Corporate Governance and Value Creation – Evidence from Private Equity

1. Corporate Governance and Value Creation – Evidence from Private Equity Viral V. Acharya, Moritz Hahn and Conor Kehoe European results March 2010 CONFIDENTIAL AND PROPRIETARY Any use of this material without specific permission of McKinsey & Company is strictly prohibited

2. PE value creation is linked to operating improvements, governance activity and ultimately to General Partner skills • Large PE funds • generate persistent outperformance • However, sources of PE fund performance opaque • Analysis of outperfor-mance on deal level • Large PE funds generate on average 22.0% net IRR, all funds only 16.3%1 • First quartile fund implies 72% chance of next fund being in top 2 quartiles2 • Impact of higher leverage ? • Impact of market/sector timing ? • Impact of fundamental PE value creation ? • Disaggregation of IRRs; remove market/sector effects, as well as leverage • Identification of operational improve-ments • Description of PE governance activities and skills at play 1 Vintage year 1993–2003, Western Europe, large funds >€500 million fund size 2 Kaplan and Schoar (2005) SOURCE: Private Equity Intelligence, McKinsey

3. Literature and main contribution • Private Equity (PE) follows an Active Ownership approach, which generates outperformance • PE generates financial under-/ outperformance on fund level • Ljungqvist and Richardson (2003) • Kaplan and Schoar (2005) • Phalippou and Gottschlag (2007) • And others • PE improves / does not improve operating performance on company level • Jensen (1989) • Kaplan (1989) • Lichtenberg and Siegel (1990) • Harris, Siegel and Wright (2005) • Guo, Hotchkiss and Song (2009)

4. Contents • Dataset • IRR disaggregation • Operating improvements • Returns and operating improvements • Returns and PE partner background • Returns and PE governance activities

5. Sample covers 110 PE transactions and 72 interviews • Deal-level data of 110 PE transactions • Initiated between 1995 and 2005 by large PE funds from Western Europe • Deal value above €50m (at acquisition) • Deals are evenly distributed across many sectors • For 102deals we also know the professional back-ground of the leading deal partner • For 72 deals interview data • 45-minute interview with General Partner (GP) of the deal • Questions on PE house strategy • Questions on the PE house involvement in the specific deal

6. d Descriptive statistics

7. 6 Sample is representative of large fund performance (1/2)

8. 6 Sample is representative of large fund performance (2/2)

9. 0 By volume By value Vendor type Our sample does not only cover infrequent public-to-private deals %, €b • 110 • 5,379 • 73.5 • 636.6 • = 100% • 4.6 • Public-to-private • 10.9 • 12.0 • 17.0 • 38.4 • Carve-out • 39.1 • 38.0 • Our sample covers by value 14% of deals with more than 100 € Mio deals size • 45.0 • 36.0 • 28.0 • Family/private-to-private • 31.8 • 13.0 • 19.0 • 13.9 • Institutional investor • 21.0 • 16.4 • Other1 • 7.0 • 6.0 • 1.8 • 1.0 • Our sample • EU universe2 • Our sample • EU universe2 1 Including ‘in receivership’ and ‘state’ 2 According to Private Equity Insight, all deals acquired 1995–2005 in Western Europe with deal size or size category available

10. Contents • Dataset • IRR disaggregation • Operating improvements • Returns and operating improvements • Returns and PE partner background • Returns and PE governance activities

11. Deal IRRs are related to sector returns, but large part of the returns are not explained by sector movements Levered IRRs in %, n = 110 • IRR of nearly 30% explained by a constant term • IRR = 29.4% + (1.1 x sector returns) • R2 = 0.1 • t-stat (constant) = 6.8 • t-stat (coefficient) = 2.8 • Deal • regression line • bisectrix • Sector 1 We use the median IRR in each deal corresponding sector, given that there are mostly less than 100 companies in each three digit sector SOURCE: McKinsey

