- You may remember that market beta is a measure
of the sensitivity between an asset or portfolio
and the risk of the overall market.
A portfolio with a beta of one 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
would be less volatile than the market.
In an efficient financial market,
risk and expected return should be related.
Based on this relationship,
we would expect higher beta stocks,
stocks with more risk relative to the market,
to have higher returns.
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.
So far, it sounds like low beta stocks are a free lunch.
More returns with less volatility.
This should be too good to be true.
And it is.
And I'm going to tell you why.
I'm Ben Felix, portfolio manager at PWL Capital.
In this episode of Common Sense Investing
I'm going to tell you what you're really buying
when you buy a low volatility ETF.
(techno music)
Low volatility stocks are, as it sounds,
stocks that are less volatile than the market.
The reason that they are interesting
is that over the last 50 years,
the lowest volatility stocks
have not only been less volatile than the market,
but they have had market-like returns.
Market-like returns with less volatility
seems to counter the idea of efficient markets.
An opportunity to get more return
without taking additional risk
is what we would call an anomaly.
In other words, it is something that is not explained
by asset pricing models.
The thing about anomalies when it comes to investing
is that as our understanding
of financial markets grows over time,
anomalies are often explained away.
The working hypothesis for much of the research
on financial markets today
is the efficient market hypothesis.
In an efficient market,
asset prices are accurate reflections
of current information,
including the riskiness of the asset.
An efficient market allows us to use asset pricing models
to understand the expected returns
of assets and portfolios.
For many years, the only asset pricing model
was the capital asset pricing model,
which uses only market beta,
the sensitivity to market risk
to explain differences in expected returns.
Under the capital asset pricing model,
about two thirds of the difference in returns
between two diversified portfolios
can be explained by their betas.
Looking at the market through the lens of market beta,
meaning that we were only comparing assets based
on their sensitivity to market risk,
certain types of stocks,
specifically small cap and value stocks,
have higher risk adjusted returns
than they should based on their betas.
Let me say that another way.
If we look at value stocks and evaluate their riskiness
only by their sensitivity to market risk,
they will have higher returns
than they would be expected to
based their sensitivity to market risk.
With the view of the capital asset pricing model,
which only looks at market risk,
the excess performance of small cap and value stocks
would be described as an anomaly.
Now, I know this was a bit of a digression
and I'm not quite done with it,
we are going to get back to low volatility stocks very soon.
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 stocks and value stocks,
which Fama and French had identified
as independent risk factors.
Through the lens of the three-factor model,
about 90% of the differences in returns
between diversified portfolios can be explained.
This means that the independent risks of the market,
small cap stocks and value stocks
explain most of the differences in returns
between any two diversified portfolios.
The reason that small cap and value stocks appeared
to be anomalies under the capital asset pricing model
was that the model was flawed, as every model is.
There were additional risks, independent of the market,
baked into asset prices.
Accounting for these risks
in the three-factor asset pricing model
eliminated the small cap and value anomalies.
The higher returns of those types of stocks were not magic.
They were due to those types of stocks being riskier.
The higher returns were not a free lunch,
they were compensation for risk.
Boom anomaly is gone.
The market is still efficient.
Now back to low volatility.
The low volatility anomaly is clear through the lens
of the three-factor model.
Stated another way,
low volatility stocks have performed better
than would be expected based on their sensitivity
to the market, size and value factors.
In 2013, Eugene Fama and Kenneth French
published a paper detailing a new asset pricing model,
a five-factor model.
They included 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 diversified portfolios
based on their sensitivity
to the five independent risks in the model.
Think about that.
If we take any two diversified portfolios,
the majority of any difference in their returns
will be explained that by their sensitivity to the market,
small stocks, value stocks, stocks with robust profitability
and stocks that invest conservatively.
With the five-factor model,
the low volatility anomaly becomes another victim
of the efficient market hypothesis.
