Analyzing Your Factor Bets

We all talk about factors. Now you can see what you’re buying.

Reviewed by: Dave Nadig
Edited by: Dave Nadig

It’s pretty rare I take the time to write about something that’s inside the four walls of, but this is one of those times.

As of a few days ago, we feature a new readout on our equity ETF fund report pages here at the MSCI FaCS Factor Box. And I genuinely think it’s solving a very real-world problem for investors.

At this point, you’re probably exhausted from a decade of talking about factors and smart beta. And that’s understandable. Smart beta funds have often been marketed as some kind of wild black box that will solve all your investing problems. And of course, we know that’s not true.

Importance Of Factor Exposure

But what gets lost in that hyperbole is that factor exposure—the core of almost every smart beta product—is in fact incredibly important. Depending on which bedside insomnia-beater paper I grab, estimates from academics suggest that as much as 80% of manager performance in the active space is actually explained simply by looking at factor tilts.

But what does that mean for you? Well, anytime you’re buying an equity ETF, one of the due diligence questions you should spend a significant number of brain cycles on is “what bets am I making?”

After all, if you’re just looking for large cap U.S. equity exposure, a fund that, say, holds mostly midcaps, is making a huge bet. Asking that question in factor terms is even more important. And finally (finally!), is able to help provide some of those answers.

What The Heck Is FaCS?

There are lots of firms out there that do factor research on stocks and funds. When I fire up my Bloomberg terminal, I can go to a portfolio and pick and choose from dozens of risk models that I can then apply to any given basket of stocks (like an ETF portfolio), and they’ll spit out reams of difficult-to-interpret data.

Don’t get me wrong, if this were all I did all day, and I were better at math, that data would be awesome: a wealth of riches. And if I’m a major institution, the chances are I have software on my desk from Barra, Axioma or any number of smarty-pants firms that can help me tease out countless nuances.

But I’m a normal person (most of the time). I kinda just want a framework that lets me compare things. So when I was first exposed to the MSCI FaCS system, I was, more than anything, relieved. Actually, it’s pretty simple.

Start With A Benchmark

First, let’s just start with a benchmark. In this case, the naive benchmark for all comparisons is the MSCI All Country World Index Investable Market Index. This is a pretty good proxy for “everything”; it’s almost 9,000 stocks in developed and emerging markets, and is 99% of the world’s market cap. That’s “the market.”

The relevant question for investors the—for any fund—is: What bets does XYZ make versus that broad market? Or to put it in factor terms, how “valuey” is this particular fund compared to, well, everything. FaCS just answers that question. For instance, here’s how FaCS looks for the good old S&P 500 (proxied by the SPDR S&P 500 ETF Trust (SPY)):


How To Read The Factor Box

So how do we interpret this factor box? Let’s start on the right. MSCI, in its great giant computer in the sky, probably tracks hundreds—if not thousands—of potential factors. It’s filtered them down into six macro factors that have shown, historically, to be significant potential drivers of returns.

Those six factors are on the right. Each one of these has its own methodology about which you could probably spend a month or two reading documentation, but for example: Value is primarily driven by book-to-price, earnings yield  and long-term reversal trends. Quality includes leverage, earnings variability, earnings quality, investment quality and profitability. And each one of those data sets has its own time-tested methodology.

Establishing A ‘Z-Score’

Each one of these factors is measured at the single-stock level, and then rolled up as a portfolio, and the metric here is a Z-score. For those of us who slept through stats class, a Z-score is a standardized way of looking at standard deviation. So a Z score of -1 would mean a given stock (or portfolio, when aggregated) is 1 standard deviation below the MSCI ACWI IMI in terms of that given factor.

In non-nerd terms, it’s reasonable to think of anything above or below 1/-1 as “a lot,” and anything inside .5/-.5 as “not very much.”

Funds that score +/- 1.5 here are extremely rare. So the box above would suggest “The S&P 500 is a little light on small companies, and a little light on low volatility stocks.” This should surprise nobody. The S&P is a large cap index, and driven by a bunch of FAANG (Facebook, Amazon, Apple, Netflix, Google) stocks that are certainly not low vol.

Fruit Rollups

Where this gets interesting is when you start comparing funds that are explicitly suggesting they’re tracking a particular factor.

For example, let’s say we we’re trying to decide between two popular value ETFs: the iShares S&P 500 Value ETF (IVE) and the Invesco S&P 500 Pure Value ETF (RPV). We know from just the names that both of these funds have the same objective (pick the value stocks out of the S&P 500). But how similar are they really?



To me it seems incredibly obvious. RPV picks smaller, higher-yielding, objectively more “valuey” stocks. The trade-off is that you’re making negative bets on momentum and quality. One of the most important things you can look at if you’re comparing these funds is if they actually do what they claim.

Beyond The Box

I hear you saying, “But, Dave, how do I make the nice little comparison chart you’ve shown here?” Well, the short answer is, we’re working on it. Right now, we’ve worked with MSCI to incorporate the data that drives the individual factor boxes you’ll see on pretty much every equity ETF. I think it’s super cool, but it’s just the beginning. Going forward, we’ll be developing more nuanced ways of making comparisons, so you can tease out these differences without having to flip between fund reports.

We’ll also be making it more intuitive to search and sort based on these factor exposures in our ETF Finder (right now, you can filter for over/underweights, but we’ve got a lot of room to add functionality here.)

But we didn’t want perfection to get in the way of the good here. This is one of the most useful additions we’ve made to the fund pages in the last few years, and we wanted to get this out there.

You’ll see us feature these metrics where it’s interesting in our daily coverage, and I hope you’ll find it useful when you start hunting for that perfect ETF to fill the empty slice in your asset allocation pie chart.

As always, I welcome your feedback at [email protected]. And stay tuned in the coming weeks as we tweak how you can use this data to make better decisions.

For more information about what’s going on under the hood here, check out this blog from MSCI, or you can download the full methodology.

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.