sr berlin 2015 - let talk about risk

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

Let’s talk about Risk !

New Approaches to bring Risk into the center stage of PE Investing

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

2

"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

3

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

4

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%

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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

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PERACS Numbers Speak Louder than words

5

"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%

Focal Procyclicality Relevant Competitor Procyclicality

Most Procyclical Quartile

Second MostProcyclical Quartile

Third MostProcyclical Quartile

Least Procyclical Quartile

44

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

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

6

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 …

Agenda

7

• PE flourishes, but what lies ahead ?

• Assessing the general riskiness of PE – new insights

• Measuring risk attributes of specific PE Portfolios

• Comparing the risk profile of individual GPs

• Conclusions

Private Equity is back strong!

Author Dataset # of Funds Studied Finding

Harris et al. 2013 Burgiss 598 US Buyout Funds pre 2009

PE funds from this sample outperform broad stock market

Gottschalg 2014 ILPA-Cambridge 819 Buyout Funds pre 2009


Gottschalg 2014 Preqin CF Data 618 Buyouts Funds pre 2009


Gottschalg 2015 PEVARA 983 Buyouts Funds pre 2009

…

Record Distributions, Record Deal Flow, Strong Fundraising … and ample evidence of the ability of the asset class to deliver strong returns in recent academic work.

Working Paper available upon request from [email protected] 8

PE Delivers Alpha during difficult economic times

9

Five-Year Joint Research Effort with Golding Capital Partners demonstrates PE’s strength in delivering Alpha in Downturn with impressive relative returns

to recent “Crisis Deals”

Alpha

-2.4%

2.7% 5.1%

Market Returns

Alpha Absolute Rate of Return

Presented at Super Return International Berlin 2013, available for download at www.peracs.com

Still, the future remains uncertain…

10

What we hear from our LP clients:

• Current deals are (too?) expensive

• Current deals are (too?) aggressively financed

• The Macro context can introduce substantial economic volatility

So it seems timely to draw attention to the other dimension of performance: Risk and look at three fundamental questions that LPs need to understand

• How risky is PE overall?

• What are the risk attributes of my specific PE portfolio?

• How much risk will a given GP add to my portfolio?

Agenda

11





• Conclusions

Consideration of Risk in PE

Typical Approach : Top Down based on aggregate times series data

Use of times series performance data from Private Equity Funds (e.g. Thomson One, Preqin)

Challenges :

• Autocorrelation of performance data needs to be eliminated

• Available databases consist largely of rather old funds (15+ years) for which NAVs were not systematically marked to market as they are today

• Generally aggregate treatment of vintage years

• No consideration of individual transactions and hence no specific treatment of different investment years, industry segments or deal sizes

2

1 Use of listed PE Proxies (e.g. LPX50, as used for Solvency II/QIS Studies)

Challenges:

• Listed Private Equity vehicles are not necessarily representative for typical unlisted PE, which leads to possible overstatement of volatility and correlation

Existing Methods provide limited insights into risk-return relationship and are unsuitable to assess/compare riskiness of different fund managers and strategies

12

Combining powerful bottom-up methodology with unique and granular data

Monte-Carlo Simulation of Deal-Level Return Pattern of PE Investments

• Confidential data collected by French PE Industry Association (AFIC) from members in multi-year data gathering effort

• 6,223 transactions, since 2000, 30B EUR Equity Invested

• Detailed information (all CFs and annual NAV from 2006 to 2013)

• 3,400 deals (10B Equity Invested EUR) made since 2006 with “complete information”, i.e. all CFs and NAV information fron inception until year-end 2013

Comprehensive data on French PE Industry (2nd largest PE market in Europe)

First-ever assessment of Value-at-Risk for PE based on a census of deal activity in major PE market using detailed times-series covering GFC period

13

Method developed jointly with Dr. Kreuter from Palladio Partners, (cf “Quantitative Assessment of PE Risks”, PETJ Q1 2013) considers observed deal-level evolution of NAV and CF from year to year to build bottom-up value-at-risk model for single deals and at the portfolio level.

• In line with the approach recommended by regulators, we calculate the maximum amount of capital that investors expect to lose for a given portfolio in a given “worst case” scenario.

• This maximum loss is called « Value-at-Risk » (VaR) and expressed as a percentage of the amount invested at the beginning of the period.

• For example, the “99.5% VaR” corresponds to the scenario of a “worst case”, equivalent to the 0.5% worst simulated outcomes. When we simulate 1000 possible scenarios for a given portfolio, the maximum capital loss in the worst 5 cases is the 99.5% VaR value.

