Sunday, February 25, 2018

Alice In Factorland: Can Momentum Investing Be Saved?—Arnott et al

Possibly the last persistent factor.
From Research Affiliates, October 2017:

By Rob Arnott Vitali Kalesnik Engin Kose Lillian Wu
Key Points
  • 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....
The series:: 
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.

September 2017
The Folly of Hiring Winners and Firing Losers
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.
May 2017 
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. 

April 2017 
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.