(Bloomberg) — BlackRock Inc.’s senior quant has bad news for the likes of Bill Gross and Cliff Asness wagering on a comeback for value stocks.
In the worldview of Jeff Shen, money managers need new investing methods because there’s no way to tell if betting on ostensibly cheap companies will work again. In fact, comparing share prices to fundamentals like corporate profits or book value is essentially futile in complex markets.
To fix misfiring quant strategies, the co-chief of the $106 billion systematic active equity group has a newfangled suggestion: Investors should scour alternative data for trading signals and end their obsession with valuation metrics.
“The market is looking at a billion of things that we only know a very small amount of,” Shen said in a telephone interview from San Francisco. “While the philosophical conversation is right in the sense that there should be some fundamentals relative to the price, how the market prices different fundamentals, different sentiment, different flows is pretty much a mystery to any one of us.”
Wall Street probably doesn’t want to hear that understanding stock valuations is effectively impossible, especially not from a 16-year executive at the world’s largest asset manager. But the idea will ring true for investors contemplating a potential bubble in tech and the S&P 500 within sight of its record, even as the coronavirus pandemic rages on.
For those systematic players betting on a rebound in cheap equities at historic discounts, it’s especially relevant.
Valuations for growth stocks have soared through the most disruptive economic crash since perhaps the Great Depression, while the cheapest shares trade at a record discount relative to the most expensive. When a company like Tesla Inc. trades at 10,392 times trailing earnings, 33 times book value and 11 times sales, which is the market using to represent fair value?
The good news is money managers can now turn to a whole new world of alternative data, says Shen, a finance PhD who joined BlackRock through its 2009 acquisition of Barclays Global Investors.
“Once you get that data, you should look for alpha opportunity associated with that rather than put that data over a price number,” he said. “Once you put the price number in, it potentially destroys the effectiveness of that new data source.”
Shen’s systematic group hasn’t conducted research on the U.S. value factor for years now. In their view, price is fickle. It naturally dominates all valuation formulas. And that means such ratios become the product of historical returns rather than predictors of future moves.
To build portfolios, the group taps into social media to gauge employee sentiment, parses online job postings to see which firms are hiring and uses machine-learning algorithms to figure out how the myriad variables in their models interact with one another.
Value traditionalists counter that their rules-based strategy is built for the long haul and will ultimately bounce back after a decade of underperformance. Even among those harboring doubts, the consensus has been to fix it — for instance by including intangible assets in book value — rather than to abandon it entirely.
Shen notes that traditional factors including value still have a place in portfolios. Just perhaps not from a unit like his that charges higher fees to find excess returns. BlackRock, after all, has at least $300 billion in factor exchange-traded funds listed in America alone.
Still, his team is rife with skepticism. Gerald Garvey, also part of the systematic equity group at BlackRock, rebuffed the case for value in a paper in the Journal of Portfolio Management. He argued that using a ratio between two valuation ratios is a flawed method which indicates higher risk rather than higher returns. Instead, a raw spread between the two metrics shows that value stocks aren’t particularly cheap.
As such, there’s no end in sight to the debate in this corner of systematic finance, but one thing is clear: the market keeps punishing anyone attempting to resurrect the long-suffering value factor.
For Shen, it all supports his vision of a quant future which lies in parsing massive amounts of alternative data to predict stock returns.
“Rather than try to invent a version 10 of an idea that’s been around for 70 years, there are so many new things out there,” he said.
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