Simulated portfolios based on momentum
add remarkable value, in most time periods and in most asset classes,
all over the world; however, live results for mutual funds that take on a
momentum factor loading are surprisingly weak.
A primary contributor to the performance
gap between the standard momentum factor’s live and theoretical results
is the price impact of trading costs associated with the strategy’s high
turnover.
In addition to thoughtful implementation,
relying on a strong sell discipline and avoiding stocks with stale
momentum can help investors capture more of the benefits of the momentum
factor.
There’s many a slip twixt cup and lip.— Old English proverb
On paper, momentum is one of the most
compelling factors: simulated portfolios based on momentum add
remarkable value, in most time periods and in most asset classes, all
over the world. So, our title may seem unduly provocative. However, live
results for mutual funds that take on a momentum factor loading are
surprisingly weak.1 No US-benchmarked mutual fund with
“momentum” in its name has cumulatively outperformed its benchmark since
inception, net of fees and expenses. Worse, because the standard
momentum factor gave up so much ground in the last momentum crash of
2008–2009, it remains underwater in the United States, not only compared
to its 2007 peak, but even relative to its 1999 performance peak. This
means 18 years with no alpha, before subtracting trading costs and fees!2
To be sure, most advocates of momentum
investing will disavow the standard model, and will claim they use
proprietary momentum strategies with better simulated, and perhaps
better live, performance. A handful (especially in the hedge fund
community) may be able to point to respectable fund performance, net of
trading costs and fees. But a careful review of the competitive
landscape reveals that most claims of the merits of momentum investing
are not supported by data, particularly not live mutual fund results,
net of trading costs and fees.3
The three traps for momentum investing are
1) high turnover, in crowded trades, which leads to high trading costs;
2) a careless sell discipline, because momentum’s profits accrue for
months, not years, and then reverse course; and 3) repeat winners
(and losers), which have been soaring (or tumbling) for so very long
they enjoy little or no momentum follow-through. Each of these traps can
be avoided. By evading these traps, we can narrow the gap between paper
and live results. Yes, momentum can probably be saved, even net of fees
and trading costs. This is the fourth and final article in the Alice in Factorland series.4
Momentum is the tendency for rising stock prices to continue rising
and for falling stock prices to continue falling. Why should stocks
behave this way? Human nature conditions us to extrapolate our recent
past experience: we want more of anything that has given us great joy
and profit, and we want less of whatever has given us pain and losses.
For this simple reason, momentum investing is popular. The mere act of
buying recent winners and selling recent losers is both comfortable and
enticing, and many investors act accordingly. Thus, human behavior may
play a large role in fueling price momentum and creating a
self-fulfilling prophecy. This may be the reason the momentum factor has
enjoyed persistent success for so many years, in so many geographic
regions. Momentum’s steam is able to power on, however, only until valuations are stretched so far that relative valuation overcomes the forces of momentum.
Momentum: Toward a Better Understanding Whereas investors have pursued momentum investing for centuries,
the “science” of understanding momentum is rather new, dating back only
about a quarter-century.5 Our understanding has been
improved through the work of many researchers, in multiple ways, ranging
from correlations between past and subsequent returns to long–short
factor portfolios.6
The most convincing explanations for momentum lie in the behavioral realm.7
Three articles are frequently cited as offering the best explanation of
the momentum effect. The three underlying theories do not contradict
each other and each is likely to be partially responsible for the
momentum effect. The first article, Barberis, Shleifer, and Vishny
(1998), suggests that when earnings surprises reach the market,
investors do not pay them enough attention, and the stock price
initially underreacts to the news.8 When the initial news is
followed by confirming news, the stock price adjusts in the same
direction (momentum), often to the point of over-extrapolation to where
the stock price is poised for mean reversion.
Daniel, Hirshleifer, and Subrahmanyam (1998) propose a second
explanation, arguing that investors overestimate precision of their
private information and underestimate precision of public information as
a result of biased self-attribution and overconfidence.9
Overconfidence encourages investors to overestimate the accuracy of
their insights or private information, which causes them to trade more
aggressively. In the case of biased self-attribution—when success is
attributed to superior skill, but failures to bad luck—investors tend to
pay attention to confirmatory signals and ignore conflicting ones,
which again inspires more aggressive trading. Both behaviors lead to
initial momentum and subsequent mean reversion in prices.
