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Deboeck G.J. Financial Applications of Self-Organizing Maps

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Deboeck G.J. Financial Applications of Self-Organizing Maps
Advisor, World Bank, December 15, 1997 — 32 p.
Applications of neural networks to finance and investments can be found in several books and articles. The great majority of these applications use supervised neural network models for forecasting market trends, creating trading models, portfolio or risk management. So far few applications of unsupervised neural networks in finance are documented in the literature.
Nevertheless unsupervised neural networks haven proven to be very successful in other fields . A vast number of applications can be found in T. Kohonen’s Self-Organizing Maps. This article provides an introduction to the use of self-organizing maps in finance, in particular it discusses how self-organizing maps can be used for data mining and discovery of patterns in large data sets.
The illustrations provided include the selection of mutual fund investment managers, mapping of investment opportunities in emerging markets, and analysis of country risks. This article is based on a comprehensive review of financial applications of self-organizing maps summarized in a book that
will be published in 1998 and is edited by the author in collaboration with T. Kohonen.
Exploratory data analysis and data mining
What is a Self-Organizing Map?
Applications of SOM in finance, economics and marketing
Selection of mutual funds investment managers
Analysis of investment opportunities in emerging
stock markets.
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