SR Berlin 2015 - Finding Winners

Prof. Oliver Gottschalg of HEC Paris Founder and Head of Research, PERACS Independent PE Track Record Analytics and Certification

Finding Winners !

GPs matter – and there are ways to help you find the best

Agenda

2

• GPs Matter !

• Findings Winners by looking at the right Performance Measure

• Findings Winners by understanding actual strategy

• Findings Winners by identifying underlying skills

• Conclusions

New Academic Research

Does Ownership Matter in Private Equity? The Sources of Variance in Buyouts’ Performance, by O. Gottschalg and F. Castellaneta, published in Strategic Management Journal, Dec 2014 (Leading Academic Journal in Management)

Analysis of 6,950 buyouts realized by 255 PE firms between 1973 and 2008 in 77 countries

Key Findings:

• PE performance is fundamentally linked to choosing the right GP

• The GP has a significant impact beyond just choosing deals with the right attributes (industry, geography and timing)

• The GP effect has been steadily INCREASING over the past decades, i.e. relative to other factors it is today MORE IMPORTANT THAN EVER to get the GP selection right

• The GP effect is greater in established economies than in emerging markets

• The GP effect has always been greatest in times of economic difficulties (= that’s when Alpha creation counts)

3

When Alpha Creation Ability Counts Most

4

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

-20% -10% 0% 10% 20% 30% 40%

ALPHA and MARKET RETURNS

By GP

MARKET RETURNS (MSCI Global)

ALPHA

Value Protecting GPs

The GP effect has always been greatest in times of economic difficulties

Bigger Picture: Challenges with Data and Measures in PE: What GPs frequently say about themselves …

5

"We are a Top Quartile Performer."

"Our value creation is based on operational

expertise."

"We proactively generate proprietary

dealflow."

"Our unique investment strategy

differentiates us from our competitors."

?

… makes LPs wonder how to spot true future outperformers in a group of GPs

6

A possible Answer: The Suite of PERACS Analytics Insights into Performance, Risk and Strategy

7

Metric 1a –Absolute and Relative Performance

Performance is measured in the excess of the cost of the forgone opportunity investing capital elsewhere, which is approximated by the MSCI World index, both in absolute multiple terms based on the PERACS Profitability Index and on an annualized basis through the PERACS Alpha, based on the duration of the investment.

Mega Partners, LLC - Aggregate

2.49x

2.18x 30.70%

TVPI Variable-RateProfitability Index

PERACS Alpha

37

Metric 1c –Components of PERACS AlphaMega Partners, LLC - Aggregate

PERACS Alpha can be driven by a (a) choice of an industry sector or geography in which also public companies out-perform the MSCI world index, (b) by a fundamental outper-formance of the acquired business(es) over their publicly traded peers (the delivered PERACS Alpha) – net of any differences in leverage, or (c)by leverage. This distinguishes between the replicable effectof incremental leverage on the performance of the publicly traded peers and the unique effect of incremental leverage on the delivered alpha.

15.7% 0.9%

6.5%

7.6% 30.7%

Delevered Alpha

Sector Choice Effect

Replicable Leverage

Eff

Unique PE Leverage

Eff

PERACS Alpha

38

Metric 3 –PERACS Value Driver BridgeMega Partners, LLC - Aggregate

The PERACS Value Driver Bridge shows the percent-age of total PERACS Alpha attributable to different drivers of performance.

47.7%

60.9% -5.8% 4.1% -6.8%100.0%

RevenueEffect

MarginEffect

MultipleEffect

De-leverage

Effect

FXEffect

Total

PERACS Alpha = 30.7%

39

Metric 2 –Relevant Peer IRR Benchmark

The Relevant Peers Benchmark represents the aggregate performance of those PE funds who have been empirically identified as similar competitor using best available purchased industry benchmarks ( that may contain IRR time bias imperfections).


