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name: momentum-strategies
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name: momentum-strategies description: Momentum strategy design — time-series, cross-sectional, and dual momentum approaches. Use when building momentum-based systems.
Time-series momentum (TSMOM) refers to the tendency of an asset's own past returns to predict its future returns. If the asset went up, buy it. If it went down, sell/short it.
Cross-sectional momentum (XSMOM) ranks assets by past returns and goes long winners, short losers.
Combines time-series and cross-sectional momentum:
Step 1: Compute 12-month return for each asset in universe
Step 2: Rank assets by 12-month return (cross-sectional)
Step 3: For the top-ranked asset(s), check if 12-month return > T-bill rate (time-series filter)
Step 4: If yes, invest in the top-ranked asset
Step 5: If no, move to safe haven (bonds or cash)
Advantage: Avoids holding losers AND avoids long positions in down markets
Returns over the past 1 week to 1 month tend to REVERSE, not continue. This is the short-term reversal effect and is the reason the 12-1 momentum signal skips the most recent month.
The lookback period determines what "momentum" you are measuring:
1 month lookback: Short-term reversal (negative autocorrelation)
2-6 month lookback: Intermediate momentum (positive, moderate)
7-12 month lookback: Classic momentum (strongest, most documented)
13-60 months: Long-term reversal (value/mean-reversion territory)
Recommended approach:
- Single lookback: 12-1 month (skip last month)
- Blended lookback: Average z-scores of 3m, 6m, 12m momentum
Blending reduces sensitivity to any single lookback
- Adaptive lookback: Use the lookback with highest recent IC (but overfitting risk)
Basic momentum signal:
MOM_i = (Price_i,t-1 / Price_i,t-12) - 1 [skip last month with t-1]
Risk-adjusted momentum (better Sharpe):
MOM_i = Return_i,12m / Volatility_i,12m
Residual momentum (Blitz, Huij, Martens 2011):
Step 1: Regress returns on Fama-French factors
Step 2: Compute cumulative residual return over lookback
Step 3: Rank by residual momentum
Advantage: Captures stock-specific momentum, not factor momentum
Lower crash risk than standard momentum
Sector momentum:
Compute momentum at sector level, then trade sector ETFs
Lower turnover, higher capacity, still profitable
Momentum strategies experience rare but severe crashes, typically during market reversals:
Historical momentum crashes:
Mechanism: After extended bear markets, past losers rally sharply (short squeeze, risk reversal) while past winners lag. The long-short momentum portfolio gets hit on both legs.
Crash characteristics:
1. Volatility scaling:
w_t = target_vol / realized_vol_t
Scale position size inversely to recent volatility
Reduces exposure precisely when crash risk is highest
Improves Sharpe from ~0.5 to ~0.8 for XSMOM
2. Dynamic momentum (conditional):
Reduce momentum exposure when:
- Market volatility > 90th percentile
- Momentum factor has negative recent returns (12-month factor return < 0)
- Value spread is extreme (momentum vs value divergence)
3. Capped shorts:
Limit short leg exposure (e.g., 30% of long leg)
Reduces crash magnitude at cost of some expected return
4. Residual momentum:
Factor-neutral momentum has much lower crash risk
Removes systematic market and value exposure from momentum signal
5. Diversification:
Run momentum across multiple asset classes
Equity, FX, commodity, bond momentum have low cross-correlation
Cross-sectional momentum portfolio:
Long: top quintile (or decile) by momentum signal
Short: bottom quintile (or decile)
Weighting within quintile: equal weight or volatility-inverse weight
Rebalance: monthly (align with momentum signal frequency)
Time-series momentum portfolio:
For each asset i:
Signal: sign(12m return) or z-score(12m return)
Position: signal_i * (target_vol / vol_i)
Aggregate: sum across all assets
Typical: 20-50 futures contracts for CTA implementation
Sector momentum:
Rank 11 GICS sectors by 12-month return
Long top 3, short bottom 3 (or long-only top 3)
Equal weight or vol-adjusted weight
Rebalance monthly
Strategy: 12-1 Cross-Sectional Momentum (US Large Cap)
Universe: S&P 500 | Rebalance: Monthly | Period: 2000-2024
Portfolio Construction:
Long: Top quintile (100 stocks, equal weight)
Short: Bottom quintile (100 stocks, equal weight)
--- Performance ---
Annualized Return (L/S): 8.2%
Annualized Volatility: 16.5%
Sharpe Ratio: 0.50
Max Drawdown: -52% (2009 crash)
Calmar Ratio: 0.16
Hit Rate (monthly): 55%
Monthly Turnover: 35%
--- With Volatility Scaling ---
Annualized Return (L/S): 9.8%
Annualized Volatility: 12.0%
Sharpe Ratio: 0.82
Max Drawdown: -28%
Calmar Ratio: 0.35
--- Factor Exposures ---
Market (beta): 0.05 (approximately neutral)
Size (SMB): 0.12 (slight small-cap tilt)
Value (HML): -0.35 (anti-value, as expected)
Quality (RMW): -0.10
--- Transaction Cost Analysis ---
Gross Sharpe: 0.82
Round-trip cost (est): 20 bps
Net Sharpe: 0.58
Break-even cost: 55 bps
Signal | IC | ICIR | Sharpe (L/S) | Turnover | Crash Max DD
12-1 price momentum | 0.035 | 0.22 | 0.50 | 35% | -52%
Risk-adjusted momentum | 0.038 | 0.28 | 0.65 | 32% | -42%
Residual momentum | 0.032 | 0.30 | 0.72 | 28% | -25%
Blended (3/6/12m) | 0.040 | 0.32 | 0.70 | 30% | -38%
Sector momentum | 0.028 | 0.18 | 0.45 | 15% | -30%
[ ] Universe defined with liquidity filters (ADV, market cap)
[ ] Lookback period selected (12-1m default, or blended)
[ ] Short-term reversal handled (skip most recent month)
[ ] Volatility scaling applied (target vol / realized vol)
[ ] Portfolio construction rules defined (quintile, decile, N stocks)
[ ] Rebalancing frequency set (monthly for standard momentum)
[ ] Transaction costs estimated and net performance computed
[ ] Crash mitigation in place (vol scaling, capped shorts, or residual)
[ ] Sector and factor neutralization considered
[ ] Capacity analysis performed (check ADV vs position size)
Before deploying a momentum strategy: