The next generation of Smart Beta strategies Optimal forward-looking equity portfolios Kristof Woutters, Global Head of Investment Solutions Geoffroy Goenen, Head of European Equity Management Noordwijk, November 22 nd, 2013
Why revisiting benchmarks? Main drawbacks of market value weighted indices Inefficient risk-return profile Trend following (Herding behavior) Inefficient risk profile Buy High / Sell Low (Overinvestment in overpriced stocks) Inefficient return profile 2
Overview of the different methodologies Main alternative weighting schemes Rule-based Basic Advanced Equal Weighted Equal Risk Weighted Fundamental (Indexing) Optimised Risk Risk / Return Minimum Variance Minimum VaR / CVaR Risk Parity Maximum Sharpe Maximum Diversification 3
Return * Results harmonised backtest European equity universe, 1991-2013, Risk / Return comparison 16% RESULTS BACKTEST European Equity Universe (1991-2013) Maximum Sharpe Ratio 14% 12% Minimum CVaR Fundamental (Indexing) 10% Minimum Variance Risk Parity Equal Risk Weighted Equal Weighted 8% Maximum Diversification Cap Weighted Random Weighted 6% 8% 10% 12% 14% 16% 18% 20% * Including estimated transaction costs Volatility 4
Results harmonised backtest Detailed Metrics Cap Weighted Equal Weighted Equal Risk Weighted Fundamental (Indexing) Minimum Variance Minimum CVaR Risk Parity Maximum Sharpe Ratio Maximum Diversification Annual Return 8.3% 8.5% 9.7% 10.5% 10.2% 10.7% 10.1% 14.8% 8.2% Excess Return 0.2% 1.4% 2.2% 1.9% 2.4% 1.8% 6.5% -0.1% In bull markets 1.1% -0.4% 2.2% -4.2% -2.9% -3.6% 4.5% -5.2% In bear markets -0.8% 5.7% 2.0% 19.5% 17.9% 17.2% 14.4% 15.1% Volatility 15.1% 17.1% 14.2% 17.0% 10.3% 10.4% 10.9% 13.1% 11.9% CVaR 1m 95% -10.1% -11.6% -9.6% -10.8% -6.5% -6.5% -6.8% -7.6% -7.2% Max. Drawdown -53% -61% -54% -56% -32% -35% -39% -44% -40% Sharpe Ratio 0.55 0.49 0.68 0.62 1.00 1.02 0.92 1.13 0.68 Beta 1.07 0.89 1.07 0.47 0.52 0.57 0.66 0.58 In bull markets 1.06 0.92 1.13 0.50 0.55 0.61 0.67 0.61 In bear markets 1.10 0.86 1.00 0.40 0.44 0.51 0.58 0.52 Tracking Error 5.7% 4.7% 5.4% 10.9% 9.9% 9.2% 9.9% 10.3% Information Ratio 0.03 0.30 0.41 0.18 0.24 0.20 0.66-0.01 Diversification 1.99 2.61 2.49 NC 3.15 3.12 3.15 2.63 3.38 Turnover 13% 46% 32% 12% 105% 116% 99% 138% 118% Overall, alternative weighting schemes deliver better risk characteristics whilst at the same time, even over a long time period, outperforming the cap-weighted benchmark As expected, most alternative weighting schemes (due to their design) perform better in bear markets and less well in bull markets resulting in a relative high tracking-error versus the capweighted benchmark 5
Low Volatility Anomaly Explanation of Smart Beta outperformance THEORY REALITY 6
One investor, one strategy Alternative weighting schemes will not replace market capitalisation due to Lower capacity & Complexity BUT should be seen as a: Diversification Tool & Risk/return enhancer No best methodology of weighting for every investor or for all types of equity investments Depending on your preoccupations and objectives, you should use of a specific weighting scheme Investor focusing on Minimising Drawdowns Adapted solution: Minimum Variance Returns Maximum Sharpe Ratio Information ratio Fundamental (Indexing) Our preferred choice Combination 7
Combination of Fundamental & Minimum Variance Strategies Objectives Combination Qualitative Fundamental Approach & Minimum Variance Optimisation Objectives Minimizing Volatility of equity investments Generate High Returns over the cycle, with Limited Downside Risk 8
Why Minimum Variance? Equity Market Premium Inefficiencies Investors should pay a significant Premium for Lower Volatility Stocks Today, no major difference in terms of Premium in Equity Markets between : Low-Risk Stable companies Highly Volatile companies Examples : (2013 P/E, as of 12/11/2013) Diageo : P/E = 18.2 Roche : P/E = 16.8 Unilever : P/E = 18.4 vs. Saint Gobain : P/E=19.6 Heidelberg Cement : P/E=15.9 Adecco : P/E=19.2 Should be higher! 9
