Optimizing Portfolio Construction

Transcription

1 Optimizing Portfolio Construction ETFs, Smart Beta, Active, Alternative? June 2018 Media Partner

2 The Lyxor Active-Passive Study A deep dive into 2017 results

3 WHAT WE LOOK AT AND HOW 2 In depth analysis of active funds performance A yearly study based on Morningstar data We study 23 universes of European domiciled active funds across both equity and fixed income asset classes. They represent 6000 active funds and more than EUR1.8trn of Assets Under Management. Based on a solid methodology The calculations are adjusted for the survivorship bias i.e. merged or liquidated funds are taken into account in this study. This allows us to represent the entire opportunity set available for investors at the beginning of each period under the scope of this study. We also disclose the survivor rate for each category i.e. the percentage of funds existing at the beginning of the period that still exist at the end of the period. Using innovative tools Lyxor Quant Research factor analysis tool to understand management style bias of active managers. Dispersion monitor to assess the environment to select outperforming funds. YEARLY + Quarterly Update

4 4 RESULTS: 1/4 ACTIVE FUNDS OUTPERFORM BENCHMARK OVER 10 YEARS AVERAGE % OF ACTIVE FUNDS OUTPERFORMING THEIR BENCHMARK % 47% 24% 28% 25% 44% 10Y 1Y 10Y 1Y 10Y 1Y Source: Morningstar and Bloomberg data from 30/12/2005 to 29/12/2017. THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

5 LIMITED CONSISTENCY OF ACTIVE FUND OUTPERFORMANCES 5 Over 10 years, 34% of active managers have, on an annual average, outperformed their benchmark. That number drops to 15.3% by the end of year two and to 7.2% in year three. This again shows just how difficult it is for active managers to perform consistently well over time. ACTIVE FUND AVERAGE PERFORMANCE CONSISTENCY THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

6 6 ACTIVE EQUITY FUNDS SHOW HIGHER RESULTS AVERAGE % OF ACTIVE FUNDS OUTPERFORMING THEIR BENCHMARK Fixed Income Equity 53% 47% 33% 32% 39% 26% Source: Morningstar and Bloomberg data from 30/12/2005 to 29/12/2017. THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

7 7 LOW DISPERSION OF ACTIVE FUND OUTPERFORMANCES The dispersion of relative performance around benchmarks has been limited. Alpha generation was concentrated on a selected number of funds and universes. It was therefore harder to pick outperformers. A more favorable environment to select the best active manager ACTIVE FUND PERFORMANCE SPREAD DISPERSION VS LT AVERAGE A less favorable environment to select the best active manager Source: Morningstar and Bloomberg data from 30/12/2016 to 29/12/2017. Peer Groups are build equally-weighted in terms of fund composition THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

8 8 IN 2017 ACTIVE FIXED INCOME MANAGERS SHOW MODEST SIGNS OF IMPROVEMENT Overall, 39% of active fixed income funds outperformed their benchmark slightly more than in 2016 (32%). One of the main reasons for the improvement was the stronger performance of US credit and global bond fund managers. AVERAGE % OF ACTIVE FUNDS OUTPERFORMING THEIR BENCHMARK ACTIVE FUNDS RELATIVE PERFORMANCE VS BBB-IG CREDIT SPREAD 2017 Source: Morningstar and Bloomberg data from 31/12/2016 to 29/12/2017. Peer Groups are build equally-weighted in terms of fund composition THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

9 9 IN % OF ACTIVE FIXED INCOME FUNDS OUTPERFORMED THEIR BENCHMARK The environment was more difficult, with a range-bound evolution of interest rates but strong credit performances in % OF FIXED INCOME ACTIVE MANAGERS OUTPERFORMING THEIR BENCHMARK More extreme results on fixed income Source: Morningstar and Bloomberg data from 31/12/2016 to 29/12/2017. Peer Groups are build equally-weighted in terms of fund composition THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

10 10 BEST ACTIVE FIXED INCOME FUNDS IN LARGE AND DIVERSIFIED MARKETS 67% of global bond active managers outperformed, largely because they have more flexibility than their underlying benchmarks. More than 50% of investment-grade corporate bond managers outperformed (57% for US credit, 52% for EUR credit). PERFORMANCE SPREAD BETWEEN BEST ACTIVE FUNDS* AND THE BENCHMARK IN 2017 FOR FIXED INCOME Best fixed income active managers outperformed their benchmark by 3% in 2017 Sources: Morningstar & Bloomberg data from 31/12/2016 and 29/12/2017. *Top 25% of funds in terms of performance THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

