE firms, I download all private equity funds (PE funds) from Capital IQ. For each LBO transaction that is collected from Capital IQ, I merge the buyer Excel Company ID of the LBO to the PE funds Excel Company ID. For the buyout sample from SDC, I run a text search for the names of PE firms in the transaction synopses and hand match with the PE funds from Capital IQ. As 18 The excess cash flow sweep and the insurance proceeds sweep specify the percentage amount of net proceeds a borrower receive from excess cash flows or insurance settlements that must be used to pay down any outstanding loan balance. Including the two sweeps ensure that all sweep covenants are considered in the modified intensity index. 19 Covenant intensity measures used in this paper indicate the presence of certain covenants in the loan contract, not the actual threshold of each covenant. This is because the thresholds for financial covenants and sweeps are related to many factors, such as the credit market conditions and the borrowers specific characteristics. Therefore, it is hard to compare the threshold directly. 20 According to Bavaria and Lai (2007), S&P define covenant-lite loans as those with no maintenance financial covenants that have to be maintained quarterly through the term of the loan. Instead, covenant-lite loans have only incurrence covenants that do not have to be met on an ongoing basis as maintenance covenants do. Incurrence covenants only restrict the borrower s ability to issue new debt, make acquisitions, or take other action that would breach the covenants. - 23 -

this paper is to look at private equity involvement at firm level, I consolidate PE funds to PE firm level. So if one PE firm has multiple active PE funds, I use the Excel Company ID at the PE firm level for the analysis. For example, both Lehman Brothers Mezzanine Fund and Lehman Brothers Capital Partners IV are identified at the PE firm level as the Lehman Brothers, Private Equity Division. I also track the name changes of the PE firms. Of the 501 LBO deals in the sample, 448 deals have at least one PE firm involved. The remaining transactions are either management buyouts or buyouts by another corporations with no PE firm involved. There are in total 234 PE firms sponsoring the 448 deals. Appendix A presents the top 25 PE firms of these 234 PE firms by the number of deals and total transaction values of buyouts they sponsored. The most frequent PE firms are Kohlberg Kravis Roberts & Co (27 deals), TPG Capital (26 deals), The Blackstone Group, (22 deals), Goldman Sachs Capital Partners (21 deals), and Bain Capital Private Equity (20 deals). Panel A of Table 5 presents the PE firms involvement in LBOs over the sample period. Column (1) shows the number of buyouts that have PE firms involved and its proportion in all LBOs in the year (in the brackets). There is an overall trend of increased PE involvement. In the late 1980s, the average proportion of LBOs with PE firms involved is around 73%. In the 1990s, almost all LBOs have PE firms. Following Officer, Ozbas, and Sensoy (2010), I categorize a PE firm as a prominent PE if it is listed as the 50 largest PE firms by the Private Equity International (PEI) magazine from the year 2007 to 2013. Starting 2007, the PEI magazine ranks PE firms based on the capital raised over the previous five-year period. I add the PE firms that are listed as the top 25 PEs in my sample from Appendix A to this list of prominent PE firms if they are not already included in the prominent list. The PE firms added are mainly those that are subsidiaries of banks (the bank-affiliated PEs), as these firms may not be on the PEI list because they may use internal capital rather than relying on external fundraising. Following Officer et al. (2010) I also add Forstmann Little and HM Capital Partners (formerly Hicks, Muse, Tate, and Furst) because they are historically prominent PE firms that have been less active in recent fundraising. Column (2) shows that 52% of the LBOs in my sample have prominent PE firms and there is a trend of more prominent PEs involved in LBOs over time. Fang et al. (2012) argue that it is important to look at bank-affiliated LBOs where the - 24 -

PE firms are subsidiaries of major banks as these deals may have different characteristics than LBOs sponsored by the stand-alone PE firms. First, bank-affiliated PEs may have better access to funds provided by their parent banks should an LBO opportunity rise and these PE firms are better able to take advantage of the favorable credit market conditions. Second, these bank-affiliated deals provide the parent banks with cross-selling opportunities (such as potential M&A advisory work, cash-management services, etc.) that increase parent banks fee income. As a result, buyout decisions may not be based on PE s expectation on efficiency improvement of target firms, but only to take advantage of these cross-selling opportunities. To confirm bank affiliation, I check whether a PE firm is a subsidiary of a bank at the LBO announcement day. 21 I further require that the parent bank provides loans for the deal. There are in total 103 bank-affiliated deals (21% of 501 LBOs) in the sample and the total transaction value of these deals is 35% of the sum of transaction value of all 501 LBOs. This is similar to the findings of Fang et al. (2012) that bank-affiliated groups account for nearly 30% of the overall private equity market, and the findings reported by Lopez-de-Silanes, Phalippou, and Gottschalg (2011) that roughly one-third of the investment in the global private equity dataset are done by PE groups that are subsidiaries of banking and finance companies. Column (3) presents the number of bank-affiliated deals by year. Bank-affiliated deals exhibits cyclicality corresponding to the LBO market activities. In the years 1989, 1998, and 2007, when the LBO activities were at the peak of the cycle, the proportions of bank-affiliated deals in total LBOs was higher. This is consistent with Fang et al. (2012) that PE firms time the market and bank affiliations allow these PE firms to take advantage of favorable credit market conditions. I then look at the club deals, where a consortium of two or more PE firms are involved in an LBO deal. Column (4) shows the number of club deals and its percentage over total LBOs each year and Column (5) presents the sum of transaction values of club deals each year and its ratio over the total transaction values of the 501 LBOs. 30% of the LBOs in my sample are club deals and the total transaction values of these deals are 50% of 21 Some PE firms started as subsidiaries of major banks but later became independent. I only consider an LBO as bank-affiliated if it is announced during the time the PE firm is subsidiary of a major bank. - 25 -

all deals. This is consistent with PE firms pooling their assets to acquire large targets. Column (6) shows the maximum number of PE firms in a club deal consortium by year. Club deals are very rare in the late 1980s and early 1990s and they start to become important in the late 1990s. During 2005-2008, club deals reached their peak almost 50% of LBOs were club deals and the total transaction value of these deals was around 80% of all LBOs during the time. The largest-ever LBO, the buyout of TXU in 2007 with a transaction value of $44.5 billion was conducted by a consortium including KKR, the TPG Capital, and Goldman Sachs Capital Partners. As shown by Column (6), there is a general trend of more PE firms cooperating in a club deal. During the whole sample period, there are on average 2.6 PE firms in the consortium of the club deals (not tabulated). The deal that has the largest number of PE firms involved is the buyout of SunGard Data Systems sponsored by seven PE firms and completed in 2005 with a transaction value of $11.5 billion. 22 4.2.2. Private Equity Reputation Having documented the time trend of PE firms involvement in LBOs, I construct measures for private equity reputation that capture PE firms past experience and skills. Previous empirical studies measure PE fund reputation in a number of different ways that include fund size, its market share, the number of recent LBO transactions, and the number of previous fund raisings 23. As the PEs in my sample are at the firm level, I use reputation measures that can be constructed for each PE firm. I follow Demiroglu and James (2010) and construct reputation measures based on the number of LBOs sponsored by the PE firm and the total transaction values of these deals in the past 36 months or since 1970. 24 In order to get the LBO transaction history, I use all Capital IQ recorded LBO, MBO, SBO transactions for U.S. target firms since 1970 plus the buyout sample from SDC a total of 19,014 deals. In the case of club deals, I consider the buyout as a full deal for each PE firm. When calculating LBO deal-level PE firm reputation, I use the reputation score of the PE 22 The seven PE firms were Silver Lake, Bain Capital, the Blackstone Group, Goldman Sachs Capital Partners, KKR, Providence Equity Partners, and the TPG Capital. 23 See Demiroglu and James (2010) for discussions on strengths and weaknesses of each reputation measures. 24 The earliest LBO deal documented by Capital IQ is in 1970. - 26 -

