Figuring Out Factor Investing

Everyone likes to believe they’re smart consumers. That’s probably why the term “smart beta,” also known as “factor investing,” is so hot right now. “Stupid beta” probably wouldn’t attract many takers.

So what is factor investing, and are you smart to use it? That’s what today’s post and related video are all about.

A simple way to think about factors is as quantitative characteristics shared across a set of securities. If you look past their current, flashy popularity, there really is some substance there. For decades, evidence-based investors have been structuring their investment portfolios to tilt toward factors that are expected to drive investment returns, without the need to rely on random stock-picking or market-timing.

In this respect, factors are pretty smart. Unfortunately, lately, I’ve seen them being used in some pretty dumb ways, to market products that may be factor-based in name, but no longer in evidence-based substance.

Factors: Bringing Order to Evidence-Based Investing

Why does one investment portfolio do better than another? Unraveling this mystery, of course, is at the heart of what evidence-based investing is all about. Before we understood factors, most of the performance difference was usually attributed to the skill of the portfolio manager. In the absence of understanding, it was an easy, if erroneous assumption to make.

As factor research emerged, it became clear that the bigger determinant of different outcomes among different diversified portfolios was, by far, not the prowess of the manager (their “alpha”) but rather certain characteristics, or factors (the portfolio’s “beta”). No wonder we started caring about what those factors were, and which ones in what combinations were expected to deliver the most powerful, positive performance differences. Isolating and incorporating positive return differences exhibited by certain types of stocks has an obvious benefit to investors.

Fast forward to today. Currently, we have factor models that can explain over 95% of the return differences among diversified portfolios. It doesn’t take a guru to incorporate these models; you just need to seek the most efficient, cost-effective funds for doing so.

This is problematic for active fund managers who are still chasing after “alpha.” In the past, their ability to “beat the market” was assumed to be due to their skill. We now know it’s far more likely to be a result of the factor exposures they’ve chosen, whether deliberately or as a random byproduct of their other activities – no prognostication skills required or desired.

In other words, if you can manage your expected returns and related risks through one or a few simple factor-based funds, who needs an active manager or the additional costs they’re expected to incur?

If you’re not quite convinced, check out my related video. In it, I describe a classic 2015 blog post, “Active Funds Exposed,” in which my PWL colleague Justin Bender runs some real-life numbers for us.

Figuring Out the Factors

Research on factors emerged in a landmark 1992 Journal of Finance paper by Nobel Laureate Eugene Fama and Kenneth French, entitled The Cross-Section of Expected Stock Returns. In the paper, they observed that, over time and in aggregate, small-company stocks outperformed large-company stocks, and value stocks outperformed growth stocks. The explanation for the return differences is that stocks with these characteristics, (small and value), were riskier – more volatile. Investors required higher expected returns before they were willing to take on these riskier assets,

In 1997, Mark Carhart added the momentum factor to the body of research; in 2012 Robert Novy-Marx added the profitability factor. This gave us five factors, which come together to explain over 95% of the return differences among diversified portfolios, as touched on above. In 2014, Fama and French came out with their own five-factor model. Their model combined market (i.e., investing in any stocks, versus risk-free assets), size, relative price (value), profitability, and investment. They left out momentum.

Which factors form THE ideal model that explains 100% of the return differences among diversified portfolios? This is unknown. Frankly, it’s unlikely we’ll ever arrive at a universal answer. But researchers continue to test new factor models aimed at inching us ever closer to the elusive nirvana of a perfect model.

And therein lies the challenge.

Faux Factor Investing

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 … and for fund companies hungry for a fresh marketing hook to differentiate themselves by injecting “new & improved” factors into the mix.

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. Cost-effectively 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.

In my video, I cover the sniff tests you can use to help decide when a new factor is really all that new or worth pursuing … and, conversely, when common sense tells us it’s safe to ignore it. To be taken seriously, I would suggest a factor should be persistent, pervasive, robust to alternative specifications, investable, and (my personal favorite) sensible. Check out my video to dig into each of these characteristics in more detail.

Now, about those fund families and their marketing programs. As we know from our experience with other retail products, “new and improved” is usually just the same old you-know-what, stuffed into a fancy new box. Same thing with factor or smart-beta investments. With some 300 “flavors” to choose from, countless new factor products have emerged, but very few of the companies creating them have impressed me.

Maybe that’s because, these days, factor research has become a commodity that any fund manager can access. The difference between implementing the evidence well or poorly comes down to how well the company vets the research, who does the vetting, how they interpret the data, and how effectively they manage the inherent limitations of factor models.

So far, Dimensional Fund Advisors – the company that introduced factor investing (with Fama and French on their board) – continues to stand apart in the field. That said, Dimensional’s funds can only be accessed through vetted advisor firms like PWL Capital. Where does that leave the DIY investor? For now, I think you’re better off focusing on simplicity rather than trying to sort out all these factors on your own.

The Canadian Couch Potato model portfolios used to pursue the size and value factors, but my colleague Dan Bortolotti 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 him in full.

Have you tried to implement a factor portfolio? Tell me how it went in the video’s comments.