The third explanation, a model proposed by Hong and Stein (1999),
observes that information is not evenly available to all market
participants. The model describes two groups of traders: “news
watchers,” who have better access to private information about specific
stocks, but are not well versed in market dynamics, so are not able to
extract information from prices; and “momentum traders,” who do not have
private information, but are well aware of market dynamics. The gradual
release of private information leads to an initial underreaction from
the news watchers, followed by an overreaction when the momentum traders
try to profit by trend chasing, which in turn is followed by price
reversion to the mean.10
Momentum in stocks is perhaps one of the best-performing signals on paper:
it has a better risk–return tradeoff than most known equity market
factors. A momentum factor pairs a long portfolio of stocks whose prices
have recently been soaring relative to the market, with a short
portfolio of stocks whose prices have been sharply underperforming the
market.11 Our research, discussed in this article, considers
three types of momentum: 1) standard momentum, which we define as the
trailing 12-month return, excluding the most recent month; 2) fresh
momentum, capturing stocks in the early part of their momentum
trajectory (which we define as standard momentum conditioned on the
opposite prior-year relative return); and 3) stale momentum, capturing
stocks in the later part of their momentum trajectory (which we define
as standard momentum conditioned on the same direction of the prior-year
relative return).
In Figure 1, we compare the cumulative relative performance of
the long portfolio (winners) versus the short portfolio (losers) (i.e.,
the standard momentum factor), on a log scale, for five geographic
regions, and globally, since 1990. Momentum was first documented by
Jegadeesh and Titman in 1993 and, anecdotally, started becoming more
popular as a quantitative investment strategy after about 1997. Before
that time, although performance-chasing strategies were commonplace, and
momentum was an element of many investment managers’ thinking, formal momentum strategies existed mostly as just a backtest.
Momentum appears to be successful, everywhere except Japan. A closer
look, however, reveals that the cumulative return for the standard
momentum factor in the United States and Japan is no better now than in
1999, and for global markets remains below its 2007 peak. Two momentum
crashes, in 2002 and 2009, took their toll on momentum factor
performance in the United States by 28% and 54%, respectively, and the
factor has not yet recovered. A momentum strategy is very vulnerable to
crashes that tend to occur when the momentum trade is relatively
expensive and in periods of heightened volatility. Momentum performance
has also shown dismayingly high global correlation—especially during the
crashes—since about 1999. All six regions show a momentum crash at the
end of the tech bubble, at the end of the 2000–2002 bear market, and a
big crash in 2009. There was nowhere to hide.
Figure 2 compares, for the same six geographic regions, the
Sharpe ratios of the relative performance of the long versus short
portfolios for momentum (winners minus losers, or WML) and the original
Fama–French factors, size (small cap minus big cap, or SMB) and value
(high book-to-price ratio minus low, or HML). Momentum dominates
everywhere except Japan.12 Since first documented in US stocks, the momentum effect has also been documented in many other asset classes.13
Again, on paper, momentum looks fantastic! Sadly, live results in the
real world hint at trouble for momentum investors, net of trading costs....
Factor tilt strategies have generally produced less alpha in live
portfolios compared to theoretical factor long–short paper portfolios
and have largely been unsuccessful in replicating smart beta strategies.
End-investors, consequently, often reap a much smaller return from
factor exposure than they expect. The winning approach to factor
investing is buying the losers: Past negative performance appears to be predictive of positive future returns.
Performance chasing in manager selection is a reliable path to poor
results. But by combining factor valuation with past performance,
investors gain a richer toolkit for making well-informed allocation
decisions among smart beta managers.
Our analysis of three first-generation smart beta strategies shows
factor-replicated portfolios are ineffective substitutes for their smart
beta counterparts, exhibiting poorer performance, high turnover, and
low capacity.
Managers who favor high factor loadings on market beta, value, or
momentum generally do not derive nearly as much incremental return as
theoretical factor return histories would suggest, and the culprit
appears to be the real-world costs of implementation.
