Document details

State detection in a financial portfolio: a self-organizing maps approach for financial time series

Author(s): Matos, Diogo Manuel Pires de

Date: 2014

Persistent ID: http://hdl.handle.net/10362/14157

Origin: Repositório Institucional da UNL

Subject(s): Financial markets; SOM; Correlated markets; Clustering over U-Matrix


Description

This study analyses financial data using the result characterization of a self-organized neural network model. The goal was prototyping a tool that may help an economist or a market analyst to analyse stock market series. To reach this goal, the tool shows economic dependencies and statistics measures over stock market series. The neural network SOM (self-organizing maps) model was used to ex-tract behavioural patterns of the data analysed. Based on this model, it was de-veloped an application to analyse financial data. This application uses a portfo-lio of correlated markets or inverse-correlated markets as input. After the anal-ysis with SOM, the result is represented by micro clusters that are organized by its behaviour tendency. During the study appeared the need of a better analysis for SOM algo-rithm results. This problem was solved with a cluster solution technique, which groups the micro clusters from SOM U-Matrix analyses. The study showed that the correlation and inverse-correlation markets projects multiple clusters of data. These clusters represent multiple trend states that may be useful for technical professionals.

Document Type Master thesis
Language English
Advisor(s) Marques, Nuno Cavalheiro
Contributor(s) Matos, Diogo Manuel Pires de
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