The primary theory espoused by many of these “technicians” is that the removal of all emotion from the trading process will undoubtedly improve returns. To some degree this is a valid hypothesis. A number of studies have shown that the average investor’s returns are stunted by emotional factors such as fear, greed, poor market timing, or by holding on to shares far too long in an attempt to recover losses or “get even.” Another touted advantage of quantitative strategies is that they remove the “story,” or qualitative bias, from investments.
Quantitative investment strategies should not be confused with high-frequency algorithm based trading where firms use supercomputers to trade large blocks of securities in a matter of microseconds to capture fleeting moves in everything from stocks, to currencies, to commodities.
Quantitative investment strategies come in many forms, from the mundane and simple to the incredibly complex. They can be as simple as buying the 10 highest yielding stocks in the Dow Jones Industrial Average, the so-called “Dogs of the Dow.” These simple models frequently have very mixed track records. Some of the more complex strategies rank and sort the investable universe by four to six key ratios or algorithms. With the wide array of information available today, the most complex strategies include dozens of variables over varying periods of time. These complex models often include a final optimization that dictates the construction of the investment portfolio. These optimization programs usually limit certain risks, as defined by the portfolio manager, such as position size or sector exposure relative to the underlying benchmark. The creators and users of these increasingly complex models spend a great deal of time, effort and resources endlessly refining both the inputs and output. See Risk/Return chart, here.
The effectiveness of quantitative methodologies is primarily dependent on two components: the accuracy of the data going into the model and the theoretical foundation of the model itself. The most effective managers not only scrub the data that goes into the model, but also regularly review the generated results. That is, once the portfolio has been assembled, each of the companies in the portfolio is reviewed to make sure that non-quantifiable factors such as lawsuits, management changes, industry competition or regulatory issues are unlikely to substantially change the potential for each company.