Finding Winners: How Advanced Private Equity Benchmarking Can Identify the Skills That Lead to Future Outperformance

Content Includes: Finding Winners: How Advanced Private Equity Benchmarking Can Identify the Skills that Lead to Future Outperformance

New Methodology

Gain insight into the drivers September 2016 of private equity value creation.

Top Performers

View the top performing private equity funds and fund managers.

Frequently Asked Questions

Find out how to interpret the results.

alternative assets. intelligent data. Preqin Special Report: Finding Winners

Introduction Prof. Oliver Gottschalg, Head of Research, PERACS

Private equity performance measurement is by design initially about the “how much” of value creation of a given fund manager. It is implicitly backward looking as today’s investors cannot “buy” a fund’s past returns but only commit to a manager’s future fund with the hope of obtaining high returns from this investment.

Commitment decisions to new funds would beneﬁ t by not only considering the absolute return of a fund but also by assessing the GP skillset, i.e. the nature of value creation, as these skills of the GP are what ultimately persists between funds. To this end PERACS have developed a novel methodology, the PERACS Manager Capability Identiﬁ er (MCI), to measure the distinct components of a fund’s performance, leveraging data on tens of thousands of underlying investments made by thousands of funds. This enables us to assess and quantify the performance impact of distinct GP skills, and can thus provide the basis for a deeper understanding of the sources of past returns, as well as the persistence of different return components for a certain GP across fund generations.

The methodology uses a combination of performance and deals data from Preqin on 1,119 funds to decompose the relative performance impact of the “timing”, i.e. how investments were timed over the investment period of the fund; the “strategy”, i.e. which type of target companies were chosen (industry sector, size category, geography) to acquire at those times; and the “implementation”, i.e. the strategic development of the business, implementation of the GP’s value creation plans and management appointments.

Through our analysis of the data, we found that statistically signiﬁ cant positive persistence occurs only in the Implementation component of returns. This is a particularly powerful message for private equity, as this component captures the impact the GP has had during its stewardship of an investment, one of the key differentiators between private equity and other forms of asset management.

In this report, we highlight the top performing funds in terms of their Implementation Skill for different vintages and fund size categories as examples of funds that were particularly successful in generating better returns with their investments than those of their competitors, with similar approaches to investment timing and target selection. Further we present the various quartiles by Implementation Skill across the 2000s vintage years, to help in benchmarking a speciﬁ c fund. Finally, we identify consistently top performing managers in terms of this persistent Implementation Skill component.

Professor Oliver Gottschalg

Professor Oliver Gottschalg is Head of Research at PERACS, a specialized advisory ﬁ rm providing advanced private equity fund due diligence and benchmarking services. He is part of the Strategy Department at HEC School of Management, Paris. He serves as HEC’s Academic Dean for the TRIUM Global Executive MBA Program, Director of the Entrepreneurship Track in the HEC MBA program, directs the HEC Private Equity Observatory and teaches courses on strategy, entrepreneurship, venture capital ﬁ nancing and management buyouts. He holds a Wirtschaftsingenieur Diploma from the University of Karlsruhe, an MBA from Georgia State University and a M.Sc. and PhD degree from INSEAD. His current research focuses on the strategic logic and the performance determinants of private equity investments. His work has been published in leading academic journals and in various publications for practitioners and was widely featured over 200 times in the business press, radio and TV. He regularly presents his research at academic conferences and private equity symposia and serves as an advisor to leading investors in the private equity industry. Most recently, he served as an advisor to the European Parliament in the context of the current debate about a possible need for regulation of the private equity industry.

www.peracs.com

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Contents

On the Methodology 3

Selected Results

Top Performing Funds 4

Top Performing Fund Managers by MCI 5

Quartile Boundaries for Implementation Skill 6

Interpreting the Results 7

Frequently Asked Questions 8

On the Methodology

The PERACS Manager Capability Identiﬁ er (“MCI”) compares the performance of various groups of private equity funds, classiﬁ ed by the nature of their underlying deals, to isolate the components of a fund’s outperformance to its peers. It measures four distinct components of a PE fund’s performance, three of which are proxies for the underlying capabilities of the fund manager.

This approach compares data on tens of thousands of underlying investments made by thousands of funds to identify funds that are similar in three different aspects: (a) similar in the time period in which the fund was raised (three year vintage cycle), (b) similar in terms of when investments were made (based on acquisition years of the underlying deal) and (c) similar in terms of which type of target companies were chosen (industry sector, size category, geography) to be acquired at those times.

