Forecasting Intraday Stock Price Trends with Text Mining Techniques
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 3 - Volume 3
Evolving neural networks for static single-position automated trading
Journal of Artificial Evolution and Applications - Regular issue
Natural Computing in Computational Finance (Studies in Computational Intelligence)
Natural Computing in Computational Finance (Studies in Computational Intelligence)
Natural Computing in Computational Finance: Volume 2
Natural Computing in Computational Finance: Volume 2
A novel similarity-based crossover for artificial neural network evolution
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
An intraday market risk management approach based on textual analysis
Decision Support Systems
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Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
Natural Computing in Computational Finance
Natural Computing in Computational Finance
Expert Systems with Applications: An International Journal
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We investigate the correlations among the intraday prices of the major stocks of the Milan Stock Exchange by means of a neuro-evolutionary modeling method. In particular, the method used to approach such problem is to apply a very powerful natural computing analysis tool, namely evolutionary neural networks, based on the joint evolution of the topology and the connection weights together with a novel similarity-based crossover, to the analysis of a financial intraday time series expressing the stock quote variations of the FTSE MIB components. We show that it is possible to obtain extremely accurate models of the variations of the price of one stock based on the price variations of the other components of the stock list, which may be used for statistical arbitrage.