Why Quant Funds Are Stumbling as Bull Market Rallies

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Quant funds — computer-driven strategies managed by Vanguard and other big firms — were originally thought to outsmart human minds and transform investing. Now, however, as their performance plunges, they are losing billions of dollars amid their worst outflow in years. That represents a huge blow for an industry that managed more than $900 billion as of early last year.


Quant Funds Sink

  • Vanguard’s quant fund down 4%, versus S&P 500’s 12% gain in 2019
  • Neuberger Berman, Columbia Threadneedle, others shut down quant funds
  • Momentum and value strategies extend 2018’s losses
  • Trend-following quants see assets plunge after worst outflow in 13 years


Factor Investing Falls Out of Favor

Robotic traders manage roughly $1 out of every $3 held in the world’s $3 trillion hedge fund industry, using models that take into account company’s profitability, trends in volatility and shift sin economic cycles to make trading decisions, per Bloomberg. Within that realm, factor investing, which typically uses single characteristics like quality and value to bet on which stocks will outperform over time, is quickly losing its luster. Vanguard’s massive quant fund is down 4% this year, compared to the S&P 500’s 12.2% gain. Meanwhile, Neuberger Berman is set become the latest major firm using factor investing to close a quant fund, shortly after Columbia Threadneedle closed its quant fund in December, as outlined by Bloomberg.

Momentum, one of the most popular factors, hasn’t managed to pull a comeback from its disastrous 2018. Value has suffered a similar demise.

“If investors believe factor returns are well-behaved, they are mistaken,” said Vitali Kalesnik, head of equity research at Research Affiliates, a firm that employs such strategies. “When investors need it the most, diversification may fade away and factors can go down together. This is exacerbated by the fact that they can go down several months in a row.”


Quants Destabilized by Fed, Trump Tweets

Trend-following quants are suffering too as they struggle to react fast enough to the unforeseen side effects of factors including the end of a decade of central bank stimulus. Trend-following quants have suffered their worst outflows in at least 13 years, a big reversal from the booming popularity of systematic trend-following quants, or CTAs, following their smooth performance throughout the 1008 Financial Crisis.

Quants even seem to be shaken by U.S. President Donald Trump.

“The models can’t move as fast as the tweets,” said Brooks Ritchey, senior managing director at Franklin Templeton’s K2 Advisors unit. The firm currently oversees $3.6 billion, and since investing significantly in trend-following quants, has exited all but one, per Bloomberg.


Some Winners Remain

Not all corners of the factor investing space are so bleak. Some riskier styles including volatility, leverage and small size have outperformed following indicators that the Fed had become more dovish in its policy, per Bloomberg. According to Credit Suisse, equity quants saw their exposure increase by roughly 9% in the first two months of the year. Bloomberg Intelligence also showed that smart beta, which typically tracks factors through long-only investments) drew a record $33 billion of inflows in the recent quarter, led by value and low-volatility.

“I don’t think institutions have given up on quant investing or factor investing, but now we have some question marks,” said Morningstar analyst Tayfun Icten. “So the firms that have an operational edge and more sophisticated infrastructure to execute will probably do better than wannabes.”


Looking Ahead

Ultimately, the idea of a future wherein computers and machines beat humans at investing seems far off. While many quant funds are still doing well, the upheavals of the past year show that human investors, in many cases, have proven to be more insightful and accurate in reacting to trends in the market. In today’s world, it looks like computers suffer many of the same weaknesses as a human brain might, and that machines for the time being may be only as smart, or as short-sighted, as the humans who program them.



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