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Alice in Factorland

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Alice in Factorland Alice in Factorland Rob Arnott Founder and CEO Research Affiliates, LLC Our Adventure in Factorland » Factor timing is difficult but possible » Relative valuation of the strategy or the factor (i.e., relative to its own history) is a powerful predictor of future return » Most investors already practice a form of market “timing”— unfortunately in the wrong direction by chasing past performance which can erode the benefits of factor investing » They fund the success of contrarian investors » Emphasizing factors or strategies that are trading cheap relative to their own historical norms and deemphasizing the more expensive factors or strategies can improve performance 2 Continue Our Journey in Factorland » Do mutual funds capture their factor returns? » Simulated factor returns are believed to describe investor opportunities » Instead, we observe the incredible shrinking factor returns in live assets » Smart beta is becoming synonymous with factor investing » In our view, this is not correct: “Smart beta” originally covered strategies that break the link between price and portfolio weight » Factor tilts don’t do this! Factor tilts are not true “smart beta” » Manager evaluation: factor tilts can help predict fund returns » Past performance is worse than useless » Funds with factor tilts that are trading cheap will tend to outperform » Troubles with momentum » Harvesting momentum premia appears to be “mission impossible” » Can we save “momentum”? Maybe. 3 PART I. Our Factor Timing Research 4 Trend Chasing Everywhere – Survivorship Bias Practitioners look for best historical performance. Academics look for best historical performance. Asset Owners look for best historical performance. » Problem: Not all factors are robust. » Selection bias and data mining are mistaken for persistent alpha1 » Rising valuations are mistaken for persistent alpha2 Harvey, Liu, Zhu (2015); Beck, Hsu, Kalesnik, Kostka (2016). 5 Fama, French (2002); Arnott, Bernstein (2002); Campbell, Shiller (1988); Cochrane (2008). Alpha Decomposition Portfolio Return Due to Change Valuation- ≈ + Alpha in Relative Valuation Adjusted Alpha “Revaluation Alpha” “Structural Alpha” » Alpha due to change in relative valuation » is mean reverting and averaging roughly zero in the long run » contributes significantly to strategy performance in the “short run” » “Short run” can mean decades! » Alpha adjusted for change in relative valuation is a good measure of unconditional expected return of a strategy 6 Valuation Cycle for Value Factor Value vs. Growth, United States (July 1968–December 2016) 4.00 1.00 D B E 2.00 0.50 Relative B Relative Performance, C D Valuation,* Value A Value vs. vs. Growth 1.00 0.25 Growth A E Biotech Bubble Global Financial C Crisis Nifty Fifty 0.50 Tech Bubble 0.13 1968 1976 1984 1992 2000 2008 2016 Value Performance Relative Valuation *Based on a blend of four valuation metrics: Price/Book, Price/5yrSales, Price/5yrEarnings, Price/5yrDividends. 7 Source: Research Affiliates, LLC, using data from CRSP and Compustat. Factor Valuations Are Predictive of Future Returns: Example: The Value Factor Value vs. Growth (July 1968–December 2016) 30% 15% Subsequent Five-Year Return 0% -15% 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 Relative Valuation (Aggregate) US Developed EM Median Valuation Source: Research Affiliates, LLC, using data from CRSP, Compustat, Worldscope, and Datastream. 8 Factor Valuations Are Predictive of Future Returns: Gross Profitability Factor Size Factor 15% 25% Correlation: -0.78 Correlation: -0.41 20% 10% t-stat: -2.06** t-stat: -7.53*** 15% 5% Yr Return Yr Return 10% - - 0% 5% -5% 0% -5% -10% -10% Subsequent Subsequent 5 Subsequent 5 -15% -15% 1 1.5 2 2.5 3 3.5 0.2 0.7 1.2 Relative Valuation (aggregate) Relative Valuation (aggregate) Momentum Factor Low Beta Factor 25% 20% Correlation: -0.27 Correlation: -0.11 20% t-stat: -1.79* t-stat: -0.79 15% 10% Return Yr Yr Return 10% - - 5% 5 0% 0% -5% -10% -10% Subsequent Subsequent 5 Subsequent -15% -20% 0 2 4 6 0 1 2 3 Relative Valuation (aggregate) Relative Valuation (aggregate) Long-Term Forecast Near-Term Forecast Source: Research Affiliates, LLC, using data from CRSP and Compustat. As of December 2016. 