29.0%

22.0%

16.0%

20.0%

28.8%

12.3%

Fund A Fund B Fund C

Focal Fund IRR Relevant Peer Benchmark IRR

40

Metric 2 –Relevant Peer TVPI Benchmark



3.3x

2.5x 2.4x2.2x 2.1x

1.4x


Focal Fund TVPI Relevant Peer Benchmark TVPI

41

Metric 5a –Lorenz Curve by % of Deals

The PERACS Risk Curve illustrates the portion of the cumulative PERACS Alpha generated by the poorest-performing x% of the port-folio (as measured by % of deals). The PERACS Risk Coefficient expresses the skewedness of returns from 0 (perfectly uniform) to 1 (perfectly concentrated Alpha).


-40%

-20%

0%

20%

40%

60%

80%

100%

0% 20% 40% 60% 80% 100%

Gini Coefficient: 0.88

42

Metric 4a –Strategic Positioning

The Strategic overlap score measures the degree of similarity in the investment characteristics of one given fund and the aggregate of all PE funds.


0.4%

1.2%

4.1%

1.1%

7.5% 7.5%


Fund Score Max Relevant Competitor Score

43

Metric 4b –Investment Timing

The Procyclicality Score measures the correlation in the timing of investments of one given fund and the aggregate of all PE funds.


9.7%

0%

20%

40%

60%

80%

100%

Focal Procyclicality Relevant Competitor Procyclicality

Most Procyclical Quartile

Second MostProcyclical Quartile

Third MostProcyclical Quartile

Least Procyclical Quartile

44

Metric 4c –Strategic Consistency

This is an assessment of the difference in investment characteristics between the investment made in the last 5 years and those made in the last 6 to 10 years.


100.0%

83.9%

48.0% 48.0%

Country Consistency

Size Consistency

Industry Consistency

Combined Consistency

45

PERACS Numbers Speak Louder than words

8

"The breakdown of our PERACS Alpha shows that our outperformance over the public

markets is largely due to fundamental value creation and not to sector choice or

leverage."

"PERACS identified our closest competitors based on an analysis of the similarity in the

acquired entities. The strong aggregate performance of these "relevant peers"

demonstrates the attractiveness of the type of deals we make – and we are proud to out-

perform that benchmark of the aggregate "relevant competitor" for all of our funds."

"Our unique deal making approach translates into an ability to time deals

independently from deal volume for other PE investors, including our closest competitors."

"As you can see from the PERACS Risk Curve, our returns are much more balanced

than the industry benchmark."

Further Examples from Actual PERACS clients available upon request

Metric 1c –Components of PERACS AlphaMega Partners, LLC - Aggregate

PERACS Alpha can be driven by a (a) choice of an industry sector or geography in which also public companies out-perform the MSCI world index, (b) by a fundamental outper-formance of the acquired business(es) over their publicly traded peers (the delivered PERACS Alpha) – net of any differences in leverage, or (c)by leverage. This distinguishes between the replicable effectof incremental leverage on the performance of the publicly traded peers and the unique effect of incremental leverage on the delivered alpha.

15.7% 0.9%

6.5%

7.6% 30.7%

Delevered Alpha

Sector Choice Effect

Replicable Leverage

Eff

Unique PE Leverage

Eff

PERACS Alpha

38

Metric 4b –Investment Timing

The Procyclicality Score measures the correlation in the timing of investments of one given fund and the aggregate of all PE funds.


9.7%

0%

20%

40%

60%

80%

100%



Second MostProcyclical Quartile

Third MostProcyclical Quartile


44

Metric 2 –Relevant Peer TVPI Benchmark



3.3x

2.5x 2.4x2.2x 2.1x

1.4x



41

Metric 5a –Lorenz Curve by % of Deals

The PERACS Risk Curve illustrates the portion of the cumulative PERACS Alpha generated by the poorest-performing x% of the port-folio (as measured by % of deals). The PERACS Risk Coefficient expresses the skewedness of returns from 0 (perfectly uniform) to 1 (perfectly concentrated Alpha).


-40%

-20%

0%

20%

40%

60%

80%

100%

0% 20% 40% 60% 80% 100%

Gini Coefficient: 0.88

42

Agenda

9

• GPs Matter !




