Applications of Fuzzy Measures and Intervals in Finance

Computational intelligence techniques are very useful tools for solving problems that involve understanding, modeling, and analyzing large data sets. One of the numerous fields where computational intelligence has found an extremely important role is finance. More precisely, the problem of selecting an investment portfolio to guarantee a given return, at a minimal risk, have been solved using intelligent techniques such as support-vector machines, neural networks, rule-based expert systems, and genetic algorithms. 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 criteria characterizing investment assets (i.e. return, risk, etc.), the ignorance of the interdependence among assets, and the assumption that all available data are precise and certain. To face these weaknesses, we suggest the use of utility-based multi-criteria decision making setting and fuzzy integration over intervals. ∗Submitted: January 19, 2009; Revised: January 11, 2010; Accepted: February 1, 2010.