A quantitative hedge fund is a pooled investment vehicle for wealthy and experienced investors that bases its decision-making process on quantitative analysis. This means that the fund's decisions to buy and sell come from statistical analysis, mathematical probabilities, and complex computer models of the stock market. Successful examples of a quantitative hedge fund, also known as a quant fund, use this approach to exploit market inefficiencies and return significant profits to its investors. The downside to such an approach is that even seemingly flawless computer models can't predict certain unforeseen events, and a fund that rigidly sticks to computerized suggestions might not be able to adjust to rapid market changes.
Hedge funds are similar to mutual funds in that they pool funds from multiple investors who then share in the profits and losses of this professionally managed investment vehicle. The difference is that hedge funds are usually limited to only hard-core investors with wealth and expertise. These funds benefit from relaxed regulatory practices, which allow fund managers to make more aggressive maneuvers on the market than what might be expected from a hedge fund. A quantitative hedge fund takes a calculated approach to such maneuvers, limiting its trades to those taken from by-the-numbers analysis and backed by computer data.
In many cases, a quantitative hedge fund can cut down on its fees because it has less use for investment analysts. Since a computer model is often the basis for the trades made by such funds, there is less need for a human element to make decisions. Other funds use the computer model as the starting point for market analysis but still allow their managers a hands-on approach to react to unexpected market movement.
The main benefit of a quantitative hedge fund is that the computer models can rapidly process information on the market and react quickly to the signals it recognizes. Models can be based on a few simple business ratios, or they can include practically endless sources of statistical information. If the model generates positive results, the method it uses to reach those results is ultimately less important than the model's rigidity in sticking to its winning formula.
That rigidity can also present huge problems for a quantitative hedge fund. Most models are based on statistical data from the past, and some unexpected event that spurs the market in a different direction in the future may not be accounted for by the past performance information. Should one of these surprising reversals of form occur, a fund that relies on computerized triggers with little human interaction can suffer great losses in a short time before a fund manager can step in and make corrections.