Smart Beta and Factor Investing Global Trends for Pension Investors Pascal Blanqué CIO Amundi
Executive summary Risk factor investing: Seeing a strong momentum among long-term investors (pension funds, SWFs, etc.); Allows long-term investors to capture outperformance over the long run; Can be reinforced with a dynamic allocation based on market conditions. Best practice: Diversified smart beta and factors strategies; Dynamic allocation among risk factors, based on statistical processes; Dynamic allocation among risk factors based on the macroeconomic scenario; Decarbonization of equity portfolios 2
Smart Beta: Global trends and mapping 3
Why Smart Beta: cap weighted indices are not well diversified 35% 30% 25% 20% Weight of sectors in global capitalization weighted indices Oil & Gas Technology Financial Capitalization weighted indices: Are poorly diversified and have a history of sector and style bubbles; Are pro-cyclical and can exhibit massive drawdown; 15% 10% Are negatively exposed to rewarded risk factors such as Value or Small Cap. 5% 0% 73 78 83 88 93 98 03 08 Source: Datastream 4
Why: cap weighted Indices deliver poor performance over the long run Market Cap Weighted Equal Weights Monkeys $100 invested with market cap weighted indexes would lead to $5,000 43 years later vs. more than $9,000 for about 50% of the cases with random (monkeys) portfolios. (1) Clare, Motson &Thomas (2013) 5
Why Smart Beta: a market monitor Survey by Longitude Research on behalf of State Street Global Advisors (2014): 300 US and Europe Asset Owners: Private Pension Funds, Public Pension Funds, Endowment Funds, Foundations, with more than USD 1bn in AuM Which of the following statements best describes your current opinion of smart/advanced beta? Most Institutional Investors have either committed a portion of their portfolios to advanced beta or intend to do so; This trend is particularly strong among Dutch, Belgian, and German pension funds; PGGM (Dutch pension fund with EUR 160bn) currently allocates 40% of its investment to advanced beta; Danish PKA, Taiwan Labor PF, UK s Strathclyde PF have allocated part of their portfolios to advanced beta products. Please indicate the extent to which you agree or disagree with the following statements. Smart beta strategies are a viable alternative to traditional index strategies Smart beta is here to stay as an evolution in indexing Smart beta is a viable alternative to active management 75% 66% 65% Source: Longitude Research on behalf of State Street Global Advisors 6
Why Smart Beta: a market monitor Survey by Russell Investments (2014): 181 US and European Asset Owners with more than USD 1 bn in AuM; Among Institutional Investors with more than $10 B, 75% are investing in, evaluating or considering smart beta; This percentage is 60% overall, 70% in Europe, 50% in USA. Source: Russell Investments Business Survey 7
Why Smart Beta: a market monitor Survey by Russell Investments (2014): 181 US and European Asset Owners with more than USD 1 bn in AuM; Russell anticipates a strong positive trend in investment flows. Source: Russell Investments Business Survey 8
What are Smart Beta? Alternative weighting schemes (Investment Process) Risk Factors (Stock Selection) Smart Beta has been traditionally identified with alternative weighting schemes, but Factor Investing is gaining remarkable momentum Source: Russell Investments Business Survey 9
Exhaustive mapping Mono Mid cap Selection + Investment Process Mono Momentum Low volatility Value Risk Factors Stock Selection Alternative weighting schemes Investment Process Min Variance Risk Parity Diversification Quality Multi Multi Dynamic allocation based on market signals Market Signals Market timing Dynamic allocation based on market signals Market regime (Markov Process) Market indicators (volatility, correlation, turbulence) 10
