5 Misconceptions About Quantitative Hedge Funds

Nathan Anderson | April 26, 2016
  1. One Bad Line of Code Will Destroy the Fund

quantitative, hedge fund, due-diligence, transparency, fat finger, glitch, errorIn 2012, Knight Capital Group, one of the largest market-makers in the industry, lost $440m in 30 minutes due to a glitch in their trading system. As CIO Magazine put it, “If that bug could affect Knight, it could happen to any company.”

Strong words and a scary thought. So why is this nightmare scenario a rare occurrence at quant funds? Knight Capital traded directly on the exchange, as have most broker/dealers that experienced flash crashes or rogue trades. That means those big mistakes when straight from the firm to the trading floor with no filter.

Quantitative funds on the other hand have a prime broker that operates as a second independent layer of risk management. The broker has just as much incentive as the fund to make sure an errant trade doesn’t destroy them, so generally both will have risk protocols in place. For a quant fund to blow up a la Knight Capital both the fund & the broker would have to fail at their risk management, which is less probable.

Due-diligence tip: Ask the fund for details on any risk limits set by the prime broker(s) to get a sense of this second point of failure. Confirm with the prime broker directly if possible.

  1. They Are Opaque

opaque, opacity, hedge fund, due-diligence, transparency, quant, quantitativeInvestors want to know everything, and the fact that quants know their models and won’t share them makes the fund seem less transparent. In reality this is a double standard. For a quant, sharing the models are akin to sharing the blueprint for (a) replicating their future trades and (b) trading against them.

So to be fair, to insist on requiring all the factors and models before investing in a quant, you should also insist that traditional managers share all of their trades in advance. That is the analogous level of complete transparency, and it is completely unrealistic to expect. To read more on this topic see here: “Quant Hedge Funds are Not as Opaque as Everyone Thinks They Are”

  1. They are Diversified
hedge fund, quantitative, due-diligence, diversification, research, transparency

A ‘diversified’ ducky portfolio

Just because a quant fund may have hundreds (or thousands) of positions doesn’t necessarily mean they are diversified. (If you have 500 positions, but they’re all correlated, you are not diversified.)

Quants make money investing in factors, and despite the number of securities represented by those factors they can actually concentrate their risk despite the large number of positions. It’s important to understand the fund’s exposure & use of leverage to truly get a sense of whether they are building a ‘diversified’ portfolio.

  1. You Need to be a Math PhD to Understand What They’re Doing

alternatives, hedge fund, due-diligence, niche, compliance, transparency, quantitative, creativityNot all quantitative funds rely on high-level advanced math. Often the most successful strategies extract their edge through:

(a) Uncovering very explainable inefficiencies in the market caused by regulatory nuance, informational disadvantages, or behavioral biases.

(b) Finding unique data sources that provide an edge over 10-Ks, 10-Qs, and other broadly available data sources.

(c) Using cutting-edge technology to draw an informational or execution advantage over the market. 

An understanding of advanced math certainly helps, but creativity & common sense are often the essential ingredients in putting together profitable quantitative strategies.

  1. The Holy Grail Exists

hedge fund, due-diligence, compliance, quantitative, investing, alts, From time to time we’ll meet a fund that has “the one” model that explains the world. “It recognizes when we’re in a different regime and shifts strategies dynamically.” “We’ve backtested it to 1936.” “We have tick data going back decades.” “We’ve developed this model over the past 20 years.”

The problem is that the market in 1936 is completely different than it is today. The ticks that happened yesterday are not the ticks that will happen tomorrow. We hear the above common sales lines when funds are using highly advanced data-mining techniques, but when it comes to explaining why the model will work tomorrow they often fall short.

The best quant funds recognize that there are hundreds of specific inefficiencies in different markets. They are simply trying to capture the highest ROI opportunities possible while recognizing that eventually the edge in those approaches will erode to zero. It is a completely different approach than the holy grail model and it has substantially better odds at generating real forward-looking returns.

Thanks for reading! Please let us know your thoughts, questions or comments.

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Nathan Anderson

Nate co-founded ClaritySpring and oversees the company's strategy and operations.

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