• If this 99.5% VaR was XXX%, the interpretation would be as follows: “With 99.5% certainty, investors in this portfolio can expect to lose no more than XXX% of the amount invested at the beginning of the period”

The methodology in a nutshell

14

How to measure the risk of capital loss for investors in PE ?

• From the 3,400 complete deals in the sample, we derive 13,000 “movements “ of CF/NAF from year-to-year

• We assign these movements to specific deal characteristics, such as

– Investment stage (venture capital, growth capital, LBO, turnaround)

– Industry sector of acquired business,

– Number of years in the portfolio (age),

– Performance to-date (classified in terciles: high, medium or low Performance).

• Using the Gottschalg&Kreuter approach we model the year-on-year VaR for PE portfolios with various characteristics based on the conditional probabilities of “movements” for each of the underlying investments, given specific deal characteristics (stage, industry, age, size, performance to-date).

Portfolio-specific Risk Assessment based on bottom-up approach

Illustrative Example – Single Deal

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Buyout Investment • Consumer Goods Sector • Large Cap • 3 Years Old • NAV in LP Portfolio

beginning of year: EUR 14M

• Performance to-date: TVPI = 1.4x (Best Performance Tercile)

1,000 Random Draws of Possible Year-End Outcomes based on year-on-year „Movements“ observed in actual data on PE industry for deals with same characteristics (stage, industry, age, performance to-date)

2.4 2.3 1.9 1.8

...

...

...

...

...

...

...

...

...

...

...

... 0.3

0.28 0.1

0 0 0

99.5% VaR Cut-Off TVPI 0.28x = 80% of Initial NAV „at risk“ ( EUR 11.2M „at risk“)

“With 99.5% certainty, investors in this deal can expect to lose no more than 80% of the amount invested at the beginning of the period” OR

“There is a 0.5% risk to lose 80% or more of the investment in this deal”

TVPI at year end:

Illustrative Example – Fund with 10 deals

16



1,000 Random Draws of Possible Year-End Outcomes for each deal based on year-on-year „Movements“ observed in actual data on PE industry for deals with same characteristics (stage, industry, age, performance to-date)

Buyout Fund, 10 investments • Deals made 2 and 3 years ago • 40% in consumer goods,

60% in Industrial • All small cap • Current TVPI tercile category

known for each deal • NAV in LP Portfolio beginning of

year: EUR 25M • Portfolio TVPI = 1.22x • Portfolio NAV = EUR 35M

2.4 2.3 1.9 1.8

...

...

...

...

...

...

...

...

...

...

...

... 0.7

0.61 0.5 0.4 0.4 0.2


1,000 simulated TVPIs across all deals at year end:

Illustrative Example – Fund with 10 deals

17




Buyout Fund, 10 investments • Deals made 2 and 3 years ago • 40% in consumer goods,

60% in Industrial • All small cap • Current TVPI tercile category

known for each deal • NAV in LP Portfolio beginning of

year: EUR 25M • Portfolio TVPI = 1.22x • Portfolio NAV = EUR 35M

2.4 2.3 1.9 1.8

...

...

...

...

...

...

...

...

...

...

...

... 0.7

0.61 0.5 0.4 0.4 0.2



99.5% VaR over years 2 to 6 based on 1,000 simulated cases for each size of the portfolio

Monte Carlo Simulation Results: VaR of French Buyouts for Different Portfolio Sizes

18

99

.5%

VaR

While single deals are inherently risky., VaR descreases rapidly with the number of underlying assets. For a portfolio made up of 100 French LBO investments (corresponding to 10 primary funds or one fund of funds), investors can expect with 99.5% likelihood to lose no more than 14% of their capital.

100%

58% 45% 40%

14%

0%

25%

50%

75%

100%

1 deal 10 deals 20 deals 40 deals 100 deals

Monte Carlo Simulation Results: VaR of different PE stages by Portfolio Size

19

Results confirm intuitive Risk Patters across different stages, while for all stages VaR decreases rapidly with the number of underlying assets

100%

85%

74%

51%

28%

67%

51% 45%

25%

58%

55%

40%

14% 0%

25%

50%

75%

100%

1 deal 10 deals 20 deals 40 deals 100 deals

French Venture capital standard risk profile

French Growth capital standard risk profile

French LBO Market standard risk profile

99

.5%

VaR

For all stages, the estimated 99.5% VaR for a portfolio of 100 underling investments is substantially lower than the implied value of the current regulatory treatment for PE (39%)

