According to Morningstar, as of June 2016, the assets in smart beta exchange traded products totaled $490 billion. BlackRock forecasts smart beta using size, value, quality, momentum, and low-volatility will reach $1 trillion by 2020 and $2.4 trillion by 2025. This annual growth rate of 19% is double the growth rate of the entire ETF market. Are factors the cure-all for our investment needs? Or are they like “active management” that everyone wanted to have instead of holding passive index funds in the 1970s?
No one then wanted to be just average. This ironically gave many investors below average returns as they used the same information to compete against one another. Superior performance was usually due more to luck than to skill. But Bill McNabb, CEO of Vanguard, points out that passive index funds have been in the top quartile of long-term performance.
Factor-based investors and advisors now think they have an advantage. They base this belief mainly on the results of theoretical asset pricing models.
Asset pricing models look at long-term long/short returns without taking into account the price impact of trading. Factors that looked good on paper may be lacking in robustness, pervasiveness, persistence, or intuitiveness. So let's look at this more closely.
Does Size Matter?
The small cap size premium was first identified by Banz in 1981. His results were influenced by extreme outliers from the 1930s.
Looking at more recent history, the oldest small cap index is the Russell 2000. It started in January 1979. Here is the Russell 2000 annual return and volatility over the life of the index compared to the S&P 500 index.
The Russell 2000 underperformed the S&P 500 by 1.3% annually and had a higher standard deviation. The Russell 2000 thus underperformed on both a risk-adjusted and non-risk adjusted basis.[1]
Here is a chart comparing the Sharpe ratios of all small and large cap stocks over a longer period of time. Small cap stocks failed to show significantly higher risk-adjusted profits than large cap stocks.
In the table below long-only small caps slightly outperformed large caps globally since 1982. But small caps have underperformed large caps in the U.S. since 1926. Where is the outperformance that Banz talked about? 
In “Transaction Costs and the Small Firm Effect,” Stoll & Whitney (1983) showed that transaction costs also offset a significant portion of the small cap size premium.
Some construct their own factor pricing models that show a small cap premium if you combine size with other factors. In other words, size matters depending on what you are able to do with it.
Front Running
Some attribute the poor performance of the Russell 2000 index to the actions of front runners. Index replicators follow formulas for trading. They have little control over what and when to trade. Their trades are also known by the public ahead of time.
I pointed out in my last post that front runners cost S&P GSCI index investors 3.6% in annual return. Front running can happen with any index or factor-based strategy having known portfolio rebalancing dates.
Front runners can initiate trades ahead of index replicators or smart beta fund managers. They take profits after the replicators and fund managers finish their trading. Front runners thereby capture part of the factor or index return at the expense of index and fund investors.
If I were still managing hedge funds, I might front run rules-based strategies like value or momentum. These strategies often hold less liquid, more volatile stocks that offer the highest front running profits. Momentum would be a particularly attractive target. Its high portfolio turnover means more opportunities for profit.
Value - The Price is Right?
We all like bargains. Advisors and fund sponsors play off that desire by promoting the idea of a value premium. This past month I read two investment blogs saying cheap value stocks have outperformed the market by 4% per year.
According to Asness et al. (2015), the only time there seemed to be a significant positive value premium in large-cap stocks was over the in-sample 1963-1981 period. Over a longer 88-year period, there was no significant value premium. They argued that value might still be useful as a diversifier in certain multi-factor portfolios.
Kok, Ribando, & Sloan (2016) showed that strategies using common fundamental metrics of value, such as book-to-market or earnings-to-price, do not deliver superior performance. Instead, these ratios identify firms with temporarily inflated accounting numbers that tend to mean revert. .
Loughran and Hough (2006) looked at the performance of all U.S. equity funds from 1962 through 2001. They used the prior 36 months to sort funds by book-to-market (top versus bottom quartile) and size (top versus bottom half).
Equal Weighted Mutual Fund Returns 1965 to 2002
Growth
|
Value
|
Difference
|
t-stat
|
|
Large Cap
|
11.30
|
11.41
|
0.11
|
-.05
|
Small Cap
|
14.52
|
14.10
|
-0.42
|
-.16
|
From 1965 through 2001, the average large cap growth fund returned 11.30% per year, while the average large cap value fund returned 11.41%. The outperformance of 0.11% forlarge cap value over growth was insignificant.
