Low Volatility
Negative of trailing 252-day daily-return standard deviation. Lower vol = higher rank.
As of 2026-06-10 · 1 names held · backtest spans 231 months
Trailing returns
Cumulative return vs SPY
Performance stats
| Metric | Factor | SPY |
|---|---|---|
Annualized return (1 + total) ^ (252/4828) − 1 | +9.93% | +10.30% |
Annualized volatility daily-return stdev × √252 | +15.22% | +19.65% |
Sharpe ratio ann return ÷ ann vol (rf = 0) | 0.65 | 0.52 |
Max drawdown worst peak-to-trough on the cumulative series | -39.58% | — |
Information ratio ann excess return ÷ tracking error (vs SPY) | -0.11 | — |
Monthly hit rate share of months where factor return > SPY | 47% (108/231) | — |
How it's computed
What. For each ticker compute the standard deviation of daily total returns over the most recent 252 trading days. Negate it so that lower realized vol → higher signal → top quintile.
Why it has worked. The low-volatility anomaly — high-vol/high-beta names underperform on a risk-adjusted basis vs CAPM predictions. Frazzini & Pedersen ("Betting Against Beta", 2014) link this to *leverage constraints*: investors who can't (or won't) use leverage bid up high-beta stocks to juice returns, leaving low-beta names systematically underpriced. A behavioral piece is *lottery preference* — retail investors overpay for volatile names with skewed upside.
Caveats. The portfolio is structurally tilted toward defensive sectors (utilities, staples, healthcare, REITs). In strong risk-on regimes it underperforms badly — and 2026 is exactly such a regime, which is why this factor's 1Y line trails SPY despite a positive absolute return.
Current sector mix
| Sector | Names | % of screen | |
|---|---|---|---|
| Consumer Staples | 1 | 100.0% |
Current top quintile (1 names)
| Ticker | Name | Sector | Signal ↓ | Z-score |
|---|---|---|---|---|
| KO | Coca-Cola Company (The) | Consumer Staples | -1.0% | 1.68 |