Towers Watson Manager Research

Towers Watson Manager Research How we se fnd performance data Harald Eggerstedt 13. März 212 212 Towers Watson. All rights reserved.

Manager selection at Towers Watson The goal is to find managers that exhibit a sstainable competitive advantage We believe that sccessfl managers exhibit certain repeating patterns or sccess factors Qalitative manager research is key there is a high lck to skill ratio in performance Qantitative research helps to provide challenge Or Philosophy 2

TW manager research: highly concentrated coverage Managers Prodcts 5,612 24,218 2,298 7,681 664 2,34 We carry ot over 2, research meetings per year and have met over 2,5 different managers 3

Alignment spports long-term performance focs What we look for: Client interests Performance fees Co-investment Alignment of interests Asset management company interests Portfolio manager interests Employee ownership 4

Typical process for rating prodcts Universe of managers Desk-based research Market knowledge, contacts, pblications, databases Experience/stability of team, fees, process/philosophy, qant analysis Initial research meetings Follow-p desk-based research & conf calls Follow-p meetings Meet key people, detail on process, independent thoghts emails Review manager s research & qant otpt, frther qant analysis Sit in on internal meetings & investment discssions, meet more people Engagement Fees, capacity framework, vehicles Devil s advocate One ASK member makes case against rating manager highly Skill 1 considerations ASK signs off Key points to cover before moving to a FREX 1 skill rating ASK debates all key isses & decides whether to assign a FREX 1 skill rating Head of stream signs off 5

Key differentiators of or manager research One of the world s largest independent teams Research-driven Experienced and stable team Ability to leverage one of the largest teams of independent manager researchers globally and or team of analysts Local on-the-grond presence in every major market Globally integrated, sbstantial resorce Asset class focs, inclding global and regional exposre Experienced in working alongside clients research teams Independent Fewer conflicts of interest than or competitors No asset management or banking operation No fnd of fnds operation Well defined research investment philosophy Focs on the traits of the great investors Past performance seless for providing answers, great for providing more qestions Qalitative research is key: Bsiness, People and Process Asset experts to enrich strategic thinking Bottom-p idea generation We are close to the best fixed income managers Manager Research feeds ideas and innovations into top-down asset research teams Qant analysis tailored to client needs Significant investment in proprietary tools analysis tailored to yor needs Ex ante risk assessment Trade analysis and capacity analysis 6

Research grop strctre Strong commitment to research with 199 researchers globally 7

Manager research locations Eropean Research Team 71 researchers Americas Research Team 31 researchers Global Research Team 132 researchers (9 fll-time eqivalents) - Asia Pacific Research Team 3 researchers Small decision making grops with an asset class focs - Area of Specialist Knowledge ( ASK ) teams 8

TW and evestment relationship How does evestment link to Dream? evestment Alliance (evestment) provides investment data and analytic technology to the instittional marketplace, and was fonded in 2 by former Watson Wyatt associates. The Dream database is TW s central repository for manager research. Prior to 29, the database was maintained internally and focsed on qalitative manager research. In anary 29, Towers Watson and evestment signed a partnership agreement to jointly develop a global manager research platform. This agreement extended or long-standing relationship with evestment in the US and Canada to a flly global relationship. D A T A Investment Managers D A T A Under this agreement, evestment serves as TW s data collection engine for investment manager qantitative data. The information is captred throgh evestment s Global Database, which is an online qestionnaire portal. Manager discovery & de diligence Manager selection exercises Manager monitoring reports 9

