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VaRSOM: A Tool to Monitor Markets' Stability Based on Value at Risk and Self‐Organizing Maps

Marina Resta (mresta@unige.it)

Intelligent Systems in Accounting, Finance and Management, 2016, vol. 23, issue 1-2, 47-64

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.

Date: 2016
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