firm with the highest reputation if the deal has multiple PE firms. The reputation score is set to zero if there are no PE firms involved in the buyout deal. Panel B of Table 5 presents the PE reputation measures. The first reputation measure is based on the number of deals. Columns (1) and (2) show the medians of the reputation measures calculated as the natural logarithm of the total number of deals completed by a certain PE firm during the past 36 months and since 1970, respectively. Both reputation measures have shown an increasing trend, suggesting that PE firms are more involved in LBO deals now than before. A second measure for PE firm s reputation is based on its market share. Market share of each PE firm at the time of a new LBO deal is calculated as the ratio of the number of deals completed by the PE firm in the prior 36 months over the total number of deals (based on the 19014 deals) during the same 36 months. I also calculate PE firm s market share as the ratio of the total transaction value of LBOs by the PE firm in the prior 36 months over the total transaction value of all LBOs during the 36 months. Column (3) and (4) shows the median reputation score based on market share. The median PE firm in my LBO sample has about 0.25% (mean 0.44%) market share by number of deals and 0.89% (mean 2.1%) market share by deal value in the LBO market. Another reputation measure is years of experience, which is calculated as the number of years based on the first ever LBO deals sponsored by the PE firm and the last LBO deals in the sample. Overall, the median PE firm in the sample has 24 years (mean: 21 years) of experience and has invested in 15 LBOs (mean: 22 LBOs) since 1970 (not tabulated). In summary, analyses of this subsection show that PE firms have become more involved in LBO deals, as evidenced by the increasing proportion of LBOs sponsored by PE firms in the more recent years and the increasing importance of club deals. Bank-affiliated LBOs have shown some cyclicality that corresponds to the credit market condition, suggesting some market timing of these deals. The reputation score for PE firms based on their market share has been decreasing. The next section will examine how these PE reputation and involvement are related to performance. - 27 -

5. Explanations for post-buyout operating performance In this section, I examine the relationship between operating performance and the LBO deal characteristics that are expected to be related to performance change. My goal is to test the different hypotheses developed in Section 2.2 to determine factors that contribute to value creation in LBOs. The analysis will also help to understand whether the documented changing characteristics of LBOs can be used to explain the reduced performance improvement observed in the more recent LBO deals. Table 6 reports the multivariate regression results for post-buyout operating performance and its drivers. The dependent variables are the industry-adjusted percentage changes in EBITDA/sales from the last full pre-buyout year (year -1) to the second full year after deal completion (year +2). This allows me to include LBO deals completed by the end of 2010 to look at the performance of LBOs during and after the 2007-09 financial crisis. Also, I use EBITDA scaled by sales, instead of total assets, to avoid the inventory write-up problems related to total assets as described in Kaplan (1989b). To control for pre-buyout characteristics of target firms, all regressions include leverage ratios at the end of year -1 and the industry-adjusted change in EBITDA/sales from year -2 to year -1. All regressions control for year and industry effect. First, I look at the disciplining effect of leverage. Column (1) shows that the effect of leverage change on performance is significantly positive. That is, firms with greater amount of leverage added during the LBOs show more post-buyout performance improvement, supporting the debt disciplining hypothesis. I next examine whether and how performance is related to the monitoring by lenders. I include the proportion of LBO debt that is funded by banks, with the expectations that banks have more incentives and advantages to monitor the borrowing firms and that the percentage of bank debt is proportional to banks monitoring effort. I also include the modified covenant intensity index that measures the presence of different covenants in LBO loans. The maturity structures of LBO debt are also used in the regression, with the expectation that shorter maturities indicate better monitoring by lenders therefore leading to better performance. 25 25 For robustness, I replace the maturity of the whole LBO loans with maturity of bank loans and maturity of - 28 -

Column (2) demonstrates a significantly positive coefficient for covenant intensity, suggesting that controlling for the leverage effect, tighter covenants further improves the post-buyout performance. These covenants generally put restrictions on the target firms use of cash flow, therefore further reducing the agency costs of the free cash flow problems. Covenants also require target firms to maintain certain financial ratios and restrict them from using more debt, possibly motivating managers to improve efficiency and to increase earnings. However, bank debt proportion and maturity are insignificant drivers for performance. I then examine the effects of PE firms reputation and involvement on performance. I use different PE reputation measures constructed in the last section as independent variables in the regression. However, none of these reputation measures is significantly related to performance (Column (3)). Column (4) shows result for a regression that includes the club deal dummy that takes the value 1 if an LBO has more than two PE firms involved and 0 otherwise. It has been argued that in a club deal, different PE firms bring different expertise to the target firm s management, therefore providing another source of value creation. 26 However, the result in Column (4) does not support this argument. Another view on club deal is that as the size of the consortium of PE firms gets bigger, it is harder to make timely operational and management decisions. Experts of the private equity industry suggested that the optimal size of the consortium is two or three PE firms. 27 I construct an optimal consortium size dummy variable that takes the value of 1 if there are two or three PE firms in a club, and 0 other wise. However, regression result presented by Column (5) shows no evidence that the optimal consortium size is related with performance. In column (6), I include a dummy variable that indicates whether an LBO is bank-affiliated deal, where bank affiliation is defined as (1) the PE sponsoring the deal is a subsidiary of a institutional loans, the results are similar. 26 For example, New York Times commented on KKR, Bain, and Vornado Realty Trust s buyout of Toys "R" Us that it was clear what each firm brought to the table. Kohlberg Kravis has a good reputation in the retail business, Bain has a good record doing turnarounds and Vornado clearly knows real estate. Source: Do Too Many Cooks Spoil the Takeover Deal, the New York Times, April 3, 2005. 27 For example, Jeffrey Walker, a managing partner of CCMP Capital, said that I find it very difficult managing a deal that has more than two or three investors. Source: Buyout Veterans Have Questions about Club Deals, Dow Jones Newswires, January 24, 2007. - 29 -

bank and (2) the parent bank provides loans for the deal. I find that bank affiliation has no significant effect on performance. In sum, regression results presented in Columns (3)-(6) provide no evidence that PE firms involvement or reputation is related to performance of target firms. I test the management participation hypothesis that LBOs tend to perform better when managers of target firms contribute equity and participate in the buyout as their incentives are better aligned with other shareholders. I use a dummy variable that indicates management participation. Specifically, the dummy variable equals 1 if Capital IQ labels a transaction as management buyout, management participated, individual investor participated when the individual investor is confirmed to be board member or management of the target firm, or the firm is bought out through an employee stock ownership plan (ESOP). If a transaction is from the SDC database, the dummy is equal to 1 if the SDC synopsis describes the deal as management led or management participated. Results in column (7) support the management participation hypothesis I next examine whether management turnover is related to performance. I go through Factiva and manually collect news on CEO and CFO change from the time of buyout announcement until the deal reaches a final outcome (bankruptcy, IPO, or a sale to another buyer). I supplement the Factiva results with the Key Development on Corporate Structure Related news from Capital IQ. From the announcement date to the final deal outcome day, 212 firms (42%) experienced a change in the CEO and 167 firms (33%) experienced CFO change. In the regression, I use a CEO change dummy variable that indicates whether there is CEO change from the buyout announcement to two full years after the buyout completion. 28 This is because the independent variable is the change in EBITDA/sale in year +2 compared with year -1. Column (8) shows an insignificant coefficient for CEO change. To conclude, regressions results of Table 6 support the debt discipline, lenders monitoring, and management participation hypotheses of value creation in LBOs, but do not support the hypotheses of private equity reputation, club deals, or bank affiliations. 28 I also use a dummy for CFO changes, or a dummy for both CEO and CFO changes. The results are the same. - 30 -