Users of evestment database Over 1,1 clients firm-wide 38 of the Top 5 global consltants (76%) are clients 15+ conslting clients overall representing US$1.6 trillion in Assets Under Advisement Clients crrently in 2 contries arond the world Pblic Fnd/ Government 3% Corporate Pension 5% Other 6% Financial Advisor 3% Fnd of Fnd 3% Fondation 1% Invst. Bank 1% Averaging 9+ new prodcts added to the database per qarter Data crrently sbmitted in 16 niqe base crrencies Consltant 21% Investment Manager 57% 1

ev: the largest instittional fnd database Dataset Eqity Fixed Income Balanced Alts & Hedge Fnds Real Estate Total United States 6,794 3,174 424 321 139 1,852 Canada 398 34 165 14 13 894 United Kingdom 182 2 3 5 35 425 Erope 479 253 8 54 41 835 Astralia 267 96 6 5 12 386 apan 226 7 11 1 245 Asia-Pacific ex-apan 457 33 7 47 13 557 Global/EAFE/Emg Mkts 3,16 882 955 849 63 5,765 Totals 11,941 4,982 1,582 1,365 324 2,194 with overall database covering 95% of global instittional marketplace 11

Introdction Towers Watson Nmber Monkey The Nmber Monkey is a tool that analyses the performance of the manager and smmarizes key statistics. In particlar for active managers achieved alpha verss a target. The tool analyses the retrn series characteristics and estimates the possible performance attribtion factors. The tool allows s to graphically display, maximm drawdown, CUSUM analysis and varios tests analysing the managers otperformance significance. The tool also evalates several key statistics sch as rolling IR's, tracking errors, Sharpe ratios, Vale At Risk, market timing skills and other metrics that reveal more abot the managers investment strategy. Or long-term clients make s ponder the managers ability to add vale over the entire market cycle. The Nmber Monkey allows s to view the managers performance nder several economic environments and jdge their ability to generate alpha over different macro and micro economic environments. 12

Relative retrn analysis ( manager alpha ) Things to look for 1. Any trends or cycles in the manager s alpha over rolling 1, 3 and 5 year periods? 2. Did the manager s ability to generate alpha differ depending on whether the market was in an p or a down cycle? 3. Does most of the alpha come from jst a few months or is it fairly consistent over time? 13

Expected Alpha Analysis (1) On the first chart, the Cm Alpha line represents the componded cmlative geometric monthly alpha achieved from the beginning to the end of the period. The Cm Alpha is compared against the Target Alpha which is a straight line that plots the cmlative retrn of an assmed constant annal alpha. The ± St.Dev and ±2 St.Dev bands plot the hypothetical cmlative standard deviation (tracking error) defined by the ser. The expected alpha and annal standard deviation are assmed to be constant from the beginning to the end of the period. 14

Expected Alpha Analysis (2) This chart shows the simple average alpha (i.e. no geometric componding), as well as a 95% confidence interval that represents the area where the alpha is not statistically different from zero. The confidence intervals are calclated sing the realised standard deviation p to the relevant date and the first 24 data points are ignored where standard error is very high. A positive alpha shold be considered as an indication of the manager s skills only if it statistically significant. Things to look for 1. How does the realised performance compare to or prior expectations? 2. Is the performance otside or confidence bands, and shold we be looking at changing or target risk and retrns? 3. If the manager is crrently below their target alpha, how extreme is the reqired retrn over the next year (in terms of nmber of std deviations) that will bring the manager s performance back on track? 15

Smmary Statistics The table smmarises the vales of varios statistics over 1, 3, and 5 year periods as well as since inception (shown as Total). The statistics are: Relative retrn (Alpha pa) ; Tracking Error, (i.e. the standard deviation of the Alpha); IR (Information Ratio=Alpha/TE); Best monthly alpha dring the period; Worst monthly alpha dring the period; Regressed Alpha (vale of the intercept calclated by regressing the manager s performance against the benchmark); Regressed Beta (vale of the slope calclated regressing the manager s performance against the benchmark); Absolte retrn of the manager s portfolio; Absolte Volatility (standard deviation of the absolte performance); Sharpe ratio = (Abs retrn - Risk free rate) / Abs volatility; Sortino ratio = (Abs retrn - Risk free rate) / Abs downside-semi-deviation). Nmbers are all annalised with the exception of best and worst monthly retrns. Things to look for 1. Any shifts relative to the longer term statistics? 2. How do these statistics compare with those of other fnds? 3. What are the confidence intervals for the main statistics over the whole history? 16

