Portfolio diversification using ant brood sorting clustering

The process of uncovering underlying intelligence in financial time series is non-intuitive; therefore, data analysis techniques such as clustering (i.e. grouping a collection of objects such that objects in the same group are more similar to each other than those in the other groups) are often used to extract intelligence from financial time series. In this paper, we investigate using the ant brood sorting clustering technique to extract a new form of intelligence from financial time series that can be used in diversifying portfolio composition. Brood sorting is a nature-inspired computing technique modeled after the natural phenomenon of cemetery organization and sorting of broods amongst ants. The technique reveals promising results that can be used in making informed decision on the collection of assets that can be owned together in order to minimize possible losses (in the case of a down-turn of the economy) or maximize gain (in the case of a growing economy).