How GPs can highlight true outperformance and demonstrate their strengths effectively g yand objectively to LPs

Prof. Oliver GottschalgHEC Paris

Proprietary and Confidential © PERACS Ltd ‐ 2012

In the current world …

• 77% of all GPs can claim to be “Top –Quartile” thanks to disagreement different benchmark providers

• The standard performance measure (IRR) has severe methodological biases:The standard performance measure (IRR) has severe methodological biases: The IRR for 30% of funds deviates by more than one 10th from the actual performance

A ltit d f th d l i l h t id tif f l l• A multitude of methodological approaches to identify sources of value leaves much room for “creative accounting”

• No standardized risk measure exists for PENo standardized risk measure exists for PE

It is challenging for LPs to identify attractive GP … .

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t s c a e g g o s to de t y att act e G

In the current world …

• 77% of all GPs can claim to be “Top –Quartile” thanks to disagreement different benchmark providers

• The standard performance measure (IRR) has severe methodological biases:The standard performance measure (IRR) has severe methodological biases: The IRR for 30% of funds deviates by more than one 10th from the actual performance

A ltit d f th d l i l h t id tif f l l• A multitude of methodological approaches to identify sources of value leaves much room for “creative accounting”

• No standardized risk measure exists for PENo standardized risk measure exists for PE

… it is equally challenging for truly outstanding GPs to distinguish themselves from “th t d ”

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“the pretenders”

Our Proposal: Five Key Metrics to Showcase GPs Strengths

A. Measuring ReturnsEnhanced Performance Measurement, avoiding issues with IRR MethodologyPublic-Market Benchmarking (PME) PerspectiveQuantification of different components of GP’s added valuep

B. Benchmarking ReturnsObjective and data-driven identification of ‘Relevant Peer Funds’ j

C. Empirical Documentation of Value Creation ComponentsGrowth, Efficiency gains, multiple expansion, debt reduction, currency fluctuation

D. Strategic DistinctivenessMeasures of distinctive strategy, unique deal flow, and strategic consistency/drift

E. Returns Volatility Measures Measures of portfolio volatility and risk based on historical return distributions

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Reminder: What’s wrong with IRR (1/2)Deal Name A IRR 298%Cash Flow Dates 01/01/1992 01/01/1993Cash Flow Amounts -100 400Deal Name B IRR 5%

400

350400

500

Deal Name B IRR 5%Cash Flow Dates 01/01/1992 01/01/2001Cash Flow Amounts -100 150

200 200 200 200 200 200 200 200

350

200

300Re‐invested at 100%IRR

Re‐invested at 100%IRR

Re‐invested at 100%IRR

Re‐invested at 100%IRR

Re‐invested at 100%IRR

Re‐invested at 100%IRR

Re‐invested at 100%IRR

Re‐invested at 100%IRR

0 0 0 0 0 0 0

150

100

200

Normal CF

Cumulative CF

0 0 0 0 0 0 0

-100

01992 1993 1994 1995 1996 1997 1998 1999 2000 2001

-200 -200

-300

-200

A hypothetical fund with 2 investments generates the cash flow pattern above. Its IRR is 100% over a 9‐year period. The standard IRR formula always makes the implicit assumption that at any given time, excess cash is re‐invested at the IRR‐rate generated up to this point in time. The 200M excess cash generated in 1993 are hence assumed to be re‐invested at the IRR rate reached by considering Cash Flows from 1992 and 1993 only: 100% IRRassumed to be re invested at the IRR rate reached by considering Cash Flows from 1992 and 1993 only: 100% IRR. This hypothetical re‐investment generates a hypothetical value of excess cash in 2001 of 100.000M. This drives overall pooled IRR to 100% over the 9‐year period.

Reminder: What’s wrong with IRR (2/2)

Year since inception Net Cash Flow1 -15,00 2 -5,00 ,3 1,004 4,005 45,006 -19,00

Over its 25 year history, GP xyz has generated an 

7 155,008 -25,00 9 -145,00 10 -14,00 11 270 00annual return of 52% to

its investors ... .

