Decision Making in Financial Markets Through Multivariate Ordering Procedure

Abstract

One of the main problems in managing multidimensional data for decision making is that it is impossible to define a complete ordering on multidimensional Euclidean spaces. In order to solve this problem, the scientific community has devolped more and more sofisticated tecniques belonging to the wide framework of Multivariate Statistics. Recently some authors [DR04] have proposed an ordering procedure in which the “meaningful direction” is the “worst-best”. The aim of this paper is to extend this approach considering that, especially in financial applications, variables are quantified using different scales and, as we will show, this can lead to undesired results. As a matter of fact, we show that, without an appropriate rescaling, variables with a large range of variation (rv) are “overweighted” with respect to variables with a small one.

Research financially supported by Local Research Project of University of Foggia and National Research Project PRIN, co-financed by MIUR, Italian Ministery for Research, 2004–2006 Titled “Non-linear models in economics and finance: interactions, complexity and forecasting”.