Portfolio construction using argumentation and hybrid evolutionary forecasting algorithms

Abstract

In the current contribution, an application for constructing mutual fund portfolios is presented. This approach comprises several Intelligent Methods, namely an argumentation based decision making framework and a hybrid evolutionary forecasting algorithm which combines Genetic Algorithms (GA), MultiModel Partitioning (MMP) theory and Extended Kalman Filters (EKF). Specifically, the argumentation framework is employed in order to develop mutual funds performance models and select a small set of mutual funds, which will compose the final portfolio. On the other hand, the hybrid evolutionary forecasting algorithm is applied in order to forecast the market status (inflating or deflating) for the next investment period. The knowledge engineering approach and application development steps are also presented and discussed.