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 Yr Return
Yr 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
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. 15 Timing with Valuations Is Not Always a Value Tilt
Allocation of Strategies and Factors Used in the Most and Least Expensive Series, Relative to Own History, United States (Jan 1977–Aug 2016)
Presence of Smart Betas Presence of Factors
Dividend Index Value (Agg)
Fundamental Value (B/M) Index
FTSE RAFI Investments Low Vol
Low-Vol Index Low Beta
Risk Efficient Size
Equal Weight Illiquidity
Maximum Momentum Diversification
Quality Index Profitability 1977 1986 1995 2004 2013 1977 1986 1995 2004 2013
Portfolio of Least Expensive Portfolio of Most Expensive
Note: Cheapest and most expensive valuations are computed as a blended average of Five-Year Average Earnings-to-Price, Five-Year Average Sales-to-Price, Five-Year Average Dividends-to-Price, and most recent Book-to-Price ratios. For each factor/valuation metric we compare their current valuation relative to long run average valuation. 16 Source: Research Affiliates, LLC, using CRSP/Compustat and Worldscope/Datastream data . Research Affiliates Smart Beta Interactive Site
Real Long-Term Expected Return, Relative Valuation, Net of Transaction Costs, US Smart Select Smart Beta Strategies Beta Strategies (as of 12/31/2016) (as of 12/31/2016) 12% 4
EM, RAFI Fundamental Index
2 8%
1 Returns (Ann.) Returns 4%
Quality Year Year - Income
5 0.5
US, RAFI Fundamental Index Valuation Relative
0% Value Momentum 0.25 Expected Expected Low Volatility Small Cap
-4% 0.125 0% 5% 10% 15% 20% 25% Volatility
US EM
Source: Research Affiliates, LLC, using CRSP/Compustat and Worldscope/Datastream data. 17 Research Affiliates Smart Beta Interactive Site
Real Long-Term Expected Return, Relative Valuation, Select Factors US Factors (as of 12/31/2016) (as of 12/31/2016) 12% 4
2 8% EM Value (Aggregate) EM Value (P/B) 1
Momentum Returns (Ann.) Returns 4%
Profitability US Value (Aggregate)
Year Year -
5 0.5 Relative Valuation Relative Investment 0% US Value (P/B) 0.25 Expected Expected Illiquidity Size Low Beta
-4% 0.125 0% 5% 10% 15% 20% 25% Volatility
US EM
Source: Research Affiliates, LLC, using CRSP/Compustat and Worldscope/Datastream data. 18 Research Affiliates Smart Beta Interactive Site Developed
Real Long-Term Expected Return, Relative Valuation, Select Factors Developed Factors (as of 12/31/2016) (as of 12/31/2016) 12% 4
2 8%
Momentum 1
Value (Aggregate) Returns (Ann.) Returns
4% Relative Valuation Relative
Year Year Investment Value (P/B) -
5 0.5 Illiquidity Profitability 0% 0.25 Expected Expected Size
Low Beta
-4% 0.125 0% 5% 10% 15% 20% 25% Volatility
Source: Research Affiliates, LLC, using Worldscope/Datastream data. 19 Research Affiliates Smart Beta Interactive Site Developed Real Long-Term Expected Return, Net of Transaction Costs, Relative Valuation, Developed Smart Beta Strategies Select Smart Beta Strategies (as of 12/31/2016) (as of 12/31/2016) 8% 4
2 RAFI Fundamental Index
4% Income
Value 1 Returns (Ann.) Returns
Quality Momentum
Relative Valuation Relative
Year Year -
5 Low Volatility 0.5 0%
Small Cap
0.25 Expected Expected
-4% 0.125 0% 5% 10% 15% 20% 25% Volatility
Source: Research Affiliates, LLC, using Worldscope/Datastream data. 20 PART II a. The Incredible Shrinking Factor Returns
21 Our Methodology
» Our database » All funds from Morningstar Direct survivorship bias-free fund universe » US open-ended long-only active equity funds, with at least two year return history from January 1990 to December 2016 » Share-class inclusion: A-share, No-load-share, and Institutional-share » We can “reverse-engineer” the factor returns earned by managers » We first measure mutual fund’s average factor loadings over time by regressing fund returns against conventional constructed factor returns » We then run a cross-sectional regression, of fund returns on fund factor loadings, to estimate monthly factor returns, as realized by live funds » The slippage in factor returns » While the conventional factor returns and the factor returns realized by the managers show ~0.9 correlation, there is often a huge shortfall