• Conclusions

This year's study focuses on the persistence of private equity performance

10

Methodology A

The repeatability of success in private equity is the subject of controversial debate in research

Persistence is checked by means of correlation and portfolio contribution

The study is carried out at transaction level

The performance indicators for IRR, multiple, alpha, holding period, loss ratio and return dispersion are analysed

1

2

3

Performance % p.a.

2008 2014

Fund I Fund II

Previous period Subsequent period

?

• Is continuous outperformance possible in private equity?

• Which performance indicators can substantiate persistence?

Methodology Question posed

Persistence is analysed using two methods

Question posed Comments Methodology

Correlation

Is there a statistical correlation between consecutive periods?

Portfolio contribution

How can an investor improve the development of their future portfolio by selecting funds that have been successful in the past?

• It is analysed whether the correlation between a period and the subsequent period is statistically significant

• If there is a statistically significant correlation, then taking a look at the relevant fund manager performance indicator in the past may help to form an opinion about the development of this performance indicator in future

• When analysing the portfolio it is tested to what extent the selection of a fund that were in the top quartile in the past in terms of the respective performance indicator has an impact on the expected value for the future portfolio

• In doing so, either (1) this selection's influence on the performance indicator itself or (2) this selection's influence on the portfolio's return is analysed

• This test is only carried out if the results for correlation are significant

?

Subsequent period's performance indicator

Previous period's performance indicator

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Portfolio performance indicator when

selecting top quartile funds

Portfolio performance indicator for market

average

?

Methodology B

Can conclusions be drawn from the previous period's performance indicator for the subsequent period?

Persistence

11

Advantages of analysis at transaction level Analysis methodology

• Analysis not on the basis of fund generations but the distribution of fund manager transactions over 4 year intervals (typical investment period for a portfolio)

• By using fixed intervals there is no distortion of analysis due to different investment cycles with different fund managers

• Transaction level allows for the use of realised transactions – no distortion of portfolio analysis by unrealised transactions

• Certain performance indicators (e.g. loss ratio and return dispersion) can only be calculated at transaction level

This study analyses the persistence of private equity at transaction level for the first time

Fund manager

A

Fund manager

B

Fund manager

C

Time 98 99 00 01 02 03 04 05 06 07 08 09

Fund I Fund II

Period 1 Period 2 Period 3

Persistence?

Fund I Fund II

Fund I Fund II

Individual transaction

Methodology A

Data at transaction level allows for a deeper and broader analysis of persistence

12

Persistence is analysed for different Performance indicators

Periods Number of

transactions IRR1) Multiple2) Alpha

Period 1 (2002-2005)

8 19% 1.6 4%

Period 2 (2006-2009)

12 21% 1.7 7%

Period 3 (2010-2013)

10 16% 1.4 8%

Future period 4 (2014-2017)

Can a similarly high IRR also be achieved in the future?

Can the multiple achieved in the past be perpetuated?

Will the fund also generate alpha in future?

1 2 3

1) IRR = return; 2) Multiple = multiple of the invested capital;

Unique dataset on 5600 realized Buyouts analyzed in collaboration with Golding Capital Partners

Methodology B

Example of period performance indicators for a fund manager

13

IRR and multiple: No significant correlation between previous and subsequent periods

Periods IRR Multiple

Period 1 (2002-2005)

19% 1.6

Period 2 (2006-2009)

21% 1.7

Period 3 (2010-2013)

16% 1.4


Can a similarly high IRR also be achieved in future?


1 2

• There is no statistically significant

correlation for IRR and multiple

between the previous and subsequent

period

• The results for portfolio contribution

are not statistically significant

• The IRR or multiple achieved in the

past does not make any statement

about the future success of a fund

manager

Results B

Results for IRR and multiple confirm results from earlier research

Question posed Comments Results

Subsequent period's IRR

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Previous period's IRR

r Persistence

No significant correlation (Correlation coefficient: +0.018)

Subsequent period's multiple

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Previous period's multiple

r Persistence

No significant correlation (Correlation coefficient: -0.001)

14

Alpha: significant correlation exists between previous and subsequent period

Significant correlation (Correlation coefficient: +0.132)

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1) Measured in PRoR, an adjusted return performance indicator, comparable with M-IRR

Results B

Alpha is repeatable – positive return contribution of 600 bps for future portfolio

