How To Measure Your Factor Exposure

How To Measure Your Factor Exposure

Unless you know what you own already, how can you know how a new product will impact your returns?

Reviewed by: Dave Nadig
Edited by: Dave Nadig

[Editor's Note: If you’re interested in smart-beta and factor Investing, check out our upcoming webinar on Monday on “Implementing Factor Strategies.” Register here]

I’m at the Inside ETFs Europe conference in London right now, and there is, as you might expect, a lot of discussion about smart beta. A common question I’m hearing from advisors is “how do I even know what I’ve got in terms of factor exposure?”

It’s a surprisingly difficult question to answer with modern smart-beta products.

In The Beginning

Factor investing got its start—or at least its big break—with the publication of a series of papers in the early 1990s by Eugene Fama and Ken French, lightly titled “The Cross-Section of Expected Stock Returns,” “Common risk factors in the returns on stocks and bonds” and “Size and Book-to-Market Factors in Earning and Returns.”

In those papers, they developed the idea that the returns of any given stock or portfolio can be broken down into three core explanatory factors:

SMB: Small-minus-big, or the size difference between stocks or portfolios

HML: High (book value minus low (book value))

These factors were added to the existing way of thinking about markets—the capital asset pricing model—which, at its simplest, only dealt with the risk-free rate of return, and beta, the overall return of the markets as a whole.

Common Investment Knowledge Now

From this early work, the popular financial press has spent 25 years repeating the basics over and over again to the point where most investors no longer even think about it as a theory; they think about it as simple truisms: There’s a difference between a small-cap stock and a large-cap stock. There’s a difference between a value stock (high book to market value) and a nonvalue stock (low book to market value).

This basic view of the market became even more ingrained when Morningstar introduced the “Style Box” as a way of short-handing active managers based on their holdings.


The Modern World

The beauty of the Fama-French model is its simplicity: It uses widely available and simplistic measurements (market cap, book value, prices) to do some very simple allocation of stocks into buckets. Few people will argue whether a $50 million company is smaller than Microsoft. But simple doesn’t always mean “best,” and since the ’90s, two things have happened that have advanced our thinking about factors:

  1. Lots of additional data has become easily available, leading to myriad ways of measuring even the obvious factors like “value.”
  2. Academics have done a lot of work identifying new factors based on all that data.

Even Fama and French have kept at it, publishing a 5-factor version of their model to include firm profitability (RMW – or robust-minus-weak), and level of internal investment (CMA – or conservative-minus-aggressive).

In the last few years, dozens of new factors have been proposed and heavily researched in academia, from the obvious to the obscure: momentum, other balance-sheet measures of quality, like return on equity, volatility, business cycle sensitivity, you name it.

But with all these factors, how is an investor supposed to make sense of their portfolio? There are two primary ways of looking at a portfolio and assessing your factor exposure. Both can be valuable, even if you’re not looking to change how you’re investing right now.


Teasing It Out

Major data providers—like MSCI, FTSE, FactSet and S&P—do a ton of work at the individual company level. Not only do they keep track of the mundane—like changes in management, balance sheet information and accurate stock prices—but they dig deep and look at what companies do, both from a business perspective and a historical performance perspective.

This means they have data that suggests, for example, how “growth-y” Microsoft is and how “momentum-y” Apple is at any given point in time.

Armed with all that data, it’s possible to load any portfolio into a risk model and construct an assessment of just how exposed you are to any given factor. This provides a good sense of where your portfolio might head given assumptions about how factors might perform going forward. For example, if you’re exposed to a lot of “value” stocks, and value is favored, you’d feel a bit more confident about your exposure.

Another Way To Measure Exposure

The other way is to take a pattern of returns and run a regression against a set of indexes constructed to represent the performance of a given factor over time. You could take the S&P 500 and compare that to the S&P 500 Value Index, and that would tell you just how exposed to value the S&P 500 had been in some past period.

This method has the advantage of being comparatively simple, but even more rearview mirror than the bottom-up approach.

In practice, nerdy portfolio analysts use both approaches, but the former requires access to a risk model, generally not free, and access to the portfolio in a convenient format. The latter simply requires performance series and a mode. The good news about the latter model is there are numerous free sources you can use, my favorite of which is the Portfolio Visualizer.

So, want to know just how much “value” is in a set of “large-cap value” ETFs? Let’s look at the top three by assets: the iShares Russell 1000 Value ETF (IWD), the Vanguard Value ETF (VTV) and the iShares S&P 500 Value ETF (IVE).

On a price-to-book basis, the winner would be IWD, with a book value of just 1.99 (versus the S&P 500’s P/B of 3.12, represented here by the SPDR S&P 500 ETF Trust (SPY)). But what do these three funds look like through the Fama French three-factor model, looking back five years?




Sources:, FactSet


What’s At Work Here

What this shows—however much on the margin—is that despite having a higher P/B than IWD, IVE has historically been more influenced by value than its competitors. The size factor is also at work here, with all three portfolios being more large-cap focused than the broad market, although not as large-cap focused as the S&P 500 itself.

You can even run a chart to see how consistent a fund has been at maintaining these factor exposures over time:



For a larger view, please click on the image above.


(The blue line at the top is market exposure, the middle orange line is our value exposure and the bottom red line is our size exposure. The fever-line is alpha, which, as you might expect, is negligible and noisy, and should basically be ignored here.)

This shows us that the value loading for IVE has been remarkably consistent (although declining as the market has become more and more pricey overall).

These kinds of analysis are extremely useful, regardless of your approach to smart-beta or factor investing, and are really the first step on any journey into the land of factors. Unless you know what you own already, how can you know how a new product will perturb your returns?

At the time of writing, the author held no positions in the securities mentioned. Contact Dave Nadig at [email protected].


Prior to becoming chief investment officer and director of research at ETF Trends, Dave Nadig was managing director of Previously, he was director of ETFs at FactSet Research Systems. Before that, as managing director at BGI, Nadig helped design some of the first ETFs. As co-founder of Cerulli Associates, he conducted some of the earliest research on fee-only financial advisors and the rise of indexing.