Starting from the funds raised in the same threeyear vintage year cycle, the two remaining peer groups correspond to sequentially fi ltering for funds that are similar to a given fund in terms of two key investment decisions of a PE fi rm: the Timing Choice, i.e. the decision of how investments were timed over the investment period of the fund, and the Strategy Choice, i.e. the decision of which type of target companies were chosen (industry sector, size category, geography) to acquire at those times. It is then possible to assess the impact of each of these decisions by comparing the average performance among peer groups. Finally, the comparison between a fund’s performance and the performance of those funds that are similar in terms of what was acquired and when, refl ects the portion of a fund’s overall performance that is attributable to Implementation Skill, i.e. the performance of the specifi c investment approaches into specifi c target companies, above the returns of other funds that made similar decisions in terms of timing and strategy.

LPs use the methodology to derive new insights on value creation when evaluating potential GPs.

GPs use the results to illustrate their relative value add and differentiation.

The results should be compared to the GP’s articulated investment strategy, and can:

► Quantitatively help backup a qualitative assessment ► Be used as part of additional advanced due diligence with a GP ► Help ﬁ lter which GP from a number should be prioritized for additional due diligence

For a more detailed explanation of the methodology and ﬁ ndings, please see “Beyond the Quartiles”, a white paper by Oliver Gottschalg, accessible at www.preqin.com/peracs

Selected Results

Top Performing Funds

To determine the best performing funds, PERACS has grouped together funds into two categories: performance of funds greater than $1bn in size; and performance of funds between $250m and $1bn. The report is read as follows:

► The “Peers” column represents the average performance of funds (represented by the average MOIC/TVPI) within a two year vintage window of the target fund;

► The “Timing” column represents the impact of the Timing Choice on the returns of the fund;

► The “Strat” column represents the impact of the Strategy Choice decision on the returns of the fund;

► The “Implement” column represents the impact of the Implementation Skill on the returns of the fund;

► The “TVPI ” column represents the Total Value to Paidin Multiple of the target fund i.e. the fund on which the analysis is run.

Please see the Interpreting the Results section on page 7 for a more detailed explanation of the Timing, Strategy and Implementation factors.

Fig. 1: Top Performing Funds by Vintage: Funds Greater than $1bn

Region Fund Size Date Vintage Firm Fund Type Peers Timing Strat Implement TVPI Focus ($mn) Reported Vista Equity North 2000 Vista Equity Fund II Buyout 1,000 1.92 0.01 0.01 1.20 3.12 31Mar16 Partners America

CVC Capital CVC European 2001 Buyout Europe 3,971 1.94 0.04 0.03 0.98 2.93 30Sep15 Partners Equity Partners III

North 2002 Berkshire Partners Berkshire Fund VI Buyout 1,700 2.00 0.00 0.15 0.68 2.53 30Jun15 America North 2003 Onex Corporation Onex Partners Buyout 1,650 1.85 0.04 0.07 1.14 2.96 30Sep15 America Hellman & North 2004 Hellman & Friedman Buyout 3,500 1.71 0.05 0.01 0.91 2.68 30Sep15 Friedman V America Goldman Sachs GS Capital Partners North 2005 Merchant Banking Buyout 8,500 1.64 0.09 0.04 0.92 2.62 30Sep15 V America Division 2006 Triton Triton Fund II Buyout Europe 1,438 1.60 0.02 0.16 0.62 2.07 30Sep15 Vista Equity North 2007 Vista Equity Fund III Buyout 1,287 1.54 0.00 0.11 0.82 2.47 30Sep15 Partners America 2008 MBK Partners MBK Partners II Buyout Asia 1,600 1.49 0.02 0.08 0.76 2.2 30Jun15 Odyssey Investment Odyssey Investment North 2009 Buyout 1,500 1.46 0.00 0.08 0.84 2.38 30Sep15 Partners Partners Fund IV America 2010 Lion Capital Lion Capital Fund III Buyout Europe 2,038 1.41 0.01 0.01 0.10 1.51 30Sep15

TVPI is based on the latest reported ﬁ gure, which covers a two-year date range. Source: Preqin and PERACS

Request MCI Metrics

Preqin has the data to compare the MCI metrics for over 1,100 funds worldwide. Should you wish to determine the metrics for a speciﬁ c fund, please reach out to us at: [email protected]

Fig. 2: Top Performing Funds by Vintage: Funds Greater than $250mn and Less than $1bn

