Trend Following Works
The academic research has provided investors with strong evidence that there is a small group of factors—or sources of returns—that have provided higher returns over the long term. To be considered among this group, the evidence should have the following characteristics:
- Persistence—it holds across long periods of time and various economic regimes.
- Pervasive—it holds across countries, regions, sectors and even asset classes.
- Robust—it holds for various definitions (for instance, there’s a value premium whether we measure value by price-to-book, earnings, cash flow or sales).
- Investable—it holds up not just on paper, but also after considering trading costs.
- Intuitive—there are logical, risk-based (economic) or behavioral-based explanations for the premium and why it should continue to exist.
- It isn’t subsumed by other well-known factors.
While there have been more than 300 factors identified in the literature—so many that John Cochrane called it a “factor zoo”—there are only a handful that meet these six criteria. Ian D’Souza, Voraphat Srichanachaichok, George Wang and Chelsea Yaqiong Yao—authors of the January 2016 study “The Enduring Effect of Time-Series Momentum on Stock Returns over Nearly 100 Years”—provide evidence supportive of the view that time-series momentum (also referred to as trend-following) is one of the few that meet all of these conditions. (Time-series momentum examines the trend of an asset with respect to its own past performance; cross-sectional momentum compares an asset with respect to another asset.)
The authors’ study covered the 88-year period from 1927 to 2014. The following is a summary of their findings:
- A value-weighted strategy of going long stocks with positive returns in the prior year and going short stocks with negative returns during the same period of time produced an average monthly return of 0.55%, and was highly significant (with a t-statistic of 5.28). It has also been present following both up and downmarkets, producing an average monthly return of 0.57% (with a t-statistic of 2.09) following downmarkets, and 0.54% (with a t-statistic of 5.30) following up markets. What’s more, it was persistent across all four subperiods the authors studied, with average monthly returns of 0.69% (with a t-statistic of 2.41) in the period 1927 through 1948, 0.47% (with a t-statistic of 3.60) in the period 1949 through 1970, 0.62% (with a t-statistic of 3.84) in the period 1971 through 1992, and 0.42% (with a t-statistic of 1.91) in the period 1993 through 2014. Thus, it meets the persistence criteria.
- Time-series momentum produced positive risk-adjusted returns in all 13 international stock markets the authors examined for the period 1975 through 2014. And it was statistically significant at the 95% confidence level in 10 of the 13 countries. The highest return for a value-weighted strategy was in Denmark, where it had a monthly return of 1.15% per month (with a t-statistic of 5.06). Thus, it meets the pervasive criteria.
- Time-series stock momentum was profitable regardless of formation and holding periods for 16 different combinations. Thus, it meets the robust criteria.
- Time-series stock momentum fully subsumes cross-sectional stock momentum, while cross-sectional stock momentum cannot capture time-series stock momentum. In addition, the other common factors of beta, size and value have little power to explain time-series momentum. Thus, it meets the criteria of not being subsumed by other factors.
- Unlike with cross-sectional momentum, time-series momentum doesn’t experience losses in January (a seasonal effect) or crashes (which occur with cross-sectional momentum during reversals).
- The time-series premium can be at least partially explained by two prominent theories describing investor underreaction (both the gradual information diffusion model and what’s called the frog-in-the-pan model). For example, if time-series momentum came from gradual information flow, there should be greater time-series momentum in small stocks (for which information diffuses more slowly). However, they found that the small size group produces the highest momentum profits (0.78% per month with an associated t-statistic of 5.52), while the large size group generates the lowest momentum profits (0.47% per month with an associated t-statistic of 4.33). The frog-in-the-pan hypothesis suggests that investors are less aware of information that arrives continuously and in small amounts than they are of information that arrives in large amounts at discrete points in time. The analogy is that frogs jump out of a pan of water following a sudden increase in temperature, but underreact to the water temperature in the pan if it is brought to a boil slowly, and so are cooked. According to the frog-in-the-pan hypothesis, if investors underreact to small amounts of information that arrive continuously, this induces strong persistent return continuation. The authors found a monotonic increase in momentum profits for stocks with discrete information compared with stocks with continuous information. Thus, we have evidence that it meets the explanation criteria.