11 11 HARDER FOR ACTIVE EUROPEAN AND EMERGING MARKET BOND FUNDS Euro government, Euro high yield and Euro inflation linked bond managers found it very hard to beat their benchmarks, only 20%, 16% and 6% respectively earned their money last year. Even in the emerging market debt area an area long assumed to be rich in alpha potential outperforming proved difficult. Just 41% managed to beat their benchmarks in PERFORMANCE SPREAD BETWEEN ACTIVE FUNDS AND THE BENCHMARK IN 2017 FOR FIXED INCOME In Euro or EM bond markets outperforming proved harder for active funds. Sources: Morningstar & Bloomberg data from 31/12/2016 and 29/12/2017. *Top 25% of funds in terms of performance THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

12 12 IN 2017, A HIGHER PROPORTION OF ACTIVE EQUITY FUNDS OUTPERFORMED COMPARED TO 2016 Overall, in the current environment of continuing global economic growth, which is marked by increased visibility, low correlation and still abundant liquidity, the best active managers were those that better captured the trend towards quality or growth. AVERAGE % OF ACTIVE EQUITY FUNDS OUTPERFORMING THEIR BENCHMARKS CORRELATION Sources: Source: Lyxor Cross Asset Research and Macrobond data from 31/12/1997 to 29/12/2017. Correlation 12W pair wise, dispersion 12w average. Source: Morningstar and Bloomberg data from 01/01/2015 to 29/12/2017. THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

13 % OF ACTIVE EQUITY FUNDS OUTPERFORMED THEIR BENCHMARK Active equity managers fared fairly well in 2017, as risk stayed on, volatility and correlations remained low and the dispersion of returns among stocks increased. % OF ACTIVE EQUITY MANAGERS OUTPERFORMING THEIR BENCHMARK Wide dispersion of results Source: Morningstar and Bloomberg data from 31/12/2016 and 29/12/2017. Peer Groups are build equally-weighted in terms of fund composition THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

14 14 BEST RESULTS IN LESS EFFICIENT AND MORE SPECIFIC ACTIVE EQUITY FUNDS Active equity managers added value in some less efficient or more specific markets. The best results for equity managers were found in the Italian, European small-caps and German markets. PERFORMANCE SPREAD BETWEEN BEST ACTIVE FUNDS *AND THE BENCHMARK* IN 2017 FOR EQUITY Best equity active managers outperformed their benchmark by 6% in 2017 Sources: Morningstar & Bloomberg data from 31/12/2016 and 29/12/2017. *Top 25% of funds in terms of performance THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

15 15 WEAK RESULTS IN ACTIVE EQUITY FUNDS ON MORE EFFICIENT MARKETS More than 50% of managers underperformed in nearly half of our universes. The worst offenders were: 76% of China large-cap managers underperformed. 70% of UK all-cap managers underperformed. 68% of US large-cap managers underperformed. 55% of Europe large-cap managers underperformed. ACTIVE FUND PERFORMANCE SPREAD DISPERSION VS LT AVERAGE In more efficient large-cap markets, it is difficult for active managers to outperform their benchmark Source: Morningstar and Bloomberg data from 31/12/2016 and 29/12/2017. Peer Groups are build equally-weighted in terms of fund composition THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

16 16 DIFFICULT FOR US EQUITY MANAGERS TO OUTPERFORM In the US, 32% of active managers outperformed their benchmark, which is above the 2016 result and above the long-term average of 25%. Quality stocks outperformed, given the economic cycle was more advanced. The best active managers were slightly overweight on momentum and quality, which were the best performing factors. The worst performers were overweight on the low beta factor. Source: Morningstar and Bloomberg data from 31/12/2007 to 29/12/2017. Top and worst active funds of the universe weighted average results (first and last decile in terms of performance) using the following five Risk Factors from J.P. Morgan Equity Risk and MSCI: MSCI US SMALL CAPS INDEX, J.P. Morgan Equity Risk Premia US MOMENTUM FACTOR Long Only Index, J.P. Morgan Equity Risk Premia US LOW BETA FACTOR Long Only Index, J.P. Morgan Equity Risk Premia US VALUE FACTOR Long Only Index, J.P. Morgan Equity Risk Premia US QUALITY FACTOR Long Only Index. The results of the regression gives very statistically significant results with most of the R2 being above 85%. THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

