Along the panoply of much-hyped hedge fund terms, “machine learning” stands out. The predominantly PhD-driven sales pitch makes stunning claims about a computer’s ability to “learn,” assign value to information, connect dots and adjust strategy sounds tempting. But new voices point to the reality, which is very different from the much-hyped promise.
“Machine learning” is a topic few question
When a pension fund, foundation or family office has meetings for quantitative “black-box” strategies, there is a troubling commonality noted. The meeting with a PhD-led team with lettered credentials following their title so long that an allocator not dare question their brilliance is what perhaps most poignantly underscores the strategy description.
Seldom are investors asked to use critical thinking. Amy Elefante Bedi, Director of Hedged Strategies at Washington University Investment Management Company in St. Louis, is different. She publicly complains about hour long meetings where the fund manager talks but nothing is said.
In large part, “machine learning” fits into this investment category. Institutional allocators are expected not to question the PhD elite, for their minds operate at a more sophisticated mathematical level than mere mortals. It isn’t until using core performance driver analysis techniques that the truth is revealed and those not wearing bathing suits in the ocean are exposed at low tide.
Machine learning systems do reveal positive insights into markets, but the little-discussed secret is that it is the human mind that is required to put the strategy together.
Machine learning is based on the concept the computer independently identifies, assimilates and assigns value to information
“Machine learning” and artificial intelligence that independently assesses information, assigns it a value and then makes trading / investing decisions is all the buzz. A recent Wired Magazine article touted Numerai, an SEC-registered hedge fund “that makes trades using machine learning models.” They are joined by the likes of Sentient Technologies, an investment firm that boasts to Bloomberg that it works by filtering “billions of pieces of data, spot trends, adapt as it learns and make money trading stocks.”
Focus on the last part of that sentence. The computer automatically “adapts as it learns” while “making money trading stocks.”
Such is the almost manic buzz and along with investor interest. But is the promise of “machine learning” more fad than fact?
While there is not a universally approved definition for machine “learning” in a hedge fund set, according to two quantitative development experts the definition looks something like this:
Machine learning concept is about a computer examining information – analyzing often vast amounts of data – and then the computer assigns allegedly meaning, relevance to that information without human assistance or background knowledge. Then the machine learning system makes investment decisions. It is this end to end “investment robot,” if you will.
Joey Krug, a Thiel Scholar who is developing prediction markets and currently sits on the board of advisors at Numerai, as well as Quantopian’s Dan Dunn, both agreed to this general definition.
Machine learning falls down based on three precepts
There are three premises upon which to question machine “learning.”
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