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Computational Intelligence for Financial Market Prediction
 Řeha, Filip ; Pilát, Martin (advisor) ; Mráz, František (referee)
Financial markets are characterized by uncertainty, which is associated with the future progress of world economics and corporations. The ability of an individual to forecast future market behaviour at least to a certain extent would give him an important competitive advantage on the market. The aim of this work is to explore neural networks and genetic programming as possible tools which could be used for financial markets forecasting and apply them on historical financial data. Experiments using neural networks and genetic programming were performed and the results show, that both tools can be employed successfully. On average, neural networks outperformed genetic programming in our experiments. In order to evaluate and visualize the results of our created strategies, the MarketForecaster application was implemented. Powered by TCPDF (www.tcpdf.org)
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