Why Not Only Minimum Variance? Backward-looking Process Pure MinVar = Backward-looking process The past does not necessarily predict the future A stable stock can become more volatile over time : If the structure of its markets evolves (competition) eg. Nokia and the evolution of the mobile (smart)phone market If the management implements a wrong strategy eg. European Telecom companies 3G license auctions (2000) If balance sheet issues appear eg. Banks in 2007-2008 and a volatile stock can become a very stable performer 10
Why Not Only Minimum Variance? Performance, Liquidity, Reactivity Lower volatility does not always provide good stock performance Example : European Utilities from 2009 until recently Illiquid stocks can be less volatile, but not for the good reasons Example : Mainly stocks with limited free float, eg. Holding companies Quant model reactions may be too slow to detect actual value destruction of a company Example : European Banks in 2007-2008 European Telecoms in 1999-2000 11
Why Not Only Minimum Variance? Slow Reaction of Volatility-Based Models 60-days Historical Volatilities vs. Performance : European Banks index Source : Bloomberg 12
Why Combining Fundamental Approach with MinVariance? Sustainable Value Creation In order to invest in the companies that will : Create the most value for shareholders (high returns over the cycle) In a stable way : Positive results evolution Increasing cash flows and well-managed risk should provide stable positive appreciation of the share price Forward-looking investment process : different from other MinVar funds (backward-looking) 13
Investment Process Step 1 : Fundamental Approach Disciplined Analysis based on our 5 Investment Criteria : 1 QUALITY OF MANAGEMENT & CORPORATE GOVERNANCE 2 CLEAR & SUSTAINABLE COMPETITIVE ADVANTAGE 3 GROWING UNDERLYING MARKET 4 CASH PROFITABILITY ABOVE THE COST OF CAPITAL 5 FINANCIAL LEVERAGE ADEQUACY 14
Investment Process Quality of Management & Corporate Governance Example: Reckitt Benkiser British multinational consumer goods company. From household goods, Reckitt has specialized in Health and Hygiene in order to better meet consumer needs Quality of Management : Stable : the CEO joined RB in 1987 (CEO since 2011). Heads of divisions : experienced and international Value-creative acquisitions : Boots (2006), Adams Respiratory (2008), SSL (2010) Mid and long-term strategy : successfully reshaped the company towards superior growth categories/geographies and where RB has competitive advantage Respect of guidance. Transparent and stable reporting Operating execution : market share gains, innovation Shareholder friendly : share buybacks, high dividend payout (50%) Adequate Corporate Governance Use of GMI rating reports as a tool to assess the corporate governance strengths and weaknesses. Source: GMI 15
Investment Process Clear & Sustainable Competitive Advantage Example: IG Group World s leading provider in financial spread betting Intrinsic analysis Products, services, brand: reliable and efficient trading platform. High quality service to clients. Costs, and R&D : economies of scale in IT costs. Strong margins despite a continuous investment in technology. Porter analysis Competition : spread betting = concentrated industry. Main competitors are not profitable. Barriers to entry : IT & marketing investments provide growing barriers to entry. IG s offer is already compliant to the new regulations, while most competitors still have to adapt their business model. Substitution products : spread betting is cheaper and more simple than warrants. Data suppliers have little bargaining power. IT is developed in-house Customers have little bargaining power (retail) As an evidence, IG has been continuously gaining market shares 16
Investment Process Growing Underlying Market Example: Syngenta Leader in Agrochemicals : #1 on crop protection and #3 in seeds Key Crops Market (crop protection & seeds) Market growth dynamics : Demand :. 2bn more people expected by 2030 (+30%, of which above 80% in emerging countries). 2011-2050 grain demand expected: +50% Offer :. Required yields improvements : >1.5% pa 2010-2025. Market value > $200bn by 2025 expected Source : Company data 17