6. Robustness 6.1. Credit Market Conditions Previous studies have shown that LBO buyers, whether they are PE firms, managers of target firms, or other corporations, take advantage of favorable credit market conditions. For example, Kaplan and Stein (1993) present evidence that the 1980s LBO boom was driven by the attractive terms of high yield bonds. This result suggests that more LBOs will be undertaken when the credit market is more favorable and leverage is cheaper to acquire and that LBO buyers may overinvest in unprofitable deals during the time. As a result, LBOs completed during the favorable credit market conditions may perform worse than other deals. In this section, I test whether the key results from my hypotheses hold when controlling for the impact of credit market conditions on performance. Following Barry, Mann, Mihov and Rodriguez (2008), I add to the baseline regression the Baa yield and the difference between the Baa yield in the month of LBO completion and its 60-month historical average. 29 I also include the term structure, calculated as the difference between 10-year T-Bond yield and three-month T-Bill yield. Column (1) of Table 7 presents the regression results. Leverage change, covenant intensity, and management participation are still significantly and positively related to post-buyout performance after controlling for interest rates. In addition, there is no evidence of worse performance for deals announced at the time of favorable credit markets. Another way to examine market conditions is to look at the hot versus cold LBO market. Following Colla, Ippolito, and Wagner (2012), I construct a hot market dummy by taking a 12-month centered moving average of the number of LBOs for each month over the sample period. Hot months are defined as above the median in the distribution of the monthly moving average across all months. The hot market dummy takes a value of 1 if a deal is completed in a hot month, and zero otherwise. Column (2) shows that controlling for the LBO market condition, leverage, covenants, and management participation are still important drivers for performance, and that deals announced during hot LBO market do not generate worse performance than other deals. 29 Regressions with the month of LBO announcement generate the same results. - 31 -

I also examine whether performance is related to the LBO loan spread. The spread at the LBO deal level is calculated as the weighted average of all-in-drawn spread across all tranches. While the Baa yield, its difference from the historical average, term structure, and the hot market dummy are related to the general credit market and LBO market condition, the loan spread measures the actual cost of debt for each LBO deal. If LBO buyers overinvest in unprofitable deals when leverage is cheaper to acquire, I expect to find less performance improvement when the LBO loan spread is lower. Column (3) shows that controlling for loan spread, the effects of leverage, lenders monitoring, and management participation in the LBO transaction are still significant. In the meanwhile, LBO loan spread is not significantly related to performance. I also look at deal price, calculated as the ratio of EBITDA over the total transaction value, adjusted by subtracting the S&P 500 market earnings/price ratios for each month of LBO completion. Column (4) shows that leverage change, covenant intensity, and management participation are still significant and that deal prices do not affect performance. To summarize, robustness analyses of this section show that leverage, covenants, and management participation are still important drivers for post-buyout performance enhancement after controlling for credit market conditions, LBO market conditions, loan spread, and buyout prices. These results again support the debt discipline, lenders monitoring, and management participation hypotheses while rejecting the private equity reputation hypothesis. In addition, I find no evidence that LBOs constructed during favorable market conditions perform worse than other deals, nor is performance related to prices paid for LBOs. 6.2. LBO deal outcome In this section, I conduct additional tests on the effects of LBO deal characteristics on performance, where performance is now measured by the ultimate outcome of these deals. I search Factiva for SEC filings and news to identify deal outcomes that include (1) bankruptcy or distressed exchange, (2) a subsequent IPO, (3) a sale to a strategic buyer (4) a sale to a financial buyer (also known as the secondary LBO), (5) still privately held by - 32 -

the same buyer, or (6) unknown. I supplement Factiva information with the Capital IQ Tearsheet and the company history from its website. Table 8 shows the post-buyout outcomes as of June 30, 2013 by LBO effective year. Over the entire sample period, 83 deals (16.6%) file for bankruptcy, initiate a financial restructuring, or go through distressed exchange. 35.3% of the LBOs exit through an IPO, 17% through a sale to a strategic buyer, and 11.8% through a sale to financial buyer. The majority of the deals in the late 2000s are still privately held by the same buyers. For all deals that have reached outcome, the median months to exit is 42 months (mean 47 months, not tabulated). Exit through an IPO or a sale to a financial or strategic buyer is generally considered as a successful outcome of an LBO. For example, Holthausen and Larcker (1996) find that the performance of LBO firms exceeds that of its industry rivals at the time of the IPO, suggesting that the IPO is related to LBO success. Harford and Kolasinski (2011) find that when a sponsor sells a firm to a public strategic buyer, the buyer s stock price reaction is positive. In order to test whether the key results from my hypotheses still hold when using deal outcomes to measure LBO success, I run logit regressions. Specifically, for the dependent variable, I create a success dummy that takes on a value of 1 if an LBO exits through an IPO or a sale to financial or strategic buyer and zero otherwise. 30 As most of the recent deals have not reached outcomes yet, I only consider LBOs that are completed by the end of 2008, taking into account that the last day of information collection on deal outcome is June 30, 2013 and that the median months to bankruptcy is 43 months (Table 8, Column (1). Column (1) of Table 9 presents results of the baseline regression using the success dummy as the dependent variable. Consistent with the lenders monitoring hypothesis, LBOs are more likely to reach successful outcomes if they are financed with higher proportion of bank debt and have tighter loan covenants. CEO changes during the time target firms are privately held by PE firms have negative impact on the deal outcome. Leverage change and the dummy for management participation are not significant. Column (1) also shows that LBOs sponsored by PEs with higher reputation score 30 Alternatively, I assign the value of 1 if the deal outcome is an IPO or a sale to financial buyer and zero otherwise, the results are similar. - 33 -

have better outcomes, supporting the private equity reputation hypothesis. I use different reputation measures that include PE s market share based on the number of deals or the total deal values in the prior 36 months or since 1970, the natural log of the number of deals in the prior 36 month or since 1970, and natural log of PE s years of experience. All measures generate significant and positive estimates on PE reputation. This result provides a clear picture of the roles of PEs in an LBO reputation is not directly related to better operating performance as measured by EBITDA and net cash flows to total assets or sales, but is important in ensuring successful outcomes of LBOs. Regression results also show that bank-affiliated LBOs are less likely to exit through an IPO or a sale, consistent with the bank affiliation hypothesis that deals sponsored by PE firms that are subsidiaries of banks tend to perform worse. This finding is also consistent with Wang (2012) and Fang et al. (2012). Wang (2012) uses accounting measures and finds that bank-affiliated LBOs in the U.K. underperform standalone deals. She argues that this is because bank-affiliated PE firms do not select good targets as other PEs do. Fang et al. (2012) find bank-affiliated deals have worse outcomes if they are consummated during the peaks of the credit market. Controlling for credit market conditions and deal prices in Columns (2)-(4), the results of bank debt proportion, CEO change, PE reputation, and bank affiliation still hold. Moreover, Column (2) shows that the probability of a successful exit strategy is significantly and positively related to the Baa spread relative to its historical average over the previous 60 months, after controlling for the absolute level of the spread. This suggests that LBOs are less likely to succeed if they are completed during the time of favorable credit market conditions. This result provides some evidence of market timing of LBO buyers that they tend to overinvest in unprofitable deals that may not exit successfully during the time when the overall credit markets are favorable and leverage is easier to acquire, as suggested by Kaplan and Stein (1993) and Axelson et al. (2009). To summarize, using the exit strategy of IPO or a sale to financial or strategic buyer as an indicator for LBO success, regression analyses show that an LBO is more likely to succeed if it uses more bank debt and tighter loan covenants, experiences no CEO change, and is sponsored by highly reputable PE firms. LBOs are more likely to fail if the buyers - 34 -

are subsidiary of banks that are also financiers of the deal. These results are in general robust to credit market conditions and deal prices. Findings of section provide evidence for the lenders monitoring, the private equity reputation, and the bank affiliation hypotheses. I also find some evidence of the market timing of LBO buyers. Alternatively, I use a failure dummy that takes the value of 1 if an LBO goes bankrupt, enters a distressed exchange, or initiates financial restructuring. Logit regressions using bankruptcy dummy as dependent variables show complementary results that LBOs are more likely to fail if they are sponsored by PE firms of low reputation, are bank-affiliated, and experience CEO change. 7. Conclusion Using a sample of 501 pubic-to-private U.S. LBO transactions completed between 1986 and 2011, I find that better performance is related to larger amount of leverage added during the buyout process, more restrictive covenants of LBO loans, and management contributing equity and participating in the buyout. These results suggest that the main source of value creation in LBOs is the reduced agency costs through the discipline effect of debt, closer monitoring by lenders, and the better aligned management incentives. These findings are robust after controlling for the credit market and LBO market conditions, costs of borrowing of target firms, and buyout prices. Findings of this paper deepens our understanding on the observed insignificant performance enhancement in more recent LBOs that use less leverage and relaxed loan covenant, which are important drivers for performance. Using deal outcome as alternative measures of performance, I find that LBO are more likely to exit through a successful strategy (an IPO or a sale to financial or strategic buyers) if they use more bank debt and tighter covenants, experience no CEO change, and are sponsored by PE firms with high reputation. These results are consistent with the lender s monitoring and the private equity reputation hypothesis in value creation in LBOs. Results of this paper suggest that controlling for deal and target characteristics and credit market conditions, private equity reputation is not related to changes in operating - 35 -

performance in the first three years after the buyout but is important in ensuring successful deal outcomes. Future research needs to examine the role of private equity firms in each stage of the buyout process in order to better understand the mechanisms through which reputable PE firms create value. - 36 -

