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dc.contributor.authorMadden, Michael G.en
dc.contributor.authorO'Connor, Niallen
dc.identifier.citation"Neural Network Approach to Predicting Stock Exchange Movements using External Factors", Niall O'Connor and Michael G. Madden. Proceedings of AI-2005, 25th International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, Dec 2005.en
dc.description.abstractThe aim of this study is to evaluate the effectiveness of using external indicators, such as commodity prices and currency exchange rates, in predicting movements in the Dow Jones Industrial Average index. The performance of each technique is evaluated using different domain specific metrics. A comprehensive evaluation procedure is described, involving the use of trading simulations to assess the practical value of predictive models, and comparison with simple benchmarks that respond to underlying market growth. In the experiments presented here, basing trading decisions on a neural network trained on a range of external indicators resulted in a return on investment of 23.5% per annum, during a period when the DJIA index grew by 13.03% per annum. A substantial dataset has been compiled and is available to other researchers interested in analysing financial time series.en
dc.subjectDow Jones industrial average indexen
dc.subjectExternal indicatorsen
dc.subjectCommodity pricesen
dc.subject.lcshDow Jones industrial averageen
dc.titleNeural Network Approach to Predicting Stock Exchange Movements using External Factorsen
dc.typeConference Paperen

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