Smart Beta. or Smart Alpha?

Transcription

1 Smart Beta or Smart Alpha? Kenneth Winther Senior Vice President, Tryg External lecturer, Copenhagen Business School november 2015

2 Smart beta in a nutshell Smart beta is the new black! Many names for same topic : Advanced beta, scientific beta, risk premia investing, risk factors etc. Concept: Overweight (usually academic) proven premia instead of market cap Great long-term smart beta excess returns Index returns (1998M12=100) MSCI World MSCI World Value Weighted MSCI World Equal Weighted MSCI World Minimum Volatility MSCI World Momentum

3 Smart beta intuition Factor Risk-based explanation Structural & behavioral explanation Value Size Low risk Momentum Costly reversibility of assets in place: High sensitivity to economic shocks in bad times Low liquidity, high distress and downside risk is compensated by higher returns Liquidity-constrained investors have to sell leveraged positions in low-risk assets in bad times when liquidity constraints become binding High-expected-growth firms are more sensitive to disappointments to expected growth trends Overreaction to bad news and extrapolation of the recent past leads to under-pricing Limited investor attention to smaller cap stocks Disagreement about high-risk stocks leads to overpricing due to short-sales constraints. Capital usage within Solvency II and many rating models do not distinguish riskiness making low risk stocks capital inefficient. Managers worry about low risk s tracking error-risk which is not compensated Investor overconfidence, initial underreaction and self-attribution bias leads to returns continuation in the short term 3

4 Smart beta in the active vs. passive debate Smart beta: The new box to dominate active and passive? Active management Passive management Smart beta Smart beta dominates due to: Average active fund do not beat market cap Winther & Steenstrup, 2016 Spring: Smart Beta or Smart Alpha? (The Journal of Investing) Smart beta dominates due to: Transparent, easy ( ) implementable static long term investment rules which outperform through academic proven weighting schemes Winther & Steenstrup, 2015: Kan ambitionsniveauet hæves højere end smart beta? (Finans/Invest) 4

5 Smart beta or smart alpha? Another revolution: From 3 to 4 boxes Manager Active Passive Focus (Universe) Active Market management cap Smart (beta) Other active funds Smart alpha Smart beta strategies Classic ETFs Passive & management index funds Smart beta strategies 5

6 Smart beta or smart alpha? Comparing apples with apples Manager Active Passive Focus (Universe) Market cap Smart (beta) True smart alpha Smart beta 6

7 Intuition behind smart alpha How can active managers potentially create value? Smart focus Active manager create value through Value Size Low volatility Ability avoid stocks that are cheap for a reason (i.e. value traps) & assess individual company risks better Smaller stocks get less attention (# of analysts and quality of analysis) and more inefficient pricing Ability to avoid overpriced low volatility stocks & understand intrinsic stability rather than just price stability Momentum [No clear intuition?] 7 + Smart active can adjust with news flow instead of waiting for the smart beta rebalancing point and avoid crowding

8 Constructing data set Groups of active smart beta funds Step 1: Focus on professional investors (separate account/cit-database * ) Step 2: Select managers that focus on smart beta Smart beta focus Active manager selection criteria Value Size Low volatility Momentum All Value -groups: All Cap Value, Foreign Large Value, Giant Value, Large Cap Core Value, Large Deep Value etc. All Mid cap -groups: Mid Core, Mid Core Growth, Mid Core Value, Mid Core Deep Value, Mid-relative Value etc. Ranking all fund according to 3yr trailing standard deviation and select 2 with lowest risk Funds with technical investment analysis smart active funds in data set 8 *Morningstar database

9 Active Passive Smart active vs. traditional passive Smart alpha? Market cap Smart Against MSCI World Value Size Low vol Momentum Numbers to be disclosed in Journal of Investing 2016 Spring issue Annualized excess return (smart alpha) 3,07% 5,02% 2,34% 2,11% Sharpe ratio (difference) 0,10 0,17 0,18 0,06 Information ratio 0,46 0,58 0,30 0,27 Outperformance probability* Against MSCI World Value Size Low vol Momentum Normal probability Beta adj. probability 1yr 62,1% 69,3% 53,5% 54,8% Numbers to be disclosed in 3yr 70,2% 82,8% 67,8% 66,9% 5yr 75,6% 88,5% 78,4% 74,4% Journal of Investing yr 80,8% 68,4% 96,1% 67, Spring issue 3yr 84,8% 73,2% 99,6% 70,8% 5yr 86,4% 82,7% 99,8% 78,5% 9 Note: Returns after fees are used in the calculations. *Average outperformance probability when choosing a random fund at a random point in time and hold for a given horizon.

