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Enhancing Self‐Organizing Map Capabilities with Graph Clustering: An Application to Financial Markets

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  • Marina Resta

Abstract

This work introduces an algorithm combining self‐organizing maps (SOMs) with graph filtering procedures and discusses how to employ it for analysing financial markets. This method defines graphs resulting from SOM leader units and the calculation of the correlation among them. The technique is applied on financial datasets, with an eye to markets at different levels of (in)stability; they were analysed both on a period of 1 year and with a moving window along a time span of 10 years, comparing graph topologies. In both cases, enhancing SOMs with graphs made it possible to emphasize relations among enterprises and to check for the emergence of critical patterns. Copyright © 2015 John Wiley & Sons, Ltd.

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  • Marina Resta, 2016. "Enhancing Self‐Organizing Map Capabilities with Graph Clustering: An Application to Financial Markets," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 21-46, January.
    • Handle: RePEc:wly:isacfm:v:23:y:2016:i:1-2:p:21-46
    DOI: 10.1002/isaf.1373
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    References listed on IDEAS

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    Cited by:

    1. Dungey, Mardi & Islam, Raisul & Volkov, Vladimir, 2020. "Crisis transmission: Visualizing vulnerability," Pacific-Basin Finance Journal, Elsevier, vol. 59(C).
    2. Javier Bajo & Philippe Mathieu & María José Escalona, 2017. "Multi‐agent technologies in economics," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 24(2-3), pages 59-61, April.
    3. Islam, Raisul & Volkov, Vladimir, 2020. "Calm before the storm: an early warning approach before and during the COVID-19 crisis," Working Papers 2020-09, University of Tasmania, Tasmanian School of Business and Economics.

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