| Abstract: | We introduce a variant of self-organizing maps (SOMs) termed VaRSOM that evaluates the similarity among inputs and nodes of the map employing value at risk (VaR). In this way we embed risk measurement within a machine-learning architecture, thus becoming particularly well-suited to analysing financial data. We tested the visualization capabilities and the explicative power of VaRSOM on data from the German Stock Exchange; we then evaluated the results in a comparative perspective, opposing the VaRSOM outcomes to those of SOM trained with more conventional similarity measures. The results lead to the conclusion that VaRSOM is a tool particularly well suited to visualize and exploit critical patterns in financial markets. This, in turn, opens perspectives for a general machine-learning framework sensitive to financial distress and contagion effects. Copyright © 2015 John Wiley & Sons, Ltd. |