With small caps, the authors were expecting different results. They were surprised that small cap value funds earned 14.10%, while small cap growth funds returned 14.52%. Small cap value underperformed small cap growth by 0.42% per year.
Israel & Moskowitz (2012) show that the value premium is insignificant among the two largest quintiles of stocks and is concentrated among small cap stocks. These results are robust over 86 years of U.S. equity data and almost 40 years of data across four international markets.
Why then did small cap value funds underperform small cap growth funds? Loughran and Hough said wide bid-ask spreads and the price impact of trading worked against the capture of a value premium in small-cap stocks. For value investing in general, they concluded, “We propose that the value premium is simply beyond reach…investors should harbor no illusion that pursuit of a value style will generate superior long-run performance.”
Some practitioners try to excuse these results by saying there are better valuation metrics than book-to-market (B/M). But the Israel & Moskowitz results were similar using valuation measures other than book-to-market. Kok, Ribando, & Sloan (2017) also found "remarkably consistent results" using different valuation ratios and weightings. Loughran & Wellman (2010) found only a .02% per month difference in performance between book-to-market (HML) and the enterprise multiple (EM), another popular valuation metric.
Some who believe in the superiority of value or small cap investing point to performance of the Dimensional Fund Advisors (DFA) funds. Their U.S. Small Cap Portfolio (DFSTX) began in March 1992 and was the first factor-based small-cap fund. DFA's U.S. Large Cap Value Portfolio (DFLVX) and U.S. Small Cap Value Portfolio (DFSVX) funds began in February and March of 1993. All these funds have positive alphas. But none of the alphas are statistically significant.[2] To the extent that the DFA funds have done reasonably well may not be entirely due to their factor tilts.
DFA serves as a market maker in the stocks they hold. This means they can be patient when adjusting portfolio positions. That reduces their costs of trading in exchange for some additional tracking error. Using a buy-sell range also reduces turnover and trading costs. Holding a large number of securities further reduces the price impact of DFA's trading.
DFA has also benefited from not being tied to an index and thereby subject to front running costs. DFA has been aggressive in lending securities for a fee. Additionally, DFA uses momentum to filter their trades. They generally avoid IPOs and stocks with high borrowing costs.
Stocks with high borrowing costs often have a large short interest. This means there is a limited supply of stock available for borrowing. Studies here, here, and here show that heavily shorted stocks have negative abnormal returns, while lightly shorted stocks outperform their benchmarks. DFA has benefited considerably from avoiding stocks with high short interest.
Source: Boehmer et al. (2009), “The Good News in Short Interest”
People may not remember that factors were once called “risk factors.” Value funds have tracking errors that can persist for 10 or more years. Tracking error is a form of risk. It can cause investors and money managers to liquidate their positions at inopportune times.
Another risk is scalability. It might not be possible for popular strategies like value to always maintain an advantage over the market. This is particularly true of value stocks that are often out-of-favor and ignored. This can make them less liquid and more expensive to trade.
In “A Taxonomy of Anomalies Costs and their Trading Costs” Novy-Marx & Velikov (2015) looked at how capital levels can affect factor trading profits. Their calculations showed that excess profits disappear once the amount in value strategies exceeds $20.7 to $50.6 billion.
The Novy-Marx & Velikov capital levels are based on a turnover reducing approach. It buys value stocks ranked in the top 10th or 30th percentile. But it does not liquidate them until stocks drop out of the top 50th percentile. DFA, MSCI and others use a similar turnover reducing approach.
U.S. Large Cap Value Index Funds
|
Assets
|
iShares Russell 1000 Value (IWD)
|
$35.2 b
|
Vanguard Value (VTV)
|
$27.6 b
|
DFA US Large Cap Value I (DFLVX)
|
$19.7 b
|
iShares S&P 500 Value (IVE)
|
$13.1 b
|
iShares Russell Mid Cap Value (IWS)
|
$9.4 b
|
Vanguard Mid Cap Value (VOE)
|
$6.6 b
|
TIAA-CREF Large Cap Value Index (TRLCX)
|
$6.3 b
|
DFA US Large Cap Value III (DFUVX)
|
$3.4 b
|
Schwab US Large Cap Value (SCHV)
|
$2.9 b
|
Total Value Assets
|
$124.3 b
|
The $124.3 billion in value funds far exceeds the upper bounds where Novy-Marx and Velikov say value profits would disappear.