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Appendix A: Top Private Equity Firms This table ranks private equity firms by the total dollar amount of transactions and the number of transactions they sponsored in my sample. Total transaction value is inflation-adjusted with the base year of 2005. Percentage in the brackets of Column (1) shows the sum of transaction values of all deals sponsored by each PE firm as a proportion of total transaction value of all 501 LBOs. Percentage in brackets of Column (2) presents the number of deals sponsored by each PE firm over the total number of LBOs in the sample. Club deals are LBOs sponsored by two or more PE firms. Parent bank is at the time of deal announcement. PEI 2013, 2000, and 2007 indicate the ranking of each PE firm by the Private Equity International magazine in the years 2013, 2000, and 2007. (1) Total Transaction Value (2) Number of Transactions (3) Club deals (4) Parent Bank (5) PEI (6) PEI (7) PEI Name of Private Equity Firm Rank Value % Rank Number % 2013 2000 2007 Kohlberg Kravis Roberts & Co. L.P. 1 $ 228,989 (25.45%) 1 27 (5.4%) 12 4 3 2 TPG Capital, L.P. 2 $ 183,501 (20.40%) 2 26 (5.2%) 20 1 4 5 Goldman Sachs Capital Partners 3 $ 142,897 (15.88%) 5 21 (4.2%) 15 Goldman Sachs 6 1 3 Bain Capital Private Equity 4 $ 100,316 (11.15%) 6 20 (4.0%) 13 9 8 8 The Blackstone Group 5 $ 83,372 (9.27%) 3 22 (4.4%) 13 3 7 4 The Carlyle Group LP 6 $ 78,450 (8.72%) 11 15 (3.0%) 7 2 2 1 Thomas H. Lee Partners, L.P. 7 $ 69,421 (7.72%) 7 19 (3.8%) 9 28 30 Lehman Brothers Private Equity 8 $ 66,788 (7.42%) 24 6 (1.2%) 1 Lehman Brothers 25 Merrill Lynch Global Private Equity 9 $ 64,841 (7.21%) 15 8 (1.6%) 5 Merrill Lynch Citigroup Private Equity LP 10 $ 55,982 (6.22%) 31 5 (1.0%) 4 34 27 Apollo Global Management, LLC 11 $ 49,629 (5.52%) 8 17 (3.4%) 8 8 5 12 Morgan Stanley Private Equity 12 $ 44,419 (4.94%) 88 2 (0.4%) 1 Morgan Stanley Providence Equity Partners LLC 13 $ 42,050 (4.67%) 20 7 (1.4%) 6 17 9 Madison Dearborn Partners, LLC 14 $ 37,172 (4.13%) 12 14 (2.8%) 7 24 32 Riverstone Holdings LLC 15 $ 30,017 (3.34%) 52 3 (0.6%) 3 11 29 Silver Lake 16 $ 28,807 (3.20%) 33 5 (1.0%) 4 27 33 19 Clayton, Dubilier & Rice, Inc. 17 $ 25,267 (2.81%) 29 6 (1.2%) 2 33 18 47 DLJ Merchant Banking 18 $ 25,024 (2.78%) 4 22 (4.4%) 11 Credit Suisse - 40 -

J.P. Morgan Partners, LLC 19 $ 18,848 (2.10%) 10 15 (3.0%) 10 JPMorgan Chase 13 Deutsche Bank AG, Investment Arm 20 $ 13,271 (1.48%) 30 5 (1.0%) 4 Deutsche Bank Warburg Pincus LLC 21 $ 13,071 (1.45%) 17 8 (1.6%) 2 5 9 14 Credit Suisse Private Equity, LLC 22 $ 12,770 (1.42%) 18 8 (1.6%) 7 Credit Suisse 31 Canada Pension Plan Investment Board 23 $ 11,545 (1.28%) 87 2 (0.4%) 2 20 Court Square Capital Partners 24 $ 9,704 (1.08%) 9 16 (3.2%) 4 Citigroup Leonard Green & Partners, L.P. 25 $ 9,679 (1.08%) 13 12 (2.4%) 5 39 31 31 Both DLJ Merchange Banking and the Credit Suisse Private Equity, LLC are PE firms listed as subsidiary of Credit Suisse. But I confirmed that they are different PE firmks. - 41 -

Table 1: LBO Year and Industry The table classifies transactions by LBO effective year and target firm industry. Eight broad industry classifications are defined according to SIC codes: (1) Agriculture/Fishing/Forestry (SIC 0-999), (2) Mining (SIC 1000-1499), (3) Construction (SIC 1500-1799), (4) Manufacturing (SIC 2000-3999), (5) Transportation/Communication/Electric/Gas (SIC 4000-4999), (6) Wholesale/Retail (SIC 5000-5999), (7) Finance/Insurance/Real Estate (SIC 6000-6799), and (8) Services (SIC 7000-8999). The percentage in the brackets of the last column shows the number of deals in each year as a proportion of total number of deals. The percentage in the brackets of the last row shows the number of deals in each industry as a proportion of total number of deals. (1) Agr (2) Mining (3) Constr (4) Mftr (5) Trans (6) Wholesale /Retail (7) Fin (8) Services 1986 6 5 1 12 (2.4%) 1987 5 1 5 1 12 (2.4%) 1988 1 18 3 8 1 5 36 (7.2%) 1989 14 3 7 3 27 (5.4%) 1990 3 2 3 8 (1.6%) 1991 1 1 (0.2%) 1992 2 2 (0.4%) 1993 2 1 1 4 (0.8%) 1994 1 1 1 3 (0.6%) 1995 3 2 1 6 (1.2%) 1996 12 1 1 1 15 (3.0%) 1997 1 21 1 4 9 36 (7.2%) 1998 1 19 6 6 9 41 (8.2%) 1999 15 2 1 2 10 30 (6.0%) 2000 1 13 3 7 3 27 (5.4%) 2001 1 5 3 1 1 11 (2.2%) 2002 1 6 1 4 3 15 (3.0%) 2003 1 1 1 17 2 1 1 4 28 (5.6%) 2004 4 16 3 7 2 6 39 (7.8%) 2005 11 4 8 8 31 (6.2%) 2006 1 6 5 2 7 23 (4.6%) 2007 1 1 1 12 6 11 6 12 51 (10.2%) 2008 2 2 4 8 (1.6%) 2009 1 1 1 3 (0.6%) 2010 1 1 7 1 5 1 3 19 (3.8%) 2011 6 1 2 4 13 (2.6%) Total 6 10 4 223 41 97 16 100 501 (1.2%) (2.0%) (0.8%) (44.5%) (8.2%) (19.4%) (3.2%) (20.0%) 100% Total - 42 -