Market Timing Market timing analysis can help to nderstand better some characteristics of portfolio retrns relative to benchmark and to provide different views on the embedded investment philosophy. An otstanding manager will demonstrate significant stock selection capabilities and sccessfl market timing skills at all market phases. The qadratic parameter (gamma) measres timing capabilities: a positive gamma will indicate that timing activities have added vale to portfolio performance. Comparing the gammas of different fnds will indicate the relative importance of timing activities in their investment policies. As sal, we have to be sre that the model we se fits or data. To trst any otcome of a regression model we have to jstify that R-sqare is high. The captre ratio is an indicator of portfolio performance relative to benchmark at different market phases. It measres compond retrn of a portfolio over compond retrn of its benchmark when the benchmark is p (pside captre ratio) or down (downside captre ratio). If the pside ratio is greater than 1 (downside ratio less than 1), this means that the portfolio beat the benchmark in pmarket (downmarket). Things to look for 1. Is manager able to assess the direction of the market and position the portfolio accordingly? 2. Did portfolio otperform the market when the index went p (down)? 17

Drawdown Analysis The drawdown chart highlights periods when a manager prodced negative retrns, how big was the sbseqent drop in performance, and how long did it take the manager to flly recover from this period of negative performance. This is particlarly sefl for accessing fnds that claim to preserve capital or fnds with high water marks in their fee strctres. Things to look for 1. How freqently does the manager prodce negative retrns? 2. How long does it take the manager to make back the money he lost? 3. When wold have been the worst possible time for a new investor to invest in the fnd and how mch wold he have lost? 18

3 Year Rolling Analysis Average 3 Year relative retrns (pa) Chart The chart plots the rolling 3 years monthly Alpha (or relative retrn) with the 95% confidence intervals, which represent the area where the alpha is not statistically different from zero. If the alpha is otside the bands, then the manager s performance is statistically different to the benchmark. Normality of retrns is assmed to plot the confidence interval. Ann Regressed alpha (36 obs) Chart The black line represents the vale of the intercept calclated by regressing rolling 36 months of retrns against the benchmark. As in the previos chart, the bands represent the interval where the alpha is not statistically significant. The light orange line is the R sqared of the regression: the higher the R sqared vale the higher the reliability of the regression. Since this is a linear regression, the vale of the R sqared varies between (the regression is not saying anything) and 1 (all the variance is explained by the model). Regressed Beta (36 obs) Chart The calclations for this chart are the same as for the regressed alpha chart bt the vale of the slope (beta) is plotted instead of the intercept (alpha). If the black line is between the confidence intervals, then the beta of the manager is not statistically different from 1. Things to look for 1. Look for when relative retrns, regressed alpha and beta became statistically significant, and if it was persistent or only temporary. 2. If the rolling 3 year statistics never fall otside the confidence bands, then the manager is statistically the same as the benchmark on that particlar measre 19

