February 22, 2017
Author, consultant and public speaker on momentum investing
Factor Zoo or Unicorn Ranch?
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 passive index funds in the 1970s?
No one then wanted to be just average. This ironically gave 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 on the results of theoretical asset pricing models, many of which have failed empirically.
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. Let us see.
Does Size Matter?
The small cap size premium was the first identified factor. Banz wrote about it 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.
Russell 2000 underperformed the S&P 500 by 1.3% annually and had a substantially 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 usually 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?
According to Shumway and Warther (1998) in “
The Delisting Bias in CRSP's Nasdaq Data and its Implications for the Size Effect
”, small caps originally showed a premium because they had an upward bias due to inaccurate returns on delisted stocks. When this bias was removed, the small cap anomaly disappeared.
In “ Transaction Costs and the Small Firm Effect ,” Stoll and Whitney (1983) showed that transaction costs offset a significant portion of the small cap size premium.
Some researchers say a small cap premium still exists if you combine size with other factors. In other words, size can be important depending on what you can do with it. We will look at using value.
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 then 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. This, however, may be a case of theoretical results differing from actual ones.
A few months back, I referenced a study by Loughran and Hough that is worth mentioning again. These authors looked at the performance of all U.S. equity funds from 1962 through 2001. They used the prior 36 months to sort funds by style (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%. This large cap outperformance of 0.11% of value over growth was insignificant.
With small caps, the authors were very surprised at the results. 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 and Moskowitz (2012) presented evidence that the value premium is insignificant among the two largest quintiles of stocks and is concentrated among small cap stocks. So, why 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 who want to believe in the superiority of value or small cap investing point to the performance of the Dimensional Fund Advisors (DFA) funds. These have done well relative to the market, but that is not just due to their value or size orientations .
DFA serves as a market maker in the stocks they hold. This means they can be patient when adjusting portfolio positions. T his reduces their c osts of trading in exchange for some additional tracking error. Using a buy-sell range also reduces turnover and tra ding costs. Holding a very large number of securities red uces the price impact of DFA's t rading.
But lower trading costs are not the only reason DFA has done better than some other fund managers. DFA has benefit ed from not being tied to a n index and thereby subject to front running costs . DFA has also been aggressive in lending securities. Additionally, DFA has avoided IPOs and stocks with high borrowing costs.
Stocks with high borrowing costs usually 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 significant negative abnormal returns.
Source: Boehmer et al. (2009), “The Good News in Short Interest”
Source: Boehmer et al. (2009), “The Good News in Short Interest”
DFA has gained a substantial advantage by avoiding stocks that have been heavily shorted.
Risk Factors
People may not remember that factors were once called “risk factors.” Value funds are known for their high tracking error that can persist for 10 years or more . 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. That can make them less liquid and more expensive to trade.
In “ A Taxonomy of Anomalies Costs and their Trading Costs ” Novy-Marx and 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 and 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
|
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. It is with investability that momentum falls short.
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 in a modest number of volatile stocks is bound to 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. The first to study this were Lesmond et al. (2002) in “ The Illusive Nature of Momentum Profits .” They found that momentum creates an illusion of profit opportunity when none really exists. Two years later, Korajcyzk and Sadka (2004) determined that profit opportunities could vanish once the amount invested in momentum-based strategies reaches $5 billion.
Counter to these findings, Frazinni et al . (2012) from AQR, based on 12 years of proprietary data, argued that the potential scale of momentum 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.
More recently, Ratcliffe et al . (2016) from BlackRock also suggested that a greater amount of capital could be traded using momentum. But they also made this disclaimer, “The exercise we conduct in this paper is hypothetical and involves several unrealistic assumptions.”
In contrast to these two studies, Fisher et al . (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 and Velikov (2015) also determined the capacity for stock momentum before profits would vanish.
This is close to the $5 billion amount where Korajcyzk and Sadka said momentum profits would disappear. Novy-Marx and Velikov used an optimization algorithm to keep them in trades longer, as discussed by Frazzini et al.
This is a conservative listing. It does not include mutual funds, managed accounts, or hedge funds. Even so, it exceeds the level of assets where both Novy-Marx and Velikov and Korajcyzk and Sadka say momentum profits would 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. The expected future growth in factor-based investing should make this situation worse.
For some guidance, let us 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.
Multi Factor Portfolios
They determined that quality, value, and momentum are a non-robust combination. Why is this important?
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. These results contradict those found by Novy-Marx and others since 1968.
Garcia-Feijoo et al. (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 great impact on low-volatility performance.
Summarized here 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
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.
Jan 1934 – Dec 2014
| S&P 500
| Low-Volatility
| Absolute Momentum
|
CAGR
| 11.1%
| 12.3%
| 13.2%
|
Standard Deviation
| 15.8%
| 12.3%
| 8.5%
|
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%
| -2.9%
|
Mar 1937 – Mar 1938
| -50.5%
| -40.1%
| -20.4%
|
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 and Kaminski (2014),
Trend Following with Managed Futures
Conclusion
[1] For more on the the Russell 2000 index and its issues, see Alpha Architect's "
A Better Way to Buy the Russell 2000
".
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