# Low Volatility and Defensive Factors

> The low volatility anomaly is the finding that less volatile stocks have delivered better risk adjusted returns. Learn the evidence, explanations and its risks.

Source: https://learn.tradelabsai.com/research/low-volatility-factor/  
Track: Research and Backtesting · Level: Advanced · Updated: 2026-10-03  
Publisher: TradeLabs AI (https://tradelabsai.com). Education, not financial advice.  
Cite as: TradeLabs Learn, "Low Volatility and Defensive Factors", https://learn.tradelabsai.com/research/low-volatility-factor/

Finance theory says higher risk should bring higher expected return. Yet research has repeatedly found that stocks with low volatility or low beta have delivered returns similar to or better than high volatility stocks, with much less risk. This low volatility anomaly is one of the most puzzling patterns in finance and the basis for popular "minimum volatility" and "low beta" funds. Understanding why it might exist, and when it struggles, helps investors use it sensibly.

## The evidence

- **Black, Jensen and Scholes (1972)** and later Haugen and Heine (1975) found that the relationship between beta and return was much flatter than the capital asset pricing model predicted.
- **Ang, Hodrick, Xing and Zhang (2006)** found that stocks with high idiosyncratic volatility had very low subsequent returns.
- **Frazzini and Pedersen (2014),** in "Betting Against Beta", found that portfolios long low beta assets and short high beta assets (leveraged to equal risk) earned positive risk adjusted returns across many markets and asset classes.

## Measuring low volatility

| Measure | Description |
|---|---|
| Total volatility | Standard deviation of returns over a past window |
| Beta | Sensitivity to the market. See [Alpha and Beta](https://learn.tradelabsai.com/portfolio/alpha-and-beta/) |
| Idiosyncratic volatility | Volatility not explained by market and factor moves |
| Minimum variance optimisation | Portfolios built to minimise total risk using a covariance matrix |

## Why might it exist?

| Explanation | Idea |
|---|---|
| Leverage constraints | Investors who cannot borrow buy high beta stocks to seek higher returns, overpricing them (Frazzini and Pedersen) |
| Lottery preferences | Investors overpay for volatile stocks with small chances of huge gains |
| Benchmark pressure | Managers judged against indices avoid low beta stocks that may lag in rallies |
| Overconfidence | Investors disagree more about volatile stocks, and optimists set prices. See [Overconfidence](https://learn.tradelabsai.com/psychology/overconfidence/) |

**Example: Same return, less risk**
Over a long period, suppose a low volatility portfolio returned 9% a year with 11% volatility, while the broad market returned 9.5% with 16% volatility. The Sharpe ratio of the low volatility portfolio is much higher. With modest leverage to match market volatility, it would have earned more than the market. Low volatility strategies have often lost less in crashes, such as 2008, which helps compounding. See [Sharpe Ratio](https://learn.tradelabsai.com/portfolio/sharpe-ratio/) and [Compounding and Geometric vs Arithmetic Returns](https://learn.tradelabsai.com/math/compounding/).

## When low volatility struggles

- **Strong rallies:** low beta stocks lag when markets surge, as in 2003 or the 2020 rebound.
- **Rising interest rates:** low volatility portfolios often hold bond like stocks such as utilities and consumer staples, which can fall when rates rise sharply, as in 2022's early months. See [Interest Rates](https://learn.tradelabsai.com/macro/interest-rates/).
- **Expensive valuations:** popularity can push low volatility stocks to high prices, reducing future returns. See [Factor Timing, Crowding and Crashes](https://learn.tradelabsai.com/research/factor-crowding/).
- **Sector concentration** in a few defensive industries.

## Relationship with other factors

Low volatility overlaps with quality: stable, profitable companies tend to be less volatile. Some research finds profitability explains part of the low volatility effect. It also tends to have negative exposure to size and momentum at times. See [Quality and Profitability Factors](https://learn.tradelabsai.com/research/quality-factor/).

## Implementing low volatility

| Approach | Notes |
|---|---|
| Minimum volatility ETFs | Optimised long only portfolios |
| Low volatility ETFs | Hold the least volatile stocks |
| Betting against beta | Long low beta, short high beta, leveraged to beta neutral |
| Sector neutral versions | Reduce concentration in defensive sectors |

## Frequently asked questions

### What is the low volatility anomaly?

The finding that low volatility or low beta stocks have delivered similar or better returns than high volatility stocks with less risk, contrary to standard theory.

### Why do low volatility stocks perform well?

Possible reasons include leverage constraints, investors overpaying for lottery like volatile stocks and benchmark pressures on fund managers.

### When does the low volatility factor underperform?

During strong market rallies and periods of sharply rising interest rates, and when low volatility stocks become expensive.

Next, learn the factor that earns from yield differences in [Carry Factor](https://learn.tradelabsai.com/research/carry-factor/).

## Continue learning

- Next lesson: [Carry Factor](https://learn.tradelabsai.com/research/carry-factor/)
- Previous lesson: [Size Factor](https://learn.tradelabsai.com/research/size-factor/)
- Related: [Size Factor](https://learn.tradelabsai.com/research/size-factor/): The size factor says small companies outperform large ones over time. Learn the original evidence, why the effect weakened, the role of quality and how to trade it.
- Related: [Quality and Profitability Factors](https://learn.tradelabsai.com/research/quality-factor/): The quality factor favours profitable, stable, conservatively financed companies. Learn how quality is measured, the evidence and how it pairs with value.
- Related: [Alpha and Beta](https://learn.tradelabsai.com/portfolio/alpha-and-beta/): Beta measures how much a portfolio moves with the market; alpha is the return beyond what that exposure explains. Learn formulas, CAPM, regression and pitfalls.
- Related: [Sharpe Ratio](https://learn.tradelabsai.com/portfolio/sharpe-ratio/): The Sharpe ratio measures return per unit of risk. Learn the formula, how to annualise it, what counts as a good Sharpe ratio, its limitations and common mistakes.
- Related: [Factor Investing Explained](https://learn.tradelabsai.com/research/factor-investing-explained/): Factor investing targets traits linked to long run returns, such as value, momentum and quality. Learn the main factors, the evidence and how they are traded.
- Related: [Interest Rates](https://learn.tradelabsai.com/macro/interest-rates/): Interest rates are the price of money and a key driver of asset prices. Learn policy vs market rates, real rates and how rates move stocks, bonds and currencies.