Quantitative asset management is a method of making financial decisions based solely on numerical and statistical data. This investment method is very popular in many large investment houses because it typically ends up with a steady stream of positive results. While other decision making methods may result in larger gains on the short term, long-term high-dollar investors generally do not desire the higher risk and erratic results found in these methods. Quantitative asset management is also a predictive method; using quantitative analysis, investors can make accurate predictions regarding market trends.
Purely mathematical methods for determining investment strategies have existed since investment began. Even so, it wasn’t until the mid-part of the 20th century that economists began codifying the methods and strategies that would become quantitative asset management. As technology progressed to current day, these methods have benefited from increased data accesses and faster computational speeds.
The science of quantitative asset management is based on hard numbers. Financiers take every available scrap of information pertaining to an asset and combine it into a larger picture. Any number is potentially useful; if it is a single number, such as base value, over a long period of time, it is especially important. The more numbers and time frames the managers have, the clearer the analysis of the asset will be. When all the information is combined together, it is possible to see how the asset changes over time in relation to other known information.
The main purpose of this information is making predictions on the outcome of future financial fluctuations. If a certain asset has proven numerically that it will always behave a certain way when compared to another variable, it is assumed that that trend will continue in the future. For example, if a certain stock price has fallen during a certain week of a certain month since it went on the market, an investor can target that week to buy into the company. This example is very simplistic; this form of correlation can cover a huge range of data from the day of the week to the phase of the moon.
Since quantitative asset management works by using past data, it works best on situations where an asset has existed for a while. This means that these data gathering and analysis methods don’t function as well on startups or new assets. Since these assets have the tendency to be the most volatile, a purely quantitative portfolio is generally abnormally stable. This stability comes at a price, as more stable investments often have a smaller overall return and a larger initial buy-in cost.
