Factor Zoo or Unicorn Ranch?

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.
 
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.


Here is a chart showing the amount of capital invested now in dedicated U.S. large and mid-cap value funds. It does not include managed accounts, hedge funds, and many of the other 400+ funds having the word “value” in their names.

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 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. 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.
Here is a table of the amounts invested in U.S. momentum exchange traded ptoducts:
 
Here is a table from the most recent study of factor capacity. It is by Beck et al. (2016) in “ Will Your Factor Deliver? An Examination of Factor Robustness and Implementation Costs .” They used a different method than Novy-Marx and Velikov to compute factor capacity.
This situation is much like the one in my last post . Those offering commodity products to the public said passive commodities are still a worthwhile diversification. But a larger number of independent researchers, with no products to promote, said the opposite. Who shall we believe?

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.
From its start through January 2017, PDP had an annual return of 6.44%, while its Russell 3000 Growth benchmark returned 8.67%. This is an average annual return shortfall of 2.23%. AMOMX had an annual return of 14.55% since its inception, while its Russell 1000 Growth benchmark returned 16.11%. This is an average annual shortfall of 1.56%. These are just short periods of time to evaluate performance. But it does suggest caution.
Besides managing seven momentum mutual funds, AQR uses momentum with their multi-style funds and large hedge fund. Even though the Frazinni et al. paper said stock momentum could handle considerably more capital, AQR now spreads out their momentum holdings to 496 stocks. This is half the fund’s available universe of 1000 stocks.
Quality
We can find intuitive reasons why size, value, and momentum might provide a premium based on  risk or behavioral factors. This becomes more challenging with quality. There is no reason to believe that higher quality stocks are riskier than lower quality ones. It is also hard to find behavioral factors that would explain why one would neglect high quality stocks causing them to command a behavioral premium. It is not surprising then that there are few signs of a premium or premium persistence across multiple definitions of quality.
Cakici (2015) found only marginal evidence that gross profitability (a subset of quality) exists globally. Hsu and Kalesnik (2014) reported in “ Finding Smart Beta in the Factor Zoo ” that two measures of quality (gross profitability and ROE) in international stocks from 1987 through 2013 showed no significant improvement in Sharpe ratio over lower quality stocks. They also found no evidence of a significant advantage in U.S. stocks using four measures of quality from 1967 through 2013:
West et al. (2016) in “ How Not to Get Fired in Smart Beta Investing ” included quality in a multi-factor environment.
More multi-factor ETFs were created in the last two years than any other category of ETF. In a January 2016 survey by Greenwich Associates, 57% of institutional investors said they used multi-factor funds in some way now. 48% said they plan to increase their use soon.
Multi-factor portfolios can have less volatility and reduced tracking error compared to single factor portfolios. In “ A Smoother Path to Outperformance with Multi-Factor Smart Beta Investing ,” Brightman et al. (2017) show that annual volatility drops from 16.4% for an average factor to 15.2-15.6% for a multi-factor portfolio. This reduction is desirable. But those familiar with portfolio theory know that factor portfolio returns are a weighted average of factor returns. If factor returns are disappointing due to lack of scalability (value and momentum), data accuracy and persistence (size), or robustness (quality), multi-factor returns will also be disappointing.
A multi-factor approach can also cut benchmark tracking error in half. But would it really matter if 10 years of factor-based underperformance were reduced to 5 years? Small cap value once underperformed the market for 42 consecutive months. If that had been 21 months, would it have made much difference? Most investors would have been gone long before then.
Low Volatility
In a Brown Brothers Harriman survey of 175 financial advisors and institutional investors, low volatility was the most popular smart-beta choice. 44% of respondents chose low volatility over other factors. But low volatility/low beta is also the most 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.
Novy-Marx also pointed out that the vast majority of low volatility profits since 1968 came from the short side. He showed that most of the benefits from low volatility investing could be achieved simply by eliminating small growth stocks from one’s portfolio.
In “ The Limits to Arbitrage and the Low-Volatility Anomaly ,” Li et al. (2014) found that the excess return associated with low volatility was present only in the first month after portfolio formation. Additionally, excess return has been weak since 1990. They also found that the low volatility premium was largely offset by high transaction costs. It was largely eliminated if you omitted stocks priced under $5 per share. It also was not present in equal weight portfolios.

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.
Getting back to the idea of short interest, Jordan and Riley (2016) show in “ The Long and Short of the Vol Anomaly ,” that short interest dominates the low-volatility effect from July 1991 through December 2012.

•    Weak since 1990
•    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
Less Downside Risk
With all these negatives, one might wonder why low-volatility has been the fastest growing factor. This may have to do with investors thinking low-volatility has less risk exposure than the market. 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.
How much risk reduction is there really from low-volatility investing? To find out, I accessed the online data provided by van Vliet and De Koning. They used the 1000 largest NYSE, AMEX, and NASDAQ stocks over $1 per share in the CRSP database. Stocks were equal weighted and sorted into deciles based on their volatility over the past 36 months. These portfolios were rebalanced quarterly.
I accessed the data starting in January 1934 to avoid the extreme returns of the late 1920s and early 1930s. I used the top two low-volatility deciles, representing 200 stocks, which is a typical fund-size portfolio. I compared the performance of the low-volatility portfolio to the S&P 500 and to a robust version of trend following absolute momentum that I use in my proprietary dual momentum models. Absolute momentum holds the S&P 500 when the model is in stocks and intermediate U.S. Government bonds when the model is out of stocks.  

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%

Conclusion
Each factor that I looked at failed to hold up under one or more of these tests: robustness, persistence, pervasiveness, intuitiveness, and investabilty. But short interest and trend appear to be effective ways to enhance portfolios. Research has shown that geographically diversified index momentum is also effective and without the scalability issues of stock momentum..
The usual factors may look good in theory and on paper. But the jury is out on whether or not they can provide superior risk-adjusted real world returns. Those who are prudent and truly interested in evidence-based investing will remain cautious. Others will continue to accept what they have been told by product sponsors and a small number of academic theorists.