Question posed Comments Results

• There is a statistically significant

correlation for alpha between the

previous and subsequent period

• The past alpha is therefore a predictor

for the expected alpha in the future

• If an investor only selects funds that

were in the alpha top quartile in the

past then they can expect a positive

return contribution of 600 basis points

compared to the market average for

their future portfolio

Periods Alpha

Period 1 (2002-2005)

4%

Period 2 (2006-2009)

7%

Period 3 (2010-2013)

8%



3

Previous period's alpha

Subsequent period's alpha

√ Persistence

600 bps


Return when selecting top quartile funds

Return for market average

1)

15

Persistence could not be evidenced for the IRR and multiple performance indicators

Persistence of alpha, holding period, loss ratio and return dispersion is statistically significant

Results B

Periods IRR Multiple Alpha


Can a similarly high IRR also be achieved in future?



Is past success repeatable?

Significant correlation r r √ Positive portfolio contribution

– – 600 bps additional

return

1 2 3

Example period performance indicators for a fund manager

16

Another Advantage of PERACS Alpha over IRR, Quartile Discrepancy between benchmark providers diminishes

17

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

EU PERACS Alpha EU IRR

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

US PERACS Alpha US IRR

Europe Buyouts Quartiles MAX - MIN

U.S. Buyouts Quartiles MAX - MIN

Comparing Top Quartile Cut-Offs across commercially available databases shows much greater disagreement for IRR than for PERACS Alpha in many vintage years …

Agenda

18

• GPs Matter !




• Conclusions

Method

Empirical assessment of the level of the client's strategic uniqueness / differentiation (fund-by-fund and at the GP / total portfolio-level) based on the overlap between each of these funds and the PE universe.

Calculation of Uniqueness Score

We divide the PE universe into "strategic cells", based on (1) industry sector, (2) time-varying deal-size quartile, (3) investment timing and (4) geography. We then measure the Percentage of activity in each "strategic cell" for (I) the focal fund and (II) all other PE investment activity.

We calculate the "strategic overlap" as the sum of joint activity (minimum of the percentage invested of the firm and the percentage invested by all firms in a given cell) over all cells. A strategic overlap of 1 means that a fund invests exactly like the average PE firm – a score of 0 indicates that no other firm makes similar investments. This method is a refinement of one of the criteria that determine the 'Fitness' score of the HEC-DowJones PE Fitness Ranking.

Empirical Assessment of Strategic Uniqueness

19

Strategic Positioning – Client Example

0.93%

0%

1%

2%

3%

4%

5%

Focal Strategic Overlap Score

Relevant Competitor Strategic Overlap Score

Highest Overlap Quartile

Second Highest Quartile

Third Highest Quartile

Least Overlap Quartile

• Low score indicates more differentiated market positioning

• Focus overlap is relative to the full universe and the relevant competitor set highlights closest competitors

20

Fund V


Empirical Assessment of Dealflow Quality

21

Intuition

We use the comparison of the pattern of client's investment activity with the pattern of investment activity of the overall PE universe as a proxy for the client's quality of deal flow, i.e. ability to continue to invest during periods when all other PE firms are decreasing their investing pace.

Calculation of Uniqueness Score

Method: We measure the correlation between client's investment pattern (i.e. number of deals by quarter) and the investment pattern of the overall PE universe. A correlation of 1 means that the client invests in a perfectly pro-cyclical fashion, i.e. with the same pattern over time as the overall PE universe – which does not manifest any proprietary dealflow, whereas a correlation of -1 means that the client invests in a perfectly counter-cyclical fashion, which can be seen as a proxy for proprietary deal flow. This method is a refinement of one of the criteria that determine the 'Fitness' score of the HEC-DowJones PE Fitness Ranking.

Investment Procyclicality

Metric 4b – Investment Timing – Client Example

Note: this presentation is to illustrate insight generated by PERACS Analytics and should not be interpreted as suggesting the funds are competitors or comparable 22

25.51%

0%

20%

40%

60%

80%

100%



Second Most Procyclical

Third Most Procyclical


Aggregate

• Lower score indicates less pro cyclical investing approach

• Industries & Finances' buy and build strategy may help allow it to be less pro-cyclical


Metric 4c: Empirical Quantification of Strategic Consistency

23

Intuition

We assess the type of investments in a GP's Track Record (by size, industry sector and geography) for the last 5 years and for the prior 5 years (i.e. the time period between 5 and 10 years ago) and measure the degree of similarity in these three dimensions.

