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Problems with smart beta – part 6: Factor dilution

This is the sixth post in a series discussing some of the problems associated with investing in “smart beta” strategies. For the previous post on how difficult it can be to tell when a factor stops working, click here.

When I was at school, I remember wondering to myself during art lessons why, whenever I mixed as many colours as possible together, the end result was always the same. No matter what colours I mixed or in what order I always ended up with a brown/black sludge. Why was the final colour always brown/black? Why not red? Or blue?

Apparently the answer is because of the way paint affects the incoming light. Paint has colour not because it’s emitting light, but because it’s absorbing colours other than the one that’s supposed to be the paint’s colour – red paint absorbs all light but red. So when mixing lots of paints together, the mixture absorbs more and more of the spectrum, until all that’s left is black.

Whilst multifactor investing can be a great idea for those who want to diversify between factors, a similar thing can happen when constructing a multifactor portfolio. By combining too many factors, or by combining a few watered-down factors, the end result can end up looking an awful lot like plain old market beta.

If we start off with an example, a case in point is the Goldman Sachs ActiveBeta ETF. It’s one of the most popular smart beta funds out there, with $4.5bn in AUM, thanks largely to its low price tag of 0.09%. The ETF states on its factsheet that it invests in stocks based on 4 attributes: good value, strong momentum, high quality and low volatility. It seems like a typical multifactor ETF. But here’s its performance versus the S&P 500 since the fund launched in 2015:

Smart beta graph 1

Source: Bloomberg

There’s not much of a difference.

The performance of the Goldman Sachs ETF being so similar to the S&P indicates that the factor definitions they’re using are such that the portfolio’s overall performance won’t deviate much from the market index.

Factor definition plays a big part in how differentiated a smart beta fund is from its benchmark. A fund that chooses to invest in the most extreme definitions of value stocks, small cap stocks, momentum stocks, etc will have the largest difference to the benchmark, but also returns that are most similar to how factors have performed in academia.

This is a double-edged sword. On the one hand, the more a factor fund deviates from its benchmark as a result of “purer” factor exposure, the higher the chance of long-run outperformance. But this deviation also increases the pain of investing in the strategy, as there are likely be prolonged periods where the strategy underperforms the benchmark. As Corey Hoffstein of Newfound Research is fond of saying, “no pain, no premium”.

This has encouraged some ETF providers to err on the side of lower factor exposure, meaning lower tracking error, and a lower chance of clients abandoning their funds. From a career-risk perspective, it’s easier to sell clients on the benefits of factor investing and provide index-like returns, than it is to provide them with actual factor returns.

If we look at the performance of a few major multifactor indices, we can see that index tracking isn’t just confined to the Goldman Sachs ETF. The graph below shows the performance of the MSCI USA multifactor index (used by UBS’ ‘MSCI USA Select Factor Mix’ ETF), the Goldman Sachs multifactor index (used for GSLC), and the JP Morgan multifactor index (used for JP Morgan’s ‘Diversified Return US Equity’ ETF):

Smart beta graph 2

Source: Bloomberg

These are all major multifactor indices, which have billions of dollars worth of client assets invested in them through the ETFs that track them. Whilst it’s not a huge track record to draw conclusions from, these multifactor indices haven’t done much apart from providing more expensive beta.

It seems like this strategy of providing index returns under the “smart beta” moniker has proved surprisingly popular. If we return to our Goldman Sachs multifactor ETF, alongside the ETF’s 0.09% TER, one of the reasons the fund might have proved so popular with institutional managers is thanks to its “active” branding. Investment managers/advisers may well know they’re getting index returns, but they can safely buy GSLC for client portfolios with the appearance of remaining “actively” invested – which many investment firms use as a selling-point to justify their fees/existence. They can espouse the benefits of smart beta investing to clients, whilst generating the performance of the S&P 500 – a notoriously difficult benchmark to beat for active managers.

So both ETF providers and their clients (those of us in the investment industry) have career-risk related incentives to producing/buying market-tracking smart beta funds. ETF providers don’t want investors redeeming due to volatility, and investment managers need to give the appearance of remaining active investors.

This makes it especially important for investors looking to allocate to a smart beta strategy to be aware of exactly what they’re buying. Whilst index-like returns aren’t necessarily a bad thing, if an investor buys something like GSLC and expect it to handily outperform the benchmark every year because they’re investing in “factors”, then they’re likely to be disappointed.

But not all multifactor smart beta ETFs are index trackers. An interesting look into multifactor ETFs versus the S&P has been taken by Stuart Cary of ETF Miner. Using their Active Share Matrix (which you can access here), they show how a selection of multifactor ETFs have varying active shares versus the S&P:

Smart beta graph 3

Source: Stuart Cary – interactive version here

As we’d expect, both the Goldman Sachs ETF and the John Hancock multifactor ETF have active shares of less than 0.3, and have closely tracked the S&P 500.

But although they’re both pretty huge in AUM terms, they seem to be in the minority when it comes to typical active share. There are plenty of multifactor funds out there that do, in fact, offer something other than index returns. Again, investors just need to be aware of what they’re buying.

Conclusion

Factor definitions matter. Two portfolios targeting the same factor, but defining the factor differently can result in hugely different returns. Investing in multifactor smart beta funds can either lead to market-like returns, or something completely different – it depends on how the index is constructed. There are incentives in place which encourage the industry to produce and to buy benchmark-hugging multifactor products, but there are multifactor ETFs out there which can give investors something different.

Investors need to be able to sort the wheat from the chaff when it comes to smart beta products. There’s certainly nothing wrong with index-like returns, but investors just need to make sure they know what they’re buying, and set their expectations accordingly.

The next post in the series will focus on how the returns from factors in academia may differ from those in real life, why this might be the case, and what that means for smart beta investors.  

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