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
• Document Type: Master thesis
• Language: English
• Advisor(s): Marques, Nuno Cavalheiro
• Contributor(s): RUN

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.