10 Active Passive Smart active vs. smart beta Market cap Smart The ultimate skill test - True smart alpha? Against smart beta benchmarks Value Size Low vol Momentum Numbers to be disclosed in Journal of Investing 2016 Spring issue Annualized excess return (true smart alpha) 1,77% 1,31% 0,78% -1,0 Sharpe ratio (difference) 0,12 0,07 0,03-0,06 Information ratio 0,26 0,14 0,12-0,09 True smart alpha exists! Active Value, Size and Low vol beat smart beta Active Momentum is lagging Note: Realized active performance vs. backstated benchmark 10 Note: Returns after fees are used in the calculations. Source: Morningstar, MSCI and Bloomberg.

11 Active Passive Smart active vs. smart beta Market cap Smart Academic smart alpha? Percentage of funds with positive estimated alphas Against Carhart 4-factor model Value Size Low vol Momentum Numbers to be disclosed in Alpha > 0 86,8% 89, 93,2% 77,9% Above 9 significance level 49,1% 50,5% 59,1% 30,6% Journal of Investing 2016 Above 95% significance level 37,4% 39,3% 50,9% 20,2% Spring issue Above 99% significance level 20,3% 20,5% 29,5% 7,8% Results equivalent to true smart alpha 11 Note: Returns after fees are used in the calculations. Values state percentage of funds with positive estimated alphas and different statistical significance. Significance level derived from regression of Carhart 4-factor model: R fund = r f + b 1 *(R m r f )+ b 2 *HML +b 3 *SMB +b 4 *MOM +α, where R fund is the return for the individual fund, r f is the risk free rate, b 1 n are factor loadings, R m is the market return measured by MSCI World Index and HML, SMB and MOM are the Carhart- Fama-French factors and α is the alpha. Source: Kenneth French Data Library, Morningstar, MSCI and Bloomberg.

12 Smart beta for tactical purposes Fluctuations in performance for active mandates poses risk in short term even when long term outperformance is expected Theoretical probability of an active fund outperforming over a given horizon: N([Exp. Alpha Trading costs]/tracking Error) + Overlay of non-smart alpha mandates (just like on sectors/countries) Use smart beta instruments on short investment horizons Example of cumulative probability of outperformance for an active mandate* 6 Tactical horizon 55% 5 45% Months 12 *Exp perf = 100bp, trading cost = (2x)20bp and Tracking error = 600bp.

13 Dynamic smart beta impossible? november 2015

14 Tactical make sense even if you are passive Passive is another way of being active: Style drift Value Size Moment um Min Vol Ot her Note: MSCI World exposures are found by optimizing with MSCI smart beta benchmarks and finding weightings that explain MSCI World s return best. Calculated on 1yr rolling basis.

15 Smart beta popularity Low volatility is especially popular among professional investors Let s focus on low volatility premium Methodology and data The survey allowed us to collect the opinions of 128 respondent, largely representative of alternative equity beta users. The survey covers different parts of the world: however, European respondent were predominant as they represent two-thirds of the sample, while 16% of respondents were from North America and 17% from other parts of the world, including Asia Pacific, the Middle East, Africa and Latin America. The respondents to the survey were mainly asset managers (64%) and institutional investors (2). The majority of respondents were also key decision-makers, including board members and CEOs (12%), CIOs, CROs, heads of asset allocation or heads of portfolio management (31%), and portfolio or fund managers (27%). Respondents were mainly from large firms having over 10 beur in assets under management (51%) or medium-sized companies with assets under management of between 100 meur and 10 beur (39%) 15 Source: EDHEC

16 Approach Potential performance drivers for low vol Operational Implied volatility (VIX) Horizon Tactical Leading indicators (ISM Man.) Strategic Valuation (P/E, P/B & P/CF) Investment alternative Bond yield (US 10yr yield) 16

