Admit it. You’ve been reading about “low-volatility” or “low-beta” ETFs, and have wondered what all the fuss is about. Are you missing out on the next big thing?

I’ve heard far crazier investment ideas, that’s for sure. So wondering about is fine. But at the end of the day, low-volatility/low-beta stocks are not expected to add value to your basic, low-cost, globally diversified index fund portfolio. To understand why, let’s dig a little deeper into what you’re really getting when you buy a low-volatility ETF.


A Low-Volatility/Low-Beta Anomaly

You may remember that market beta is a measure of the sensitivity between an asset or portfolio, vs.the risk of the overall market. A portfolio with a beta of 1 moves with the market, so if the market drops 10%, we would expect the portfolio to do the same. A portfolio or asset with a lower beta should be less volatile than the market. We should expect higher-beta stocks to deliver higher returns, since they exhibit more risk relative to the overall market. In other words, in efficient markets, risk and expected return should be related.

Except this is not what has happened. For the last 50 years, lower-beta stocks have had higher returns and higher risk-adjusted returns than higher-beta stocks. More returns, with less volatility? This is an anomaly that sounds too good to be true. And it is! I am going to tell you why. But first, we need to review the history of market anomalies.


A History Lesson on Market Anomalies

A market anomaly is something that is not explained by our asset pricing models. That is, they conflict with our assumption that markets are highly efficient. But there’s an important catch we can learn by heeding history. When it comes to investing, our understanding of market anomalies has grown over time. Many earlier anomalies have been explained away in newer models.

The Efficient Market Hypothesis (EMH) is the working theory for much of today’s financial market research. In an efficient market, EMH suggests that asset prices are accurate reflections of current information including an asset’s riskiness. This allows us to use asset pricing models to understand expected returns.

CAPM – a Single-Factor Model: For many years, the capital asset pricing model (CAPM) was the only model around. It uses a single factor – market beta, or the sensitivity to market risk – to explain differences in expected returns. Under the CAPM, we can use beta alone to explain about twothirds of the difference in returns between diversified portfolios.

Looking at the market through the single-factor, market beta lens, we only compare assets based on their sensitivity to market risk. In this context, it was observed that small-cap and value stocks have higher risk-adjusted returns than they should have based on their betas alone. Let me say that another way: These stocks have higher returns than we’d expected based solely on their sensitivity to market risk. Viewed through the CAPM, this excess performance appears to be an anomaly.

Fama/French Three-Factor Model: In 1992, Eugene Fama and Kenneth French introduced a three-factor asset pricing model. Instead of only looking at the sensitivity to market risk, the three-factor model accounts for sensitivity to small-cap and value stocks, which Fama and French had identified as independent risk factors. Through the lens of their three-factor model, we can explain about 90% of the differences in returns between diversified portfolios. This means that the independent risks of (1) the market, (2) small-cap stocks, and (3) value stocks explain most of the return differences between diversified portfolios.

In other words, the higher returns for small-cap and value stocks were neither magic nor a free lunch. They were compensation for those types of stocks being riskier than the overall market. Boom. The anomaly is gone, and the market is still (relatively) efficient.


Modeling Away Low Volatility

Now, back to low volatility. Low-volatility stocks have performed better than expected based on their sensitivity to the three factors: market, size, and value. Then again, time and market models have moved on.

Fama/French Five-Factor Model: In 2013, Fama and French published another paper detailing a new asset pricing model: a five-factor model, adding profitability and investment as new risk factors. With the updated five-factor asset pricing model, we can comfortably explain nearly 100% of the differences in returns between two diversified portfolios, based on their sensitivity to these five independent risks.

Think about that. If we take any two diversified portfolios, almost all of any difference in their returns will be explained by their sensitivity to the market, small-cap stocks, value stocks, stocks for companies with robust profitability, and stocks for companies that invest conservatively.