Table 2: Annual Medians for Deal Pricing This table presents the annual medians of total capital, buyout prices relative to fundamentals, market-adjusted prices relative to fundamentals, and buyout premiums. Buyouts are listed by year in which final transaction are completed. Total capital equals the sum of (1) the market value paid for the firms equity; (2) the value of firm s outstanding debt; and (3) the fees paid in the transaction; less (4) any cash removed from the firm to finance the buyout. Net cash flow equals EBITDA less capital expenditures in the last full year before the leveraged buyout announcement. Market E/P ratio is the ratio of earnings to price for the S&P 500 in the month the buyout deal is effective. Market-adjusted ratios are calculated by subtracting the market E/P ratio from the EBITDA to capital or NCF to capital. Premium is the percentage difference between the price paid for a firm s equity and the price one month before the first announcement of buyout activity. ***, **, and * denote that the nonparametric trend test statistics is statistically significant at 1%, 5%, and 10% level, respectively. year (1) Total Capital ($ millions) (2) EBITDA to Capital (%) (3) NCF to Capital (%) (4) Adjusted EBITDA to capital (%) (5) Adjusted NCF to capital (%) (6) Premium (%) 1986 1,548.52 16.59 7.15 10.68 1.11 54.58 1987 990.22 12.13 3.72 6.81-1.71 41.38 1988 430.55 13.90 6.42 5.76-1.32 43.70 1989 858.29 15.42 10.24 7.85 3.06 49.52 1990 226.58 17.96 7.25 11.39 1.14 27.60 1991 222.17 23.21 14.40 17.95 9.15 24.46 1992 414.55 14.68 12.33 9.82 7.47 14.29 1993 154.47 20.08 14.54 15.57 10.03 10.60 1994 376.30 14.39 11.31 8.65 5.58 15.00 1995 821.23 16.22 13.29 9.83 6.90 22.52 1996 237.80 12.23 8.06 6.93 2.66 26.79 1997 386.26 12.87 7.62 8.00 3.33 24.55 1998 421.46 9.85 6.66 6.15 2.54 21.80 1999 485.46 13.20 10.66 10.18 7.26 24.11 2000 485.58 11.75 7.31 8.31 3.67 33.97 2001 408.47 12.53 7.68 9.60 4.98 20.51 2002 185.44 14.85 11.22 12.60 8.03 38.96 2003 727.19 11.60 8.51 7.83 5.19 19.47 2004 697.01 10.83 8.19 6.37 3.93 19.22 2005 738.43 9.72 7.62 4.43 2.30 31.51 2006 1,278.44 10.66 7.17 4.87 1.48 17.29 2007 1,956.71 8.47 5.24 3.01 0.16 21.08 2008 1,863.38 9.31 7.39 4.38 2.56 33.93 2009 531.70 14.12-1.94 12.92-5.62 11.78 2010 822.24 12.83 9.00 6.64 2.81 29.22 2011 2,223.02 9.64 8.11 3.44 1.71 26.60 1986-2011 620.97 11.75 7.67 6.84 2.51 27.60 Time Trend (+)*** (-)*** (-)** (-)*** (-)* (-) - 43 -

Table 3: Median Changes in Operating Performance Year -1 is the last fiscal year prior to completion of the buyout. Year +1 is the first full fiscal year following the year of buyout completion. Data are obtained from Compustat and Capital IQ. Significance levels of medians are based on a two-tailed Wilcoxon rank test. ***, **, and * denote levels that are significantly different from zero at 1%, 5%, and 10% level, respectively. Time trend is examined using the nonparametric trend test, and ***, **, and * next to the bracket denote the nonparametric trend test statistics is significant at 1%, 5%, and 10% level, respectively. Panel A: Median Changes in Operating Performance between 1986 and 2011-2 to -1-1 to +1-1 to +2-1 to +3-2 to -1-1 to +1-1 to +2-1 to +3 Unadjusted Change Industry-adjusted Change EBITDA/sales 1.0%*** 0.8% -0.1% -0.8% 3.7%*** 7.0%** 6.9%** 8.9%** NCF/sales 0.1% 0.5% -3.8% -2.6% 9.6%*** 18.5%** 13.9%** 19.6%*** EBITDA/assets 9.6%*** -26.3%*** -26.6%*** -24.1%*** 13.0%*** -12.2%** -9.8%** -6.1% NCF/assets 7.2%*** -29.7%*** -31.0%*** -26.5%*** 12.7%*** -5.7%* 5.3% 3.4% Panel B: Median Changes in Operating Performance in Sub-Periods -2 to -1-1 to +1-1 to +2-1 to +3-2 to -1-1 to +1-1 to +2-1 to +3 Unadjusted Change Industry-adjusted Change EBITDA/sales 1986-1993 0.5% 7.6%*** 4.7% 2.5% 3.3% 10.6%** 8.1%* 8.4%* 1994-2001 1.5%** -0.3% -2.8% -3.6%* 2.7%** 7.7%** 6.1%* 10.7%** 2002-2011 1.0%** 0.0% 0.1% -0.7% 5.1%*** 4.8% 6.6% 8.5% time trend (-) (-)** (+) (-) (+) (-)* (-)* (-) NCF/sales 1986-1993 -12.2% 15.3%** 13.4%** 11.4%** 5.7% 32.7%*** 28.2%** 31.5%** 1994-2001 -0.9% -2.3% -7.0% -3.0% 6.0%** 18.5%** 13.7%** 29.8%*** 2002-2011 1.8% -2.8% -4.0% -7.4% 12.9%*** 13.3%** 8.4% 5.7% time trend (+)** (-)** (-) (-) (+) (-)*** (-)*** (-)** EBITDA/assets 1986-1993 4.8%* -18.2%*** -22.4%*** -14.2%*** 7.6%*** -8.9%* 0.0% 0.0% 1994-2001 13.0%*** -17.7%*** -17.8%*** -21.2%*** 18.6%*** 0.9% -4.7% -0.2% 2002-2011 9.6%*** -34.0%*** -34.4%*** -32.1%*** 13.2%*** -27.5%*** -22.5%*** -11.5%** time trend (+) (-)*** (-)*** (-)* (+) (-)*** (-)*** (-)** NCF/assets 1986-1993 -9.6% -12.7% -16.3%* -11.4% 7.1% 10.1% 14.9% 13.4% 1994-2001 10.7%** -20.7%** -20.2%* -16.1%* 11.7%*** 7.7%** 20.5%*** 19.2%*** 2002-2011 10.6%*** -40.3%*** -40.7%*** -44.9%*** 15.3%*** -24.2%*** -6.7% -8.4% time trend (+)** (-)*** (-)* (-)** (+) (-)*** (-)** (-)*** - 44 -

Table 4: Leverage, Debt Structure, and Debt Contractual Features Panel A: Leverage This table presents the annual medians of leverage. Pre- and post-buyout debt, equity, and total assets are from Compustat and Capital IQ and missing data are filled from SEC filings. Pre-buyout leverage, post-buyout leverage, and post-buyout equity ratio are calculated using total assets. ***, **, and * denote that the nonparametric trend test statistics is statistically significant at 1%, 5%, and 10% level, respectively. year (1) Pre-buyout Leverage (%) (2) Post-buyout Leverage (%) (3) Change in Leverage (%) (4) Compustat Leverage Change (%) (5) Post-buyout Equity ratio (%) 1986 17.07 70.37 48.70-0.05 6.15 1987 25.74 75.88 51.37-0.23 2.83 1988 32.03 73.57 37.20 0.20 7.66 1989 29.19 63.80 26.34 0.28 10.65 1990 23.64 44.33 14.45 0.03 23.65 1991 27.72 34.35 6.63-0.56 39.72 1992 28.34 36.93 8.59-0.73 35.64 1993 19.89 34.88 14.75-0.95 49.79 1994 31.90 75.77 39.15-0.18 8.48 1995 37.53 84.84 27.39-0.22 16.86 1996 14.00 58.84 44.84-0.43 26.29 1997 33.66 84.03 52.97 0.09 8.92 1998 33.90 83.18 63.95 0.88 6.57 1999 21.49 82.24 64.86 0.30 5.56 2000 35.77 64.43 28.81 0.22 28.78 2001 54.27 70.90 14.26 0.17 20.40 2002 37.51 57.63 14.06 0.17 29.69 2003 52.40 53.43 11.41-0.61 27.21 2004 34.32 59.64 30.55-0.68 19.35 2005 29.03 61.33 34.09-0.37 18.29 2006 30.54 55.71 25.50-0.28 17.91 2007 26.74 60.83 34.74 0.15 25.46 2008 41.81 48.73 22.57 1.08 36.63 2009 33.39 26.66 2.50-1.26 60.17 2010 41.83 54.59 15.40-0.77 28.29 2011 30.10 53.59 24.92-0.21 26.22 1986 31.92 64.35 30.55-0.15 20.42-2011 Time Trend (+) (-)*** (-)** (-) (+)*** - 45 -