Insights from bottom-up Monte-Carlo Simulation of Value-at-Risk

Conservative estimate of VaR in PE based on Census of times-series data from French PE industry 2006 to 2013 reveals that:

• In line with expectations, the VaR is greater for early stage VC, followed by late stage VC/Growth Capital, while BOs have the lowest VaR in comparison

• Individual PE deals are inherently risky

• VaR in PE rapidly decreases for larger and more diversified portfolio

• For a typical (reasonably diversified) investor, the estimated VaR lies substantially below the level implied by current regulation (39%)

20

Agenda

21





• Conclusions

Monte-Carlo Simulation of Value-at-Risk for specific PE Portfolios

22

60% 57%

40% 43%

Investment volume Count

Mid

Small

Portfolio By Size

100%

Portfolio By Stage

100%

41% 39%

36% 39%

23% 22%


Growth Capital

VC

Buyout

Portfolio By Sector

100%

53% 50%

47% 50%


IT

Comm

36% 35%

31% 35%

18% 18%

15% 12%


5 Year

4 Year

3 Year

2 Year

Portfolio By Age

Generic Example

For any given PE portfolio with given characteristics deal-by-deal (age, industry, stage, size, performance to-date), the exact VaR can be estimated based on observed

movements of PE deals evolving across time from AFIC-type database.

100%

Simulation of VaR for specific PE Portfolio

23

...

This approach enables LPs to estimate their specific VaR given portfolio characteristics to accurately consider risk attributes of PE portfolio in calibration

of capital reserves.


x x x X

... ... ... ... ... ... ... ... ... ... ... ... Y Y Y Y Y

99.5% VaR Cut-Off


Generic Example

Agenda

24





• Conclusions

Approach used to assess Income Inequality across countries

0

20

40

60

80

100

0 20 40 60 80 100

Brazil China Perfect

Cu

mu

lati

ve In

com

e S

har

e

Cumulative Population Share

A Concept for Measuring Risk: The PERACS Portfolio Risk Curve Inspired by Lorenz curve in Macroeconomics

25

Similar to the 'Gini Coefficient' for wealth distribution, we capture the distribution of performance in a single measure, the 'PERACS Risk 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. The 'PERACS Risk Coefficient' measures the area underneath a given risk curve relative to the area underneath the diagonal line at a 45 degree angle.

The "PERACS Investment Risk Curve" of a Typical PE Fund

26

-20%

0%

20%

40%

60%

80%

100%

1

5

9

13

17

21

25

29

33

37

41

45

49

53

57

61

65

69

73

77

% o

f va

lue

cre

atio

n

Number of transactions

Alpha Contributors

Alpha Drags

Vertex

Break Even Point

Insight

Overall assessment of the distribution (uniform vs. expo-nential) of returns in the portfolio as a new risk measure for PE portfolios.

Benchmarking

Comparison with average perform-ance distribution from HEC PE database, based on different portfolio characteristics.

Generic Example

PERACS Investment Risk Curve by % of Deals

Doughty Hanson & Co – Aggregate (Fund IV + V)

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

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

27

0.90

0.91

0.61

0 0.5 1

Benchmark EU

Since 2005 Large-Cap

DH

PERACS Risk Coefficient

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

Real-World Client Example

PERACS Investment Risk Curve by % of Deals

Nordic Capital Fund V versus Fund VI

28

0.90

0.91

0.59

0.45

0 0.5 1

Benchmark EU

Since 2005 Large-Cap

Nordic Capital Fund V

Nordic Capital Fund VI

PERACS Risk Coefficient

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

Real-World Client Example

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

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

Risk and Return distribution across sample of GPs

Across 152 PE GPs with > 9 realized deals in Track record

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6

PER

AC

S R

isk

Co

effi

cie

nt

Aggregate PERACS Alpha

29

High Alpha, Low risk GPs

…

Presented at Super Return International Berlin 2014, available for download at www.peracs.com

Agenda

30





• Conclusions

Conclusions

31

• PE can deliver outperformance, in particular in difficult times

• The probability of loosing the capital invested decreases more than proportionally with increasing portfolio size

• The VaR is greater for early stage VC, followed by late stage VC/Growth Capital, while BOs have the lowest VaR in comparison

• For a typical and reasonably diversified investor, the estimated VaR lies substantially below the level implied by current regulation (39%)

• This should open up the asset class for investors until today limited to invest by restrictive regulation

Thank you for your attention !

32

About Professor Oliver Gottschalg

33

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

34

PERACS in the ILPA Newsletter

35

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

36

• 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 - let talk about risk

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