IMPORTANT DISCLAIMER: Blank Capital Research ("BCR") is a technology platform, not a registered investment advisor or broker-dealer. The algorithmically generated signals, scores, and rankings provided on this site ("God Mode" Signals) are for informational and research purposes only and do not constitute financial advice, investment recommendations, or an offer to sell or solicit an offer to buy any securities.
HYPOTHETICAL PERFORMANCE RESULTS: The "timing scores" and "regime signals" displayed are based on quantitative models. Hypothetical or simulated performance results have certain inherent limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not actually been executed, the results may have under-or-over compensated for the impact, if any, of certain market factors, such as lack of liquidity.
RISK OF LOSS: Trading in financial markets involves a high degree of risk and may result in the loss of your entire investment. Data provided by third-party sources (Intrinio, Snowflake) is believed to be reliable but is not guaranteed for accuracy or completeness. Past performance is not indicative of future results.
© 2026 Blank Capital Research. All rights reserved. System Version: Aegis V8 (God Mode).
Less volatile stocks deliver surprisingly competitive returns with less risk.
The low-volatility anomaly is one of the most counterintuitive findings in finance. Classical theory predicts that higher-risk stocks should deliver higher returns as compensation. In reality, the opposite is true: low-volatility stocks have historically delivered similar or better returns than high-volatility stocks, with significantly less risk.
This anomaly was first documented by Black, Jensen, and Scholes in 1972 and has been confirmed by Baker, Bradley, and Wurgler (2011), who showed that the lowest-volatility quintile of stocks outperformed the highest-volatility quintile on both an absolute and risk-adjusted basis over a 41-year period.
The explanations include leverage constraints (investors who can't use leverage buy risky stocks instead, overpricing them), benchmark-hugging (institutional investors chase high-beta stocks for tracking purposes), and the lottery effect (retail investors overpay for high-volatility stocks hoping for outsized gains).
Our stability score combines five risk measures, each inverted so that lower risk produces a higher score:
• Realized Volatility (25% weight) — 60-day annualized standard deviation of daily log returns
• Beta (25% weight) — Market sensitivity via 252-day regression of stock returns against SPY
• Idiosyncratic Volatility (20% weight) — Residual volatility after removing the market factor, capturing stock-specific risk
• MAX Effect (15% weight) — Largest single-day return in the lookback period (Bali, Cakici & Whitelaw 2011), as lottery-like payoffs predict underperformance
• Maximum Drawdown (15% weight) — Largest peak-to-trough decline, measuring tail risk
All five metrics are percentile-ranked within sector peers and inverted, then weighted into a composite stability score from 0-100. Stability receives a 10% weight in our composite.
Baker, M., Bradley, B., & Wurgler, J. (2011)
“Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly”
Financial Analysts Journal
Ang, A., Hodrick, R., Xing, Y., & Zhang, X. (2006)
“The Cross-Section of Volatility and Expected Returns”
Journal of Finance
Frazzini, A. & Pedersen, L. (2014)
“Betting Against Beta”
Journal of Financial Economics
The low volatility (stability) factor selects stocks with lower price fluctuations and market sensitivity. We measure five dimensions: realized volatility, beta, idiosyncratic volatility, the MAX effect (largest daily return), and maximum drawdown. Lower risk on all dimensions produces higher scores.
Several explanations exist: investors who can't use leverage buy risky stocks instead (overpricing them), institutional investors chase high-beta stocks for benchmark performance, and retail investors overpay for volatile "lottery ticket" stocks. These biases systematically overprice risk.
Yes. It has been documented across multiple decades, geographies, and asset classes. Baker, Bradley, and Wurgler (2011) showed the pattern persists in U.S. stocks from 1968-2008. Frazzini and Pedersen (2014) confirmed it globally.
Low-volatility stocks are particularly well-suited for retirement portfolios because they minimize drawdown risk — a critical concern when withdrawing from a portfolio. Their competitive returns with lower risk make them ideal for capital preservation.
Quality measures fundamental business strength (profitability, margins, earnings consistency), while stability measures price behavior (volatility, beta). They are correlated — high-quality businesses tend to be more stable — but capture different dimensions.