22 Long−Short Factor Returns vs. Realized Factor Returns Captured by Managers Market Size 12% 8% Correlation = 0.92 Correlation = 0.96
Slope = 1.01 4% Slope = 1.00 Captured 4% Captured
0%
Managers
Managers by by
by by -4%
Factor Return -4% Size Size
Market Market Factor Return -12% -8% -12% -8% -4% 0% 4% 8% 12% -8% -4% 0% 4% 8% Observed Market (Mkt - RFR) Factor Return Observed Size (SMB) Factor Return Value Momentum 8% 16% Correlation = 0.89 Correlation = 0.90 Slope = 0.95 Slope = 0.98
4% 8%
Captured Captured
0% 0%
Managers
by by Managers Factor Return
Factor Return -4% -8%
by by Value Value -8% -16%
-8% -4% 0% 4% 8% -16% -8% 0% 8% 16% Momentum Momentum Observed Value (HML) Factor Return Observed Momentum (UMD) Factor Return
Source: Research Affiliates, LLC, using data from CRSP, Compustat, Morningstar Direct, and the website of Kenneth French. 23 Jannuary 1991–December 2016. Monthly Correlations are shown in all four graphs. Factor Slippage Momentum Factor
Cumulative Returns Cumulative Difference, Realized Versus Theoretical $400 $120 Shortfall (per ann):- 5.2% t-stat = -3.43
$247
$200 $60
scale)
(log (log 100 (log scale) (log 100 $111
$100 $30
Growth of $100 of Growth Growth of $ of Growth
$50 $15 1990 1996 2001 2006 2011 2016 1990 1996 2001 2006 2011 2016
Return Captured by Manager Theoretical L/S Factor Return
Source: Research Affiliates, LLC, using data from CRSP, Compustat, Morningstar Direct, and the website of Kenneth French. 24 Jannuary 1991–December 2016. Note: Cumulative returns are compounded using arithmetic returns. Factor Slippage Market Factor Market-Risk Free
Cumulative Returns Cumulative Difference, Realized Versus Theoretical $400 $200
$314
$200 $205 $100
scale)
(log (log 100 (log scale) scale) (log 100
$100 $50
Growth of $100 of Growth Growth of $ of Growth Shortfall (per ann): -4.2% t-stat = -3.54
$50 $25 1990 1996 2001 2006 2011 2016 1990 1996 2001 2006 2011 2016
Return Captured by Manager Theoretical L/S Factor Return
Source: Research Affiliates, LLC, using data from CRSP, Compustat, Morningstar Direct, and the website of Kenneth French. 25 Jannuary 1991–December 2016. Note: Cumulative returns are compounded using arithmetic returns. Factor Slippage Value Factor
Cumulative Returns Cumulative Difference, Realized Versus Theoretical $256 $125
$194 $157 $100 $128
scale) $75 (log (log $64
Growth of $100 of Growth $50
$32 Growth of $100 of Growth $25 Shortfall (per ann): -1.4% t-stat = -1.38 $16 $0 1990 1996 2001 2006 2011 2016 1990 1996 2001 2006 2011 2016
Return Captured by Manager Theoretical L/S Factor Return
Source: Research Affiliates, LLC, using data from CRSP, Compustat, Morningstar Direct, and the website of Kenneth French. 26 Jannuary 1991–December 2016. Note: Cumulative returns are compounded using arithmetic returns. Factor Slippage Size Factor Small Cap–Large Cap
Cumulative Returns Cumulative Difference, Realized Versus Theoretical $200 $125 $185
$167
scale) (log (log
$100 $100
Growth of $100 of Growth Growth of $100 of Growth
Excess (per ann): 0.7% t-stat = 1.13 $50 $75 1990 1996 2001 2006 2011 2016 1990 1996 2001 2006 2011 2016
Return Captured by Manager Theoretical L/S Factor Return
Source: Research Affiliates, LLC, using data from CRSP, Compustat, Morningstar Direct, and the website of Kenneth French. 27 Jannuary 1991–December 2016. Note: Cumulative returns are compounded using arithmetic returns. Do Mutual Funds Capture Their Factor Returns? No!