17 Low volatility premium history Low vol premium has outperformed historically MSCI World Minimum Volatility index relative to MSCI World Low volatility outperforms broad equity market Low volatility underperforms broad equity market

18 Operational horizon: Implied volatility VIX vs. low vol excess return 1M excess return 3M excess return 1 15, 5% -5% -1 6M excess return 3 15% -15% -25,0-12,5 0,0 12,5 25,0 1M change in VIX (% -point) 7,5% 0, -7,5% -15, M change in VIX (% -point) 12M excess return 3 15% -15% M change in VIX (% -point) M change in VIX (% -point) Horizon 1M 3M 6M 12M Slope 0,14*** 0,09*** 0,24*** 0,29*** Constant 0,01 0,09 0,34 0,66* R 2 15,3% 8,8% 22,1% 13,5% 18 Note: Data from 1990M1-2015M1. *, ** and *** indicate statistical significance at 1, 5% and 1% level, respectively.

19 Tactical horizon: Leading indicator ISM Man. vs. low vol excess return 1M excess return 3M excess return 1 2 5% -5% M change in ISM 6M excess return 3 15% -15% M change in ISM % -15% M change in ISM 12M excess return M change in ISM Horizon 1M 3M 6M 12M Slope -0,10* -0,35*** -0,50*** -0,52*** Constant 0,09 0,30 0,61** 1,03*** R 2 1,1% 13, 25,4% 26,6% 19 Note: Data from 1990M1-2015M1. *, ** and *** indicate statistical significance at 1, 5% and 1% level, respectively.

20 Strategic horizon: Valuation Low vol increasingly more expensive P/E 40 P/E-spread (right) Low vol-equity MSCI World Spread 30 P/CF-spread (right) Low vol-equity MSCI World P/CF Spread P/B P/B-spread (right) Low vol-equity MSCI World Spread Note: Data from 1999M M1. Due to data quality in the P/B-serie values have been interpolated between 2000M2-2000M11 and 2003M2-2003M11.

21 Strategic horizon: Valuation Valuation-spread explains future low vol excess return Slope Horizon (yrs) P/E-spread 0,49*** 0,29*** 0,20*** 0,04 0,01 0,04* 0,07*** 0,05*** 0,11*** 0,12*** P/B-spread 9,53*** 7,37*** 5,52*** 3,16*** 2,74*** 1,99*** 1,61*** 1,78*** 2,58*** 1,78*** P/CF-spread 2,24*** 2,01*** 1,15*** 0,91*** 0,99*** 0,52*** 0,40*** 0,47*** 0,63*** 0,51*** Horizon (yrs) P/E-spread 10,5% 9,9% 12,7% 1,4% 0,1% 2,8% 15,6% 7,3% 26,7% 37,6% P/B-spread 25,6% 38,2% 51,3% 31,4% 28,7% 36,5% 46,8% 50,2% 87,5% 75,4% P/CF-spread 28,8% 53,3% 36,4% 38, 54, 33,3% 34,7% 46,2% 63,9% 67,7% R 2 Excess return 5 yrs excess return (left) P/CF-spread (right, 5 yrs lead) 2 15% 1 5% -5% Spread 10,0 7,5 5,0 2,5 0,0-2,5-5,0 21 Note: Data from 1999M M1. *, ** and *** indicate statistical significance at 1, 5% and 1% level, respectively.

22 Investment alternative: Bond yield US 10yr yield vs. low vol excess return (in-/outflow) 1M excess return 1 5% -5% M change in US 10yr (bp) 6M excess return 3 15% -15% 3M excess return M change in US 10yr (bp) 12M excess return 3 15% -15% M change in 10yr (bp) 12M change in US 10yr (bp) Horizon 1M 3M 6M 12M Slope -0,024*** -0,033*** -0,042*** -0,043*** Constant 0,034 0,043 0,007-0,073 R 2 11,6% 22,7% 30,2% 28,1% 22 Note: Data from 1988M5-2015M1. *, ** and *** indicate statistical significance at 1, 5% and 1% level, respectively.

