Abstract
This paper introduces Financial Self–Organizing Maps (FinSOM) as a SOM sub–class where the mapping of inputs on the neural space takes place using functions with economic soundness, that makes them particularly well–suited to analyze financial data. The visualization capabilities as well as the explicative power of both the standard SOM and the FinSOM variants is tested on data from the German Stock Exchange. The results suggest that, dealing with financial data, the FinSOM seem to offer superior representation capabilities of the observed phenomena.
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Resta, M. (2014). Financial Self-Organizing Maps. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_98
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DOI: https://doi.org/10.1007/978-3-319-11179-7_98
Publisher Name: Springer, Cham
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