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There is an abundance of intelligent agent decision support systems (DSS) that have been used to assist with portfolio management decisions. These systems have tended to focus on fundamental, technical and trader behaviour analyses whereas efficient markets hypothesis, as argued by researchers such as Clarke et al., suggest that effort should rather be spent on strategic asset allocation. All of the artificial intelligent studies that have been found have focused on analysis methods which are aimed at identifying investment opportunities with above average returns. According to Clarke et al, although giving support to these types of decision is possible with today’s computing power, to be effective these systems must identify an opportunity before the rest of the marketplace. There is evidence from the efficient markets hypothesis that the stockmarket reflects new information so quickly that these types of analysis technique are ineffective. As an alternative to these methods Markowitz developed an approach known as mean-variance efficiency analysis which aimed at allowing an investor to gain a specific level of return for a corresponding degree of risk. Sharpe introduced an alternative way of implementing meanvariance efficiency theory by defining stocks and other investments in terms of asset classes. A literature search has revealed that there has as yet been no attempt to investigate this potential. This paper therefore aims to fill this gap by reporting on research that involves the incorporation of quadratic calculations in an intelligent agent and case based reasoning tool for strategic asset allocation. This paper provides the results of an evaluation into the effectiveness of intelligent agents and case based reasoning in dealing with this problem.

Strategic asset allocation using quadratic programming with case based reasoning and intelligent agents