Annualized Factor Returns, US Equity Funds (Jan 1991–Dec 2016) 8.2%
5.7%
4.1% 3.3% 3.6% 2.6% 2.2%
0.4%
Mkt (Mkt-Rf) Size Value Momentum
Theoretical L/S Portfolio Realized Return in Live Portfolio
» The returns realized by mutual fund managers can fall short due to: » Transaction costs, fees, bid−ask spreads, and trades that “get away” » Hard to replicate features of theoretical factor portfolios (usually constructed ex post, bringing in data-mining and selection bias)
Source: Research Affiliates, LLC, using data from CRSP, Compustat, Morningstar Direct, and the website of Kenneth French. 28 Similar Slippage in International Equity Funds
Annualized Factor Returns, International Equity Funds (Jan 1991–Dec 2016)
6.3% 6.6%
4.9%
2.3% 2.1% 1.6% 1.6%
Mkt (Mkt-Rf) Size Value Momentum -0.6% Theoretical L/S Portfolio Realized Return in Live Portfolio
Source: Research Affiliates, LLC, using data from Worldscope, Datastream, and Morningstar Direct. 29 PART II b. Why Factor Tilts Are Not Smart “Smart Beta”
30 Factor Tilts = “Smart Beta”?
» The definition of “smart beta” has been vastly extended » Now, almost anything formulaic, other than a full-market cap-weighted index, seems to qualify for the “smart beta” label » Factor tilt and multi-factor strategies are sold as “smart beta” even though most of these strategies begin with, and anchor on, cap weighting » Let’s replicate a few “generation one” smart beta strategies with factors:* » Fundamental Index™ » Equally Weighted Index » Minimum Volatility Index » How well do the factor replicated “smart beta” strategies fare? » … much is lost in translation.
*These theoretical factors are Market, SMB, HML, MOM, and BAB. 31 Early Criticism of the Fundamental Index™ – It Is Repackaged Value Return Performance and Factor Loadings for Fundamental Index, Equal Weight, and Minimum Variance Strategies (January 1974−June 2016)
Panel A: Fama−French Three−Factor Model Plus Momentum
Alpha (Ann.) Market Value Size Momentum R-Sq.
0.97% *** 0.98 *** 0.35 *** -0.08 *** -0.07 *** 97.92% Fundamental Index (2.74) (147.47) (33.46) (-8.07) (-10.23)
0.67% 1.03 *** 0.18 *** 0.24 *** -0.02 *** 97.23% Equal Weight (1.48) (120.96) (13.78) (19.59) (-2.73)
1.59% ** 0.83 *** 0.16 *** -0.16 *** 0.05 *** 87.38% Low Volatility US (2.1) (58.05) (6.97) (-7.61) (3.1)
Panel B: Fama−French Three−Factor Model Plus Momentum and BAB Factor
Alpha (Ann.) Market Value Size Momentum BAB R-Sq.
