Not an implementation claim. Both momentum families are heavily documented and heavily crowded; this guide teaches their mechanics — the construction differences that determine risk, evidence standards, and failure modes — not a recommendation to run either.
This is the applied companion to cross-sectional vs time-series predictability: momentum is the flagship example of the distinction, big enough to earn its own mechanics.
Construction, side by side
| Cross-sectional (relative strength) | Time-series (trend) | |
|---|---|---|
| Signal | Asset’s return rank among peers | Sign of asset’s own past return |
| Position | Long top decile, short bottom | Long/flat/short each asset independently |
| Net exposure | ~zero by construction | Structurally directional; varies with regime |
| Natural habitat | Single-asset-class universes (equities) | Multi-asset futures (managed futures) |
| Characteristic crash | Momentum crashes: short-leg rips in sharp reversals (2009’s is canonical) | Whipsaw at trend turns; gap reversals (2020’s V) |
| Skew | Strongly negative | Historically positive-ish (long-vol-like in crises) |
The skew line is the deepest difference: cross-sectional momentum sells crisis-reversal insurance (it’s short the recently-crushed names that bounce hardest), while time-series momentum has historically provided crisis offset (it’s short whatever was falling) — the “crisis alpha” claim of the managed-futures industry. Same word, opposite tail products.
The evidence bases differ in kind
Cross-sectional momentum’s evidence: large effective samples (breadth × time), robust across countries and zoo studies — one of the few survivors of harsh corrections — but with well-documented post-publication decay and brutal crash episodes. Time-series momentum’s evidence: centuries-long backtests across asset classes look imposing, but the effective sample is trend episodes, not months — a few dozen independent bets per asset class — and the strategy’s returns concentrate in a handful of big-trend years, making its statistics regime-mixture averages. Hold TSM papers to episode-counting standards; their t-stats flatter.
Mechanics that decide live behavior
- Lookback/holding grids are trial factories: 12-1, 6-1, vol-scaled, skip-months — every published variant is a cell in a searched grid; apply the plateau standard and DSR counting.
- Vol scaling changes the strategy: sizing positions inverse to volatility (near-universal in modern TSM) embeds a volatility-targeting layer with its own leverage dynamics — and much of “improved” TSM performance attributes to the vol layer, not the trend signal.
- Turnover asymmetry: CSM rebalances a whole book monthly (heavy turnover tax); TSM trades only at signal flips but concentrates its trading in exactly the volatile turns where costs spike.
Common ways each fails in production
Cross-sectional: the crash arrives via the short leg during broad reversals — junk rallies 40% in a quarter while your longs lag; borrow on crushed names vanishes precisely then; and sector-neutralization (the standard crash mitigant) taxes the gross every calm month to soften the rare bad one.
Time-series: years of whipsaw bleed between trends (the strategy’s “cost of carry” is psychological and financial); position flips at turns execute into the worst liquidity; and the crisis-alpha promise is conditional on crises trending rather than gapping — a V-reversal punishes both directions in weeks.
Both: crowding. Momentum is the most-run signal family on earth; its unwinds are correlated events, and your backtest’s regime mix contains a limited number of them.
Questions to ask a momentum paper
- Which family — and if both appear, are results kept separate?
- What share of TSM profits comes from the top 5 trend episodes?
- Is the crash episode in the sample, and does the paper report performance through it?
- How much of the improvement over vanilla momentum is the vol-scaling layer?
What our scoring captures
Momentum papers are a rigor-axis showcase: the genre’s best work reports crash episodes, subperiods, and costs unprompted, and ranks accordingly in the factor hub; the genre’s worst reports a 40-year gross Sharpe and a lookback grid’s best cell. The two-family distinction itself is what to verify first — papers that pool the families have averaged two different risk products into one flattering number.
The general distinction: cross-sectional vs time-series predictability → · The layers on top: volatility targeting → · turnover →