17 17 MORE EUROPEAN EQUITY MANAGERS OUTPERFORMED In Europe, 45% of active managers outperformed above their 2016 but below their 2015 mark. Given the cycle was less advanced, more factors outperformed, with value, size and momentum leading the way. The best managers were all overweight on the size factor. Small-cap stocks were sensitive to the economic recovery and better positioned than large-caps in the context of a weakening USD. Source: Morningstar and Bloomberg data from 31/12/2007 to 29/12/2017. Top and worst active funds of the universe weighted average results (first and last decile in terms of performance) using the following five Risk Factors from J.P. Morgan Equity Risk and MSCI: MSCI EUROPE SMALL CAPS INDEX, J.P. Morgan Equity Risk Premia EUROPE MOMENTUM FACTOR Long Only Index, J.P. Morgan Equity Risk Premia EUROPE LOW BETA FACTOR Long Only Index, J.P. Morgan Equity Risk Premia EUROPE VALUE FACTOR Long Only Index, J.P. Morgan Equity Risk Premia EUROPE QUALITY FACTOR Long Only Index. The results of the regression gives very statistically significant results with most of the R2 being above 85%. THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

18 18 Q RESULTS: DIFFICULT START OF THE YEAR FOR ACTIVE MANAGERS AVERAGE % OF ACTIVE FUNDS OUTPERFORMING THEIR BENCHMARK * 24% 28% 25% 44% 39% 10Y 1Y 10Y 1Y Q1 Source: Morningstar and Bloomberg data from 30/12/2005 to 30/03/2018. THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

19 19 Q1 2018: FIXED INCOME ACTIVE MANAGERS FOUND THE ENVIRONMENT DIFFICULT 32% of fixed income active managers outperformed their benchmark in Q vs. the 39% we saw last year. Credit managers were again largely to blame, with only 18% of Euro and 19% of US corporate bond managers outperforming. Average % of active funds outperforming their benchmark Q EURO IG credit active managers performance vs BBB yield spread Source: Morningstar and Bloomberg data from 31/03/2017 to 29/03/2018. Peer Groups are build equally-weighted in terms of fund composition THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

20 20 Q1 2018: FIXED INCOME ACTIVE MANAGERS FOUND THE ENVIRONMENT DIFFICULT Global bond managers again enjoyed the best results with 74% of them finishing ahead vs. the 67% that beat the benchmark in Most benefited from increasing US interest rates and the positive trend for credit we saw at the very beginning of the year. Euro inflation linked bonds are the only area of real improvement with 29% outdoing their index vs. 9% in Average % of active funds outperforming their benchmark Q Global Bonds EUR Hedged active managers performance vs BBB yield spread Source: Morningstar and Bloomberg data from 31/03/2017 to 29/03/2018. Peer Groups are build equally-weighted in terms of fund composition THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

21 21 Q1 2018: DIFFICULT START OF THE YEAR FOR EQUITY ACTIVE MANAGERS Over the quarter, equity managers failed to come to terms with the more volatile environment and increasing correlations. The main weaknesses were found in the largest, more efficient markets such as World, Japan, US and Eurozone. Interestingly, performance also tailed off in some less efficient markets like the US, European small-caps and Italy. In contrast, we saw the biggest improvement in the UK all-cap universe, where 75% outperformed in Q Average % of active funds outperforming their benchmark Q UK all caps Equity benchmark relative performance compared to the GBP/EUR exchange rate Source: Morningstar and Bloomberg data from 31/03/2017 to 29/03/2018. Peer Groups are build equally-weighted in terms of fund composition THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

22 What can we derive for portfolio construction? Going one step further

23 23 USING OUR RESEARCH TO BUILD A BETTER PORTFOLIO What we learn from the 2017 results: Combining active and passive is key to generating long term performance: 44% of active funds in Europe outperformed benchmarks in Add active in less efficient and specific equity markets, position passive more prominently in other segments. Choose active in large and diversified bond markets, use ETFs elsewhere. Therefore a portfolio combining active funds on the most favorable universes and ETFs otherwise would have added value compared to a portfolio fully composed of active funds. AVERAGE % OF FUNDS OUTPERFORMING THEIR BENCHMARK OUTPERFORMANCE INDICATOR* Source: Morningstar and Bloomberg data from 31/12/2007 to 29/12/2017 * Europe large-cape universe.

24 24 WHAT DOES THE RESEARCH MEAN FOR PORTFOLIO CONSTRUCTION Using 34% the average percentage of active funds outperforming their benchmark in any one year over the last decade, we derive an efficient portfolio should include between 30% and 40% of active funds and the remaining 60-70% in traditional passive or smart beta. The passive component (whether traditional ETF or smart beta) can be used to: - add diversification, - capture market premium more efficiently, - make more precise allocations, - manage liquidity, - capture risk factors efficiently. The active part of the portfolio should be comprised of selected active managers with a genuine ability to generate alpha (alternative managers or niche stock pickers) in order to enhance diversification. SUGGESTED PORTFOLIO COMBINING ACTIVE AND PASSIVE

25 25 IN 2017, 76% OF FLOWS GO INTO ACTIVE FUNDS IN EUROPE Overall in Europe, active equity managers are under pressure but are at least resisting the trend to passive more effectively than their US counterparts. They represent 76% of flows. Fixed income exposures gathered the bulk of those inflows, with active funds ahead at $319bn vs. $57bn to passive. In equities, passive management flows reached a record high and were still well above those of active management ($126bn vs. $76bn). INTO EQUITY EUROPE CUMULATED FLOWS INTO FIXED INCOME Passive funds: 62% of flows in 2017 Active funds: 85% of flows in 2017 Sources: Morningstar universe of open-end funds domiciled in respectively the US and Europe, excluding Money-market funds and FoFs, data in USDbn from 31/12/2011 to 29/12/2017. THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

26 26 IN THE US, PASSIVE FUNDS COLLECT 97% OF TOTAL FLOWS Overall, US investors seem to decide that passive is the right choice for their equity exposures but the debate remains open on fixed income given its rebound. Active equity funds continued to see significant outflows of $227bn vs. record high inflows of $443bn to their passive equivalents. Active fixed income funds recovered with of $211bn of inflows vs. $216bn to their passive equivalents. INTO EQUITY US CUMULATED FLOWS INTO FIXED INCOME Passive funds: 100% of inflows in 2017, twice active fund outflows Passive funds: 51% of flows in 2017 Sources: Morningstar universe of open-end funds domiciled in respectively the US and Europe, excluding Money-market funds and FoFs, data in USDbn from 31/12/2011 to 29/12/2017. THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

27 27 IN TERMS OF ASSETS, PASSIVE FUNDS REPRESENT LESS THAN 30% OF TOTAL MUTUAL FUNDS* Globally, passive funds currently represent 27% of total funds managed vs. 16% in GLOBAL ACTIVE VS PASSIVE FUND ASSET UNDER MANAGEMENT USD TRN IN % Active 23.8 Passive AuM x 3 in 7 years 27% Total AuM: $32.8 trn % Source: Bloomberg data Source: Bloomberg data, *only including open-end funds

28 28 PASSIVE FUNDS: 16% OF ASSETS IN EUROPE; 37% IN THE US In Europe, passive funds represent 16% of total fund AUM vs. 12% in In the US, they represent 37% of total fund AUM vs. 20% in GLOBAL ACTIVE VS PASSIVE FUND ASSET UNDER MANAGEMENT Passive funds IN EUROPE 16% Total AuM: $8.4trn Passive funds 37% IN THE US Total AuM: $18.1trn 84% Active funds 63% Active funds Source: Bloomberg data Source: Bloomberg data, *only including open end funds

29 29 WHAT THE RESEARCH MEANS FOR PORTFOLIO CONSTRUCTION We believe portfolio composition in Europe currently underplays the role passive has to play: The weighting towards passive investments should be increased, While the emphasis on the active side should be on identifying those active managers with a genuine ability to generate alpha. GLOBAL ACTIVE VS PASSIVE FUND ASSETS UNDER MANAGEMENT 26% 35% 74% 65%

30 Blending Active and Passive A portfolio simulation

31 31 BLENDING ACTIVE & PASSIVE: PORTFOLIO SIMULATION We use 8 universes from the 23 of our study: Europe LC, UK all caps, US LC, Japan, EM LC, Euro HY, US HY, Global bonds. We calculate the average performance of all funds in each universe as well as the performance of the selection of funds for each universe done by the Lyxor fund selection team. We compare them to the performance of passive funds for each universe as represented by the benchmark net of fees (average fees of all the ETFs of each universe). We combine active and passive funds based on a simple methodology: selecting the best performing investment tool each year (active or passive) and asset weighting the overall portfolio. OUT / UNDERPERFORMANCE OF ACTIVE VS PASSIVE FUNDS* ALL ACTIVE & PASSIVE FUNDS Year Europe LC UK AC US LC Japan EM LC Euro HY US HY Global bonds % 0.08% -1.66% 0.11% -0.90% 0.38% 2.70% 0.61% % -3.38% -2.82% -1.11% 0.33% -1.70% -3.54% -1.18% % 0.60% -1.36% -0.56% 1.01% -4.64% -0.48% 2.85% % 0.82% -1.21% 0.80% 4.14% -1.73% -1.02% -0.41% % -1.12% -0.80% -2.02% 0.86% -2.61% -1.22% 0.12% % -0.25% -1.69% -1.12% 1.77% 0.62% 0.62% -0.63% % -3.21% -2.22% -0.67% -2.46% -0.11% -3.24% 1.16% % -2.31% -0.16% 0.59% 1.06% -0.11% -0.26% 1.28% SELECTED ACTIVE & PASSIVE FUNDS** Year Europe LC UK AC US LC Japan EM LC Euro HY US HY Global bonds % 6.23% -0.85% -0.28% 6.40% 6.46% 3.22% % 0.47% -1.84% -0.49% 2.12% -0.94% -1.16% % 4.52% -2.86% -4.79% 1.08% 9.29% -5.88% % 5.22% 2.51% 5.38% 3.09% 3.97% -2.35% % 2.43% -5.27% 0.35% 0.79% -2.02% -5.01% % 0.38% -2.22% -0.42% 3.88% -1.22% 1.44% 0.98% % -5.15% -1.72% 2.31% -5.50% 1.13% -0.54% -2.07% % -0.69% -0.95% -3.38% -1.49% 1.83% 0.14% -0.55% Sources: * Passive funds is estimated by benchmark minus fess. Active funds data are from Morningstar between 31/12/2009 and 29/12/2017, ** selected active funds are based on Lyxor fund selection aggregated performances by universe. Calculation from Lyxor Quant research. THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