Investment Process Cash Profitability above the Cost of Capital Example : Croda Croda is a global leader in speciality chemicals, sold to a wide range of markets : personal care, health care, crop care, coatings and polymers. The company uses natural origins raw materials. Croda should continue to consistently deliver ROCE well above its cost of capital 18
Investment Process Financial Leverage Adequacy Example : Assa Abloy Assa Abloy is the largest global supplier of intelligent locks and security solutions. The group has recorded an average Net debt/ebitda level of 1,8x from 2008-2012. We see low financial risks to this relatively high level of debt ratio, as cash flow generation is very strong and accelerating. Assa Abloy is expected to continue to use its balance sheet strength for acquisition purposes rather than share buybacks or special dividends. Source : Company data, Dexia AM estimates Dividend payment of 1.9 bn SEK in 2013 and 2,2 bn SEK forecast in 2015e (assuming an EPS CAGR of c.10% and an unchanged payout of 35%) Assa Aboy is committed to maintain a credit rating of at least A- No pension fund deficit. No off-balance sheet commitment. 19
COMPANIES Investment Process Companies Assessment ASSESSMENT COMPANY COLOUR GRADE QUALITY OF MANAGEMENT & CORPORATE GOVERNANCE CLEAR & SUSTAINABLE COMPETITIVE ADVANTAGE STRONG POSITIVE CONVICTION 5 Green Criteria GROWING UNDERLYING MARKET MEDIUM CONVICTION Neither Green nor Red Company CASH PROFITABILITY ABOVE THE COST OF CAPITAL FINANCIAL LEVERAGE ADEQUACY STRONG NEGATIVE CONVICTION At least 1 Red Criteria 20
Investment Process Companies Assessment Example : Food sector Company name Comp. Adv. Management Market growth Value creation Financial adequacy GMI Flags Food & staples retailing TESCO SAINSBURY MORRISON AHOLD CARREFOUR CASINO JERONIMO MARTINS COLRUYT DELHAIZE Food& Ingredients DANONE NESTLE UNILEVER KERRY ABF LINDT VISCOFAN Household & Personal Products RECKITT L'OREAL BEIERSDORF HENKEL 21 Source: Dexia AM
Fundamental European Equity An Experienced Team Geoffroy GOENEN : Head, 16 years experience ENTERPRISE Koen POPLEU : Laurent MILLIAT : Deputy Head & I.T., Industrials Specialist, 15 years experience Industry, Energy, Utilities Specialist, 9 years experience Nicolas RUTSAERT : Materials, Energy Specialist, 15 years experience Frédérique BETTE : Consumer Staples Specialist, 15 years experience CONSUMER Caroline DE TROYER : Consumer Discretionary Specialist, 12 years experience Antoine HAMOIR : Health Care, Consumer Specialist, 9 years experience FINANCE Christian SOLÉ : Yassine FKI : Finance Specialist, 16 years experience Assistant Fund Manager & Junior Finance Analyst Anne-Catherine DELAYE : Small & Mid Caps Specialist, 18 years experience 22
Investment Process Step 2: Minimum Variance optimisation => Europe Optimum Quality 5 Investment Criteria ALL Companies in our Coverage European Large Caps + Small & Mid Caps 600 Stocks (approx.) GREEN-RATED Companies Profitability Forecasts 100 Stocks* (approx.) Valuation Liquidity Minimum Variance Optimisation QUALITY Universe 60 Stocks* (approx.) (*) : as of 24/10/2013 23 EUROPE OPTIMUM QUALITY 50 Stocks (approx.)
Portfolio Sector Exposure : Europe Optimum Quality vs. Cap-weighted European Index As of 30/09/2013 : Source : Bloomberg 24
Portfolio Style Exposure : Europe Optimum Quality vs. Cap-weighted European Index As of 30/09/2013 : Source : Bloomberg 25
Portfolio Risk Characteristics As of 30/09/2013 Over 5 Years (annualized) Volatility Volatility Tracking Error Beta Cap-Weighted European Index 14.54 26.85 Quality Universe 12.81 22.24 9.14 0.78 50 pos - Equal-Weighted Optimum Quality 11.91 20.43 10.69 0.71 50 pos - MinVar Optimisation Pure MinVar 10.70 17.06 14.28 0.56 50 pos - MinVar / Cap-Weighted European Index Volatility calculation is based on 10-years history (weekly frequency), with a half-life of 6 months for Volatility, and 12 months for Correlations Source : Bloomberg European Risk Model 26
Conclusion The next generation of Smart Beta Strategies Qualitative Fundamental Approach & Minimum Variance Optimisation Soon : EUROPE OPTIMUM QUALITY 27
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