Investing features many ways to make and -- unfortunately -- lose money. Finding ways to maximize investment gains while minimizing downside risk is one of the ultimate goals of the investor, and there are several well-recognized approaches to this process. One is quantitative investing, a strategy that uses detailed statistical models to differentiate stocks.
Statistical analysis of the world's economies and stock markets moved from academic abstraction to Wall Street reality during the second half of the 20th century, as computers became more powerful and less costly. Most investment management firms use this kind of quantitative analysis to inform their decision-making process, but "quant investing" as a distinct style places stat analysis front and center. Quant investing attempts to reduce the process to its scientific and statistical core, eliminating emotional, fallible human judgment from the equation as much as possible.
Constructing the Models
The actual "product" of a quant investment firm isn't the portfolio itself, but the software model that guides their investment decisions. Market-savvy programmers -- or code-savvy investors -- create these by sifting through huge quantities of stock and trading data, in search of patterns. Once a promising pattern is identified, the programmers will create an algorithm to track those factors and assess the optimal time to buy and sell a given security. The code must be tested rigorously under simulated market conditions and with live data, before it's put into use as an investment tool.
Approaches to Quant
Some quant funds and institutional quant investors take what's referred to as a "black box" approach. In this style of investing, the computers can make trades without direct human supervision. It's especially well suited to arbitrage, in which high-speed trades can be conducted in huge volumes to profit from minor fluctuations in the value of stocks. Other managers use quantitative analysis as a way to create a real-time "shortlist" of securities worthy of their consideration. The manager then chooses among these investments through additional research, personal experience or other factors.
The Bottom Line
A study of quantitative and traditional strategies performed by Karl Mergenthaler of J.P. Morgan Investment Analytics demonstrated that, on the whole, quant investing can work well. The Sharpe ratio for quant investment strategies -- a measure of return vs. risk -- showed quant investment outperforming conventional investment on large- and mid-cap stocks, and remaining competitive in small-cap stocks. Quantitative funds also demonstrated lower management costs, in part because of their automated nature and correspondingly low salary requirements. Stable and predictable markets work best for quant investing, allowing statistical models to generate steady gains. During volatile markets, such as the 2008-09 recession, traditional investing has outperformed quant investing.
Fred Decker is a trained chef and certified food-safety trainer. Decker wrote for the Saint John, New Brunswick Telegraph-Journal, and has been published in Canada's Hospitality and Foodservice magazine. He's held positions selling computers, insurance and mutual funds, and was educated at Memorial University of Newfoundland and the Northern Alberta Institute of Technology.