Region Fund Size Date Vintage Firm Fund Type Peers Timing Strat Implement TVPI Focus ($mn) Reported GCP Capital Greenhill Capital North 2000 Buyout 423 1.91 0.03 0.08 1.88 3.9 31Dec15 Partners Partners America CBPE Capital 2001 CBPE Capital Buyout Europe 288 1.94 0.04 0.04 1.56 3.5 31Mar16 Fund VI Wellspring Capital Wellspring Capital North 2002 Buyout 640 2.00 0.01 0.11 0.30 2.2 31Mar16 Management Partners III America 2003 Altor Altor 2003 Fund Buyout Europe 7,099 1.85 0.06 0.01 1.31 3.23 30Sep15 Herkules Private 2004 Herkules Capital Buyout Europe 329 1.71 0.08 0.03 3.01 4.82 31Mar14 Equity Fund I 2005 Axcel Axcel III Buyout Europe 491 1.64 0.00 0.06 4.31 5.88 30Sep15 Thoma Cressey North 2006 Thoma Bravo Buyout 765 1.60 0.02 0.07 1.20 2.89 30Sep15 Fund VIII America HIG Capital North 2007 H.I.G. Capital Buyout 750 1.54 0.00 0.07 0.71 2.31 30Sep14 Partners IV America Thoma Bravo North 2008 Thoma Bravo Buyout 823 1.49 0.02 0.10 1.55 3.17 30Sep15 Fund IX America Egeria Capital Egeria Private 2009 Buyout Europe 787 1.46 0.01 0.09 0.51 1.9 30Sep15 Management Equity Fund III Wynnchurch Wynnchurch North 2010 Buyout 603 1.41 0.00 0.02 0.68 2.11 30Sep15 Capital Partners Capital Partners III America

TVPI is based on the latest reported ﬁ gure, which covers a two year date range. Source: Preqin and PERACS

Top Performing Fund Managers by MCI

PERACS generates quartile rankings for individual funds according to their vintage. The quartile rankings are assigned using the Implementation Skill component of MCI which per PERACS research was identiﬁ ed as the most material and persistent measure of fund manager value add. The tables are compiled using only funds for which PERACS assigns a quartile ranking, and so for this reason, funds with more recent vintages (2013, 2014, 2015 and 2016) have been excluded as these funds are too early in their fund lives to generate meaningful return metrics. Only managers with three or more funds were considered to have enough data to be deemed consistent.

The lower the average score, the more consistently the manager has outperformed its peers. The scores are calculated by assigning topquartile funds with a score of one, secondquartile funds with a score of two and so on, and then an average of the scores is taken. Only ﬁ rms with average an quartile ranking of 2.00 or below are considered. No assurances as to the completeness of this list can be given; PERACS is limited to a sample of 1,100 funds based on the performance and deals data available.

Fig. 3: Consistently Top Performing Fund Managers by Implementation Skill

No. of Average Quartile Ranking of Firm Firm Location Funds Implementation Skill Altor US 3 1.00 Clayton Dubilier & Rice US 3 1.00 KPS Capital Partners US 3 1.00 Procuritas Partners Sweden 3 1.00 Waterland Netherlands 5 1.20 Leonard Green & Partners US 4 1.25 American Securities US 3 1.33 Archer Capital Australia 3 1.33 Erhvervsinvest Denmark 3 1.33 Harvest Partners US 3 1.33 Nautic Partners US 3 1.33 Odyssey Investment Partners US 3 1.33 Sentica Partners Finland 3 1.33

Source: PERACS

Quartile Boundaries for Implementation Skill

Fig. 4: Median Implementation Skill and Quartile Boundaries by Vintage Year

0.80

0.60 Bottom Quartile Implementation Skill 0.40 Boundary

0.20 Median Implementation 0.00 Skill

-0.20

Implementation Skill Top Quartile -0.40 Implementation Skill Boundary -0.60

-0.80 200020012002200320042005200620072008200920102011201 2

Vintage Year Source: PERACS

Fig. 5: Median Implementation Skill and Quartile Boundaries by Vintage Year

Bottom Quartile Implementation Median Implementation Top Quartile Implementation Vintage Skill Boundary Skill Skill Boundary 2000 0.37 0.12 0.50 2001 0.33 0.27 0.62 2002 0.56 0.25 0.15 2003 0.43 0.09 0.42 2004 0.15 0.09 0.51 2005 0.40 0.12 0.15 2006 0.44 0.08 0.27 2007 0.27 0.04 0.20 2008 0.21 0.01 0.27 2009 0.23 0.03 0.26 2010 0.24 0.02 0.14 2011 0.23 0.09 0.11 2012 0.15 0.01 0.14

Source: PERACS

Interpreting the Results

1. What does Timing Choice mean?

This attribute measures the impact of the timing decision taken by a GP, namely, how investments were timed during the investment period of the fund.

2. What does Strategy Choice mean?

This attribute measures the impact of the decision taken by a GP as to which types of company they invest in, i.e. industry sector, investment size, and geography.

3. What does Implementation Skill mean?

This attribute measures relative stock selection value add of the GP in addition to the impact the GP has had during its stewardship of an investment i.e. the strategic development of the business, implementation of its value creation plan, management appointments etc; its ‘key ingredients’ of value creation.

