sector momentum orthogonal
What it checks
If a stock has beaten the market by a lot over the past 6 months, it tends to keep winning; if it's been losing badly, it tends to keep losing — long the leaders, short the laggards.
Mechanism
Industry/sector momentum after orthogonalizing to market return — long top-quintile sector relative-strength, short bottom-quintile. Captures slow-diffusion industry-level news the cross-section can't.
Signal rule
6m return minus SPY 6m return (smoothed 21d), self-ranked vs 252d. Rank >= 0.8 -> LONG; rank <= 0.2 -> SHORT. T+1 entry; hold 21/63/126 trading days.
Data dependencies
daily_pricesAdjusted-close OHLCV for every US-listed ticker; primary price feed.
Expected edge
- Paper alpha
- ~0.7-1.0%/month long-short
- Paper window
- 21/63/126 trading days
Grinblatt-Moskowitz 1999; ~0.7-1.0% per month long-short.
Related families
meta hrpMetaLopez de Prado 2016 introduces HRP as a quasi-optimal allocator that avoids the unstable matrix-inversion at the core of mean-variance. HRP delivers higher Sharpe out-of-sample than 1/N (DeMiguel-Garlappi-Uppal 2009) and minimum-variance, especially when asset count grows. Recipe: correlation matrix → distance matrix → single-linkage hierarchical clustering → quasi-diagonalization → recursive bisection inverse-variance allocation. Applied here over the K=5 canonical sub-signals (same set as meta_equal_weight) for head-to-head HRP-vs-1/N comparison on every ticker.
lazy pricesEventStocks whose 10-K (or 10-Q) text barely changes year-over-year OUTPERFORM those with big language shifts. The intuition: boring filings ≈ stable business ≈ slow-and-steady cash flow. Big text changes signal management hiding bad news with new boilerplate. Effect size: ~0.4 Sharpe alone, still replicating in 2020-2024. Long when the most recent 10-K's cosine similarity to prior year is in the top quartile (≥ 0.85); hold ~12 months until the next filing.
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