32 32 ACTIVE & PASSIVE PORTFOLIO ALLOCATION With active fund selection, the weight of active funds is increased from 33% to 50% when creating the perfect mixed portfolio. ALLOCATION WEIGHTS OF THE PORTFOLIO COMBINING ACTIVE & PASSIVE ALL ACTIVE & PASSIVE FUNDS SELECTED ACTIVE & PASSIVE FUNDS Avg Active 50% Avg Active 33% Sources: * Passive funds is estimated by benchmark minus fess. Active funds data are from Morningstar between 31/12/2009 and 29/12/2017, ** selected active funds are based on Lyxor fund selection aggregated performances by universe THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

33 A PORTFOLIO BLENDING ACTIVE & PASSIVE OUTPERFORMED 33 A portfolio combining selected active funds and passive funds outperformed significantly both a portfolio only comprised of passive funds and one only comprised of selected active funds. The Sharpe ratio of such a portfolio is also significantly better. PORTFOLIO PERFORMANCES COMBINING ACTIVE & PASSIVE FUNDS Year Selected Active + Passive Selected Active Passive % 22.94% 20.08% % -6.65% -6.17% % 15.23% 14.18% % 14.14% 10.98% % 11.63% 12.82% % 7.22% 5.40% % 5.75% 9.15% % 10.72% 10.95% Annualised Return % 9.81% 9.42% 2017 Volatility 9.42% 9.79% 9.50% Sharpe Ratio Sources: * Passive funds is estimated by benchmark minus fess. Active funds data are from Morningstar between 31/12/2009 and 29/12/2017, ** selected active funds are based on Lyxor fund selection aggregated performances by universe. THE FIGURES RELATING TO PAST PERFORMANCES REFER TO PAST PERIODS AND ARE NOT A RELIABLE INDICATOR FOR FUTURE RESULTS. THIS ALSO APPLIES TO HISTORICAL MARKET DATA.

34 34 WHAT IT MEANS FOR PORTFOLIO CONSTRUCTION Defining the target breakdown of a portfolio between active and passive investment vehicles. Current breakdown* Target breakdown 33% 26% Without active selection 67% 74% With active selection *Current breakdown of the overall mutual fund (active +passive) asset under management source Morningstar open end funds excluding money market funds as of 29/12/2017.

35 Portfolio Construction: Key Recommendations

36 36 OUR RECOMMENDATIONS Adding passive tools (traditional, smart beta factors), namely Lyxor ETFs, in portfolio construction to come closer to our optimal portfolio proposal: 60-70% passive management, 30-40% active management. Relying on an optimal active management selection process set up, namely our Lyxor fund selection process, in order to be positioned on the optimal active funds according to your goals. FINDING THE RIGHT BLEND

37 Principles and Challenges of our Active-Passive Analysis A current investment outlook

38 JUNE 2018 ACTIVE vs PASSIVE SELECTION CROSS ASSET RESEARCH >> OUTLOOK Q3 2018

39 A SEASON FOR EVERY INVESTMENT STYLE CROSS ASSET RESEARCH >> OUTLOOK Q

40 There s a season for every management styles Passive and Active interests are usually at odds Defining the quality of their environment is complex: A combination of multiple factors at play Some are cyclicals others are structural Signals are rarely black or white Heavy data monitoring required given the wide Passive & Active operating universes Environments change over time, regions and market segments Managers talent and active funds selection can overcome unfavorable conditions CROSS ASSET RESEARCH >> OUTLOOK Q