11 270,0012 -131,00 13 290,0014 -230,00 15 180 0015 -180,00 16 700,0017 200,0018 430,0019 -10 0019 10,00 20 -135,00 21 -800,00 22 900,0023 -550,00 24 380,0025 500,00

IRR 52%MIRR 14%

Metric I - Measuring Returns: The « PERACS Alpha » (1/3) From Total Return Multiple to PERACS Alpha( ) p p

C id ti f

2 6x

Consideration of Opportunity Cost

of Capital

18%

2,1x

2,6xAnnualizing returns based on

Duration: 4,4 yrs

18%

PERACS AlphaVariable‐Rate Profitability Index

TVPI

The “PERACS Alpha” is a measure of pure value generation that is corrected for the biases of standard IRR and expresses returns relative to the ‘opportunity cost’ of not investing in the public market. Following Phalippou and Gottschalg (2009), we measure the ‘Profitability Index’ of each investment as the ratio of Present Value of Distributions (or NAV) over Present Value of Takedowns, using time-matched stock-market returns as discount rate for each Cash Flow. We then measure the PERACS Alpha as the corresponding annualized rate of value increase, given the investment duration (where duration if the difference between the value weighted average Takedown date and the value weighted average

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investment duration (where duration if the difference between the value-weighted average Takedown date and the value-weighted average Distribution date. The PERACS Alpha of a fund corresponds to the aggregate PERACS Alpha of the underlying investments, using the product of (duration * Present Value of Takedowns) as weight in the aggregation (Phalippou and Gottschalg, Review of Financial Studies, 2009).

Metric I - Measuring Returns: The « PERACS Alpha » (2/3) Linking PERACS Alpha to IRR( ) g p

36%

26%

Effect of IRR Bias due to reinvestment hypothesis

18%

26%

18%

8%

GP IRR Market ReturnsPeracs Rate of Return

PERACS Alpha

A GP’s “PERACS Alpha” is a measures of pure value generation that is corrected for the biases of standard IRR and expresses returns relative to the ‘opportunity cost’ of not investing in the public market. It is based on a refinement of the method developed by Acharya Gottschalg et al (Forthcoming in the Review of Financial Studies)method developed by Acharya, Gottschalg et al. (Forthcoming in the Review of Financial Studies)

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Metric I - Measuring Returns: The « PERACS Alpha » (3/3) Components of PERACS Alpha( ) p p

C t f PERACS Al h

8 0%

2,0% Unique PE Leverage Effect

Replicable

Components of PERACS Alpha

Total Leverage Eff t 10%8,0%

3,0%

18%

Sector Choice Effect

Leverage Effect Effect 10%

The “PERACS Alpha” is further broken down in three/four distinct components to illustrate sources of returns, based on a refinement of

5,0% Delevered Alpha

p p ,the method developed by Acharya, Gottschalg et al. : • effect of sector choices, i.e. The incremental return over the broad stock index return (with actual index leverage) that would have

been achieved by replicating the PE portfolio’s industry mix in a PME investment strategy (with actual sector leverage) • ‘delevered Alpha’, i.e. The difference between the returns to a sector-choice adjusted PME investment strategy (with actual sector

leverage) and the de-levered PE returns, i.e. simulating PE performance based on a level of financial leverage that corresponds to the gearing of the publicly-traded sector peers

• Effect of different financial leverage: The portion of returns attributable to the difference in leverage between the PE portfolio and the leverage of the sector-choice-adjusted PME investment strategy (i.e. actual sector leverage) . This can be further broken down into the (1) the Replicable Leverage Effect, i.e. the incremental return over the sector-choice adjusted PME investment strategy(with actual sector leverage) that could have been achieved by ‘leveraging-up’ the sector-choice adjusted PME investment strategy to a level of financial leverage that corresponds to the PE portfolio and (2) the remaining unique PE Leverage Effect, which can be seen as the added performance from incremental leverage on the De-levered Alpha.

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Metric II: ‘Relevant Peer Benchmark’: An Innovative PE Performance Benchmark based on empirically - derived ‘Relevant Competitor Funds’ p y p

Fact:

Existing PE benchmarks based on vintage year peer groups are of limited accuracy: (a) the vintage year criterion groups funds with very different investment focus and (b) funds with similar investment focus from consecutive vintage years compete for similar deals and LP commitments.

