Quants: Beyond multi-factor models Nomura Global Quantitative Equity Conference May 21st 2010
Generating alpha from quant models It is like the old story of the man who was going to fight a duel the next day. His second asked him, "Are you a good shot? "Well," said the duelist, "I can snap the stem of a wine glass at twenty paces" and he looked modest. "That's all very well," said the unimpressed second. "But can you snap the stem of the wineglass while the wineglass is pointing a loaded pistol straight at your heart?" (Jesse Livermore Quote)
The story so far or how we arrived here
Quant has been a long-term value creator US Quantitative managers versus the S&P 500. Average relative return and Range around it (1995 through first-half 2009) 25 % 20 15 10 Average 5 0 (5) (10) (15) (20) (25) 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 Source: evestment Alliance, Empirical Research Partners Analysis. 1 Fifteen large core funds including products managed by Acadian Asset Management, Analytic Investors, Aronson + Johnson + Ortiz, Barclays Global Investors (select poducts), Battertmarch Financial Management, Chicago Equity Partners, INTECH Investment Management, Jacobs Levy Equity Management, LSV Asset Management, Nicholas-Applegate Capital Management, Numeric Investors, PanAgora Asset Management, Quantitative Management Associates and State Street Global Advisors.
Performance since 2006 has not been stellar, but positive nonetheless. 2009 deserves an explanation US Quantitative managers: Share of Actively-Managed Equities Held in Defined-Benefit Plans (LH) and average performance (RS) 20 % 6.0 18 5.0 16 14 12 10 8 6 4 2 Average = 2.9% Average = 0% 4.0 3.0 2.0 1.0 0.0 (1.0) (2.0) 0 00 01 02 03 04 05 06 07 08 09 (3.0) Source: Federal Reserve Board, Pensions & Investments, Greenwich Associates, evestment Alliance, Empirical Research Partners Analysis
A tragedy in four acts: how quant moved from value-based models to momentum and dynamic factor allocation Hedge Fund Market Neutral Index (LHS) Correlation with the performance of momentum factors (RHS) 4 Momentum breaks down 1150 0.80 1100 2 Correlation to momentum increases Average = 60% 0.60 1050 0.40 1000 0.20 950 Average = 0.0 3 Aug 07: models become more dynamic - 900 1 Value starts to fail (0.20) 850 (0.40) Mar- 03 Sep- 03 Mar- 04 Sep- 04 Mar- 05 Sep- 05 Mar- 06 Sep- 06 Mar- 07 Sep- 07 Mar- 08 Sep- 08 Mar- 09 Sep- 09 Mar- 10 Source: HFRX Equity Market Neutral Index; Aviva Investors
Our story so far Multi-factor models were initially value driven models Gradually, as value became out of favour, models became more momentum driven Increasing quant research and new entrants caused them to converge As we all realised this in August 2007, we enhanced the models Part of this enhancement meant more dynamic models: which lead to more momentum These dynamic approaches broke down in May 2009
Evolution and the fallacy of optimisation
Multi-factor models evolved in different ways Evolution of multi-factor models 1 Dynamic models Models based on either momentum or dispersion of factors Momentum based rotation creates significant exposure to reversals Dispersion based rotation can lead to periods of poor performance 2 New alpha models Efforts to identify new alpha factors lead to a lot of data mining No compelling evidence that new factors were identified after Aug 07 Better and more diversified models were a tangible output 3 Static models Do not fall into the momentum and dispersion traps Replicable by passive factor strategies Consultants and other buyers believe it lacks innovation / research Source: Aviva Investors
Multi-factor models are created from a combination of market anomalies Building up multi-factor models In building multi-factor models, we aim to Relative Return (Q1-Q5) 12% 11% 10% 9% Quant Model Capital Allocation Momentum Maximise our information ratio (i.e. return per unit of volatility) 8% 7% Growth Value Maximise our hit-rate (i.e. number of months we outperform the market) 6% 5% Minimise our turnover 4% 3% Earnings Quality Minimise our drawdowns 2% 5% 7% 9% 11% 13% 15% Source: Aviva Investors Tracking error
The fallacy of optimisation Our desire for monthly/quarterly leveraged out-performance lead us to all of this We forgot that these inefficiencies exist because they do not work all the time In optimising our models for higher hit-rates, we were undermining the alpha of the underlying strategies Value investors did not stop being value investors in 1998-1999, but we stopped believing in what we were doing because it did not work in 2007-2008 But the alpha from these strategies is still there
Breaking the paradigm: decomposing our alpha
A number of sub-strategies have emerged Quant sub-strategies 1 Enhanced Multi-Factor Models 2 Quant + Fundamental Quant model + analyst input Static and dynamic models Quant Universe 5 Alpha Decomposition Selling Implied alpha in segregated strategies! 3 4 High Frequency trading Mathematical Algorithmic trading Relative volatility and correlation Source: Aviva Investors
Quants can do more by acting at SAA level Strategic Asset Allocation Process 1 2 3 Constraints Established Strategic Asset Allocation Manager Selection Quant manager ALM Study Long term economic drivers Philosophy Competitor s Review Sustainable asset returns Process Broader Constraints Portfolio optimisation People Performance Equities as capweighted benchmark Performanc e versus benchmark Source: Aviva Investors