Panel B: LBO Debt Structure This table presents the structure of LBO debt. Data are obtained from Dealscan and Capital IQ at tranche level. Bank debt includes the revolving credit facilities (revolvers) tranche and the Term Loan A tranche. Institutional debt includes the Term Loan B, C, and D tranche and the Note tranche. Junior debt includes the bridge loans and the mezzanine debt. ***, **, and * denote that the nonparametric trend test statistics is statistically significant at 1%, 5%, and 10% level, respectively. year (1) Revolvers (%) Revolver to LBO debt %with Revolver (2) Term Loan A (%) Term A to LBO debt %with Term A (3) Bank Debt=(1)+(2)(%) Bank debt to LBO debt %with Bank debt (4) Institutional Debt (%) Inst. debt to LBO debt %with Inst. debt (5) Junior Debt (%) Junior debt to LBO debt %with Junior debt 1986 60.8 52.2 36.2 34.8 97.0 87.0 1.3 4.3 1987 17.0 36.8 38.8 31.6 55.8 68.4 38.3 23.7 1988 18.6 36.8 20.4 28.5 38.9 65.3 60.5 31.3 1989 10.3 31.9 41.5 30.2 51.8 62.1 1.2 4.3 46.2 27.6 1990 32.1 36.0 19.5 24.0 51.6 60.0 46.4 32.0 1991 39.4 40.0 45.5 40.0 84.8 80.0 15.2 20.0 1992 53.5 50.0 36.6 25.0 90.1 75.0 9.9 25.0 1993 23.1 42.9 76.9 57.1 100.0 100.0 1994 52.2 41.2 36.1 23.5 88.4 64.7 8.3 29.4 3.3 5.9 1995 18.9 27.3 22.5 22.7 41.4 50.0 41.7 40.9 1996 67.6 38.0 15.0 30.0 82.6 68.0 16.5 30.0 0.9 2.0 1997 38.2 38.9 13.3 17.8 51.5 56.7 41.6 35.6 6.4 5.6 1998 26.6 30.6 16.1 21.7 42.7 52.2 48.4 40.8 8.8 5.7 1999 22.9 30.3 21.2 27.0 44.1 57.4 48.3 38.5 7.5 3.3 2000 23.9 37.3 23.6 29.3 47.5 66.7 52.5 32.0 2001 20.7 32.4 29.5 35.3 50.2 67.6 49.8 32.4 2002 21.4 40.6 6.4 25.0 27.8 65.6 37.2 18.8 35.0 16.3 2003 23.4 37.5 12.2 16.7 35.6 54.2 44.3 36.1 20.1 18.3 2004 31.4 44.7 7.5 11.7 39.0 56.4 52.6 36.2 8.5 13.2 2005 20.3 41.9 6.7 13.5 27.0 55.4 37.8 33.8 35.1 15.4 2006 20.4 43.6 13.5 15.4 33.9 59.0 41.2 25.6 24.9 25.4 2007 12.7 33.0 9.7 17.0 22.4 50.0 64.3 35.1 13.3 19.8 2008 12.5 34.1 11.5 20.5 24.0 54.5 52.3 27.3 23.7 28.2 2009 38.6 50.0 61.4 50.0 100.0 100.0 2010 18.1 44.7 2.0 6.4 20.2 51.1 60.5 34.0 19.3 24.9 2011 13.7 34.3 1.2 5.7 14.9 40.0 58.4 37.1 26.8 22.9 1986-2011 28.4 38.7 24.0 25.4 52.4 64.1 40.3 31.2 20.5 13.3 Time Trend (-)* (-) (-)** (-)** (-)*** (-)* (+)*** (+) (-) (-) - 46 -

Panel C: Spread, Maturity, and Loan Covenant This table presents the median all-in-drawn interest spread over 6-month London Interbank Offered Rate (LIBOR) in bps, the median maturity (in months) of loans, and the median loan covenants used in financing the 501 LBOs in my sample. Covenant-lite loans are loans with no financial maintenance covenants. ***, **, and * denote that the nonparametric trend test statistics is statistically significant at 1%, 5%, and 10% level, respectively. Year (1) Bank debt Spread (2) Inst. debt Spread (3) Spread Diff Inst. vs Bank (4) Bank debt Maturity (months) (5) Inst. debt Maturity (months) (6) Maturity Diff Inst. vs Bank (months) (7) Covenant Intensity Index (8) Modified Covenant Intensity Index (9) Cov-lite Loans 1986 237.5 46 1 (52%) 1 1987 255.7 73 0.5 (34%) 0.5 1988 278.4 69 1 (56%) 1 1989 268.1 450.0 81 98 1 (73%) 1 1990 335.0 75 1 (64%) 1 1991 300.0 56 1 (60%) 1 1992 275.0 36 120 1 (10%) 1 1993 318.8 60 1 (57%) 1 1994 225.0 362.5 87.5 61 101 32 4 (94%) 5 1995 255.0 334.4 75.0 66 104 31 5 (77%) 5 77% 1996 243.7 320.8 66.7 61 98 19 5.5 (86%) 8 26% 1997 246.5 275.0 51.8 74 99 16 5 (74%) 7.5 33% 1998 234.7 276.5 42.1 75 100 16 5 (82%) 9 29% 1999 286.7 353.5 62.9 70 93 18 5 (78%) 10 31% 2000 300.8 353.3 65.5 63 88 21 6 (76%) 8 29% 2001 373.8 356.8 17.5 72 74 12 5 (82%) 8 26% 2002 356.9 333.3 37.5 60 83 18 6 (88%) 9 19% 2003 331.1 373.2 50.0 54 76 7 4 (78%) 6.5 38% 2004 463.6 272.8-25.0 74 82-1 5 (95%) 9 22% 2005 402.5 271.0-50.0 74 80 1 5 (92%) 9 22% 2006 262.3 251.3 0.0 67 81 4 3.5 (80%) 7 40% 2007 366.3 307.6 6.3 74 81 3 3 (89%) 6 39% 2008 302.2 390.0 15.0 76 82 5 1 (64%) 1.5 39% 2009 462.5 48 0 (0%) 0 100% 2010 347.9 441.1 25.0 62 76 9 1 (81%) 1 64% 2011 450.9 473.1-37.5 75 79 9 1 (77%) 2 54% 1986-2011 Time Trend 314.6 344.2 28.8 65 88 13 5 (1995-2011) (+)*** (+) (-)** (+) (-)*** (-)** (-)** (1995-2011) 7 (1995-2011) (-)*** (1995-2011) 34% (+)* - 47 -