41 Active & Passive management s Summers and Winters Passive products are favored when Beta is the dominating source of performance Active managers are favored when Alpha adds value, and/or when Beta becomes weak or unstable ACTIVE BACKDROP PASSIVE BACKDROP Stock / Themes differentiation Driven by companies fundamentals With catalysts for a change Medium volatility Transversal or speculative drivers dominate Micro fundamentals ignored Abnormally low volatility Strong identifiable market or thematic trends Stable and benign macro forces Unappealing micro opportunities Frequent macro turns Fragile financial conditions or rising uncertainty Abnormally high volatility CROSS ASSET RESEARCH >> OUTLOOK Q

42 Active & Passive s Relative Appeal over the Business Cycle Recession Early Cycle Mid Cycle Late Cycle Passive - - Active Long - Active AI + + Passive + + Active Long + Active AI - - Passive +/- Active Long +/- Active AI +/- Passive + Active Long + + Active AI = Straightforward Active/Passive Allocation Shifting Active/Passive Allocation CROSS ASSET RESEARCH >> OUTLOOK Q

43 KEY CRITERIAS FOR PASSIVE ALLOCATION CROSS ASSET RESEARCH >> OUTLOOK Q

44 Criteria 1 Stage of the Business Cycle The stage of the cycle is decisive for Passive products We monitor the phase, the dynamic and the fluidity of the Business Cycle: Macro Fundamentals: G20 and global trade pulse Macro Momentum: consensus surprises & expectations, lead indicators, economic volatility MACRO MOMENTUM Business Cycle Indicator (0-10) Global Liquidity: macro liquidity, financial stress, credit access Macrobond, Lyxor AM CROSS ASSET RESEARCH >> OUTLOOK Q

45 Criteria 2 Factors that can prolong the Business Cycle These factors are used as tilts to our Business Cycle scoring Monetary Policy: Stance: size of B/S, market expectation, key rates Direction: Taylor rule, output gap Guidance: IR volatility, volatility around Government Policy: Fiscal: budget deficit, public capex, credit growth, tax rate, VAT Regulation: reserve ratio, discretionary scoring CROSS ASSET RESEARCH >> OUTLOOK Q

46 Criteria 3 Factors that can break the Business Cycle These factors are used as tilts to our Business Cycle scoring Macro risk: wide moves in currency, commodity price, CPI Recession risk hitting threshold Leverage risk: corporate leverage, NPL ratio, delinquency rate, covenants Uncertainties: in policies, politics, geopolitics, game-changing vote, external shocks (using indicators and discretionary scoring) Macrobond, Lyxor AM CROSS ASSET RESEARCH >> OUTLOOK Q

47 Criteria 4 Equity Directionality Monitoring how Equities are impacted by macro developments Earning Cycle: trend, revision, guidance, earnings dispersion & volatility Valuation stage Thematic pulse: share of our theme baskets displaying trends Tactical pulse (sentiment, positions, flows, technical) to spot pending rotations Macrobond, Lyxor AM CROSS ASSET RESEARCH >> OUTLOOK Q

48 Criteria 5 Cross-Asset Directionality Using cross-asset markets as insight for Passive products in Equities Directional pulse in G20 countries: Aggregate Pulse in G20 in G20 Directional Assets Aggregate trend Aggregate stretched and range trading assets Share of assets showing correlation anomalies Signal dispersion Trend length Relative pulse in key asset classes: similar measures as above Share of G20 Directional Trades with 12M zscore >2 sigma (rhs) Macrobond, Lyxor AM CROSS ASSET RESEARCH >> OUTLOOK Q

49 Passive Allocation Summary Business Cycle Stage Monetary & Fiscal Policies PASSIVE ALLOCATION SCORING Tail Risks Equities Directionality Cross-Asset Directionality CROSS ASSET RESEARCH >> OUTLOOK Q

50 KEY CRITERIAS FOR ACTIVE ALLOCATION CROSS ASSET RESEARCH >> OUTLOOK Q

51 Criteria 1 Stock Arbitrage Potential Active managers need arbitraging potential Dispersion in stock prices and in valuations (extreme highs/lows are adverse) Relative stock volatility vs. indices (same comment) Analysis broken down at Country, Sectors, Stocks, Factors levels Macrobond, Lyxor AM CROSS ASSET RESEARCH >> OUTLOOK Q

52 Criteria 2 Nature of Stock Prices Drivers Active managers need idiosyncratic equity drivers Correlation across Countries, Sectors, Stocks and Factors The weakest, the better, suggesting independant and diversified drivers (more opportunities, less crowded) Source of stock returns: Market, Sectors, Company specifics Sector leadership (ideally not to broad but not too thin) 100% Share of returns post-eps-announcement explained by : Market, Sector, Company-specifics ( top 100 of S&P500) 80% 60% 40% 20% 0% Macrobond, Lyxor 18 AM CROSS ASSET RESEARCH >> OUTLOOK Q