23 Investment alternative: Bond yield Not explained by downtrend in interest rates 1M excess return Rising rate Falling rate 3M excess return Rising rate Falling rate 1 2 5% 1-5% M change in US 10yr interest rate (bp) M change in US 10yr interest rate (bp) 6M excess return 3 Rising rate Falling rate 12M excess return 3 Rising rate Falling rate 15% 15% -15% -15% M change in US 10yr interest rate (bp) M change in US 10yr interest rate (bp) 23 Note: Data from 1988M6-2015M1.

24 Putting it all together Go for the smart alpha! Smart alpha for Value, Size and Low volatility Smart beta is still useful! Smart beta for Momentum (consider insourcing) Use for tactical purposes on both return and risk (like sectors) Dynamic indicators seems possible Manager consequences Focus on rewarded styles (use smart alpha) Evaluate and monitor active managers against smart beta Use cheap smart beta for fee discussions 24

25 Smart beta in the future? Smart beta has a future but not as rosy as backtests Academia s evidence is basically a backtest and not realized returns historical returns are a bit too rosy Factor zoo! Number of new smart beta: Increasing smart beta popularity can bid up valuation future premia will be lower (but will not disappear) Smart beta s transparency makes them pray for index arbitrageurs future returns will be lower Hot and a lot of money in and out of smart beta increase risk 25 Source: and the Cross-Section of Expected Returns (Harvey, Liu & Zhu, 2015) via SSRN

26 Appendix Supporting numbers (Gross returns) Percentage of funds beating benchmark Smart alpha Benchmark: MSCI World Value Size Low Volatility Momentum Excess return 91,5% 94,6% 89,4% 80,5% Sharpe ratio (difference) 84,5% 88,6% 91,2% 73,6% Information ratio 91,8% 95,2% 89,4% 80,6% True smart alpha Benchmark: Smart beta Value Size Low Volatility Momentum Excess return 88,9% 78,8% 82,4% 49,2% Sharpe ratio (difference) 89,4% 76, 76,4% 51,6% Information ratio 89,1% 79,2% 82,4% 49,2% 26

27 Appendix Supporting numbers (Net returns) Percentage of funds beating benchmark Smart alpha Benchmark: MSCI World Value Size Low Volatility Momentum Excess return 85,3% 90,8% 81,4% 74,8% Sharpe ratio (difference) 73,4% 81,5% 82,7% 61,6% Information ratio 85,4% 91,1% 81,4% 74,8% True smart alpha Benchmark: Smart beta Value Size Low Volatility Momentum Excess return 77,4% 69,1% 63,3% 35,9% Sharpe ratio (difference) 78,3% 64,6% 58,2% 37,2% Information ratio 77,5% 69,4% 63,2% 35,7% 27

28 Investment alternative: Bond yield Monitoring low vol excess return 3M chg in US 10yr (6M chg, left) Implied rate chg (3M chg, left) Excess return (3M, right) bp % Interest rate rises Low vol underperforms -15% % % Interest rate falls Low vol outperforms 15% Note: Implied rate change from forward rate curve

29 Appendix - Measuring low risk Low risk premium seems robust across definitions 29 Source: Deutsche Bank MSCI World & MSCI USA from 1995 to Rebalanced quarterly, long only, max 5% in each stock and max 25% in each sector.

30 Appendix - Low risk on long horizons Past 40 yrs may be exceptional in low vol returns Low risk exposure towards size on the rise 30 Source: Dimensional Fund Advisors (Upper) and Deutsche Bank (Lower). Note to upper graph: US Beta quintiles rebalanced annually. Stocks in each quintile are weighted by market capitalization. Note to lower graph: Data: MSCI World from 1995 to Rebalanced quarterly, long only, max 5% in each stock and max 25% in each sector.

