Multi-Attribute Portfolio Selection: New Perspectives

The Multi-Attribute portfolio selection problem involves the choice of a set of stocks (assets, securities) based on incommensurable and conflicting objectives such as return, risk and liquidity. These objectives cannot be optimized simultaneously. Thus, the Financial Decision Maker has to make some compromises between objectives in order to obtain the most satisfactory portfolio with a specified amount of money to invest. Various approaches have been proposed for the portfolio selection problem such as: stochastic dominance models, multi-attribute utility models, multi-objective programming models, discriminant analysis, heuristic methods, neural networks, optimization models and multi-criteria decision aid methods. The aim of this editorial note is to provide a summary of the contributions made by some papers presented during the second International Workshop on Multi-Attribute Portfolio Selection that took place in Montreal (Canada) in 2007.