53 Criteria 3 Fundamental Rationality Active managers need prices to respond to the companies fundamentals that they picked them for Stock Returns vs. Fundamentals: a rolling assessment every 1 month 1/2 with the EPS season Stock volatility vs. Macro volatility also emphasizes investors rationality SP500 Post-Report Return vs. EPS Surprise (100 largest stocks, in zscore) Stock overbullish Stock overbearish Macrobond, Lyxor AM CROSS ASSET RESEARCH >> OUTLOOK Q

54 Criteria 4 Freshness of Opportunities Active managers need opportunities that are mature enough but not too old, so that managers can identify them and benefit from them Measuring the share of stocks going through a lasting expansion or weakening phase vs. the share of stocks at inflection points (peaking or bottoming) Macrobond, Lyxor AM CROSS ASSET RESEARCH >> OUTLOOK Q

55 % sectors w/ up v s down rev ersal risk Criteria 5 Reversal Risk Active managers need relatively stable opportunities Share of stock and sector with reasonable auto-correlation Sector and Factor rotation risks using tactical indicators Broad volatility regime 100% 80% 60% 40% 20% 0% -20% -40% -60% US Sectors & Styles Rotation Risk Signal -80% Bear -100% Based on sectors' trading patterns incl. flows, position, valuation, liquidity. Bull US Sector and Style Pulse Signal (in Z -score) ENERGY MATERIAL INDUST INFO TECH CONS DISC CONS STPL HEALTH CR FINANCIA L TELCO UTILITY GROWTH VALUE LARGE SMALL CYCLICAL BUYBA CK Pulse Sign al Index price Macrobond, Lyxor AM CROSS ASSET RESEARCH >> OUTLOOK Q

56 Criteria 6 & 7 Catalysts Intensity & Structural Constraints Catalyst to move prices toward fundamental target Corporate activity M&A, IPOs, buybacks, extraordinary meeting, asset sales Stock sensitivity to macro drivers: the smallest the better US Corporate Activity Number of operations (3M avg) Extraord. Shldr. Meetings Rights Issues Spin offs Keeping eye on structural constraints weighting on Active managers Information edge: BBG, 13F filings Dispersion killer: Passive volume Flow Distortion: algorithmic trading Depth: trading desk & deriv. Volume Crowdedness: sensitivity to top held stocks, Active managers AuM "Animal Spirit": risk management, funds liquidity, implied leverage Regulation: capital cost Herding due to social media Access to less covered markets CROSS ASSET RESEARCH >> OUTLOOK Q

57 Active Allocation Summary ACTIVE ALLOCATION SCORING Arbitrage Potential Nature of Stock Drivers Fundamental Rationality Opportunities Freshness Reversal Risks Catalysts Intensity + Structural constraints CROSS ASSET RESEARCH >> OUTLOOK Q

58 PASSIVE / ACTIVE OUTLOOK CROSS ASSET RESEARCH >> OUTLOOK Q

59 US Slight Advantage for Passive and Smart Beta in Q3 Focusing only on Deep Value & Tactical within Active (1/2) Slight preference for Passive Marginally supportive cycle for Active. Fiscal cycle prolongation, but monetary tightening and further trade stress suggest frequent risk on-offs Decent equity environment but weak upward directionality expected for Q3 Mainstream Active to suffer, favor Deep Value style Elevated correlation suggest that soft-catalyst stocks will remain driven by macro, monetary and dollar trends Dispersion is fine, but concentrated on few sectors and driven by macro rotation Poor stock return rationality with their fundamentals, amid earnings sustainability doubt US Sector Return Dispersion (weekly returns, avg 12wk) All Sectors Ene rgy Basic Mat Industr ial Macrobond, Lyxor AM Cons Discr Cons Staples Healthcare Financial Info Tech Telecom Utili ties CROSS ASSET RESEARCH >> OUTLOOK Q

60 US Slight Advantage for Passive and Smart Beta in Q3 Focusing only on Deep Value & Tactical within Active (2/2) Weaker theme intensity: ageing or cross-current Ageing: tax-reform, defense, infra. spending, tech safe-haven, energy revival themes In cross-current: banking deregulation vs curve flattening, capex sectors vs. trade risks But plentiful corporate activity: M&A, asset sales, strategic consolidation Appeal for deep value and tactical styles: Softer valuation issue, cash repatriation, structural shift in telco, cons disc. etc. Improving Smart beta / Quant backdrop: More macro/cyclical driven rotation Better volatility backdrop, lower leverage More stocks loyalty to their factors More differentiation across factors Short opportunities rebuilt: higher number of stocks in a peak or weakening phase Macrobond, Lyxor AM CROSS ASSET RESEARCH >> OUTLOOK Q