Table 5: Private Equity Involvement and Reputation Panel A: Private Equity Involvement This table presents the involvement of private equity (PE) firms in LBOs over time. Information on PE firms is from Capital IQ. A firm is a prominent PE if it is listed as the 50 largest PE firms by the Private Equity International (PEI) magazine from the year 2007 to 2013. I add to the PEI list firms listed in Appendix A and Forstmann Little and HM Capital Partners that were historically prominent PE firms. Bank-affiliated PEs are PE firms that are subsidiaries of banks at the time of deal announcement. Club deals are LBOs with two or more PE firms involved. ***, **, and * denote that the nonparametric trend test statistics is statistically significant at 1%, 5%, and 10% level, respectively. Year (1) Number of LBOs with PE (2) Number of LBOs with Prominent PE (3) Number of LBOs with Bank- Affiliated PE (4) Number of Club Deals (5) Total Transaction Value ($million) of Club Deals (6) Max # of PEs in the club 1986 10 (83%) 8 (67%) 6 (50%) 1987 9 (75%) 4 (33%) 3 (25%) 1988 21 (58%) 8 (22%) 7 (19%) 1 (3%) 233 (1%) 2 1989 20 (74%) 10 (37%) 10 (37%) 4 (15%) 11,126 (14%) 3 1990 5 (63%) 3 (38%) 1 (13%) 1991 1 (100%) 1992 2 (100%) 1 (50%) 1 (50%) 1 (50%) 723 (87%) 3 1993 1 (25%) 1994 3 (100%) 2 (67%) 1 (33%) 1 (33%) 450 (50%) 2 1995 6 (100%) 5 (83%) 1 (17%) 1996 15 (100%) 6 (40%) 3 (20%) 4 (27%) 3,880 (39%) 3 1997 36 (100%) 16 (44%) 5 (14%) 10 (28%) 5,601 (30%) 3 1998 40 (98%) 21 (51%) 12 (29%) 10 (24%) 7,425 (24%) 4 1999 30 (100%) 16 (53%) 7 (23%) 10 (33%) 5,978 (34%) 3 2000 25 (93%) 9 (33%) 7 (26%) 12 (44%) 8,069 (31%) 4 2001 8 (73%) 3 (27%) 2 (18%) 5 (45%) 2,950 (54%) 3 2002 13 (87%) 6 (40%) 2 (13%) 6 (40%) 2,487 (52%) 4 2003 24 (86%) 18 (64%) 4 (14%) 6 (21%) 5,452 (17%) 3 2004 36 (92%) 24 (62%) 4 (10%) 16 (41%) 17,011 (44%) 4 2005 31 (100%) 20 (65%) 5 (16%) 14 (45%) 47,465 (72%) 7 2006 21 (91%) 15 (65%) 4 (17%) 12 (52%) 72,038 (88%) 4 2007 48 (94%) 35 (69%) 15 (29%) 22 (43%) 179,252 (66%) 5 2008 8 (100%) 7 (88%) 1 (13%) 3 (38%) 49,625 (85%) 3 2009 3 (100%) 1 (33%) 1 (33%) 1 (33%) 6,107 (91%) 4 2010 19 (100%) 12 (63%) 1 (5%) 5 (26%) 5,135 (23%) 3 2011 13 (100%) 11 (85%) 0 (0%) 6 (46%) 18,554 (64%) 3 1986-2011 Time trend 448 (89%) 261 (52%) 103 (21%) 149 (30%) 449,562 (50%) (+)*** (+)*** (-) (+)*** (+)*** (+)** - 48 -

Panel B: Private Equity Reputation This table presents the medians of five reputation measures for PE firms. PE reputation measures are based all Capital IQ recorded LBO, MBO, SBO transactions for U.S. target firms since 1970 plus the buyout sample from SDC. ***, **, and * denote that the nonparametric trend test statistics is statistically significant at 1%, 5%, and 10% level, respectively. Year (1) Reputation: Ln (number of LBO deals by the PE in the last 36 months) (2) Reputation: Ln (number of LBO deals by the PE in since 1970) (3) Reputation: Market share of PE based on the number of deals in the last 36 months (4) Reputation: Market share of PE based on the dollar value of deals in the last 36 months (5) Median PE Age in years 1986 0.84 0.89 0.6% 2.4% 27 1987 1.70 1.91 2.5% 4.2% 32 1988 1.19 1.30 0.4% 0.7% 27 1989 1.29 1.39 0.6% 1.0% 25 1990 1.47 1.89 1.2% 2.3% 25 1991 1.39 1.39 0.7% 0.5% 21 1992 1.65 2.56 1.1% 2.7% 30.5 1993 1.10 2.08 0.6% 1.3% 24 1994 1.56 1.98 1.0% 3.8% 25 1995 1.65 2.85 0.8% 2.9% 29 1996 1.68 2.50 0.6% 2.5% 26 1997 1.53 2.23 0.4% 1.1% 24.5 1998 2.01 2.59 0.4% 1.7% 22 1999 1.87 2.57 0.3% 1.2% 25 2000 1.81 2.28 0.3% 0.5% 19 2001 2.38 3.06 0.4% 0.6% 23 2002 1.47 2.28 0.1% 0.3% 24 2003 1.71 3.29 0.2% 1.1% 26 2004 1.64 2.90 0.2% 1.0% 25 2005 2.13 3.28 0.2% 0.7% 29 2006 2.53 3.47 0.4% 1.3% 24 2007 2.12 3.02 0.2% 0.6% 25 2008 2.90 3.95 0.4% 3.2% 24.5 2009 0.37 0.37 0.0% 0.1% 5 2010 1.90 3.49 0.1% 0.4% 23 2011 1.78 3.46 0.1% 1.9% 25 1986-2011 Time trend 1.68 2.42 0.25% 0.89% 24 (+)** (+)*** (-)** (+) - 49 -

Table 6: Regression for Post-buyout Performance: Baseline Regression This table reports the multivariate regression results for post-buyout performance. Dependent variables are the industry-adjusted percentage changes in EBITDA/sales from the last full pre-buyout year (year -1) to the second full year after deal completion (year +2). All regressions control for the year and industry effect. P-values are in parentheses. Dependent variable is the industry-adjusted change in EBITDA over sales. All regressions are OLS with heteroskedasticity adjusted standard errors. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively. VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) Change ROS year-1 0.149 0.135 0.148 0.139 0.140 0.142 0.140 0.138 [0.177] [0.215] [0.182] [0.199] [0.196] [0.196] [0.194] [0.200] Pre-buyout leverage 0.230 0.116 0.227 0.132 0.126 0.129 0.140 0.111 [0.216] [0.522] [0.225] [0.471] [0.491] [0.478] [0.434] [0.537] Leverage change 0.348** 0.265** 0.351** 0.279** 0.281** 0.279** 0.297** 0.278** [0.0131] [0.0330] [0.0127] [0.0417] [0.0407] [0.0431] [0.0258] [0.0403] Bank debt pctg 0.0586 0.0479 0.0472 0.0438 0.0564 0.0254 [0.666] [0.732] [0.736] [0.752] [0.683] [0.854] Covenant intensity 0.0248** 0.0275*** 0.0284*** 0.0281*** 0.0248*** 0.0280*** [0.0193] [0.00278] [0.00189] [0.00217] [0.00873] [0.00224] Maturity 0.00144 [0.468] PE Reputation -0.372 [0.543] Club deals dummy 0.0370 [0.657] 2 or 3 PE in club -0.0369 [0.636] Bank-affiliated -0.0252 [0.721] Mgmt participation 0.190** [0.0237] CEO change -0.120 [0.243] Constant 5.506 14.38 5.927 15.41 13.32 14.89 8.695 11.31 [0.595] [0.273] [0.570] [0.269] [0.328] [0.262] [0.498] [0.401] Year fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Observations 419 419 419 419 419 419 419 419 R-squared 0.086 0.101 0.086 0.100 0.099 0.099 0.111 0.107 Robust pval in brackets *** p<0.01, ** p<0.05, * p<0.1-50 -