In their 2014 paper titled
"Dissecting Anomalies with a Five-Factor Model",
Fama and French looked specifically at low beta stocks,
and found that they have positive exposure
to the new profitability and investment factors,
which in addition to the size and value factors,
explains their previously anomalous higher average returns.
Low beta stocks are not magical.
They have just had exposure to risk factors
that had not been included in asset pricing the models
until relatively recently.
This could still be be a good thing
for low volatility investment products.
If low beta stocks offer consistent exposure to factors
that explain differences in returns,
then investing in the might be a good idea.
However, there are some problems.
In his 2016 paper,
Robert Novy-Marx came to a similar conclusion
as Fama and French regarding the low volatility anomaly.
He explained that "While low volatility stocks
"have performed well over the last 50 years,
"their performance is not anomalous after controlling
"for size, relative price and profitability."
He concludes the paper stating that,
"while investors would have benefited
"from a defensive tilt over the period,
"these benefits derive effectively
"from an unprofitable, small cap growth exclusion,
"which could have been implemented more efficiently
"and at lower costs directly."
This is an important nuance in product implementation.
Novy-Marx is saying that instead of buying
only low volatility stocks in a portfolio,
it would be more efficient to buy the market
and exclude the small growth stocks with weak profitability.
Let's look at USMV as an example.
It is Ishares Edge MSCI min vol USA ETF.
It has only 213 holdings as of May 7th, 2019,
compared to 3,552 holdings
for the IShares core S&P total U.S. market ETF.
Having 213 holdings in USMV
clearly decreases diversification,
which decreases the reliability of the outcome.
The low volatility specification
also increases the portfolios turnover.
USMV turns over about 25% holdings each year,
while ITOT generally turns over less than 10%.
Turnover increases costs and decreases tax efficiency.
Now, to be clear,
ITOT does not exclude unprofitable small growth stocks,
but they are a tiny portion of the portfolio.
Portfolio efficiency is not the only reason
that low volatility may not be a good place is to invest.
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 that while low volatility stocks
have tended to have these characteristics,
they do not always have these characteristics.
We would call this time varying factor exposures.
In a 2012 paper, Pim van Vliet found that on average
low volatility strategies tend to have exposure
to the value factor.
Historically low volatility stocks have been
in a value regime only about 62% of the time
and a growth regime about 38% of the time.
This regime shifting behavior over time
affects the performance of low volatility strategies.
When low volatility stocks have a value exposure,
on average, they've outperformed the market by 2%.
However, when low volatility stocks have growth exposure,
they underperformed by 1.4% on average.
The low volatility anomaly is no longer an anomaly.
Under the capital asset pricing model
and the Fama French three-factor model,
it appeared that low volatility stocks
were violating the relationship
between risk and expected returns.
Just like small cap and value stocks before them,
the higher risk adjusted returns of low volatility stocks
have now been explained by known risk factors
as our understanding of asset pricing has evolved.
It just took the science a little while to catch up
to the empirical observation.
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 buying a basket of stocks
that will probably give you exposure to value stocks
with robust profitability most of the time.
But sometimes you will be getting growth stocks
depending on whether low volatility
is in a value or a growth regime at the time.
You will also be getting a concentrated portfolio
with relatively high turnover
and higher fees compared to a regular index fund.
As Robert Novy-Marx pointed out in his paper,
"Most of the benefits from low volatility
"come from the fact that focusing on low volatility stocks
"ends up excluding small cap growth stocks
"with weak profitability,
"which have generally performed a 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,
which is exactly how Dimensional Fund Advisors
approaches this problem.
But even if we look at a total market ETF, like ITOT,
only 2% of its holdings are in small growth stocks.
That's not an exclusion,
but it is a very small portion of the portfolio.
I think as usual,
that investing in good old fashioned index funds
is going to be the best approach for most people.
I know that many of you have considered low volatility ETFs
because it comes up a lot in the comments.
Did this video change your view?
Thanks for watching.
My name is Ben Felix of PWL Capital
and this is Common Sense Investing.
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please share it with someone who you think
could benefit from the information.
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(upbeat music)