Ideally, PE funds should be benchmarked against their ‘relevant peers’

Idea:

A GPs self-selection of peers is often seen by LPs as subjective and potentially biased.

Problem:

Solution:

We can empirically derive those funds which directly compete with a given GP for investment opportunities, i.e. those which are active in the same ‘space’ within the PE universe in an objective fashion.

Solution:

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This document describes a method to derive a benchmark that meets this requirement.

How to empirically identify ‘Relevant Competitor Funds’ *

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 (VEIC classification at the 1 Digit level)

– region

– investment time period (based on 3-year window around date of investment) andinvestment 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 2000 fall into the same ‘ t t i ll’‘strategic cell’

* Proprietary – US Patent Pending 11

How to empirically identify ‘Relevant Competitor Funds’ *

Calculating the ‘strategic overlap score’ between two PE funds:

• Measure the percentage of activity (based on the # of deals) in each “strategic cell” for each PE fund.

• Calculate the “strategic overlap” for each pair PE funds as the sum of joint activity (i.e. the minimum of th t i t d i i t t i ll b b th f d ) ll t t i llthe 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.

• ‘Comparable’ funds that should be benchmarked against each other can then be derived as a function of

Identification of relevant competitor fund based on ‘strategic overlap score:

p gthe ‘strategic overlap score’

• Accordingly, we consider funds as ‘relevant competitors’ whenever their ‘strategic overlap score’ exceeds 10% and consider the degree of ‘competitive overlap’ in the calculation of the benchmark:10% - and consider the degree of competitive overlap in the calculation of the benchmark:

The ‘Relevant Competitor Benchmark (RCB) for a given funds is the average performance of all its ‘relevant competitors’ weighted by their ‘strategic overlap score’

12* Proprietary – US Patent Pending

competitors’, weighted by their strategic overlap score’

Intuitive Validity of the empirical approach:Examples for most relevant ‘Competitor Funds’ according to our method:p p g

Permira Europe II Montagu III

• BC European Capital VII• Third Cinven Fund, The• Blackstone Capital Partners IV, L.P.• CVC European Equity Partners III LP• KKR European Fund

• Halder GIMV Germany Fund• CICLAD 4 FCPR• Egeria Private Equity Fund II• Halder Investments III, B.V.• Alpha Private Equity Fund 5• KKR European Fund

• Hellman & Friedman Capital Partners IV, L.P.• Charterhouse Capital Partners VI• Carlyle Europe Partners, L.P.

• Alpha Private Equity Fund 5• Riverside Europe Fund II• Close Brothers Private Equity Fund VII• AXA LBO Fund III

• Vestar Capital Partners III, L.P.• TPG Partners III L P

• Bridgepoint Europe II• Nordic Capital V

Clayton, Dubilier & Rice Fund V, L.P. : 3i Eurofund IV :

• TPG Partners III, L.P.• American Securities Partners II, L.P.• Welsh, Carson, Anderson and Stowe VIII, • Madison Dearborn Capital Partners III, L.P.• Stonington Capital Appreciation 1994 Fund

C

• Nordic Capital V• Global Private Equity IV Program• Italian Private Equity Fund IV• Barclays Private Equity European Fund II• Duke Street Capital V

G• Riverside Capital Appreciation Fund 1998• Monitor Clipper Equity Partners, L.P.

• Gresham III, L.P.

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Metric II – Relevant Peer Benchmark

24% 2,3

15%

1,7

Focal Fund IRRRelevant Peer Benchmark IRR

Focal Fund TVPIRelevant Peer Benchmark TVPI

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Metric III – Documenting Sources of Returns: The « Value Driver Bridge » g

13%

3%

4%

5%

1%

3%5%

TotalLeverageFXDeleverageMultipleMarginRevenue

The « Value Driver Bridge » is a refinement of the widely used “Value Attribution” analysis which (1) quantifies value driversThe « Value Driver Bridge » is a refinement of the widely-used Value Attribution analysis, which (1) quantifies value drivers on an annualized basis (either IRR or variable-rate MIRR) and (2) includes currency effects and separates simple leverage effects from deleverage/debt-repayment.