Data Source: GP's investment Track Record

Calculation of Strategic Consistency Score

Method: We measure the consistency of investment activity as the degree of overlap in the percentages in each category of a given dimension across the two time periods. Example: if the volume split between the four size-categories was 20%, 20%, 50%, 10% in the distant past and 10%, 40%, 30%, 20% in the recent past, then the size-consistency score is the sum of the minimum between the two periods (10%+20%+30%+10%)= 70%.

This method is a refinement of one of the criteria that determine the 'Fitness' score of the HEC-DowJones PE Fitness Ranking

Metric 4c – Strategic Consistency

100%

86.7%

33.3%

Country Consistency Size Consistency Industry Consistency

24

Aggregate

Note: this presentation is to illustrate insight generated by PERACS Analytics and should not be interpreted as suggesting the funds are competitors or comparable

This is an assessment of the difference in investment characteristics between the investment made in the last 5 years and those made in the last 6 to 10 years.

• Investment consistency can be executed by GPs operating at very different ends of the deal spectrum

• Industries & Finances demonstrates consistency of a country focused fund targeting small buy-and-build

Agenda

25

• GPs Matter !




• Conclusions

Beyond the Vintage Year Quartile: The PERACS 'Relevant Peer Benchmark' An Innovative PE Performance Benchmark based on empirically-derived 'Relevant Competitor Funds'

Fact

Existing vintage year benchmarks are of limited accuracy: • Vintage criterion groups funds with very different investment focus • Does not account for market dynamics where funds with similar focus from

consecutive vintages compete for similar deals and LP commitments

Problem Self-selection of peers is often seen by LPs as subjective and potentially biased

26

Idea PE funds should be benchmarked against their 'relevant peers'

Empirically derive funds that directly compete with a given GP for investment opportunities to objectively capture funds active in the same 'space' within the PE universe

Solution

How to empirically identify 'Relevant Competitor Funds'*

27

Assessment of investment activity by 'strategic cells'

• Compose data set on fund-level performance and investment activity (individual deals) of large sample of PE funds

• Define a space within the PE universe ("strategic cell") as the combination of a given:

– Industry sector (GICS classification at the 2-digit level)

– Region

– Investment time period (based on 3-year window around date of investment) and

– Size category (based on time-varying deal-size quartile)

• For example, all US upper-mid-cap automotive deals made between 1997 and 2009 fall into the same 'strategic cell'

* Proprietary – US Patent Pending

Calculating the 'strategic overlap score' between two PE funds

• Measure the percentage of activity (based on the investment volume and the # of deals) in each "strategic cell" for each PE fund

• Calculate the "strategic overlap" for each pair of PE funds as the sum of joint activity (i.e. the minimum of the percentages invested in a given strategic cell by both funds) over all strategic cells

• A strategic overlap of 1 means that a firm invests exactly like the average firm – a score of 0 indicates that no other firm makes similar investments

How to empirically identify 'Relevant Competitor Funds'*

28

Identification of relevant competitor fund based on 'strategic overlap score'

• 'Comparable' funds that should be benchmarked against each other can then be derived as a function of the 'strategic overlap score'

• For example, we consider funds as 'relevant competitors' whenever their 'strategic overlap score' (by investment volume) exceeds 15% – and consider the degree of 'competitive overlap' in the calculation of the benchmark:

The 'Relevant Competitor Benchmark' (RCB) for a given fund is the average perform-ance of all its 'relevant competitors', weighted by their 'strategic overlap score'

* Proprietary – US Patent Pending

Intuitive Validity of the Empirical Approach

29

Illustrative Example based on Data Provided by Thomson Reuters

Fund to be benchmarked: Large Cap Europe Fund

List of Relevant Competitors (by order of strategic overlap)

• BC European Capital VII

• Third Cinven Fund, The

• Blackstone Capital Partners IV, L.P.