Sustainable return Asset allocators see equities as a single asset class represented by a cap-weighted index Mean-variance comparison: S&P500 versus non-cap weighted alternatives 10% 9% EM equities EM Inflation-Linked Bonds 8% UK equities AP equities 7% Global equities EU equities 6% UK IG Credits Global Converts UK Property NA equities 5% UK Gilts Global IG Credits EM USD Bonds JP equities 4% Global Developed Bonds UK index-linked EU Bonds US Bonds 3% 2% JP Bonds 1% UK Cash 0% 0% 5% 10% 15% 20% 25% 30% 35% 40% Expected Volatility Source: Aviva Investors
Equities should not be synonymous with to cap-weighted indexation Mean-variance comparison: S&P500 versus non-cap weighted alternatives Realised Return 18% 17% Intellidex DJ Select Dividend 16% 15% Mergent GWA VTL 14% RAFI 1000 13% 12% 11% WisdomTree Dividend S&P 500 10% 10% 11% 12% 13% 14% 15% 16% 17% 18% EDHEC Risk and Asset Management Research Centre
Alpha decomposition: Illustrative example (1) Fundamental strategies, as a proxy for the value premium Fundamental Strategy (Value Driven): UK Market 1990-2010 160 150 140 130 120 110 100 Potential for significant periods of underperformance 90 80 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 Source: Aviva Investors
Alpha decomposition: Illustrative example (1) There are ways to measure the potential from this strategy United Kingdom Composite valuation dispersion 1988-2010 4.0 United Kingdom Composite Valuation Dispersion As Of 04/23/10 3.0 Very High 2.0 1.0 High 0.0 Normal -1.0 Low -2.0 Very Low 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 Source: Bernstein Quant Research
Alpha decomposition: Illustrative example (2) Low vol strategies present much higher risk-adjusted returns Global portfolio: relative returns and Sharpe ratios of low vs high volatility strategies 5.0 4.0 3.0 2.0 1.0 0.0-1.0-2.0-3.0-4.0-5.0-6.0 Alpha (LHS) % Sharpe Ratio (RHS) % D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 0.60 0.50 0.40 0.30 0.20 0.10 0.00 Source: Aviva Investors
Hit rate (%) Alpha decomposition: Illustrative example (2) There are also ways to measure the potential from this strategy Volatility Strategies: Hit Rates versus direction of the market 90.0 80.0 Up Down 70.0 60.0 50.0 40.0 30.0 20.0 10.0 Source: Aviva Investors 0.0 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 Decile
% Alpha decomposition: Illustrative example (3) Index funds continue to grow and so arbitrage opportunities US: Index Fund and ETF Assets as a share of all long-term mutual fund and ETF Assets (1993 Through July 2009) 18 16 14 12 10 8 6 4 2 0 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 Memo: Institutions Index Funds ETFs Indexed Share 08 Source: Investment Company Institute, Greenwich Associates, Empirical Research Partners Analysis and Estimate.
Alpha decomposition: Illustrative example (3) Index arbitrage: an anomaly that has been increasing in size S&P 500: Relative performance of additions versus deletions between announcement and effective dates 20% 15% 10% Additions Deletion 14.7% 5% 5.0% 3.9% 0% -5% -5.9% -2.8% -10% -8.5% Announcement Date Announcement date to Event Date Post-Event Date Source: The price response to S&P 500 index additions and deletions: Evidence of asymmetry and a new explanation. Chen, Noronha and Singal, The Journal of Finance, 2004 (data analysis from July 1962 to December 2000)
Alpha decomposition can fight cap-weighted indexation Why are we allowing such huge pools of assets to be moved to passive strategies when we can offer better risk-adjusted solutions? Cap-weighted indexation is a poor solution: it has the known problem of overweighting overpriced securities and even equal-weighted portfolios can generate better riskadjusted returns. In order to be able to pitch our proposition to asset allocators and fight indexation, we should simplify and decompose our strategies and therefore be much more transparent about the alpha opportunity we are presenting.
This is not about a new passive approach Implementing any of the above-mentioned strategies is far from trivial. It requires extensive modelling to understand risk-adjusted returns, hit-rates, drawdowns and turnover associated with each of them. They can often be implemented in a number of different ways (sector neutral, beta neutral, maximum weights for single stocks). Only Quants can perform the necessary modelling to successfully implement these strategies. And all of them should represent a better solution than classical indexation.
Breaking the paradigm: decomposing our alpha
Alpha decomposition, the story so far Delivering Alpha Decomposition Multi-factor Quant Models Multi-factor Quant Models Multi-factor Quant Models Multi-factor Quant Models Global Income Funds Global Income Funds Global Income Funds Fundamental Strategies Fundamental Strategies Increasing depth of research Index Arbitrage Minimum Variance
Quant will continue to develop into a multitude of decomposed alpha strategies Decomposing multi-factor models into new products Fundam. Funds Income funds Index Arbitrage Multi-factor Quant Models Long-Only and Long-Short Funds Minimum Variance Trend Following? Quality Strategies? Structured Solutions Quantitative Research Trend Following High Quality Structured Solutions
Recap on the main points discussed today Multi-factor models will continue to be the core of our proposition as quants, and each of us will continue to try to differentiate ourselves in this space But there is more to our proposition than multi-factor models. We should fight capweighted indexation We are the only ones that can provide a credible alternative to this trend For doing that, we should be prepared to decompose our alpha and work together with the asset allocation process