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Metric IV- a: Empirical Assessment of Strategic Uniquenessq

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

D t S PE I t t A ti it f Th R tData Source: PE Investment Activity from Thomson Reuters

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 XXX advised funds and (II) all other PE investment activity

Calculation of Uniqueness Score:

strategic cell for (I) the XXX advised funds 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 th t f d i t tl lik th PE fi f 0 i di t th t th fi k i ilthat 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.

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Metric IV- a: Empirical Assessment of Strategic Uniquenessq

33%

26%

17%

14%14%

Fund CFund BFund AOverall Portfolio

The strategic uniqueness of each fund (and overall) can be compared to/benchmarked-

Fund CFund BFund AOverall Portfolio

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The strategic uniqueness of each fund (and overall) can be compared to/benchmarkedagainst a set of comparable ‘peers’.

Metric IV- b: Empirical Assessment of DealflowQuality – Investment Procyclicalityy y y

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 theirinvesting paceinvesting pace.

Data Source: PE Investment Activity from Thomson Reuters

Method: We measure the correlation between client’s investment pattern (i.e. number of deals by quarter) d th i t t tt f th ll PE i A l ti f 1 th t th li t i t i

Calculation of Uniqueness Score:

and the investment patter of the overall PE universe. A correlation of 1 means that the client invests in a perfectly procyclical 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

fi t f f th it i th t d t i th 'Fit ' f th HEC D J PE Fitrefinement of one of the criteria that determine the 'Fitness' score of the HEC-DowJones PE Fitness Ranking.

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Metric IV- c: Empirical Quantification of Strategic Consistency y

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

Method: We measure the consistency of investment activity as the degree of overlap in the percentages in each

t f i di i th t ti i d E l if th l lit b t th f

Calculation of Strategic Consistency Score:

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-DowJonesPE Fitness Ranking

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PE Fitness Ranking.

Metric IV- c: Empirical Quantification of Strategic Consistency y

Strategic Consistency Score

82%

63%

39%

20%

DetailedIndustrySizeCountry

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The strategic consistency of each fund (and overall) can be compared to/benchmarked-against a set of comparable ‘peers’.

Metric V: Empirical Assessment/Benchmarking of Performance Sensitivityy

Intuition:

Assessment of the sensitivity of average returns to eliminating the best x or the worst x deals

Method:

Calculation and plotting of the aggregate performance (based on variable-rate MIRR) for any percentage of ‘best’ and any percentage of ‘worst’ deals in the portfolio. The corresponding ‘PERACS Performance Curve’ captures cumulative distribution function of the distribution of performance in a PE portfolio, analogy to the Lorenz curve, which economists long use as a graphical representation of the cumulative distribution function of the empirical probability distribution of wealth in different economies. Similar to the ‘GiniCoefficient’ for wealth distribution, we capture the distribution of performance in a single measure, the ‘??? Coefficient’, which makes it possible to compare and benchmark the risk of different PE portfolios in a measure that is independent of the performance of these portfolios.

Overall assessment of the distribution (uniform vs. exponential) of returns in the portfolio as a novel risk measure for PE portfolios

Insight:

measure for PE portfolios.

Comparison with average performance distribution from HEC PE Database

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Comparison with average performance distribution from HEC PE Database, based on different portfolio characteristics.Benchmarking

Metric V – PERACS Risk Curve – inspired by lorenzcurve in macroeconomics

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The “Lorenz Curve” of a typical PE fundyp

100%

60%

80%

on

40%

of value

creatio

Alpha Contributors

0%

20%% o

Vertex

Capital Impaired

‐20%

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79

Number of transactions

PERACS Risk Curve – Reference Points

24

PERACS Risk Curve – Reference Points

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The PERACS Approach

• Independent, Formulaic, and Standardized review of track record

• PERACS is not just another performance benchmark, but it provides customized and insightful metrics to based on the best available benchmarking data, while recognizing and incorporating inevitable remaining data limitations.

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

• Granular analysis built up from company by company portfolio analysis

• Trusted standardized comparisons, consistent with ILPA Principles

• Since early last year, GPs who have solicited PE track record analytics services from PERACS and/or Professor Gottschalg amount to approx. 10% of the PE industry, measured by worldwide buyout fundraising activity since 2000.

PERACS: Becoming the Global Standard for PE Performance Analytics26