Source: Moskowitz, T. J., Ooi, Y. H. & Pedersen, L. H. (2012). Journal of Financial Economics 104(2), 228–250. DOI: 10.1016/j.jfineco.2011.11.003
TL;DR
An asset's own past 12-month excess return positively predicts its next-month return. A strategy that goes long instruments with positive trailing-year returns and short those with negative returns — each scaled to a constant ex-ante volatility — earns large, diversified abnormal returns across 58 liquid futures/forwards spanning equity indices, currencies, commodities, and bonds, with little exposure to standard factors and the best performance during extreme markets ("crisis alpha"). Twelve-month TSMOM is profitable for every one of the 58 contracts.
What anomaly it documents
Predictor: an instrument's own past 12-month excess return.
Direction: positive — past 12-month returns predict continuation for ~1 to 12 months, then partial reversal over longer horizons (consistent with initial underreaction and delayed overreaction).
Shape: sign-based (long if past-year return > 0, short if < 0). Pooled-regression t-statistics are significantly positive for the first ~12 lags and turn negative thereafter.
Distinct from cross-sectional momentum: it uses an asset's own return, not its rank vs peers. TS-MOM loads positively on cross-sectional momentum (UMD) but is not subsumed by it.
How to construct it
Signal: sign of the past 12-month excess return of each instrument.
Position sizing: scale each position to a target ex-ante volatility (the paper sizes each to ~0.60% so the combined diversified portfolio targets ~10% annual volatility; ex-ante vol from a univariate GARCH-type estimate), so no single instrument dominates.
Universe: diversify across asset classes (country equity-index, currency, commodity, and sovereign-bond futures/forwards).
Rebalancing: monthly.
Evidence and replication
Sample: January 1965 – December 2009 (the diversified-factor analysis relies on 1985–2009 to ensure a comprehensive instrument set of all 58 contracts).
A diversified TS-MOM portfolio earns a large, significant alpha to the Fama-French + UMD factors of about 1.26% per month (≈3.8% per quarter), and 0.94% per month (≈2.65% per quarter) against the Asness-Moskowitz-Pedersen value-and-momentum-"everywhere" factors — i.e., not explained by cross-sectional momentum.
The coefficient on the squared market return is significantly positive: TS-MOM pays off most in the most extreme market episodes (up or down), giving its hedge-like, "crisis alpha" profile.
Returns are linked to the trading of speculators vs hedgers: speculators appear to profit from TS-MOM at hedgers' expense. A return decomposition (Lo-MacKinlay / Lewellen) attributes most of the effect to positive own-return autocovariance, not lead-lag cross-correlations or mean dispersion.
Why it might work
Underreaction then overreaction: investors update slowly to news, then extrapolate, producing trends that persist before reversing.
Risk transfer / hedging demand: speculators earn a premium for absorbing hedgers' positions.
Crisis alpha: trends tend to persist during prolonged drawdowns, so trend following often profits when equities fall — a diversification benefit, not just a return source.
Limitations and risks
Whipsaw: in choppy, trendless, mean-reverting markets the strategy bleeds via repeated reversals.
Capacity and costs: strong in liquid futures, but turnover and slippage matter at scale.
Robustness debate: Huang, Li, Wang & Zhou (2019), Time Series Momentum: Is It There?, argue the effect is statistically fragile once the near-always-long tilt and time-varying means are accounted for — a useful caution that the headline result is sensitive to specification.
Key references
Moskowitz, T., Ooi, Y. H. & Pedersen, L. (2012) — Time Series Momentum — Journal of Financial Economics
Hurst, B., Ooi, Y. H. & Pedersen, L. (2017) — A Century of Evidence on Trend-Following Investing — Journal of Portfolio Management
Huang, D., Li, J., Wang, L. & Zhou, G. (2019) — Time Series Momentum: Is It There? — Journal of Financial Economics
Asness, C., Moskowitz, T. & Pedersen, L. (2013) — Value and Momentum Everywhere — Journal of Finance
Reference replication on ConvexPi
An open, verified replication of this strategy is maintained at convexpi/replications. It recomputes the strategy from underlying building blocks and scores it out of sample (the McLean & Pontiff test):