• CVC European Equity Partners III LP

• KKR European Fund

• Hellman & Friedman Capital Partners IV, L.P.

• Charterhouse Capital Partners VI

• Carlyle Europe Partners, L.P.

Example for most relevant 'Competitor Funds' according to our method:

Hypothetical Example: Relevant Peer TVPI Benchmark

30

3.3x

2.5x 2.4x 2.2x 2.1x

1.4x



Mega Partners, LLC – Aggregate

Key Insights into two distinct GP skills:

• High Relevant Peer Benchmark = Superior Target Choice

• Focal Fund beats Relevant Peer Benchmark = Superior Execution / Implementation

Metric 3: PERACS Value Driver Bridge

31

Fund B

40.4%

70.8% -8.0% 12.4% -15.7%

100.0%

Revenue Effect

Margin Effect

Multiple Effect

Deleverage Effect

FX Effect

Total

The « PERACS Value Driver Bridge » is a refinement of the widely-used “Value Attribution” analysis, which

(1) quantifies value drivers on an annualized basis and

(2) includes currency effects and separates revenue growth from margin improvements as drivers of EBITDA growth.

20%

20%

20%

50% -10% 100%

Revenue Effect

Margin Effect

Multiple Effect

Deleverage Effect

FX Effect

Total

20%

90% -40% 30%

0% 100%

Revenue Effect

Margin Effect

Multiple Effect

Deleverage Effect

FX Effect

Total

Different Value Drivers Reflect Different Strategies

32

80%

20%

20% -15%

5% 100%

Revenue Effect

Margin Effect

Multiple Effect

Deleverage Effect

FX Effect

Total

20%

40%

60% -30%

10% 100%

Revenue Effect

Margin Effect

Multiple Effect

Deleverage Effect

FX Effect

Total

Growth Player: Growth drives margin and Multiple improvement, but requires additional cash investment

Buy and Build strategy requires additional borrowing, leads to synergies = improved margins and substantial multiple expansion

Leveraged play in a favorable macro environment Difficult environment pushes for efficiency gains and careful cash management to ensure survival

In-depth understanding of the GP’s strategy and value creation skills serve as basis of assessment of likely future performance

Agenda

33

• GPs Matter !




• Conclusions

Conclusions

34

• If you look for a predictor of future performance don’t consider IRR anymore, look at PERACS Alpha instead

• When a GP states it’s investment strategy is different, we can prove this in numbers

• Likewise, when a GP states he doesn’t go with the cycle, we can show also this in numbers

• When a GP states he didn’t change it’s strategy, we have some numbers to show it

Thank you for your attention !

35

About Professor Oliver Gottschalg

36

Education • Dipl. Wirtschaftsingenieur (TU Karlsruhe)

• MBA (Georgia State University)

• MSc. of Management (INSEAD)

• Ph.D. (INSEAD)

Work Experience • Federal Reserve Bank, US

• Bain & Company – Private Equity Practice

Teaching • HEC Grande Ecole Program

• HEC Executive Education

• Harvard Executive Education

• TRIUM Global EMBA Program

• INSEAD Executive Education

• LBS Executive Education

• Tsinghua University Executive Education

• Company-Specific Executive Programs

Current Positions • Director of the HEC PE Observatory

• Academic Dean of the TRIUM Global Executive MBA Program

• Founder and Head of Research, PERACS PE Track Record Analytics

Research • Published in the Review of Financial Studies, Harvard Business

Review, Academy of Management Review, Strategic Management Journal, Journal of Banking and Finance, etc.

• Featured over 100 times in the business media (press, radio, TV and online) in the past 2 years, including The Economist, Financial Times, Wall Street Journal, Financial News, Les Echos, etc.

Consulting • Tailored projects for leading sponsors, institutional investors

and advisors. Repeatedly served as advisor to policy makers at the national and European level in questions related to the possible regulation of private equity.