D’Souza, Srichanachaichok, Wang and Yao also examined a strategy that combined the two (time-series and cross-sectional) momentum strategies. Their dual momentum strategy buys the strongest winner portfolio and sells short the weakest loser portfolio, basically making it a market-neutral strategy. They found that the average annualized return of the dual momentum strategy was 22.4%.
The strategy, however, was associated with high volatility (37.5% per year). The data was statistically significant and also held up to tests that employed different combinations of formation and holding periods.
Using historical data from a number of sources, AQR Capital Management constructed a time-series momentum strategy all the way back to 1880 and found that it was consistently profitable throughout the past 135 years. AQR’s researchers constructed an equal-weighted combination of one-, three- and 12-month time-series momentum strategies for 67 markets across four major asset classes (29 commodities, 11 equity indexes, 15 bond markets and 12 currency pairs) from January 1880 to December 2013. (Full disclosure: My firm, Buckingham, recommends AQR funds in constructing client portfolios.)
Their results include implementation costs based on estimates of trading costs in the four asset classes. They further assumed management fees of 2% of asset value and 20% of profits, the traditional fee for hedge funds. Following is a summary of AQR’s findings:
- The performance was remarkably consistent over an extensive time horizon that includes the Great Depression, multiple recessions and expansions, multiple wars, stagflation, the global financial crisis of 2008, and periods of rising and falling interest rates.
- Annualized gross returns were 14.9% over the full period, with net returns (after fees) of 11.2%, higher than the return to equities, but with about half the volatility (an annual standard deviation of 9.7%).
- Net returns were positive in every decade, with the lowest net return being the 5.7% return for the period beginning in 1910. There were also only five periods in which net returns were in the single digits.
- There was virtually no correlation to either stocks or bonds. Thus, the strategy provides strong diversification benefits while producing a high Sharpe ratio of 0.77. Even if future returns are not as strong, the diversification benefits would justify an allocation to the strategy.
Researchers at AQR observed that “a large body of research has shown that price trends exist in part due to long-standing behavioral biases exhibited by investors, such as anchoring and herding, as well as the trading activity of non-profit-seeking participants, such as central banks and corporate hedging programs. For instance, when central banks intervene to reduce currency and interest-rate volatility, they slow down the rate at which information is incorporated into prices, thus creating trends.”
A Historical Trend
The authors then continue: “The fact that trend-following strategies have performed well historically indicates that these behavioral biases and non-profit-seeking market participants have likely existed for a long time.”
They noted that trend-following has done particularly well in extreme up or down years for the stock market, including the most recent global financial crisis of 2008. In fact, they found that during the 10 largest drawdowns experienced by the traditional 60/40 portfolio over the past 135 years, the time-series momentum strategy experienced positive returns in eight of 10 of these stress periods and delivered significant positive returns during a number of these events.
AQR also noted that these results were achieved even with a “2-and-20”fee structure. And today there are funds that can be accessed with much lower costs (including AQR’s own Managed Futures Fund, AQMIX, which has an expense ratio of 1.23%, as well as the R6 version of the fund, AQMRX, which has a lower expense ratio of 1.15%).
Additionally, AQR has found that their actual trading costs have been only about one-sixth of the estimates used for much of the sample period (1880-1992) and approximately one-half of the estimates used for the more recent period (1993-2002). These results demonstrate that time-series momentum meets the last of our six criteria: investability and implementability.
As an investment style, trend following has existed for a very long time. The data from the two aforementioned studies provides strong out-of-sample evidence beyond the substantial evidence that already existed in the literature.
It also provides consistent, long-term evidence that trends have been pervasive features of global markets. Another paper that reviews this topic is “Time-Series Momentum” by Tobias Moskowitz, Yao Hua Ooi and Lasse Pedersen, which was published in the May 2012 issue of the Journal of Financial Economics.
Addressing the issue of whether we should expect trends to continue, AQR’s researchers concluded: “The most likely candidates to explain why markets have tended to trend more often than not include investors’ behavioral biases, market frictions, hedging demands, and market interventions by central banks and governments. Such market interventions and hedging programs are still prevalent, and investors are likely to continue to suffer from the same behavioral biases that have influenced price behavior over the past century, setting the stage for trend-following investing going forward.”
The bottom line is that given the diversification benefits, and the downside (tail risk) hedging properties, a moderate portfolio allocation to trend-following strategies merits consideration.
This commentary originally appeared March 4 on ETF.com
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