12. = un-levered return deal i (Return on total assets) • = cost of debt for deal i Methodology used to disaggregate portfolio company financial performance 1 • Un-levering deal performance to purge the effect of financial leverage • = levered return deal i (Return on Equity) 2 • Excess return generated at the enterprise level of a portfolio company • Assumption throughout the document • Findings are robust to alternative assumptions of

13. 12.1 PE deals generate alpha (outperformance) • In %

14. Contents • Dataset • IRR disaggregation • Operating improvements • Returns and operating improvements • Returns and PE partner background • Returns and PE governance activities

15. PE companies are stable pre-acquisition • In % PE does NOT follow a momentum or reversal trading strategy Acquired companies do not systematically display an upward or downward trend pre-acquisition, which PE firms could build upon or help to recover

16. 0 Two distinctive PE value creation strategies % • Strategic focus • n = • 110 • Internal improvements • Organic growth • Productivity enhancements, including divestments • Organic • 66 1 • ‘Buy and build’ • Major acquisitions during PE ownership1 • First acquisitions on average in first year during PE ownership 2 • Inorganic • 34 1 We use M&A events during PE ownership as reported by the PE house or as mentioned in the press, Capital IQ database, or PE house website. M&A event are major if they altered sales or enterprise value of the deal by more than 20%.

17. PE companies increase margins and multiples during PE ownership

18. Contents • Dataset • IRR disaggregation • Operating improvements • Returns and operating improvements • Returns and PE partner background • Returns and PE governance activities

19. Margin and multiple increase above sector is related to abnormal performance

20. Nearly half of abnormal performances is explained by operational improvements n = 85, deals with operational data available • Source of improvements • 1 • Organic deals (n = 55) • EBITDA margin increase • 6.5 • 1 ppt increase in EBITDA margin relates statistically significantly to 1.1 ppt increase in abnormal performance 1 • On average 2.6 ppt EBITDA margin increase during PE ownership for organic deals (1.1 x 2.6 = 2.9) • 2.9 • Abnormal performance • 2 • Inorganic deals (n = 30) • EBITDA multiple increase • 12.4 • 1% increase in EBITDA multiple relates statistically significantly to 0.3 ppt increase in abnormal performance 1 • On average 17.8% EBITDA multiple increase during PE ownership for inorganic deals (17.8 x 0.3 = 5.3) • 5.3 • Average abnormal performance 1 Based on multivariate OLS regressions, controlling for duration, entry time periods and other operating improvements

21. Contents • Dataset • IRR disaggregation • Operating improvements • Returns and operating improvements • Returns and PE partner background • Returns and PE governance activities

22. A • Finance Leading deal partners have different professional backgrounds,the majority of deal partners have finance background %, abnormal performance,1 medians in parenthesis, n = 102 • Last profession before joining PE industry1 (n = 102) • Banking • Legal/ Accounting • Industry manager • B • Operations • Consultant 1 Professional background of the partner interviewed if not mentioned otherwise in Capital IQ database, PE house website, or press articles. For deals without an interview available or if the interviewed partner is not the leading deal partner, we use the professional background of the leading deal partner if mentioned in Capital IQ database, PE house website, or press articles

23. 0 • A • Finance Operation deal partners are better at organic deals, finance deal partners better at inorganic deals %, abnormal performance,1 medians in parenthesis • Background • B • Strategy • Operations • (12.1) • 1 • Organic • 16.0 • (1.8) • 1.6 • n=22 • n=45 • 2 • Inorganic • (13.5) • (4.1) • 13.0 • 6.5 • n=30 • n=5 1 Simple average

24. Deal partner background is related to performance depending on deal strategy (1/2) • = abnormal performance of deal i • = 1 if partner background finance, 0 otherwise • = 1 if deal with M&A during PE ownership, 0 otherwise

25. Deal partner background is related to performance depending on deal strategy (2/2)

26. Contents • Dataset • IRR disaggregation • Operating improvements • Returns and operating improvements • Returns and PE partner background • Returns and PE governance activities