Momentum – the Premier Anomaly
Momentum is the strongest market anomaly based on academic research. Momentum has been studied now for more than 25 years. It meets all the tests of robustness, pervasiveness, persistence, and intuitiveness.
Momentum performs best in focused, concentrated portfolios. Momentum is a high turnover strategy. Momentum stocks are often volatile with wide bid-ask spreads. Trading billions of dollars around the same time in a modest number of volatile stocks may impact trade execution. It would be like trying to force a dozen people through a small door opening.
Academics have long been concerned about the price impact of momentum trading. One of the first studies of this was by Lesmond, Schill & Zhou (2002) in their “The Illusive Nature of Momentum Profits.” They found that momentum creates an illusion of profit opportunity when none really exists. Momentum is a high turnover strategy. Momentum stocks are relatively less liquid with disproportionally large trading costs. Two years later, Korajcyzk & Sadka (2004) found that profit opportunities could vanish once the amount invested in momentum-based strategies reaches $5 billion.
Counter to these findings, Frazinni, Israel & Moskowitz (2012), based on 16 years of actual AQR data, argue that the potential scale for factor trading is more than an order of magnitude greater than previous studies suggested. They said this capacity could increase even further by using optimized trading methods. Their data is for all factor-based strategies in their database though, not just momentum. Even if their study had been limited to momentum stocks, AQR has been trading non-focused momentum portfolios (over 400 stocks in their large cap U.S. momentum fund). This may not be truly representative of the impact from momentum trading.
More recently, Ratcliffe, Miranda & Ang (2016) from BlackRock suggested that a greater amount of capital could be traded using momentum.
In contrast to these two studies, Fisher, Shah & Titman (2015), using observed bid-ask spreads, got results much closer to those of Lesmond et al. and Korajcyzk & Sadka than Frazinni et al.
Novy-Marx & Velikov (2015) also determined the capacity for stock momentum before profits would vanish.
This is close to the $5 billion amount where Korajcyzk & Sadka said momentum profits would disappear. Novy-Marx & Velikov used an optimization algorithm to keep them in trades longer, as discussed by Frazzini et al.
It does not include mutual funds, managed accounts, or hedge funds. Even so, it exceeds the level of assets where both Novy-Marx & Velikov and Korajcyzk & Sadka say momentum profits should no longer exist.
With $10 billion invested in large cap momentum, the value added by momentum goes from +2.7% per year before transaction costs to -3.4% after transaction costs. This is with monthly portfolio rebalancing. If you rebalance quarterly instead of monthly, your additional annual return goes from +2.0% before trading costs to -1.6% afterwards.
To help find out, we can look at the performance of the oldest publicly available momentum funds. First is the PowerShares DWA Momentum ETF (PDP) managed by Dorsey Wright. It began on March 1, 2007. The second is the AQR Large Cap Momentum (AMOMX) mutual fund. It began on July 9, 2009.
AMOMX had an annual return of 14.55% since its inception, while its chosen Russell 1000 Growth benchmark returned 16.11%. This is an average annual shortfall of 1.56%. Besides managing seven momentum mutual funds, AQR uses momentum with their multi-style funds and large hedge fund. AQR spreads their large-cap U.S. momentum holdings among 496 stocks. This is half the fund’s available universe of 1000 stocks. Research by Alpha Architect shows that momentum works best with focused portfolios of 50 or fewer stocks. Why wouldn't AQR use more focused portfolios if there is little impact from trading momentum stocks?
Ten years of underperformance for PDP and 8.5 years for AMOMX are not long enough to draw meaningful conclusions. But there is another possible explanation for underperformance other than or in addition to trading costs. Momentum itself may have lost its mojo since it became widely known and followed in the 1990s.
Battacharya, Li & Sonaer (2016) found that momentum profits from U.S. stocks have been insignificant since the late 1990s. Robert Arnott of Research Affiliates said their research shows stock momentum has underperformed since 1993 [2]. This was the same year Jegadeesh & Titman published their seminal study on stock momentum. Hwang & Rubesam (2013) showed that the momentum premium for stocks disappeared in the early 1990s.