61 EU Potential to emerge for Active Passive to remain under pressure from politics and soft-patch Unfavorable backdrop for Passive Reflation struggling to get traction Italian stress to come and go, Brexit uncertainty to intensify EMU reforms halted, Foreign investors to stay on the side lines But electoral agenda cleared and EMU systemic concern to abate A respite in tail risk to favor Active in Q3 Greater stocks differentiation. Wide set of stocks with upside from operational leverage Appealing fundamental rationality Low correlation consistent with multiple themes: corporate activity, regulation/tax changes, Greek & Italian consolidation, defense, commodity linked, export/domestic focused, French reforms Post-Report Return vs. EPS Surprise (100 largest StxxEMU stocks, in zscore) Stock overbullish Stock -0.8 overbearish Share of returns post-eps-announcement explained by : Market, Sector, Company-specifics ( top 100 of EuroStoxx) 100% 80% 60% 40% 20% 0% Macrobond, Lyxor 16AM CROSS ASSET RESEARCH >> OUTLOOK Q

62 Japan Mixed for Active and Passive as reforms abate Macro status quo means boring but relative advantage for Passive Reflation struggling to anchor BoJ on hold, JPY dominant stock driver Uncertainties to intensify: trade risk, politics and corruption allegation to cripple reforms But healthy corporate fundamental pulse Searching for catalysts to support Active Average correlation and dispersion Weakening set of themes as reforms prospects lose traction Poor fundamental rationality as macro drivers dominate Speculative catalysts also a drag for Active Macrobond, Lyxor AM CROSS ASSET RESEARCH >> OUTLOOK Q

63 EM Too early for Passive but prepare to reweight. Current backdrop still favors Active Adverse Passive: persisting focus on EM vulnerabilities, but prepare to reweight Sensitivity to monetary normalization, dollar liquidity take center stage Speculative catalysts dominate: trade, politics Poor equity directionality and earnings trends But looking for entry point at later stage Macro differentiation still benefits Active Elevated stock and country differentiation Diversity of themes and reduced correlation: Asian tech supply chain, trade sensitivity, Chinese market access, Eastern Europe domestic pulse + U.S./Russia détente, domestic consumption Reasonable fundamental rationality Macrobond, Lyxor AM CROSS ASSET RESEARCH >> OUTLOOK Q

64 MICRO MACRO MICRO MACRO MICRO MACRO MICRO MACRO Global MT Outlook Start combining Active & Passive Active managers navigated with constraints from poor economic volatility and distortions from quantitative easing in the recent years Both trends are expected to gradually reverse as we move into late cycle (no recession before at least 1y) and as monetary normalization moves forward In store: more fundamental pricing, more asset dispersion, a diversity of themes, greater volatility, both at a macro and micro levels Supporting a balance between Active and Passive Monetary Normalization & Late Cycle Fundamental pricing Dispersion Themes Greater Volatility Countries' Growth / Inflation / Productivity / Leverage become stronger market drivers Greater economic volatility and asset dispersion, as central banks buffer fades Implications for countries committing to reforms or policy rupture matter more Higher sensitivity to policy disappointment and risk perception Receding QE wealth effect means less rate distortion and more valuation discrimination Stock dispersion as rates spur discounted cash flows and leverage differentiation Sectors/Stocks to reflect tax / spending / regulation changes / political backdrop More frequent sector and stock rotations CROSS ASSET RESEARCH >> OUTLOOK Q

65 Scoring Summary Matters more for Passive Scoring Matters more for Active Scoring ACTIVE / PASSIVE SCORECARD US EU JP ASIA MACRO ENVIRONMENT + = = - Macro cycle pulse + = = = Monetary Support - = + - Fiscal Support + + = + + Recession Risks + = - = Political / Geopolitical Risk EQUITY ENVIRONMENT + = = - Valuation Attractivity = = = + Earning trend & Corp. Profitability Corporate Failure Risk = Corporate Operations = + CROSS-ASSET PULSE = Directional Pulse = Relative Pulse = ALPHA ENVIRONMENT = Arbitrage Potential (Dispersion) + + = = Nature of Driver (Correlation) = Fundamental Rationality - + = Opportunities Freshness = Reversal Risks - = = - Catalyst Intensity Structural Alpha Constraint CROSS ASSET RESEARCH >> OUTLOOK Q

66 Active Funds vs. Benchmark Blending Active and Passive Key Recommendations

67 67 KEY RECOMMENDATIONS As we are moving towards a late cycle phase, the environment for alpha is set to improve. Meanwhile, in the beta space more granularity will add value through sectors/countries, themes and smart beta. Blending active and passive funds for Q US Europe Japan Emerging Markets Beta = Sectrs/Cntries = Themes Smart Beta Alpha = Overweight - Underweight

68 Thank You!

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