Factor investing is currently one of the hottest terms used to sell financial products. You may
have also heard the term smart beta, which is referring to the same concept. A simple way to
think about factors is that they are quantitative characteristics shared across a set of securities.
The reason that we care about factors is that those characteristics can be used
to structure an investment portfolio to outperform
the market without the need to rely on stock picking or market timing.
Factors are on the cutting edge of financial market research, but they
are also being used to market products that may be detrimental to investors. Don’t get me wrong,
factors are more than a sales pitch. They are the mechanisms that drive asset returns.
I’m Ben Felix, Associate Portfolio Manager at PWL Capital. In this episode
of Common Sense Investing, I’m going to tell you about factor investing.
Before we understood factors, researchers were noticing that diversified portfolios
of small stocks were outperforming diversified portfolios of larger
stocks. At the time there was no explanation for this difference,
and the performance difference may have been attributed to the skill of the portfolio manager.
As factor research emerged, it became clear that stocks with certain characteristics could
explain a lot of the differences in returns of diversified portfolios. The reason that
we care about factors is that those performance differences have been positive. Capturing positive
return differences exhibited by certain types of stocks has an obvious benefit to investors.
Currently, factor models explain over 95% of the return differences between diversified
portfolios. This is problematic for active fund managers because their ability to beat the market,
which was previously assumed to be due to their skill, can in many cases be explained by factor
exposure. This is a big deal for investors because if you can get market beating returns
with a factor index fund as opposed to an active manager you will save a lot on fees.
Here is a concrete example to explain what I mean. In a classic 2015 blog post,
my PWL colleague Justin bender took a handful of actively managed market-beating mutual
funds suggested by Globe and Mail columnist Rob Carrick and performed a three-factor regression.
In other words, he used some analysis to show how much of their performance could
be attributed to factor exposure as opposed to manager skill. In most cases the outperformance
was fully explained by factor exposure, and in one case it was mostly explained.
This means that while these active funds did beat the market, they did so by holding more
small cap and value stocks than the market, not by skillfully picking the right stocks at the
right time. Holding more small cap and value stocks than the market is something that an
index investor can replicate at a fraction of the cost of an actively managed fund.
Research on factors emerged in the 1992 paper by Eugene Fama and Ken French titled
The Cross-Section of Expected Stock Returns. In the paper, they observe that small stocks
outperformed large stocks over time, and value stocks outperformed growth stocks over time.
The explanation for the return differences is that stocks with these characteristics, small
stocks and value stocks, are riskier. Investors must expect higher returns to own riskier assets,
In 1997, Mark Carhartt added the momentum factor to the body of research, and later,
in 2012 Robert Novy-Marx added the profitability factor. This gave us five factors which together
explain over 95% of the return differences between diversified portfolios. Fama and
French came out with their five-factor model in 2014, combining market, size, relative price,
profitability, and investment, while ignoring momentum. The ultimate factor model is unknown,
but researchers continue to test new factor models to increase the explanatory power of the model.
Factor research has become not only important to our understanding of finance and investing,
but a way for academic researchers to make a name for themselves. After all,
Fama was awarded the Nobel Memorial Prize in Economic Sciences in 2013 for his work
on asset pricing. This academic competition for discovery of the next factor has resulted in many,
many research papers being published claiming to have identified new factors.
Duke University’s Campbell Harvey, Texas A&M’s Yan Liu, and University of Oklahoma’s
Heqing Zhu have identified over 300 factors in academic literature. This is problematic for
investors. Targeting five factors in a portfolio is hard enough. What do you do if there are 300
of them? Unfortunately for the researchers, and fortunately for investors, many of these factors
do not pan out. In many cases they turn out to be a re-packaging of the original factors.
There is a sniff-test for investors to know when a factor is worth pursuing,
and when it should be ignored.
To be taken seriously a factor should be persistent, pervasive,
robust to alternative specifications, investable, and sensible.
It is worth digging into each of these characteristics. For a factor to be
persistent it must show up through time and not be limited to a specific time
period. To be pervasive a factor must hold true across various countries, regions,
and sectors. Robust to alternative specifications means that the factor
should not be affected if you slightly change how the characteristic is defined.
Investable is extremely important - it means that if the factor cannot
be cost-effectively captured in portfolios it is not helpful to investors. Momentum
is an example of this. The momentum factor meets many of the previous characteristics,
but it is a high-turnover strategy. This makes it expensive to implement in a portfolio.
If there is no sensible explanation for a factor, then it may not be expected to persist. Again,
momentum is an example. Unlike the risk explanation for small and value stocks,
momentum does not have a sensible explanation.
While many factor products have emerged, there are very few companies creating factor products
that get me excited. One company that has done and continues to do an excellent job
in this space is Dimensional Fund Advisors. The research on factors is a commodity - anyone can
access it. The difference between implementing factors well and poorly comes down to how the
company vets the factor research, who does the vetting, how they interpret the data,
and their ability to understand of the limitation of factor models.
The founder of Dimensional Fund Advisors, David Booth, has said “The research is out
there for anybody to access. What distinguishes Dimensional is the way we implement the ideas.”
While I do believe that a factor portfolio is optimal, Dimensional Fund Advisors’ products
can only be accessed through specific firms, like PWL Capital. Based on this,
and with a lack of ETFs, especially in Canada, that are effectively capturing
well-researched factors at a reasonable cost, I think that DIY investors are probably
better off, at least for now, focusing on simplicity rather than pursuing factors.
The Canadian Couch Potato model portfolios used to pursue the size and value factors,
but Dan changed the models in 2015 to ignore factors entirely. Part of his explanation
was that “many DIYers make costly mistakes when they try to juggle too many funds. Meanwhile,
there are exactly zero investors in the universe who failed to meet their financial goals because
they did not hold global REITs or small-cap value stocks.” I agree with Dan in full.
Have you tried to implement a factor portfolio? Tell me how it went in the comments.
Thanks for watching. My name is Ben Felix of PWL Capital and this is Common Sense
Investing. I will be talking about a new common sense investing topic every two weeks,
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