Multi Factor Portfolios
They determined that quality, value, and momentum are a non-robust combination. Why is this important?
However, low volatility is a problematic factor. The first cautionary sign is a chart of pre-1967 performance in the appendix of Novy-Marx’s (2016) paper “Understanding Defensive Equity.” Volatility and beta are estimated using daily data from the prior year when available. Otherwise, Novy-Marx uses 5 years of monthly data.
There is little difference between the lowest and highest volatility quintiles. With respect to beta, low beta is the worst performer, while high beta turns in the second-best performance.
Garcia-Feijoo, Kochard, Sullivan & Wang (2015) in “Low-Volatility Cycles: The Influence of Valuation and Momentum on Low-Volatility Portfolios,” showed that the excess return from low-volatility is reliably positive only when low-volatility stocks are much cheaper than high volatility stocks as shown by a high book-to-price (B/P) ratio.
Using U.S. stock data from 1929 through 2010, van Vliet (2012) found low-volatility has had time-varying exposure to the value factor. When low-volatility stocks had value exposure, they returned an average of 9.5% annually versus the market’s 7.5%. But when low-volatility stocks had growth exposure, they returned 10.8% annually versus the market’s 12.2%.
High volatility stocks with low short interest had extraordinarily positive returns. High volatility stocks with high short interest had extraordinarily poor returns. Low volatility stocks had a similar, but less dramatic, disparity in performance based on short interest. Short interest has had a large impact on low-volatility performance.
Summarized below are the issues associated with the low-volatility premium:
• Absent in higher priced stocks
• Exists mostly on the short side
• Largely offset by transaction costs
• Reliably positive only when cheap
• Not present in equal weight portfolios
• Present only in the first month after formation
It is not surprising that investors are more risk-averse now. They have experienced two bear markets over the past 20 years where stocks lost half their value.
Jan 1934 – Dec 2014
|
S&P 500
|
Low-Volatility
|
Absolute Momentum
|
CAGR
|
11.1%
|
12.3%
|
13.2%
|
Standard Deviation
|
15.8%
|
12.3%
|
11.3%
|
Sharpe Ratio
|
0.53
|
0.73
|
0.85
|
Worst Drawdown
|
-50.9%
|
-40.1%
|
-31.5%
|
Worst U. S. Bear Markets 1934- 2014
S&P 500
|
Low-Volatility
|
Absolute Momentum
|
|
Jul 2007 – Feb 2009
|
-50.9%
|
-38.3%
|
+5.0%
|
Apr 2000 – Sep 2002
|
-43.8%
|
+24.2%
|
+17.4%
|
Jan 1973 – Sep 1974
|
-41.8%
|
-37.5%
|
+2.0%
|
Nov 1968 – Jun 1970
|
-29.3%
|
-22.9%
|
|
Mar 1937 – Mar 1938
|
-50.5%
|
-40.1%
|
-9.1%
|
Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Please see our Disclaimer page for more information.
The low-volatility portfolio outperformed the S&P 500. But absolute momentum was more effective at both reducing drawdown and enhancing return.
Source: Greyserman & Kaminski (2014), Trend Following with Managed Futures
The viability of trend-following momentum back to the 13th century is strong evidence that it is not an artifact of data mining.
There are plenty of research papers and articles extolling the virtues of factor based investing. Factors look good in theory and on paper. But whether they provide superior risk-adjusted real world returns is another story [3]. Those promoting factor investing may be taking a page out of the political playbook. If you say something repeatedly, people will start to believe you and ignore contrary evidence. Investors may think they can do better than market by using higher cost factor-based investing. But not everyone can be above average.
[1] For more on the the Russell 2000 index and its issues, see Alpha Architect's "A Better Way to Buy the Russell 2000." Other small cap indices have done better than the Russell 2000, but have still been disappointing versus large cap indices.
[2] https://ritholtz.com/2018/07/mib-rob-arnott-research-affiliates/
[3] When I looked at the 41 factor-based funds with more than 5 years of price history, only three had an alpha that was statistically significant at the 5% confidence level.
