Computational methods for investment portfolio

the use of fuzzy measures and constraint programming for risk management

Tanja Magoč

François Modave

Martine Ceberio

Vladik Kreinovich

pp. 133-173

in: Ajith Abraham, Francisco Herrera, Aboul-Ella Hassanien (eds), Foundations of computational intelligence volume 2, Berlin, Springer, 2009

Abstract

Computational intelligence techniques are very useful tools for solving problems that involve understanding, modeling, and analysis of large data sets. One of the numerous fields where computational intelligence has found an extremely important role is finance. More precisely, optimization issues of one's financial investments, to guarantee a given return, at a minimal risk, have been solved using intelligent techniques such as genetic algorithm, rule-based expert system, neural network, and support-vector machine. Even though these methods provide good and usually fast approximation of the best investment strategy, they suffer some common drawbacks including the neglect of the dependence among among criteria characterizing investment assets (i.e. return, risk, etc.), and the assumption that all available data are precise and certain. To face these weaknesses, we propose a novel approach involving utility-based multi-criteria decision making setting and fuzzy integration over intervals.