The Differences Between Intelligent Computer Systems And Traditional Quantitative Systems
Thomas E. Berghage
Many people mistakenly classify Artificially Intelligent (AI) computer systems as a form of quantitative analysis. There are two distinct differences between advanced AI systems and traditional quantitative analysis. They are: (1) who makes up the selection rules and weighting, and (2) what information is used to discriminate between good and poor performing securities.
In most quantitative systems, even in an advance Expert System form, humans make up the investment rules and mathematically derive the weightings associated with the rules. Computer systems that depend on outside human intelligence to program their actions are not inherently intelligent. In advanced AI systems, the computer makes up its own rules and weightings. The computer learns from examples of good and poor performing stocks, and determines its own ways for discriminating between them. The procedures that are derived by the computer are often so complex that they defy human understanding.
In addition to making up its own rules, advanced AI systems look at corporate financial data differently. Just like in the human brain, where information is not stored in the brain cells, but rather in the connections and relationships between cells, so too is corporate performance information stored in the relationships between financial numbers. Assessing the performance of companies is not so much in the numbers as it is in the connections between the numbers. Financial analysts recognized this early on and have used first order relational information in the form of financial ratios for many years (price/book, debt/equity, current assets/current liabilities, price/earnings, etc.). Now with advanced AI systems we are finally able to look at and evaluate high order interrelationships in financial data that have been far too complex to analyze with less sophisticated systems. These then are the fundamental differences between what has been used in the past and what will be used in the future.