Table 7: Market Timing This table reports the multivariate regression results for post-buyout performance. Dependent variables are the industry-adjusted percentage changes in EBITDA/sales from the last full pre-buyout year (year -1) to the second full year after deal completion (year +2). All regressions control for the year and industry effect. P-values are in parentheses. Dependent variable is the industry-adjusted change in EBITDA over sales. All regressions are OLS with heteroskedasticity adjusted standard errors. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively. VARIABLES (1) (2) (3) (4) ChgROS year-1 0.147 0.137 0.142 0.138 [0.182] [0.206] [0.190] [0.198] Pre-buyout leverage 0.148 0.139 0.156 0.145 [0.436] [0.441] [0.389] [0.421] Leverage change 0.316** 0.290** 0.314** 0.301** [0.0294] [0.0305] [0.0206] [0.0246] Bank debt pctg 0.0757 0.0618 0.0791 0.0307 [0.574] [0.660] [0.564] [0.832] Covenant intensity 0.0301** 0.0246** 0.0257*** 0.0237** [0.0201] [0.0106] [0.00788] [0.0120] PE reputation 0.0584 0.116 0.110 0.0321 [0.925] [0.859] [0.862] [0.959] Mgmt participation 0.188** 0.190** 0.181** 0.196** [0.0309] [0.0265] [0.0353] [0.0230] baa 0.0921 [0.373] Baa-HBaa -0.0397 [0.600] term -0.0103 [0.801] hot 0.0625 [0.524] LBO loan spread 0.0483 [0.140] Adj-EBITDA/Capital 0.204 [0.205] Constant -35.53 9.037 9.812 7.478 [0.479] [0.475] [0.453] [0.558] Year fixed effect Yes Yes Yes Yes Observations 419 419 419 419 R-squared 0.115 0.114 0.118 0.116 Robust pval in brackets *** p<0.01, ** p<0.05, * p<0.1-51 -

Table 8: LBO Year and Exit Strategy This table presents the post-buyout outcomes as of June 30, 2013. The number of observations is reported, followed in parentheses by the proportion of the outcome in all LBOs each year. LBO Year (1) Bankruptcy (2) IPO (3) Acquired by Corp (4) Secondary LBOs (5) still private (6) Unknown 1986 5 41.7% 3 25.0% 2 16.7% 2 16.7% 0 0.0% 0 0.0% 1987 6 50.0% 4 33.3% 1 8.3% 1 8.3% 0 0.0% 0 0.0% 1988 8 22.2% 19 52.8% 6 16.7% 2 5.6% 0 0.0% 1 2.8% 1989 6 22.2% 6 22.2% 11 40.7% 2 7.4% 1 3.7% 1 3.7% 1990 0 0.0% 3 37.5% 3 37.5% 0 0.0% 0 0.0% 2 25.0% 1991 1 100.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1992 0 0.0% 0 0.0% 1 50.0% 1 50.0% 0 0.0% 0 0.0% 1993 0 0.0% 1 25.0% 1 25.0% 1 25.0% 0 0.0% 1 25.0% 1994 0 0.0% 2 66.7% 1 33.3% 0 0.0% 0 0.0% 0 0.0% 1995 0 0.0% 3 50.0% 2 33.3% 1 16.7% 0 0.0% 0 0.0% 1996 6 40.0% 6 40.0% 1 6.7% 2 13.3% 0 0.0% 0 0.0% 1997 7 19.4% 15 41.7% 7 19.4% 5 13.9% 1 2.8% 1 2.8% 1998 10 24.4% 11 26.8% 11 26.8% 7 17.1% 2 4.9% 0 0.0% 1999 6 20.0% 15 50.0% 5 16.7% 3 10.0% 1 3.3% 0 0.0% 2000 2 7.4% 8 29.6% 11 40.7% 3 11.1% 1 3.7% 2 7.4% 2001 1 9.1% 1 9.1% 1 9.1% 6 54.5% 1 9.1% 1 9.1% 2002 2 13.3% 6 40.0% 1 6.7% 5 33.3% 1 6.7% 0 0.0% 2003 2 7.1% 11 39.3% 4 14.3% 8 28.6% 3 10.7% 0 0.0% 2004 6 15.4% 23 59.0% 5 12.8% 2 5.1% 3 7.7% 0 0.0% 2005 3 9.7% 17 54.8% 4 12.9% 2 6.5% 5 16.1% 0 0.0% 2006 5 21.7% 5 21.7% 1 4.3% 3 13.0% 8 34.8% 1 4.4% 2007 6 11.8% 13 25.5% 4 7.8% 2 3.9% 26 51% 0 0.0% 2008 0 0.0% 2 25.0% 0 0.0% 0 0.0% 6 75.0% 0 0.0% 2009 0 0.0% 1 33.3% 1 33.3% 0 0.0% 1 33.3% 0 0.0% 2010 0 0.0% 2 10.5% 1 5.3% 1 5.3% 15 78.9% 0 0.0% 2011 1 7.7% 0 0.0% 0 0.0% 0 0.0% 12 92.3% 0 0.0% Total 83 16.6% 177 35.3% 85 17.0% 59 11.8% 87 17.4% 10 2.0% Months to Outcome Median(mean) 43 (49.5) 36 (39.5) 50 (57.9) 44 (48.6) - 52 -

Table 9: Bankruptcy The table presents the results from a logit regression where the dependent variable is equal to 1 if an LBO exits through an IPO or a sale to financial or strategic buyers and 0 otherwise. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively. (1) (2) (3) (4) (5) Deal size 0.0204 0.142 0.0293 0.0206 0.000246 [0.836] [0.172] [0.764] [0.834] [0.998] Change ROS year-1 0.269 0.179 0.226 0.271 0.281 [0.197] [0.410] [0.284] [0.192] [0.185] Pre-buyout leverage 0.549 0.255 0.514 0.552 0.578 [0.195] [0.562] [0.252] [0.193] [0.178] Leverage change 0.192-0.00354 0.145 0.196 0.200 [0.563] [0.992] [0.697] [0.558] [0.552] Bank debt pctg 0.834** 0.667* 0.912** 0.844** 0.862** [0.0290] [0.0846] [0.0198] [0.0293] [0.0254] Covenant intensity 0.0787** 0.0205 0.0713** 0.0790** 0.0787** [0.0275] [0.613] [0.0482] [0.0270] [0.0284] Maturity 0.00754 0.00803 0.00796 0.00758 0.00740 [0.118] [0.116] [0.107] [0.117] [0.127] PE Reputation 0.288*** 0.250** 0.306*** 0.288*** 0.288*** [0.00905] [0.0193] [0.00475] [0.00896] [0.00894] Bank-affiliated -0.720*** -0.682** -0.755*** -0.719** -0.720*** [0.00984] [0.0127] [0.00706] [0.0102] [0.00982] Club deal dummy 0.116 0.105 0.141 0.119 0.123 [0.658] [0.702] [0.597] [0.650] [0.640] Mgmt participation 0.200 0.155 0.160 0.197 0.198 [0.406] [0.518] [0.515] [0.412] [0.412] CEO change -1.091*** -1.207*** -1.135*** -1.090*** -1.084*** [1.06e-06] [2.97e-07] [7.90e-07] [1.17e-06] [1.25e-06] Baa -0.574** [0.0289] Baa-Hbaa 0.437** [0.0292] term 0.149 [0.181] Hot mkt dummy 0.731*** [0.00473] LBO loan spread 0.000154 [0.872] Deal price -0.395 [0.448] Constant 152.5*** 565.4*** 154.1*** 153.2*** 153.5*** [3.88e-05] [0.000116] [2.78e-05] [3.45e-05] [3.26e-05] Year dummy Yes Yes Yes Yes Yes Observations 466 466 466 466 466 Pseudo R-squared 0.170 0.171 0.183 0.170 0.171-53 -

Figure 1: A typical LBO Transaction and Hypotheses in LBO Value Creation Lenders Banks Institutional investors H1.2: Lenders Monitoring (bank loan proportion, covenant, maturity) Public debt holders Debt H2.3: Bank-affiliated LBO Equity Investors Private Equity firms Management of Targets Equity H1: Debt Disciplining Hypothesis H2.1: Private Equity Reputation; H2.2: Club Deals H3: Management Participation (CEO change) - 54 -

Figure 2: LBO Transactions Each Year The figure shows the number of LBO deals and total transaction value by LBO effective year. The solid line that corresponds to the left y-axis plots the number of LBO deals each year. The bar that corresponds to the right y-axis shows the inflation-adjusted total transaction value, based on the 2005 dollar. LBO transaction sample is constructed from the Standard and Poor s Capital IQ and the Securities Data Company s (SDC) U.S. Mergers and Acquisitions Database. - 55 -