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

• Autor(es): Matos, Diogo Manuel Pires de
• Data: 2014
• Identificador Persistente: http://hdl.handle.net/10362/14157
• Origem: Repositório Institucional da UNL
• Assunto(s): Financial markets; SOM; Correlated markets; Clustering over U-Matrix
• Tipo de Documento: Dissertação de mestrado
• Idioma: Inglês
• Orientador(es): Marques, Nuno Cavalheiro
• Contribuidor(es): RUN

Descrição

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.