0.67% * 0.98 *** 0.33 *** -0.08 *** -0.08 *** 0.04 *** 98.01% Fundamental Index (1.9) (150.06) (29.2) (-8.31) (-11.47) (4.83)
0.38% 1.02 *** 0.16 *** 0.24 *** -0.04 *** 0.04 *** 97.30% Equal Weight (0.83) (121.87) (11.25) (19.77) (-3.8) (3.57)
0.51% 0.82 *** 0.08 *** -0.16 *** 0.00 0.16 *** 88.94% Minimum Variance (0.71) (61.28) (3.31) (-8.24) (0.26) (8.45) » What about those alphas?
*** Significance at the 1% level, **Significance at the 5% level, * Significance at the 10% level. 32 Source: Research Affiliates, LLC, based on data from CRSP and Compustat. Factor Replications Miss the Mark…
Return Performance of Various Strategies (January 1974−June 2016)
Relative to Cap US 1000 Information Investment Allocation Returns Volatility Sharpe Ratio Value Add Tracking Error Ratio Fundamental Index original 12.9% 15.3% 0.53 1.8% 4.3% 0.42 Full Long/Short Factor 12.0% 15.2% 0.47 0.9% 3.4% 0.28 Replicated Factor Replicated Long Only 12.0% 15.7% 0.46 0.9% 3.2% 0.29
Equal Weight US 1000 original 13.1% 16.9% 0.49 2.0% 4.8% 0.41 Full Long/Short Factor 12.8% 17.4% 0.46 1.7% 5.3% 0.32 Replicated Factor Replicated Long Only 12.6% 16.8% 0.46 1.5% 5.1% 0.30
Minimum Variance original 12.4% 13.3% 0.57 1.3% 5.7% 0.23 Full Long/Short Factor 12.2% 13.7% 0.54 1.1% 3.0% 0.38 Replicated Factor Replicated Long Only 12.3% 14.4% 0.52 1.2% 2.6% 0.46
Cap Weight US 1000 11.1% 15.4% 0.41
*These theoretical factors are Market, SMB, HML, MOM, and BAB. 33 Source: Research Affiliates, LLC, based on data from CRSP and Compustat. When It Comes to Implementation They Miss the Mark Big Time!
Portfolio Trading Costs, Capacity, Turnover, and Leverage (January 1974–June 2016)
Ann. Average Average Annual Return Long Leg Short Leg Trading Cost Capacity Long Leg Short Leg Return Net of Turnover Turnover (bps) ($Bn) Leverage Leverage Costs
Fundamental Index original 12.9% 12.9% 11% 1 615 100% 0%
Factor Replication 12.0% 11.7% 57% 15% 32 16 113% 13%
Factor Replication Long Only 12.0% 11.8% 50% 20 25 100% 0%
Equal Weight original 13.1% 13.0% 18% 4 116 100% 0%
Factor Replication 12.8% 12.6% 36% 2% 23 19 104% 1%
Factor Replication Long Only 12.6% 12.4% 65% 20 25 100% 0%
Minimum Variance original 12.4% 12.2% 25% 19 26 100% 0%
Factor Replication 12.2% 11.9% 37% 14% 35 14 111% 11%
Factor Replication Long Only 12.3% 12.1% 75% 23 22 100% 0%
Source: Research Affiliates, LLC, based on data from CRSP and Compustat. 34 Why Factor Tilts Are Not True “Smart Beta”
» Fundamental Index, as one example, is not a repackaged value » Smart beta strategies have factor tilts, but they are much more than this » Smart beta strategies deliver alpha net of their Fama–French four- or five−factor regressions; factor-replicated strategies do not » Factor loadings show us some of the systematic drivers of return » But, simple factor tilt strategies based on theoretical factors are not the best way to capture return premiums » Replication portfolios have lower performance, higher turnover, and smaller capacity » Construction details matter! » Efficient implementation taking into account transaction costs can help better capture the premia
35 PART II c. Manager Evaluation
36 The Big Picture
» Trend−chasing is tempting and damaging » Investors tend to focus overwhelmingly on past performance in making investment decisions, especially for fund selection » But, past performance is worse than useless » To win, ask if the price is right » Valuation is a powerful tool in gauging future performance » Fees and turnover can eat away take-home returns » Implication for manager evaluation » We find reversal, not persistence, in mutual fund manager performance » We think there are reliable quantitative metrics that can help » Fund style return expectation* » Fees and turnover
* The first metric is drawn from the methodology of Arnott, Beck, Kalesnik (2016a, b, and c). 37 Mutual Fund Data Sample
» Fund inclusion criteria » We used Morningstar Direct survivorship bias-free fund universe » US open-ended long-only active equity funds » We included A-share, No-load-share, and Institutional-share » For funds with multiple subclasses (share-classes), we select the subclass with the oldest inception date » Funds with at least two-year return history from January 1990 to December 2016 » Our sample data » A total of 3,331 funds, classified into 9 Morningstar categories: » Large Blend/Growth/Value » Mid-Cap Blend/Growth/Value » Small Blend/Growth/Value » Fund returns at monthly frequency and are net-of-fees 38 Does Past Skill Predict Future Skill?