Corporate - Bond Fnd vs UK Credit Since Inception till Dec-11 25 2 15 Geometric Relative retrn analysis Achieved Alpha Target (%) Achieved (%) vs. Target Alpha Alpha.9 -.2 TrackErr 1.5 4. 25 2 1 5 15 1 5-5 -1-5 -1-15 -15-2 -2 an-2 l-2 Monthly an-3 l-3 an-4 l-4 in rising market in falling market an-5 l-5 an-6 l-6 an-7 l-7 an-8 l-8 an-9 l-9 an-1 l-1 an-11 Roll 1Y %pa Roll 3Y %pa Roll 5Y %pa l-11-25 31/1/2 3/11/2 3/9/3 31/7/4 31/5/5 31/3/6 31/1/7 3/11/7 3/9/8 Target Alpha ± St.Dev ±2 St.Dev Cm Alpha 31/7/9 31/5/1 31/3/11 y = -12.561x 2 + 1.389x +.11 R² =.7964 8% Manager retrn 6% 4% 2% Market Timing % -6% -4% -2% % 2% 4% 6% -2% -4% -6% -8% -1% -12% Benchmark retrn Qadratic parameter. Upside 1.27 Captre Beat the benchmark in pmarket Downside 1.28 Captre Not beat the benchmark in downmarket Smmary statistics Total 5Y 3Y 1Y Description 1Ys Ms Conf Interval Relative Retrn (%) -.22 [-.2, 1.2] -1.3 3. 1.9 Tracking error (%) 4. [3.6, 4.4] 5.6 5.4 1. IR -.6 -.2.6 2. Best monthly otp (%) 4.16 n/a 4.2 4.2.6 Worst monthly otp (%) -5.93 n/a -5.9-4.4 -.3 Regr Alpha (%) -.14 [-.2,.3] -.2..2 Regr Beta 1.3 [.85, 1.16] 1.4 1.4.9 Absolte Retrn (%) 5.22 [2.8, 5.6] 3.4 11.7 8.9 Absolte volatility (%) 7.51 [6.7, 8.3] 9.8 9.6 4.5 Sharpe ratio.16 -.1.8 1.1 Sortino ratio.18 -.1 1. 3.6 Towers Watson Limited - Performance Wizard 2

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Corporate - Bond Fnd vs UK Credit Since Inception till Dec-11 DrawDown (absolte retrns) Relative Retrn Distribtion Analysis -5-1 an-2 an-3 an-4 an-5 an-6 an-7 an-8 an-9 an-1 an-11 6 5 4 3 95% c.l. Fnd Benchmark VaR -3.2% -1.9% Modified VaR -3.7% -2.% CVaR -6.% -2.9% Active VaR -2.% Modified Active VaR -2.2% Active CVaR -4.% -15-2 -25 2 1 Moments of the distribtion Mean Standard Deviation Skewness Krtosis (excess).% 1.2% -1.4 8.9-3 -5.7-4.5-3.3-2.1 -.9.3 1.5 2.7 3.9 5.1 Distribtion does NOT look normal Distribtion DOES exhibit serial correlation CUSUM and Likelihood IR CUSUM Likelihood Alarm Average Relative Retrn Analysis (first 2Y ignored) 5 1 15 2 25 an-2 Ag-2 Mar-3 Oct-3 May-4 Dec-4 l-5 Feb-6 Sep-6 Apr-7 Nov-7 n-8 an-9 Ag-9 Mar-1 Oct-1 6 4 2-2 -4-6 -8.4.3.2.1 -.1 -.2 an-4 an-5 an-6 an-7 8Y Qs an-8 an-9.524345 1.4659661 1.2735 1.5124.7747.7747-1.4457 -.3372.65957.1292659 1.3272.8966393 4.2327 2.5394264 4.169984 3.2951314.9458-1.86729 an-1 an-11 3-1 -.3 35-12 -.4 Towers Watson Limited - Performance Wizard 22

