I burned some digital ink on these pages discussing the utility of quantitative investment processes over more qualitative approaches. The thesis was, in essence, as follows:
- Simple statistical models outperform the judgements of the best experts
- Simple statistical models outperform the judgements of the best experts, even when those experts are given access to the simple statistical model.
The reason? Humans are fallible, emotional and subject to all sorts of biases. They perform better when they are locked into some process (see here, here, here and here for the wordier versions).
I also examined some research on the performance of quantitative funds and their more qualitative brethren. The findings were as one might expect given the foregoing:
[Ludwig] Chincarini [the author] finds that “both quantitative and qualitative hedge funds have positive risk-adjusted returns,” but, ”overall, quantitative hedge funds as a group have higher [alpha] than qualitative hedge funds.”
All well and good. And then Morningstar spoils the party with their take on the matter:
The ups and downs of stocks since the credit crisis began roiling the equity markets in 2007 haven’t been kind to most stock-fund managers. But those who use quantitative stock-picking models have had an especially difficult time.
What went wrong?
Many quant funds rely primarily on models that pick stocks based on value, momentum, and quality factors. Those that do have been hit by a double whammy lately. Value models let quants down first. Stocks that looked attractive to value models just kept getting cheaper in the depths of the October 2007-March 2009 bear market. “All kinds of value signals let you down, and they’re a key part of many quant models,” said Sandip Bhagat, Vanguard’s head of equities and a longtime quant investor.
Morningstar quotes Robert Jones of GSAM, who argues that “quant managers need more secondary factors”:
Robert Jones, former longtime head of Goldman Sachs Asset Management’s large quant team and now a senior advisor for the team, recently asserted in the Journal of Portfolio Management that both value and momentum signals have been losing their effectiveness as more quant investors managing more assets have entered the fray. Instead, he calls for quant managers to search for more-sophisticated and proprietary measures to add value by looking at less-widely available nonelectronic data, or data from related companies such as suppliers and customers. Other quants have their doubts about the feasibility of such developments. Vanguard’s Bhagat, for example, thinks quant managers need more secondary factors to give them the upper hand, but he also wonders how many new factors exist. “There are so many smart people sorting through the same data,” he said. Ted Aronson of quant firm Aronson+Johnson+Ortiz is more blunt: “We’re not all going to go out and stumble on some new source of alpha.”
Jones’s comments echo Robert Litterman’s refrain (also of GSAM) in Goldman Sachs says P/B dead-as-dead; Special sits and event-driven strategies the new black. Litterman argued that only special situations and event-driven strategies that focus on mergers or restructuring provide opportunities for profit:
What we’re going to have to do to be successful is to be more dynamic and more opportunistic and focus especially on more proprietary forecasting signals … and exploit shorter-term opportunistic and event-driven types of phenomenon.
Just remember, once a model becomes ineffective, it gets forgotten. Once forgotten, it becomes effective again.
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[…] Quant funds need to step up their game. (Greenbackd) […]
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I believe I saw you state that WACC was useless on this site just a few days ago because it allows volatility to represent risk, but I noticed in the link to your older post that you posted research showing that quantitative funds had higher alpha. Do you believe that this is relevant, since alpha also uses volatility to represent risk, or were you just sharing the research to allow people to make up their own minds on the matter?
Thanks,
Scott
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I do think alpha is a meaningless measure, but it is an academic article and it is therefore drafted in the language of the academic orthodoxy, which includes words like “alpha”, “beta” etc. While it’s not a great measure, it’s still a measure of market beating return, and we as value investors can interpret it as excess return over the market.
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Thanks, I appreciate your thoughts on the matter – this is one of my favorite websites.
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