4. What does a positive score indicate?

A positive score in a particular dimension indicates that the GP has taken decisions in that dimension that led it to perform better than the average fund’s GP in the reference group, as described above in the Timing Choice, Strategy Choice and Implementation Skill descriptions.

What a positive Timing Choice score indicates: This GP took timing decisions that improved its performance over the universe of funds raised in the same ﬁ vevintageyear period.

What a positive Strategy Choice score indicates: This GP made choices related to the industry, size and geography of the acquired assets that improved its performance over those funds raised in the same ﬁ vevintageyear period that made comparable timing decisions.

What a positive Implementation Skill score indicates: This GP was better able to implement its chosen investment strategy than those funds raised in the same ﬁ vevintageyear period that made comparable timing and strategy decisions.

5. What does a negative score indicate?

A negative score in a particular dimension indicates that the GP has taken decisions in that dimension that led it to perform worse than the average fund’s GP in the reference group, as described above in the Timing Choice, Strategy Choice and Implementation Skill descriptions.

6. Which is more important: Timing Choice, Strategy Choice, or Implementation Skill?

While each of these attributes is important, a user needs to ﬁ rstly compare the results to the articulated strategy and approach of the GP in question to help answer questions such as: “Is the key skill of this GP its ability to be a great market timer?” Only the Implementation Skill shows positive persistence of returns which would imply this characteristic is the most important one when selecting future outperformers.

7. How do I benchmark the relative score sizes?

Identify a set of GPs that are comparable from your perspective and look at the relative score they each have in the different MCI components.

8. How do I interpret these scores and apply this to the other diligence I am completing on the target GP?

This methodology helps a user identify the sources of value creation by a GP in an objective, formulaic and independent manner. The results need to be compared to the GP’s articulated investment strategy, and can either quantitatively help backup the qualitative assessment of a GP and/or be used as part of additional advance due diligence with a GP and/or help ﬁ lter which GP from a number a user would like to prioritize for additional due diligence.

Frequently Asked Questions

1. Why is understanding MCI important?

Understanding how a GP creates value is important as it gives a greater insight into the nature of the skills of a particular GP, which can be compared to the way their strategy is articulated. More importantly, performance driven by a GP’s Implementation Skill shows persistence over time, and hence is a strong indicator that the GP has a higher chance of continuing to perform in the future.

2. Is a strong Implementation Skill Score an indicative feature to demonstrate persistence?

Yes.

3. How is the universe of comparables calculated?

A competitor ‘Peer Universe’ is created by identifying all funds raised in a similar period to the GP in question, i.e. those funds raised in +/ 2 years of the GP’s defi ned vintage year. Within this universe we then fi lter to only include funds with similar timing of investments and then fi lter again to only include funds with a similar strategy, i.e. similarity in terms of the size, the industry and the geography of the underlying assets, to the GP in question.

4. What data is needed to run the analysis on a fund?

In order to run the analysis we need the nettoLP performance data of the fund as well as at least ﬁ ve deals from the fund including information on the size, timing, sector and location of that deal.

5. How does this Peer Universe differ from the traditional vintage year approach?

This approach creates a Peer Universe based on the similarity of underlying deals done by the GP in question to consider GPs which have also completed similar deals rather than the often used method of creating a peer set based on the vintage year a fund happened to be raised in.

6. How big is the universe for funds?

There was sufﬁ cient data to run the analysis on 1,119 PE funds.

7. How big is the universe for deals?

Information on 26,771 PEbacked deals was used to support the analysis of the 1,119 funds.

8. How many funds are typically in the five year vintage group peer set?

On average ﬁ ve times more than in a typical vintage year.

9. Can I see which funds make up the peer benchmark for a target fund?

Identifying similarity based on the underlying deals a fund makes as opposed to vintage year often leads to nontraditional results that require a more detailed explanation of the methodology. To understand more about the peer set for a given fund please contact PERACS directly at [email protected]

10. What are the limitations of this methodology?

While this methodology has been designed to provide directionally correct insights despite possible imperfections in the underlying data on fund performance and investment activity, the method only considers deals that actually happened. As such it cannot capture that funds may have been in regular competition for similar deals, but eventually ended up making investments that were not very similar as per our deﬁ nition of similarity.

Data limitations prohibit us from considering other deal attributes than size, industry and geography, so that we do not capture which fund are similar with respect to their choice of buyandbuild versus standalone buyout strategies. The method uses net toLP performance data only. Based on the way that similarity is determined the method inherently assumes that deal size and a deal’s impact on overall fund TVPI are perfectly aligned. While this may not hold true for individual funds, we ﬁ nd that across a universe of >1000 funds these factors are highly correlated. Given the above, the Strategy choice metric for specialist funds is often understated.

For a detailed explanation of the methodology, or the results for a speciﬁ c fund, please contact Preqin client services: [email protected] .

September 2016

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