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Neural Network Approach to Predicting Stock Exchange Movements using External Factors

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dc.contributor.author Madden, Michael G. en
dc.contributor.author O'Connor, Niall en
dc.date.accessioned 2009-05-15T10:31:11Z en
dc.date.available 2009-05-15T10:31:11Z en
dc.date.issued 2005 en
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.identifier.uri http://hdl.handle.net/10379/193 en
dc.description.abstract The 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.language.iso en en
dc.subject Dow Jones industrial average index en
dc.subject External indicators en
dc.subject Commodity prices en
dc.subject.lcsh Dow Jones industrial average en
dc.subject.lcsh Prices en
dc.title Neural Network Approach to Predicting Stock Exchange Movements using External Factors en
dc.type Conference Paper en

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