Size
Negative log market cap (shares × price). Smaller companies rank higher.
As of 2026-06-10 · 1 names held · backtest spans 182 months
Trailing returns
Cumulative return vs SPY
Performance stats
| Metric | Factor | SPY |
|---|---|---|
Annualized return (1 + total) ^ (252/3810) − 1 | +12.33% | +11.61% |
Annualized volatility daily-return stdev × √252 | +41.41% | +20.08% |
Sharpe ratio ann return ÷ ann vol (rf = 0) | 0.30 | 0.58 |
Max drawdown worst peak-to-trough on the cumulative series | -65.11% | — |
Information ratio ann excess return ÷ tracking error (vs SPY) | 0.21 | — |
Monthly hit rate share of months where factor return > SPY | 47% (85/182) | — |
How it's computed
What. Market cap = latest known shares_outstanding_diluted (PIT with the 60-day filing lag) × latest closing price. We rank stocks by *−log(market cap)* so the smallest names land in the top quintile.
Why it has worked. The small-firm effect, documented since Banz (1981). Possible drivers include illiquidity premia, information asymmetry (fewer analysts, more mispricing), and survivorship in the tail. The size effect is the original Fama-French "SMB" factor.
Caveats. Within the S&P 500 the size effect is muted — every constituent is by definition large-cap. The factor here ranks "small" vs "very large" within the index, not actual small-caps vs large-caps. Academic small-cap premium is largely a micro-cap phenomenon and has been weak since 2000 even in broader universes.
Current sector mix
| Sector | Names | % of screen | |
|---|---|---|---|
| Unknown | 1 | 100.0% |
Current top quintile (1 names)
| Ticker | Name | Sector | Signal ↓ | Z-score |
|---|---|---|---|---|
| CTRA | -2386.0% | 0.00 |