» The belief that manager outperformance* is persistent is false » Manager performance is mean-reverting. Among many possible predictors for return,* one of the most “powerful and reliable” predictor is the simple past return … with the wrong sign!
» Pooled correlation between past three-year simple return and subsequent three-year simple return is -26%, with an adjusted t-stat of -6.75
» “Positive” reasons to hire a manager (i.e., good things, like past alpha) are never even half as powerful
*We looked at an array of return measurement which included: simple return, excess return relative to benchmark or peer group, 39 CAPM risk-adjusted return, and Fama−French alpha, net of factor returns. A Naïve Contrarian Strategy Can Work
Average Mutual Fund Subsequent Three-Year Performance, Sorted by Prior Three-Year Returns, US Long-Only Equity Funds (Jan 1990–Dec 2016)
11.7%
11.0% 10.6% 10.3% Average Subsequent 10.2% 10.1% 10.3% 9.9% 10.0% 3-Year 9.8% 9.7% Average Annualized Return
1 2 3 4 5 6 7 8 9 10 = Lowest Decile Portfolios, Based On Prior 3-Year Returns = Top Decile Decile
This result is arguably created by our industry’s favorite decision rule: Three bad years and out!
Source: Research Affiliates, LLC, based on data from Morningstar Direct. 40 Factor Tilts Can Help Predict Future Relative Performance
» Managers with exposure to out-of-favor factors outperform the market » We can measure fund factor loadings » We can estimate factor expected returns based on valuations* » Therefore, we can predict fund returns » Pooled correlation between fund style expectation (i.e., fund return expectation based on manager factor tilt) and subsequent one-year excess return relative to market is 29%, with an adjusted t-stat of 10.30 » This is also the strongest relationship we found
* The factor return forecasts (valuation-ratio based model plus structural alpha) are done following the methodology introduced in 41 Arnott, Beck, Kalesnik, West (2016), and Arnott, Beck, Kalesnik (2016a and b). Fees and Turnover
» Managers with higher fees tend to underperform their peers » In the median-run (future three-year) and long-run (future five-year and above), 10 bps more expense delivers more than 10 bps lower relative net-of-fee performance » The relationship between turnover and future performance is inconclusive » Managers with higher turnover seem to underperform their peers » But, high turnover seems to be associated with higher return relative to market
42 PART II d. Momentum: Crowded Space and the Problem of Stale Momentum Crashes
43 Troubles with Momentum: Factor Premia Hard to Capture and Funds Underperform
Average Annualized Relative Performance Across Funds (Jan 1991−Dec 2016*)
Average Value-add Average Keyword No. of Funds Relative to Market FF4 Alpha
RAFI** Funds 9 2.0% 0.8%
Small 684 1.8% -1.0%
Multi-Factor*** 62 1.3% -1.0%
Value 732 0.8% -0.3%
Opportunity 50 0.6% -0.7%
Growth 966 0.1% -1.0%
Research 37 -0.1% -1.1%
Advantage 39 -0.1% -1.0% Dynamic 22 -0.2% -2.1%
Fundamental 30 -0.3% 0.2%
Dividend 103 -0.4% 0.0%
Contrarian 12 -0.5% -0.2%
Volatility 34 -0.8% -2.0%
Large 454 -0.9% -1.1%
Momentum 10 -1.1% -2.5%