9 Two-Tail statistical significance: * = 10% threshold; ** = 5% threshold; *** = 1% threshold. What We Saw in June 2016 U.S. Dev ex U.S. Emerging Markets (1967 – Mar 2016) (1983 – Mar 2016) (1996 – Mar 2016) Aggregate Aggregate Aggregate 8 Legend Factor is Expensive 4 Current Valuation 2 Median Valuation Factor is Cheap 1 0.5 Relative Relative Valuation of Factors 0.25 0.125 0.0625 Gross Low Small Value Value Momentum Illiquidity Investments Profitability Beta Cap (B/P) (Blend) Source: Research Affiliates, LLC, using data from CRSP and Compustat, 1967–Mar 2016. The chart was originally published in “To Win With “Smart Beta” Ask if the Price is Right,” June 2016, Arnott, Beck, and Kalesnik. 10 What Happened Afterwards in 2016 Value Won, Quality & Momentum Lost, and Low-Vol Cratered Everywhere, Jul–Dec 2016 Performance Relative to the Benchmark Absolute Index Performance Region Index Jul-Dec Prior Prior Jul-Dec Prior Prior 2016 3 Years 5 Years 2016 3 Years 5 Years S&P 500 7.8% 39.7% 76.8% FTSE RAFI US 1000 3.9% -5.3% -2.8% 11.7% 34.4% 74.0% Russell 1000 Value 2.6% -7.0% -5.6% 10.4% 32.6% 71.2% S&P 500 Low Volatility -9.6% 9.4% 21.6% -1.7% 49.1% 98.4% United United States MSCI USA Quality -0.8% 0.8% 1.6% 7.0% 40.5% 78.5% S&P 500 Momentum -4.2% -3.0% -7.9% 3.6% 36.6% 69.0% MSCI World 6.8% 22.3% 37.8% FTSE RAFI Developed 4.1% -5.3% -8.4% 10.9% 17.0% 29.5% MSCI World Value 3.9% -6.5% -7.2% 10.7% 15.9% 30.7% S&P Developed Low Volatility -8.7% 9.2% 18.1% -1.9% 31.5% 56.0% MSCI World Quality -3.3% 10.8% 20.4% 3.5% 33.2% 58.3% Developed Developed Market S&P Momentum Developed -5.7% -0.5% 8.5% 1.1% 21.9% 46.3% MSCI Emerging Markets 4.5% -4.6% -17.5% FTSE RAFI Emerging 10.6% -3.1% -7.7% 15.1% -7.7% -25.3% MSCI Emerging Markets Value 2.5% -4.9% -7.2% 7.0% -9.6% -24.8% S&P Emerging Markets Low Volatility -7.0% -6.2% 10.7% -2.5% -10.8% -6.8% MSCI Emerging Markets Quality -4.1% 7.5% 11.5% 0.4% 2.9% -6.0% Emerging Emerging Market S&P Emerging Markets Momentum -3.7% 12.7% 26.2% 0.8% 8.1% 8.7% Source: Research Affiliates, LLC, using Bloomberg data. 11 Most Academics Are Trend Chasers! Return Degradation Before and After Factor Publication United States (Jan 1967–Aug 2016) Value Value Low Annualized Results Momentum Size Illiquidity Profitability Investment Average (Blend) (B/P) Beta Year Published 1977 1977 1993 1981 2002 1975 2013 2004 Before Publication 9.8% 9.1% 5.4% 7.0% 2.5% 7.4% 1.2% 3.5% 5.8% After Publication 2.3% 1.4% 3.7% 0.8% 5.0% 2.1% 5.0% -1.0% 2.4% Difference -7.5% -7.8% -1.8% -6.2% 2.5% -5.4% 3.8% -4.5% -3.3% » After-Publication Alpha is Not Large! » 2.4% is for long-short portfolio … 1.2% per side » That’s before trading costs, implementation shortfall, and fees » Residual alpha for end customers could easily be zero! Source: Research Affiliates, LLC, using CRSP/Compustat and Worldscope/Datastream data. 12 Most Product Providers Are Trend Chasers! Return Degradation Before and After Smart Beta Index Launch United States (Jan 1967–Aug 2016) Fundamental Equal Low-Vol FTSE RAFI Quality Dividend Risk Maximum Annualized Results Average Index Weight Index Low Vol Index Index Efficient Diversification Year Launched Nov-05 Jan-03 Feb-11 Apr-13 Dec-12 Nov-03 Jan-10 Nov-11 Before Launch 2.0% 1.3% 1.2% 2.2% 0.4% 2.9% 2.7% 1.6% 1.8% After Launch 0.5% 2.3% 2.1% 0.1% 0.1% 1.3% 0.9% 4.1% 1.4% Difference -1.5% 1.0% 0.9% -2.1% -0.4% -1.6% -1.9% 2.5% -0.4% » Here, at least, there’s some hope … » 1.4% after launch is not bad, not far below prior simulated results » Again, this is before trading costs, implementation shortfall, and fees » But, many of these have low turnover, and most have delivered live results ahead of benchmark since launch, net of all fees and costs Source: Research Affiliates, LLC, using CRSP/Compustat and Worldscope/Datastream data. 13 Most Investors Are Trend Chasers! 9.78% 9.36% S&P 500 8.81% 8.66% Index 8.38% 8.05% 8.23% 8.97% 6.87% 6.76% 5.22% All Funds Growth Funds Value Funds Small-Cap Funds Large-Cap Funds Dollar-Weighted Return Buy-&-Hold Return S&P 500 Index Source: Hsu, Myers, and Whitby, “Timing Poorly: A Guide to Generating Poor Returns While Investing in Successful Strategies,” 14 Journal of Portfolio Management (Winter 2016). Trend Chasing Is Costly Performance Characteristics of Trend Chasing and Contrarian Allocations, United States (Jan 1977–Aug 2016) Smart Beta Strategies Factors Trend Chasing and Contrarian Strategies Trend Chasing and Contrarian Strategies Value Add (Ann.) Information Ratio Average Alpha (Ann.) Sharpe Ratio 0.66 6.1% 0.52 0.34 0.33 0.25 2.4% 2.0% 1.5% 0.14 1.2% 1.2% Equally Weighted Three Best Three Cheapest Equally Three Best Three Cheapest Smart Beta Performing Smart Smart Beta Weighted Factor Performing Factors Factors Allocation Beta Strategies Strategies Allocation (1,3,5,10 yr (1,3,5,10 yr Performance) Performance) Source: Research Affiliates, LLC, using CRSP/Compustat and Worldscope/Datastream data.
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