About PERACS

• PERACS is not just another performance benchmark, but it provides customized and insightful metrics to quantify relevant elements of past performance, risk attributes and strategic differentiators

• Independent, credible, trustworthy, global, conflict-free, and singularly focused

• Granular analysis built up from company by company portfolio analysis

• Formulaic and transparent. Trusted standardized comparisons

• Dynamic quarterly updates and annual reviews

• Methodology of leading industry academics and investors

• Value added service provided by GPs to their LPs

• Used in GP marketing materials with success

PERACS: The Global Standard for PE Performance Analytics

37

PERACS in the ILPA Newsletter

38

Milestones of the establishment of PERACS as the World Standard for Advanced PE Track Record Analytics

Spring 2012 Exploratory Conversations with leading GP and LPs

Summer 2012 First PERACS Client Work Performed

January 2013 20% of Fundraising GPs (buyouts in EU and US, by volume target fund size) use PERACS numbers

Spring 2013 "LP Champion" Initiative launched, supporting LPs with risk/return analysis of existing portfolio and in fund due diligence

Winter 2012/2013 Research Project ILPA-CA-HEC, leveraging PERACS methods

January 2014 PERACS Client-announced Fund Closings exceed USD 55B

February 2014 "LP Champion" projects set up with LPs of all types, from fund-of-funds, over Sovereign Wealth investors to insurance companies, public and private pension funds, university endowments and family offices from basically all relevant parts of the world

Winter 2011/12 Design of Standardized Metrics, Value Proposition and Business Model

2000 - 2010 10 years of applied research, developer of Wall Street Journal PE Rankings, consulting engagements

2011 Exploratory Consulting Engagements with three of the World’s 50 Largest GPs

Confidential 39

February 2015 1000th fund analyzed on behalf of LP Clients, PERACS GP Client-announced Fund Closings exceed USD 70B, PERACS Performance Metrics available on Bloomberg Terminal, first GP Client engagements in Mezzanine, VC, Emerging Market PE …

Diverse Senior Team Delivers Innovative Services

Oliver Gottschalg

PERACS Founder and Head of Research; senior strategic consultant to global corporations; Head of the Private Equity Observatory at HEC Paris; Academic Dean of TRIUM Global Executive MBA Program; leading private equity researcher

Gerry Flintoft

Extensive track record with oversight of PE at the $50 billion LACERA pension plan; Director of Alternatives with PineBridge (formerly AIG Investments); advised clients on portfolio construction, emerging markets, private credit, and hedge fund seeding; ILPA Board Member and Chartered Alternative Investment Analyst (CAIA)

Peter Mayrl

18 years of experience in European PE, both on the direct side (Permira, Lyceum) and on the FoFs side (Allianz, Idinvest); experience in strategic consulting at Bain & Co

Fernando Vazquez

Co-Founder of Conversus listed PE fund; MD and investment committee member of Bank of America’s PE fund business; corporate finance and banking experience in developed and emerging markets; ILPA Research Chair and CFA

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• Exceptional access to applied research and experienced investing professionals

• Client focused senior professionals based in Europe and North America

• Deep analytic resources

• In-house systems development

Industry Leading Advisory Board

John Breen Chairman of the PERACS Advisory Board, Head of Private Investments & Investment Committee Member – Sanabil Investments, Saudi Arabian Investment Company; Former Head of Funds and Secondaries, Canada Pension Plan Investment Board (CPPIB); former Vice Chairman – Institutional Limited Partners Association

Thomas C Franco Partner with Clayton, Dubilier & Rice, LLC

Jeff Gendel Managing Director at Gen II Fund Services, LLC

John Higgins Managing Director of the Americas, PEI Media

Kathy Jeramaz-Larson Executive Director of the Institutional Limited Partners Association (ILPA)

Andrea Lowe Chief Executive of LPEQ, the Listed Private Equity Association

Stephen Marquardt CEO of Doughty Hanson & Co

Spencer Miller Managing Director and Head of London Office of OPTrust Private Markets Group

Tom Rotherham Formerly Director – Head of Private Markets BTPS/ Hermes Equity Ownership Services Limited

Sheryl Schwartz Managing Director, Caspian Private Equity

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SR Berlin 2015 - Finding Winners

Documents

Transcript of SR Berlin 2015 - Finding Winners