Income 142 -1.1% -0.4%
Quality 18 -1.7% -0.3%
Source: Research Affiliates, LLC, based on data from Morningstar Direct. *Data sample includes all US equity mutual funds, including non-survivors, with "A" or No-load or institutional share-classes. The oldest share class is kept for funds with multiple share classes. The reported performance stats are averaged within each month, and 44 compounded over the full span. ** Funds with "RAFI" or "RAE" or "Fundammental Index" in the name ***Funds (ETFs and Open-ended Funds) labeled as "multi-factor" in their "Strategic Beta Attribute" by Morningstar Direct, with at least 12 month return history. Troubles with Momentum: Factor Premia Hard to Capture and Funds Underperform
Average Annualized Relative Performance by Style, Jan 1991−Dec 2016*
Panel A. Average Value-Add Relative to Market Top Decile minus t-stats Sorting Variable Top Decile Funds Bottom Decile Funds Bottom Decile (Top - Bottom)
Market Beta 1.3% -0.8% 2.0% 0.72
Size Beta 2.3% -1.0% 3.3% 1.21
Value Beta 1.4% 0.5% 0.9% 0.34
Momentum Beta 1.2% 0.1% 1.1% 0.53
Panel B. Average FF4 Alpha Top Decile minus t-stats Sorting Variable Top Decile Funds Bottom Decile Funds Bottom Decile (Top - Bottom)
Market Beta -1.9% 0.5% -2.4% -2.09
Size Beta -1.2% -0.4% -0.8% -0.96
Value Beta -0.2% -1.0% 0.8% 0.65
Momentum Beta -1.7% -0.4% -1.3% -1.43
Source: Research Affiliates, LLC, based on data from Morningstar. *Data sample includes all US equity mutual funds, including non-survivors, with "A" or No-load or institutional share-classes. The oldest 45 share class is kept for funds with multiple share classes. Decile funds are sorted based on fund full-sample FF4 factor loading estimates. Does Momentum Trade Get More Crowded in Bubble Stocks?
Cumulative Returns Over Subsequent Months, United States (Jan 1963−Dec 2016) 8%
4%
Cumulative 0% Returns
-4%
-8% 0 6 12 18 24 30 36 Months
Standard Momentum Stale Momentum
Source: Research Affiliates, LLC, based on data from Compustat and CRSP. 46 Stale Momentum – Poor Performance and Crashes
Dollar Growth of Standard and Stale Momentum, United States (Jan 1963 − Dec 2016) 1000.0
100.0
Dollar Growth 10.0 (Log)
1.0
0.1 1963 1973 1983 1993 2003 2013 Year
Standard Momentum Stale Momentum
Source: Research Affiliates, LLC, based on data from Compustat and CRSP. 47 Saving Momentum
» Harvesting momentum premium seems to be mission impossible » First, there may be no momentum effect left; it’s basically flat-lined since 1999 » And that’s for long/short paper portfolio investors, before fees! » And before trading costs! » Is momentum trade crowded? » What’s the Achilles’ Heel for momentum? Well, there are two: » How about shunning stale momentum? » Once momentum has been running for two years or more, stocks are very expensive, running out of gas. » It’s dreadful!! Shun stale momentum. » How about momentum conscious trading of other strategies? » If a strategy wants to buy a stock in free fall, why not wait a month? » If a strategy wants to sell a stock that’s soaring, why not wait a month? » Trading costs? Zero-to-negative! 48 Thank You
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