Hedge Fund Secret Source

The New Yorker‘s John Cassidy recently asked why some hedge funds make so much money.


Cassidy like hedge fund critic Les Leopold focuses on two primary reasons: (1) the ‘2 and 20’ fees that hedge fund managers charge investors where the funds charge a 2% administration fee and take 20% of the profits; and (2) carried interest loopholes in United States tax laws that hedge funds are structured to take advantage of.


Yet the other reason Cassidy does not explore is the hedge fund secret source: their trading strategies and transaction execution capabilities.


The vanilla version of hedge fund strategies is well known. For instance, ‘long / short’ funds take a long (upside) position in financial securities whilst ‘shorting’ (downside) others. Global macro funds profit from geopolitical risk and central bank monetary policy. Distressed debt and special event funds make profits from turnarounds or from creating situations where there are crowded trades and rational herding among investors.


The secret source is how a vanilla strategy is transformed into one where there is an edge or positive expectancy that is in the hedge fund’s favour. Some pre-quant hedge fund managers learned this from formative childhood experiences playing backgammon and poker. The quants studied Andrey Kolmogorov‘s probability work, and applied it to market microstructure patterns of the order book, and price / volume dynamics. Others benefited from geopolitical events: the 1973-74 growth of offshore Eurodollar markets (Paul Tudor Jones); the European Union’s Exchange Rate Mechanism and Black Wednesday (George Soros); or understanding bubble dynamics in the 1995-2000 dotcom bubble (trader Dan Zanger) and 2007-09 global financial crisis (John Paulson).


One of the keys to this is having a transaction execution capability. It means having a prime broker relationship with more favourable terms than retail traders get. It means having the complex event processing / stream processing capabilities to identify edges / positive expectancy and to trade them in many different financial instruments, markets, and timeframes. This is why some retail traders look at Edwards & Magee-style technical analysis and signals software; successful proprietary traders use trading psychology and market microstructure theory; and quantitative hedge funds use computational intelligence, machine learning, and software agents.


Cassidy and Leopold rail against hedge fund managers as a financialisation symbol of extreme income inequality. Their arguments resonate with many people who are legitimately angry about how much money some hedge funds make Рeven though there is survivorship bias. But what Cassidy and Leopold may obscure is the fact that Рto quote the mid-1990s television show The X-Files Рthe information to create hedge fund-like capabilities is out there, scattered, waiting to be identified and reassembled into new forms. William Gibson, Bruce Sterling, and Charles Stross have already given fictional hints in their novels about what this proto-cyberpunk world might resemble.


When these hedge fund capabilities ‘cross the chasm’ from the hedge fund managers (1%) to the multitudes (90%) then things will get even more interesting.