Even though quantative funds have been in existence for decades, they have only come into mainstream during 2006. Many of the top 20 hedge funds have some form of quantative methods of investment. These quant-funds utilize algorithmic trading, also known as black-box trading that uses mathematical based computer programs to enter trade orders, with the math algorithm deciding many aspects of the trade such as price, size of order, and hedges. The algorithm takes advantage of for example – various types of arbitrage and even-driven opportunities, so long as the market is imperfect. Such methods are helpful in managing market risk in the sense that the electronically based models make decisions much quicker than the decision process of human. Many of the institutional hedge funds have hired brilliant PhD’s in physics, math, and computer science to build these mathematical based models and update them as the market changes. As the computers take over many tasks that used to be executed by humans, the role of responsible fiduciary becomes vague, and often neglected. Of course, the most obvious disadvantage is that the model is still built by humans, and there can always be mistakes built into the model. At the same time, such mathematical algorithms can be reverse engineered. For example, if the algorithm has been deciphered by a competitor, the competitor can “front-run” the order, which means buying at a discount before the algorithm initiates buy orders. As the market changes, these models have to be constantly updated to ensure that the model is step ahead of its competitors, because the slightest technological advantage can mean an extraordinary amount of profit or loss. During the credit meltdown in the second half of 2007 and early 2008, the data has shown that these quant-funds have suffered huge losses, up to billions of dollars, with some top funds down -30%.
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