Portfolio optimization using classification and functional data analysis techniques

In this paper, we propose an algorithm for hierarchical
classification, based on an ultrametric distance. We study its properties and develop
an application in Microsoft Visual Studio, based on the algorithm proposed, using C#
language. The software obtained will be used to classify the shares from Bucharest
Stock Exchange which had profit during the last two years, in order to find similarities
and differences between these shares and build a diversified portfolio. We prove that
this portfolio is representative for the shares from Bucharest Stock Exchange and
study the evolution of the obtained portfolio at different moments of time, using
functional data analysis methods ( STATIS ) . In order to evaluate our methodology,
we provide a numerical experiment .We demonstrate the performance of the proposed
algorithm by comparing the obtained results with the evolution of BET index, BET-C
index or BET –XT index, which are representatives for the capital market in
Romania.