GBP Corporate - Bond Fnd! vs UK Credit Since Inception till Dec-11-1 -2-3 -4-5 -6-7 DrawDown (absolte retrns) 8 l-9 9 l-9 l- 1 l- 2 l- 3 l- 4 l- 5 l- 6 l- 7 l- 8 l- 9 l- l-1 1 l-1 8 7 6 5 4 3 2 1-1.9-2.3-2.7-1.1-1.5 - Relative Retrn Distribtion Analysis.2.8.4 1. 2.6 2 95% c.l. Fnd Benchmark VaR -1.8% -1.8% Modified VaR -1.8% -1.8% CVaR -2.6% -2.7% Active VaR -.8% Modified Active VaR -.7% Active CVaR -1.4% Moments of the distribtion Mean Standard Deviation Skewness Krtosis (excess).%.5%. 6.2 Distribtion does NOT look normal Distribtion does not exhibit serial correlation 5 8 l-9 CUSUM and Likelihood IR CUSUM Likelihood Alarm 9 9 1 2 2 3 3 4 4-9 b p -9 r- v - e e p o n - n - g - - r- F S A N a a t- y c a c - 5 6 6 7 7 8 9 9 - e l- b p - r- v - A M O M D e e p o n - n - g - r-1 F S A N a a t-1 c A M O 5 4 3.6.4 l- 1 l- 2 l- Average Relative Retrn Analysis (first 2Y ignored) 3 l- 4 l- 5 l- 8Y Qs 6 l- 7 l-.94838.357571 1.98117.23256 8 l- 9 l- l-1 1 l-1 1 15 2 25 2 1-1 -2.2 -.2 -.4 2.165.1311-2.2179 -.875.43769.234697.926853 1.26767 6.45654 1.3792173 4.371293 3.628612 1.19432-3.6966 3-3 -.6 Towers Watson Limited - Performance Wizard 23

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Corporate - Bond Fnd vs UK Credit Market cycle analysis Benchmark Matrity spread: Long - Short Qality spread: BBB - AAA 5 4 3 2 1-1 -2-3 -4 5 4 3 2 1-1 -2-3 -4 5 4 3 2 1-1 -2-3 -4 Bll market Bear Market 15+ -5 BBB AAA Pos 43 22 Pos 38 27 Pos 45 2 Neg 33 22 Neg 34 21 Neg 27 28 Matrity Factors: 3Y Rolling Performance Qality Factors: 3Y Rolling Performance Long Short 3 Year Relative Retrn BBB AAA 3 Year Relative Retrn 5 25 2 15 5 1 5-5 -5-1 Dez 4 Okt 5 Ag 6 n 7 Apr 8 Feb 9 Dez 9 Okt 1 Ag 11 Dez 4 Okt 5 Ag 6 n 7 Apr 8 Feb 9 Dez 9 Okt 1 Ag 11 26

GBP Corporate - Bond Fnd! vs UK Credit Market cycle analysis Benchmark Matrity spread: Long - Short Qality spread: BBB - AAA 8 6 8 6 4 6 4 4 2 2 2-2 -2-4 -2-4 -6-4 -6 Bll market Bear Market 15+ -5 BBB AAA Pos 59 36 Pos 52 43 Pos 57 38 Neg 46 21 Neg 35 32 Neg 37 3 Matrity Factors: 3Y Rolling Performance Qality Factors: 3Y Rolling Performance Long Short 3 Year Relative Retrn BBB AAA 3 Year Relative Retrn 2 15 1 5-5 -1 n 1 Apr 2 Feb 3 Dez 3 Okt 4 Ag 5 n 6 Apr 7 Feb 8 Dez 8 Okt 9 Ag 1 n 11 n 1 Apr 2 Feb 3 Dez 3 Okt 4 Ag 5 n 6 Apr 7 Feb 8 Dez 8 Okt 9 Ag 1 n 11 27

