Fuzzy Math in Quant ETFs? A $1 Trillion Boom Draws Naysayers

(Bloomberg) -- Today’s rules-based investing models were supposed to take the guesswork out of Ben Graham’s age-old principle of using “established standards of value” in deciding what to own.

In practice, some argue, they’ve made things more confusing.

Take three well-known products that aim to replicate that tried-and-true strategy of buying low. Depending on the one you look at, U.S. value stocks are either up 1 percent in 2018, down 6 percent, or down 8 percent. That’s great, if you happen to own the one that’s rising, an index from Deutsche Bank. But if you owned an ETF tracking the Russell 1000 Value Index? Not so much.

Fuzzy Math in Quant ETFs? A $1 Trillion Boom Draws Naysayers

It’s not just an academic exercise. With the rise of low-cost passive investing, billions of dollars have poured into so-called factor funds and ETFs, which use sophisticated, math-based formulas to pick stocks with predefined characteristics. Whether value, growth, momentum or size, they’re designed to provide steady exposure and remain immune to subjective (and often faulty) human judgments. But as the disparities show, small tweaks in how you define a strategy can make a big difference in how it performs.

“You’re telling yourself that what you’re actually doing is passive, but you’re actually doing something active, and potentially doing it in a poorly informed way,” said Eddie Perkin, chief equity investment officer at Eaton Vance. “You’re relying on someone else’s definition.”

Those definitions aren’t secrets -- they’re in prospectuses for all to see. And nobody’s saying these funds and indexes aren’t behaving exactly as they’re designed, usually with a variety of risk profiles. The issue is their proliferation: so many funds track so many different styles nowadays that some critics see a return to the same random walk of unpredictable outcomes that passive was supposed to address.

Others just see an industry providing clients with lots of options.

“We’re not here to be a universal cohesive marketplace. We’re here to offer choice, and choice requires research and thinking,” said Simon Mott, head of marketing at HANetf, a European ETF provider, and former global head of ETF and wealth management marketing for FTSE Russell. “Things can be pretty similar, but they offer a different flavor. It’s up to the investor to look at that.”

In theory, any investment that tries to mimic the performance of an index can be considered rules-based. In that way, the entire $5 trillion industry of index funds and ETFs is just one grand exercise in quantitative finance.

But as competition shrinks profits on plain-vanilla passive funds, money managers have made a small fortune selling costlier quant versions, marketed to individuals as smart-beta or factor ETFs. These funds, built to track indexes that use quantitative analysis to precisely target distinct styles like value and momentum, have in a few short years amassed over $1 trillion.

It’s within these strategies, which often carry the stamp of scientific certitude, where the gaps in returns often open up.

Take growth for example. Among 81 ETFs in that admittedly broad category, the best and worst of these funds this year are separated by 26 percentage points, data compiled by Bloomberg show. Among 67 value ETFs, the gap is 13 percentage points. Sure, the definitions incorporate a host of methodologies and sizes, but all of them share elements of the value credo of buying cheap.

It might seem odd to see returns on lookalike quant-driven products diverge so sharply. But according to Meb Faber, chief investment officer at Cambria Investment Management, the phenomenon is “totally normal” as fund managers try to differentiate themselves in an increasingly crowded market.

“The word value is sorta like the word ‘dog’ -- a beagle looks nothing like a Great Dane but both ‘dogs,”’ he wrote in an email. The products are “all under same umbrella but selection criteria means you can have totally different performance even with similar philosophy.”

Bloomberg LP, parent company of Bloomberg News, provides models to track factor returns.

Fuzzy Math in Quant ETFs? A $1 Trillion Boom Draws Naysayers

And these days, nowhere are incongruities more striking than in value. Popularized by Graham and Warren Buffett, the principles of value investing -- which aim to identify stocks that are trading for less than their intrinsic value -- are perhaps the most time-tested and academically robust of any model.

Yet since the global financial crisis, how value should be defined has drawn increasing scrutiny -- and criticism. Unsurprisingly, much of the debate has centered on widely followed indexes that have persistently underperformed. Based on indexes compiled by MSCI, cheap stocks in the developed world plunged to the lowest versus growth shares since 2000 last month. Over the last decade, they are behind by 43 percentage points.

To some, it’s been a cause for soul searching.

One argument gaining currency is that in today’s knowledge-based economy, traditional ways to determine whether a stock is cheap or expensive are obsolete and no longer apply. Compounding the problem is lower post-crisis growth levels, which have undermined economically sensitive value shares and left investors to crowd into tech darlings like Netflix and Amazon.com.

Olivia Engel, chief investment officer of active quantitative equities at State Street Global Advisors, sees book value as a culprit.

A pillar of the Fama-French model and a Ben Graham favorite, book value simply looks at a company’s assets minus its liabilities. Investors compare that number with its market value to determine whether a stock is cheap.

But these days, intangible assets -- like patents, brand value and proprietary technologies -- have become more important to businesses, something that book value doesn’t capture. That’s because in the U.S., intangibles are mostly expensed rather than capitalized, meaning that they don’t show up as assets. (Intangibles acquired in mergers are the exception.)

McDonald’s, for example, has a negative book value, but it’s hard to believe the world-famous fast-food giant’s assets can’t cover all its liabilities. Biotechnology company Acadia Pharmaceuticals, which spent nearly $150 million on research and development last year, has but $5.5 million of intangibles from a licensing agreement. Yet price-to-book ratio is used to determine inclusion into major value indexes like the Russell, MSCI and S&P Global.

“What you might think is highly unattractive from a valuation standpoint is maybe only mildly unattractive from a valuation standpoint when you start to look at R&D expensing,” said Engel, whose firm incorporates intangibles into its equity models.

Fuzzy Math in Quant ETFs? A $1 Trillion Boom Draws Naysayers

Invesco’s Thorsten Paarmann prefers price-to-cash flows. He says it’s the most predictive, and an “honest” metric that’s less prone to accounting shenanigans. Meanwhile, Eaton Vance’s Perkin says enterprise value is better than market capitalization alone because the former accounts for debt. (This might explain the Deutsche Bank basket’s outperformance, which picks stocks by comparing cash flow to enterprise value.)

Michael Hunstad, Northern Trust Asset Management’s director of quantitative research, points to another shortcoming: some industries are always more expensive than others, which means too many value models end up disproportionately skewed toward certain sectors, like financials or energy.

“The big issue that we have in the industry is again a metaphysical problem of what is value, what is quality, what is growth,” he said. “There is no one uniform definition and therein lies the problem.”

For investors less concerned with a metaphysical quant debate and more with returns, the problem is various constructions of the same strategy can often show lucrative results in backtests. Believing in Buffett is not enough.

“The fact that quant managers show a lot of data doesn’t mean they have greater ability to guarantee a particular investment outcome,” said Jason Hsu, chief investment officer at Rayliant Global Advisors and co-founder of Research Affiliates, pioneer of smart beta. “More accurately, you should think of it as scientific marketing: using a lot of data to help sell products.”

©2018 Bloomberg L.P.