With the five-factor model, the low-volatility anomaly becomes another victim of the EMH. In their 2014 paper, Dissecting Anomalies with a Five-Factor Model, Fama and French looked specifically at low-beta stocks and found they have positive exposure to the new profitability and investment factors. Combined with exposure to the existing size and value factors, this explains their previously anomalous higher average returns.

Thus we have determined that low-beta stocks are not magical. Instead, they have exposure to risk factors that had not been included in asset pricing models until relatively recently. In other words, the low-volatility anomaly is no longer an anomaly. It just took the scientific explanation a little while to catch up to the empirical observation.


Applying What We’ve Learned

You may still be wondering: If low-beta stocks offer consistent exposure to factors that explain return differences, does this bode well for low-volatility investment products? Should you invest in them?

Let’s look at why this is problematic. In his 2016 paper, Robert Novy-Marx substantiated Fama and French’s conclusions regarding the low-volatility anomaly. While low-volatility stocks have performed well over the last 50 years, Novy-Marx explained that their out-performing anomaly disappears after controlling for size, relative price, and profitability. He concludes:

“While investors would have benefited from a defensive tilt over the period, these benefits derive effectively from an unprofitable small growth exclusion, which could have been implemented more efficiently, and at lower cost, directly.”


This is an important nuance in product implementation. Novy-Marx is saying, instead of buying only low-volatility stocks in a portfolio, it would be more efficient to buy the market and exclude small-cap growth stocks with weak profitability.

For example, consider the iShares Edge MSCI Min Vol USA ETF (USMV). As of May 7, 2019, it had only 213 holdings, compared to 3,552 holdings for the iShares Core S&P Total U.S. Stock Market ETF (ITOT). USMV’s 213 holdings clearly decreases diversification, which decreases the reliability of the outcome. The low-volatility specification also increases portfolio turnover, which increases costs and decreases tax efficiency. USMV turns over about 25% of its holdings each year, while ITOT generally turns over less than 10%. (To be clear, ITOT does not exclude unprofitable small-cap growth stocks, but they are a tiny portion of the portfolio.)

Portfolio efficiency is not the only reason you may want to avoid allocating to low-volatility stocks. We have talked about the potential benefits of exposure to the value, profitability, and investment factors as driving the success of low-volatility stocks. The problem is, while low-volatility stocks often have these characteristics, they do not always have them. There’s even a fancy term for this: it’s called time-varying factor exposure.

In a 2012 paper, Pim van Vliet found that, while on average low-volatility strategies tend to have exposure to the value factor, low-volatility stocks have historically been in a value regime only about 62% of the time. The rest of the time, they were in a growth regime. This regime-shifting behavior affects the performance of low-volatility strategies. When low-volatility stocks have had value exposure, they’ve outperformed the market by 2.0% on average. However, when they’ve had growth exposure, they’ve underperformed by 1.4% on average.


Eliminating the Low-Volatility Middle Man

Now that we understand where the additional returns for low-volatility stocks have come from, we have to question the value (no pun intended) of investing in low-volatility ETFs.

When you buy a low-volatility ETF, you are probably buying a basket of stocks that will usually give you exposure to value stocks with robust profitability. But when low-volatility is in a growth regime, you’ll be getting growth stocks. Compared to a regular index fund, you will also be getting a concentrated portfolio with relatively high turnover, and higher fees.

As Novy-Marx pointed out in his paper, most of the benefit from low-volatility stocks results from their having excluded small-cap growth stocks with weak profitability (which generally have performed very poorly). A much more efficient approach would be to maintain exposure to the market as a whole, and directly exclude small-cap growth stocks with weak profitability.

This is exactly how Dimensional Fund Advisors approaches this problem. Even if we look at a total market ETF like ITOT, only 2% of its total holdings are in small-cap growth stocks. So, as usual, I think investing in good, old-fashioned index funds is still going to be the best approach for most people. Now you know why.

I’ve received a lot of comments about low-volatility ETFs. Did this video change your view? Please share it with anyone else you think could benefit from the information, and stay extra informed by tuning into our weekly episodes of the Rational Reminder Podcast wherever you get your podcasts.