Towers Watson Manager Research Model Portfolios What are they? How are they rn? A theoretical mltimanager portfolio Investment decisions are proposed and tracked on a qarterly basis Model portfolios are rn by the Towers Watson manager research team in the same way as if they were given delegate responsibility for client assets in a particlar asset class Each model assmes an initial accont size (typically 5m or 1m), benchmark, base crrency and makes assmptions for transaction costs The manager research team discsses changes to model portfolios and portfolio constrction before officially proposing changes to be implemented from the start of the next qarter Performance is sorced each qarter and a management fee for a typical Towers Watson client is removed to generate realistic net retrn nmbers What are they sed for? To demonstrate Towers Watson s skill in manager research The models aim to demonstrate Towers Watson s skill in manager research, in particlar the ability to select managers and strctre portfolios They replicate, as accrately as possible, how Towers Watson wold rn a similar client mandate The aim is to maximise the net information ratio whilst also delivering an attractive level of retrn What else shold I note? The models cannot flly replicate the complexities of the real world Fee strctres are becoming increasingly complex, hence some simplifying assmptions are necessary to allow s to proxy the exact fees on a qarterly basis The transaction cost applied for transitioning between prodcts is an approximation Model portfolios generally ignore practical implementation costs, which may be incrred otside of the vehicle, sch as tax, legal and cstody The model portfolios may not be investible by a client at a point in time as some of the prodcts may crrently be closed to new bsiness Towers Watson never rely solely on performance when assessing managers. Clients, too, shold not jst rely on the model performance nmbers when assessing Towers Watson, bt develop an nderstanding of the Towers Watson philosophy and process Towers Watson believes that short-term performance contains very limited information on skill, hence the model portfolio track records are only displayed once they have at least a three-year track record and there is a preference to focs on since inception retrns Despite potentially having more than a ten-year track record, one model on its own does not demonstrate skill with mch statistical significance 28

Towers Watson model portfolios All (EUR) Track record since inception to 31 December 211 % 7 6 5 4 3 2 1 n/a n/a n/a n/a 1 2 Gross Relative Retrn (annalised) Net Relative Retrn (annalised) All figres are annalised since inception and are expressed in Ero terms. Please note that inception dates vary, bt all the models above have been rnning for at least three years. Past performance is not necessarily a gide to ftre investment performance. 29

Towers Watson model portfolios Over ten-year track records (EUR) Track record since inception to 31 December 211 % 7 6 5 4 3 2 1 1 Gross Relative Retrn (annalised) Net Relative Retrn (annalised) All figres are annalised since inception and are expressed in Ero terms. Please note that inception dates vary, bt all the models above have been rnning for at least ten years. Past performance is not necessarily a gide to ftre investment performance. 3

Model inception dates and benchmarks Model Inception Date Benchmark Global eqities 1/1/2 MSCI World (ND) EAFE eqities 1/1/23 MSCI EAFE (ND) US eqities 1/1/2 S&P 5 1 US small-cap eqities 1/1/26 Rssell 25 Eropean eqities 1/1/2 FTSE W Erope Erope ex UK eqities 1/1/2 FTSE W Erope ex UK UK eqities 1/1/2 FTSE All-Share UK small-cap eqities 1/1/2 FTSE Small-Cap ex IT Asia ex apan eqities 1/1/22 MSCI AC Asia ex apan (GD) 2 apanese eqities 1/1/2 Topix Hong Kong eqities 1/7/25 FTSE MPF Hong Kong 3 Astralian eqities 1/4/23 ASX/S&P 3 Emerging market eqities 1/1/2 MSCI EM (GD) Unconstrained eqities 1/1/25 MSCI World (ND) 4 Global aggregate bonds 1/1/21 Composite of manager benchmarks 5 Global government bonds 1/1/2 Composite of manager benchmarks 6 Global credit bonds 1/1/24 Composite of manager benchmarks 7 Global high yield bonds 1/7/26 Composite of manager benchmarks 8 US fixed income bonds 1/1/23 Barclays US Universal Ero aggregate bonds 1/1/22 Composite of manager benchmarks 9 Ero credit bonds 1/1/27 Composite of manager benchmarks 1 UK aggregate bonds 1/4/22 Composite of manager benchmarks 11 UK government bonds 1/1/2 Composite of manager benchmarks 12 UK credit bonds 1/1/23 Composite of manager benchmarks 13 Astralian fixed income bonds 1/4/23 UB SA Composite Emerging market debt 1/4/22 Composite of manager benchmarks 14 Global listed property secrities 1/1/27 FTSE EPRA/NAREIT Global Developed UK property 1/1/2 HSBC All Balanced Fnds Index Long-short eqities 1/1/25 HFRI Eqity Hedge (Total) Index Broad hedge fnds 1/1/28 HFRI Fnd of Fnds Composite Index Core fnd of hedge fnds 1/4/27 HFRI Fnd of Fnds Composite Index Niche fnd of hedge fnds 1/4/27 HFRI Fnd of Fnds Composite Index Notes 1. Model portfolio was measred against the FTSE AW USA, ntil 31 March 22, when the benchmark was changed to the S&P 5 2. MSCI Far East ex apan since inception ntil 3/6/26 when it changed to MSCI AC Asia ex apan 3. 9% FTSE HK + 1% HSBC since inception ntil 3/9/29 when it changed to FTSE MPF HK 4. Model was measred against MSCI World ntil 3 September 211 after which the benchmark changed to MSCI AC World 5. Benchmark is a composite of the hedged and n-hedged Barclays Capital Global Aggregate Index 6. Benchmark is Citigrop WGBI or similar 7. Benchmark is Barclays Global Aggregate Credit or similar 8. The Global High Yield model incldes Eropean, US and Global high yield strategies all measred against the managers respective benchmarks 9. Composite of Barclays Capital Ero Aggregate and iboxx Ero BIG Index 1. Composite of Barclays Capital Ero Aggregate Credit and Corporate and iboxx Ero Corporates 11. Benchmark is Merill Lynch Sterling Broad or similar 12. Benchmark is FTSE Gilts All Stocks or similar 13. Benchmark is Merill Lynch Sterling Non-Gilts or similar 14. Benchmark was P EMBI+ since inception to 31/3/26 when it changed to the P EMBI Global Diversified, ntil 31/12/26. Now measred against a Composite of the PM EMBI Global Diversified and the PM GBI-EM 31

