Time-series momentum examines the trend of an asset with respect to its own past performance. This is very different than cross-sectional momentum (often referred to as Carhart momentum), which compares the performance of an asset with respect to the performance of another asset.
Ian D’Souza, Voraphat Srichanachaichok, George Jiaguo Wang and Chelsea Yaqiong Yao, who authored the 2016 study “The Enduring Effect of Time-Series Momentum on Stock Returns Over Nearly 100 Years,” provide evidence that supports the view that time-series momentum (also referred to as trend-following) is one of the few investment factors that meet five important criteria for inclusion in a portfolio; specifically, it is persistent, pervasive, robust, investable and intuitive. Their study covered the 88-year period from 1927 to 2014.
Study Results
Following is a summary of their findings:
- A value-weighted strategy of going long stocks with positive returns in the prior 12 months (skipping the most recent month) 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 down markets, producing an average monthly return of 0.57% (with a t-statistic of 2.09) following down markets 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.
- 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.
- Time-series stock momentum was profitable regardless of formation and holding periods for 16 different combinations.
- The common investment factors of market beta, size and value have little power to explain time-series momentum.
- Unlike with cross-sectional momentum, time-series momentum does not experience losses in January (a seasonal effect) or crashes (which occur with cross-sectional momentum during reversals).
- The time-series momentum premium can be at least partially explained by two prominent theories describing investor underreaction (both the gradual information diffusion model and what is 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). Indeed, the authors found 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 a frog will 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, it induces strong persistent return continuation. The authors found a monotonic increase in momentum profits for stocks with discrete information compared to stocks with continuous information. Thus, we have evidence that time-series momentum meets the explanation criteria.