31 Appendix Investment alternative: Bond yield Steepnnes in falling and rising rate environment Regression results Down Up Slope Constant R 2 # Obs Slope Constant R 2 # Obs 1M -0,031** 0,009 7,1% 165-0,02*** 0,130 3,7% 154 3M -0,048*** -0,200 16,9% 168-0,025*** 0,277 5,6% 149 6M -0,056*** -0,019 19,8% 179-0,029*** 0,281 6,1% M -0,03*** 2,215 5,1% 203-0,03*** 0,297 14,7% 104 Slope differences Slope Up - Down Down Up Diff Diff in % Up rate steepness 1M -0,031-0,020 0,011 35,3% Less steep 3M -0,048-0,025 0,023 47,3% Less steep 6M -0,056-0,029 0,028 49, Less steep 12M -0,030-0,030 0,000-1, More steep Note: Data from 1988M6-2015M1. 31 Coefficients scaled by 100. *, ** and *** indicate statistical significance at a 1, 5% and 1% level using a two-sided t-test

32 Appendix - Low risk and interest rate risk 32 Source: Goldman Sachs

33 Appendix Smart beta correlation Example: EDHEC factor correlation of excess returns EDHEC SciBeta US long-term track records (Dec 1974 Dec 2014): Diversified multi-strategy Momentum Low volatility Value Low investment High profitability Mid cap 0,67 0,63 0,86 0,85 0,74 Momentum 0,61 0,64 0,74 0,65 Low volatility 0,70 0,82 0,60 Value 0,84 0,51 Low investment 0,69 Note: All statistics are annualized and daily total returns from 31 December 1974 to 31 December 2014 are used for the US Long-term universe. The universe contains 500 stocks. The full names of the indices used are: SciBeta United States LTTR Mid-Cap Diversified Multi-Strategy, SciBeta United States LTTR High-Momentum Diversified Multi-Strategy, SciBeta United States LTTR Low-Volatility Diversified Multi-Strategy, SciBeta United States LTTR Value Diversified Multi-Strategy, SciBeta United States LTTR Low Investment Diversified Multi-Strategy and SciBeta United States LTTR High Profitability Diversified Multi-Strategy. 33 Source: EDHEC

34 Appendix Smart beta in single stocks or indices? Momentum possible in sectors while you need stock pickers on other factors 34 Source: AQR

35 Appendix - Smart arbitrage? Some of smart beta s key selling points are fixed investment rules and transparency but is it smart to tell everyone what you buy in advance? Smart index arbitrage can lower smart beta s return Already well-known effect from traditional passive investing in e.g. S&P 500 and Russell 2000 Negative effect rises as smart beta volume rises in the future Especially the momuntum factor has high turnover and is easy to exploit 35

36 Appendix Smart beta AuM ETP US

37 Appendix Smart beta AuM ETP EU

38 Appendix Passive sector drift Passive is another way of being active: Sector drift Market overweighing the technology sector in the early 2000 Index funds suffered the loss from not being able to get out in time Source: S&P, FactSet, ING U.S. Investment management 38

39 Performance persistence on active management Past performance for active managers is simple and works (as starting point) Enhance value by selecting active fund managers who have a history of superior performance, or avoiding managers with the worst track records (Harlow & Brown, 2006) Persistence is much more pronounced for the top and bottom performers (Vidal- Garcia, 2012) The best use of past relative performance information is to avoid persistently poor performers (Aragon & Ferson, 2006) Performance persistence is even more pronounced for emerging equity funds (Huij & Post, 2011) 39

40 Performance persistence (US) 3yr historical performance is simple starting point to chose managers US equity funds ranked on 3yr past performance. Worst funds have 3yr average performance <-2,5%. Best funds are residual. Median for groups 6% Distributions: Best Distributions: Worst Distributions: All Observations 4% 2% -2% -4% -6% -8% Source: Own construction. Data from Bloomberg. 40

41 Performance persistence globally Able to detect performance persistence on all markets US: Best vs. Worst EU: Best vs. Worst Worst Best Best Worst 14% 12% 1 8% 6% 4% 2% -25% -2-15% -1-5% 5% 1 15% 2 25% EM: Best vs. Worst % 2 15% 1 5% -25% -2-15% -1-5% 5% 1 15% 2 25% Average performance Best Worst EM US EU 18% 9% 16% 8% 14% 7% 12% 6% 1 5% 4% 8% 3% 6% 2% 4% 1% 2% -25% -2-15% -1-5% 5% 1 15% 2 25% -25% -2-15% -1-5% 5% 1 15% 2 25% Source: own construction. Data from Bloomberg,