Smmary TW has an extensive global research infrastrctre designed to assist clients in their strategy formlation, risk management and manager selection needs Or manager valation is not qant -driven, bt based on a qalitative analysis (bsiness, people, process). Performance data are sed to check whether prodct strategies are implemented sccessflly and consistently over time. We look for sstainable otperformance after fees over 3 or (better) 5 years evestment Alliance allows s easy access to performance data, however, we depend on the managers willingness to feed high-qality data into the database Or systems allow s to rn detailed relative retrn analyses, which help s to assess a manager s style and his ability to adjst to changes in market conditions Qantitative data play an important role in or Manager Watch process (for top-rated managers) and in fidciary mandates We rn model portfolios across a wide range of prodcts in order to monitor or own sccess in selecting best in class managers 32

Disclaimer Towers Watson has prepared this docment for marketing prposes only. No action shold be taken based on this docment as it does not inclde any detailed analysis into yor own scheme specifics. In preparing this docment we have relied pon data spplied to s by third parties. Whilst reasonable care has been taken to gage the reliability of this data, we provide no garantee as to the accracy or completeness of this data and Towers Watson cannot be held accontable for any errors or misrepresentations in the data made by any third party. This docment is provided to the recipients solely for their se, for the prpose indicated. This docment is based on information available to Towers Watson at the date of the docment and takes no accont of sbseqent developments after that date. It may not be modified or provided by the recipients to any other party withot Towers Watson s prior written permission, except as may be reqired by law. In the absence of or express written agreement to the contrary, Towers Watson and its directors, officers and employees accept no responsibility and will not be liable for any conseqences howsoever arising from any third party's se of or reliance on this docment or the opinions we have expressed. The terms Towers Watson, we, or or s above mean Towers Watson Limited and Towers Watson Investment Services, Inc, both Towers Watson companies, as well as all other entities directly or indirectly owned or controlled by Towers Watson & Co. Towers Watson Limited is athorised and reglated by the Financial Services Athority. Towers Watson Investment Services, Inc., is a registered investment adviser with the Secrities and Exchange Commission. 33