Market caps bias EM portfolios to sub-optimal solutions: Enhancing performance through an active approach

Transcription

1 Market caps bias EM portfolios to sub-optimal solutions: Enhancing performance through an active approach César Cuervo Director of Research, Sura Asset Management (King Irving Funds Management) EMERGING MARKETS The Past, The Present and The Future

2 REGIONAL ALLOCATION an alpha generator within Emerging Markets?

3 A market cap approach in EM tilts the outcome towards Asia Region Weight EM Americas 11,0% EM EMEA 14,0% EM Asia 75,0% Emerging Markets 100,00% 80% Asia represents +75% of the MSCI Emerging Markets Index, and it is expected to continue growing Weight of Asia in MSCI EM Index 60% 40% 20% Source: MSCI. May

4 explained by the growing weight of China China is nearly 30% of the MSCI EM Index and has been growing over time The Chinese equity market is vast; MSCI indexes are designed to have breadth and flexible mechanisms to accommodate further expansions (e.g. IPOs) By targeting a % of the free-float market cap coverage, MSCI methodologies aim to remain representative of the opportunity set over time China is almost 30% of the MSCI EM Index Source: MSCI. June

5 but EM investable universe is more than China EM as an asset class is extremely diverse (once its components are properly understood) Source: Bloomberg, IM Sura.

6 Risk/return profiles differ widely between EM regions Risk-Adjusted Returns (monthly AUD) Risk-Adjusted Returns (monthly AUD) ASIA ASIA EMEA EMEA BRAZIL BRAZIL PA PA 0.00% 0.03% 0.05% 0.08% 0.10% 0.13% 0.15% -0.07% -0.05% -0.03% -0.01% 0.01% 0.03% 0.05% 0.07% 0.09% Optimal allocation may require research, but the effort pays off Source: Bloomberg, IM Sura.

7 Active regional allocation enhances risk-adjusted returns Monthly Mean Return (AUD) 1.2% 1.0% 0.8% 0.6% 0.4% 0.2% GEM vs Optimised Portfolio 10% active hard cap Risk/return arrows show direction & quantum of return added & risk by optimising portfolio vs GEM % 2.5% 3.0% 3.5% 4.0% 4.5% Volatility 30% 25% 20% 15% 10% 5% 0% Risk-adjusted returns (AUD) Global EM Index Optimal portfolio 2015 Risk-return profile of Global EM Index and Optimal Portfolios (10% active hard cap) Source: Bloomberg, IM Sura.

8 Active regional allocation enhances risk-adjusted returns Monthly Mean Return 1.6% 1.4% 1.2% 1.0% 0.8% 0.6% 0.4% 0.2% GEM vs Optimised Portfolio No hard cap Risk/return arrows show direction & quantum of return added & risk by optimising portfolio vs GEM % 2.5% 3.0% 3.5% 4.0% 4.5% 5.0% Volatility 35% 30% 25% 20% 15% 10% 5% 0% Risk-Adjusted returns (no hard cap) AUD Global EM Index Optimal portfolio 2015 Risk-return profile of Global EM Index and Optimal Portfolios (No active hard cap) Source: Bloomberg, IM Sura.

9 Investing in EM equities: using AI > 500 variables. Macro, fundamental market data. Machine learning algorithms. Predict returns, volatilities and other variables, approximate a value function. Find an optimal allocation policy With: good out-ofsample performance. Testing results.

10 Investing in EM equities: using AI LSTM Recurrent Neural Networks Adaptive Booting Support Vector Machines Random Forest Sequential online learning with expert advice Approx 110bp alpha per year * Data from MSCI. May Training Testing

11 MANAGER SELECTION Does being local make a difference? If yes how much?

12 Is there any local knowledge advantage in Emerging Markets? Our first attempt suggested that on-site managers might have an edge but concerns were relevant 1. BUILDING BLOCKS 2. SURA CENTER OF EXCELLENCE 3. ANALYZING THE DATASET 4. FIRST FINDINGS 5. CONCERNS Asia Pacific Ex - Japan Emerging Europe Brazil Asia Ex Japan Latam Large Caps Latam Small Caps 1,000 funds 30 categories 10 risk/return indicators χ Too many funds with unspecified characteristics χ Many investment styles CATEGORY TOP MANAGERS % LOCAL ASIA PAC EX JAPAN 20% EM EUROPE 80% BRAZIL 60% ASIA EX JAPAN 60% LATAM LC 20% LATAM SC 60% χ Size bias χ Commercial footprint of asset managers χ Findings not conclusive

13 Is there any local knowledge advantage in Emerging Markets? We decided to use a different sample. Interesting results called for further research. Asia Ex Japan 1Y 3Y 5Y 1. NEW BUILDING BLOCKS -Asia Ex Japan -Asia Pacific Ex Japan -Brazil -Latin America 2. DIFFERENT SOURCE 172 funds NO filters 3. CONCLUSIONS Local managers outperform in 10/12 combinations Asia Pacific Ex Japan Brazil LatAm 1Y 3Y 5Y 1Y 3Y 5Y 1Y 3Y 5Y

14 EM manager allocation: Local vs Remote manager Sharpe Ratio Asia (ex Japan) Asia Pacific (ex Japan) Brazil Latin America 1 Year 2 Year 5 Year +1bp +1bp -3bp +12bp +9bp +12bp +24bp +13bp +14bp +9bp +6bp -2bp In 10 out of 12 cases, local PMs outperformed non-local managers * Mean Difference Test prove significant difference between two samples of the same type Source: Moringstar, IM Sura.

15 Pacific Alliance Increase return & reduce risk Average Monthly Return (%) 1.8% 1.3% 0.8% 0.3% Pacific Alliance Pacific Alliance ASIA ex Japan World Colombia Perú Emerging Asia India BRIC Chile Mexico LatAm GEM MENA Brazil Source Sura Asset Management Bloomberg Data Returns in US dollars from April 2001 to April Calculated monthly and aggregated Index MSCI PA index created 2014, simulated from underlying country indices. -0.3% Emerging Europe 4% 5% 6% 7% 8% 9% 10% 11% Monthly Standard Deviation (%)