Feature clustering with self-organizing maps and an application to financial time-series for portfolio selection

The portfolio selection is an important technique for decreasing the risk in the stock investment. In the portfolio selection, the investor's property is distributed for a set of stocks in order to minimize the financial risk in market downturns. With this in mind, and aiming to develop a tool to assist the investor in finding balanced portoflios, we achieved a generic method for feature clustering with Self-Organizing Maps (SOM). The ability of neural networks to discover nonlinear relationships in input data makes them ideal for modeling dynamic systems as the stock market. The method proposed makes use the remarkable visualization capabilities of the SOM, namely the Component Planes, to detect non-linear correlations between features. An appropriate metric - the improved R_v coefficient - is also proposed to compare Component Planes and generate a distance matrix between features, after which an hierarchical clustering method is used to obtain the clusters of features. Results obtained are empirically sound, although at this moment we do not provide mathematical comparisons with other methods. Results also show that feature clustering with the SOM presents itself as a viable method to cluster time-series.