Two Anomalies That Shouldn't Exist
Standard finance theory has a simple prediction: more risk means more return. Volatility = risk. Therefore, high-volatility stocks should outperform low-volatility stocks, and stocks with no recent price trend should outperform stocks with strong recent trends (as markets price in the information quickly).
Both of these predictions are wrong.
The momentum premium — the tendency of recent winners to keep winning — was documented by Jegadeesh and Titman in 1993. The low-volatility anomaly — the tendency of low-risk stocks to outperform high-risk stocks — was documented by Black, Jensen, and Scholes in 1972. Both have survived decades of out-of-sample testing across global markets.
These are not small, noisy effects. They are among the largest and most consistent return anomalies in the empirical finance literature.
The Momentum Premium
Momentum is the tendency of stocks that have performed well over the past 3–12 months to continue performing well over the next 1–6 months.
The most common explanation is behavioral: investors are slow to process information. Good news about a company arrives gradually, and the market takes time to fully reprice. Early momentum reflects the initial underreaction. Eventually, the market overcorrects and momentum reverses over very long horizons (3–5 years). The sweet spot is the 3–12 month window.
In the FactorLens system, momentum is captured through five separate signals:
- Price to 52-week high: Stocks near their highs are in structurally strong trends
- TRSD30D: Risk-adjusted 30-day momentum
- 26-week price change vs. industry: Relative strength within peer groups
- Sub-industry aggregate return: Sector-level momentum
- 4-month vs. 6-month price ratio: Medium-term price acceleration
Using five different momentum signals — across different time windows, peer groups, and normalizations — reduces the noise inherent in any single momentum measure. If all five point in the same direction, the signal is much stronger.
The Low-Volatility Anomaly
The low-volatility anomaly says that portfolios of low-risk stocks have historically outperformed portfolios of high-risk stocks — the opposite of what finance theory predicts.
The explanation involves structural constraints on institutional investors. Many funds use benchmarks with fixed risk targets. To beat the benchmark, managers reach for high-volatility, high-beta stocks. This creates persistent overpricing of risky stocks and underpricing of boring, low-volatility names.
Additionally, individual investors exhibit a "lottery preference" — they overpay for the possibility of extreme upside (think meme stocks, biotech speculations). This lottery-ticket demand inflates prices of high-volatility stocks and depresses returns.
In the FactorLens system, we capture low-volatility exposure through two factors:
- PctDev(252, 1): 252-day price deviation. Stocks with lower annual volatility score higher.
- Interest Coverage TTM: Companies that comfortably cover their debt service are financially stable and typically less volatile.
The combination of price-based and fundamental-based volatility measures captures both market-level volatility and underlying business stability.
Why Momentum and Low Volatility Together?
Conventional wisdom says momentum and low volatility are in tension. Momentum stocks are often the recent market darlings — high-flying, high-volatility names. Low-volatility stocks are often boring defensives that lag in bull markets.
But this tension disappears at the stock level when you look carefully. The best momentum stocks are not always the most volatile — they are often the stocks with steady, persistent price appreciation driven by improving fundamentals. These are precisely the stocks that tend to have low realized volatility relative to their returns: consistent upward drift with limited drawdowns.
The intersection of high momentum and low volatility is rare — but when a stock achieves it, it tends to be an exceptional investment. Our 18-factor composite naturally surfaces these stocks because both signals must be satisfied simultaneously to achieve a top score.
The Combined Effect in the FactorLens Backtest
Between 2009 and 2026, the combination of momentum, low volatility, quality, and value signals in the FactorLens system generated a Sharpe ratio of 1.62 — meaning roughly $1.62 of return per unit of risk taken.
For context, the S&P 500 has generated a Sharpe ratio of approximately 0.5–0.7 over long periods. The multi-factor composite — specifically in the small and micro-cap universe where all these signals have more room to work — produces a substantially higher risk-adjusted return.
The correlation with the S&P 500 is 0.65 — meaningfully lower than most equity strategies. This indicates that a meaningful portion of the strategy's return comes from factors other than simply being long equities. The diversification benefit is real.
Practical Implications
For investors building a FactorLens-based portfolio, the momentum and low-volatility signals have a practical implication: the screener will frequently surface stocks you've never heard of. These are not the names in the financial press. They are not discussed on Twitter or Reddit. They are steady, underloved businesses with improving fundamentals and low analyst coverage.
That obscurity is the point. The less discussed a stock is, the more likely its factor scores reflect information that the market hasn't fully priced. The screener's job is to systematically find those overlooked opportunities before the market does.
Key Takeaways
- The momentum premium (past winners keep winning over 3–12 months) and low-volatility anomaly (low-risk stocks outperform high-risk stocks) are two of the most robust return factors in finance
- Momentum arises from investor underreaction to information; low volatility arises from structural biases among institutional investors
- Using multiple momentum signals (across time windows, peer groups, and normalizations) reduces noise vs. any single measure
- Momentum and low volatility are complementary, not contradictory — the best combination is steady, persistent price appreciation in fundamentally stable businesses
- In the small-cap universe, where information is sparse and behavioral biases are strongest, these two premiums are particularly large and consistent