27. Sourcing Due diligence Deal structuring First 100 days Medium/ long term Exit Active ownership practices deployed mainly before or right after acquisition • 1. Proprietary Deal insight • Leverage privileged knowledge • Bring board members and expertise • 2. Value creation plan • Create a new value creation plan • Refine plan during first 100 days • Adjust/ implement systems to monitor plan evolution • Immediately react to plan deviations • 3. Early manage-ment changes • Strengthen manage-ment before closing • Find addi-tional repla-cements within first 100 days • Pre • 4. Substantial but focused incentives • Very significant value at stake for top executives • 5. Significant time spent upfront • Invest significant time in first 100 days (>1.5 days per week) • Interact with CEO/CFO multiple times a week • 6. External support • Use external support both in the first 100 days and in the medium/ long term • Post

28. According to deal interviews PE funds actively manage companies Answers with yes in %, n = 68-74 deals1 in first 100 days or before • CEO 1 • Changed management • CFO • 39 • Other management • Organic growth 2 • Launched value creation initiatives • Productivity • Revised management plan 3 • Shaped value creation plan • New Key Performance Indicators • 56 • Acted immediately on deviations • Multiple CEO interactions oer week 4 • Provided management support • Frequent CFO interactions • 52 • High PE time commitment2 • Used in acquisition phase 5 • Leveraged external support • Used in 1st 100 days • Mgmt. with high equity share2 6 • Incentives • Mgmt. with high cash-multiple2 • 53 • 1st & 2nd mgmt. line with equity 1 Based on 45 min interviews, which was structured around a questionnaire, with leading deal partner per deal 2 Above cross-sectional average SOURCE: Interviews

29. 6 • Statistically significant difference1 Management turnover is higher for top performing deals,and low for deals with Finance deal partners • Organic deals only, n = 49-37, answers with “yes” in percent • Replacement • Total management changes3 • CEO • CFO • Other • Top performer2 • 67 • 50 • 58 • 58 • Mid tercile • 38 • 33 • 42 • 38 • Bottom tercile • 23 • 31 • 23 • 26 • Finance partner • 35 • 35 • 32 • Operating partner • 58 • 42 • 67 1 At least at a 10% level, t-test between 1) top-performing deals and 2) mid and bottom tercile 2 Top tercile deals, measured by abnormal performance 3 We fill data holes for deriving subtotals with the average score of the sub questions per deal SOURCE: Interviews

30. Statistically significant difference1 PE support is higher for top performing deals, and low for deals with finance deal partners • Organic deals only, n = 49-36, answers with “yes” in percent • Multiple CEO interaction4 • Frequent (regular) CFO interaction • High PE Time commitment • Total PE support3 • Top performer2 • 77 • 100 • 56 • 79 • Mid tercile • 46 • 100 • 73 • 68 • Bottom tercile • 30 • 95 • 50 • 53 • Finance partner • 36 • 97 • 48 • 56 • Operating partner • 77 • 100 • 82 • 86 1 At least at a 10% level, t-test between 1) top-performing deals and 2) mid and bottom tercile 2 Top tercile deals, measured by abnormal performance 3 We fill data holes for deriving subtotals with the average score of the sub questions per deal SOURCE: Interviews

31. 4 • Statistically significant difference1 External support and organic growth initiatives relate to performance, but seem not to be different by partner background • Organic deals only, n = 53-34, answers with “yes” in percent • At least one organic growth initiative • External support used in first 100 days • Top performer2 • 87 • 57 • Mid tercile • 47 • 27 • Bottom tercile • 59 • 14 • Finance partner • 29 • 68 • Operating partner • 42 • 54 1 At least at a 10% level, t-test between 1) top-performing deals and 2) mid and bottom tercile 2 Top tercile deals, measured by abnormal performance SOURCE: Interviews

32. Q&A