Institutional Investors in the Netherlands: approach to Smart Beta investing All of the 20 largest pension funds in the Netherlands have an allocation to Smart Beta; The typical first step has been an allocation to an alternative weighting scheme; the most widely adopted are minimum volatility and maximum diversification; The next step is a multi-factor approach where institutional investors typically allocate 20-40% to a combination of risk factors to (i) harvest targeted risk premia, (ii) balance the existing exposure to risk premia via existing active managers, (iii) reduce drawdown; The most popular factors are Value, Low Volatility and Quality due to defensive characteristics/benefits. 11
Institutional Investors in the Netherlands: Pension Fund case study Asset Manager of a multi-national company Pension Fund (employees in 80 countries); EUR 20bn in assets under management; In 2012 converted its Value/Growth mix into a 5-factor mix: Value, Momentum, Quality, Low Volatility, and Dividend Yield; In 2013 introduced a defensive mix (Low Volatility, Maximum Diversification, and Equal Risk Contribution). 12
French public Pension Fund FRR mixing alternative weighting schemes Weighting Scheme Risk Efficient What it does Increases portfolio diversification Minimizes portfolio Min Variance volatility Equalizes Equal Risk contribution to risk Contribution between stocks in the portfolio Weighs stocks based on RAFI fundamental characteristics (dividend, sales ) How you measure it Diversification ratio Volatility Entropy, effective number of stocks Fundamental score Constant mix of alternative weighting schemes; 2.0bn in Equity Smart Beta strategies; Combining several Smart Beta strategies: Help reduce the portfolio tracking error Without reducing expected record Exploiting implicit exposures to Value, Size (small), Low Volatility; In 2014: 2.38% outperformance in the Eurozone; 3.23% outperformance in North America. 13
Swiss private Pension Fund combines diversification scheme with dividend bias Weighted Average Crossed Correlations Diversification focused weighting schemes; 60% 50% 40% 30% 20% 10% 0% D-03 D-04 D-05 D-06 D-07 D-08 D-09 D-10 Optimum Diversification World MSCI World 160m in High Diversification High Dividend Equity Smart Beta, managed by Amundi; High diversification: Reduces risk and maximum drawdown; Does not impact expected return. Weighting schemes can be mixed with factor biases: Dividend, Value, Momentum 14
Investing with Risk Factors 15
Why factor investing makes sense Theme Risk-Based explanation Behavioral Value Momentum Captures excess returns on stocks that have low prices relative to their fundamental value Captures excess returns on stocks with stronger past performance Costly reversibility of assets in place leads to high Overreaction to bad news sensitivity to economic and subsequent reversal. shocks in bad times. Zhang Lakonishok et al. (1994) (2005) High Sensitivity to shock to expected growth. Liu and Zhang (2008) Underreaction to good news and return continuation. Daniel et al. (1998) Among available factors, some have positive long term expected rewards; Low Risk Size Quality Captures excess returns on stocks with lower than average volatility Captures excess returns of smaller firms relative to their larger counterparts Captures excess returns on stocks that are characterized by low debt, stable earnings growth, etc. Liquidity constraints. Frazzini Pedersen (2014) - Leverage constraints. Frazzini Pedersen (2013) Higher Earning uncertainty and distressed risk. Fama French (1992) Quality of accounting data affects systematic risk and the cost of capital. Campbell, Polk, and Vuolteenaho(1992) Two-layer portfolio theory and lottery-type investments. Shefrin and Statman (2000) - Hong Sraer (2012) Limited investor attention to small cap stocks Risk-seeking investors drive up the price of low quality/high-risk stocks. Hunstad (2013) They are supported by empirical evidence; They rely on solid economic rationale. 16
