Exploiting topography of neural maps: a case study on investment strategies for emerging markets

Investments can be spread over many possible assets to avoid risk (at the cost of obtaining only an average performance) or it can be focused on clusters of only a few promising assets (at the cost of increased risk). A trade-off between these two objectives can be reached by using the self-organizing map (SOM), a neural network paradigm which achieves a clustering of data points while simultaneously preserving their inherent neighborhood relations (topography). This amounts to a combination of clustering with local smoothing. In a case study involving investments in emerging stock markets the author illustrates the application of SOMs in investment decisions, with an improvement of about 30% in returns over other, more simple investment strategies