Risk factors outperform cap-weighted indexes over the long run All main factor indices have outperformed the market capitalization based index; But they can face some major drawdowns. MSCI World Source: Amundi Quantitative Research, FactSet. data from 05/31/1994 to 02/28/2014 Performance in USD. 17
Diversifying allows performances to be increased MSCI Indices: 1994-2014 Annual return Sharp ratio Active return MSCI World Minimum Volatility Moment um Quality Value Small & Mid Cap High Dividend Equally Weighted Basket 7.0% 7.9% 10.6% 10.6% 8.1% 8.1% 9.3% 9.3% 0.24 0.42 0.45 0.52 0.3 0.29 0.4 0.43 0.9% 3.6% 3.6% 1.1% 1.1% 2.3% 2.3% Information ratio 0.13 0.43 0.75 0.35 0.19 0.39 0.82 Single-Factor Indices have a better likelihood to perform (vs. indexes): 3 years: 60-70%; 5 years: 65-90%. Volatility 15.4% 11.1% 16.2% 14.1% 16.1% 16.9% 15.1% 14.0% Max Drawdown 54.0% 43.5% 52.8% 44.9% 57.9% 56.4% 59.4% 52.7% Tracking Error 7.1% 8.2% 4.8% 3.3% 6.0% 5.8% 2.8% Max Relative DD 20.1% 20.3% 19.6% 13.1% 34.3% 23.4% 4.7% Probability of outperformance (3Y) 62% 76% 76% 57% 66% 72% 97% Probability of outperformance (5Y) 74% 93% 75% 65% 80% 70% 100% Source: Amundi Quantitative Research, FactSet data from 05/31/1994 to 02/28/2014 Performance in USD. Multi-Factor Indices have even: Better returns; Lower Volatility; Lower max relative drawdowns Higher likelihood to outperform: 3 years: 96.5%; 5 years: 100%. 18
Timing factors with a statistical process 120 100 80 60 40 20 Distribution of returns in turbulent and quiet regime Probability of being in a high volatility regime: 1995-2015 2500 2000 1500 1000 500 0 0 6/9/1995 6/9/1997 6/9/1999 6/9/2001 6/9/2003 6/9/2005 6/9/2007 6/9/2009 6/9/2011 6/9/2013 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% A Markov process is a statistical tool that can be used to discriminate between market regimes: Quiet regime: returns tend to be high, volatility low; Turbulent regime: returns to be low, volatility high. A dynamic risk factor allocation invests: in a Defensive Mix when the Markov Model is detecting turbulent regimes (risk-off); in a Dynamic Mix when the Markov Model is detecting quiet regimes (risk on). Example - Defensive Mix: Minimum Volatility, Quality, High Dividend Yield Example - Dynamic Mix Momentum, Equal Weight (Size), Enhanced Value In-Sample Proba Vol HighRegime SP500 Multi Factor Dynamic Allocation 19
Timing factors according to the economic cycle 1- Recovery Winners Mid Cap Value 2- Expansion Winners Momentum Quality 3- Deceleration Winners Min Vol High Div. 4- Recession winners Min Vol Quality How to combine risk factors depending on economic cycles Statistical tests enable detection of opportunities Mid Cap & Value (Recovery) Momentum & Quality (Expansion) Min Vol & High Div. (Deceleration) Min Vol & Quality (Recession) and risks Quality (Recovery) (Momentum) Quality Losers Min Vol Losers (Growth) (Momentum) Losers Momentum Losers 0.2 Min Vol (Expansion) Growth & Momentum (Deceleration) Momentum (Recession) Source: Amundi Quantitative Research Factors in bracket are less statistically significant Smart Beta & Risk Factors 20
Minimizing Turnover 21
Specificities of Smart Beta strategies Compared to Market Cap weighted indices, Smart Beta strategies exhibit challenging specificities for index funds: Higher turnover; More liquidity issues; Less concentrated weight distribution; Greater difficulties in predicting quarterly index changes (for optimization based indices). A proper indexing process will have addressed those specificities: Within the replication phase; When implementing value added strategies. 22
Conclusion 23
Conclusion In a low yield environment, all asset owners are chasing returns: Long-term investors are benefiting from a competitive advantage: their investment time horizon; They can monetize it by implementing simple and transparent risk-factor investing strategies. In both cases, there is a need to: Have a clear understanding of the mechanisms (no black box); Deal with trusted partners that will work with them over the long run. 24
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