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dc.contributor.authorHalliday, R.
dc.date.accessioned2013-05-14T02:16:47Z
dc.date.available2013-05-14T02:16:47Z
dc.date.issued2004
dc.identifier.citationHalliday, R. (2004), Equity trend prediction with neural networks, Research Letters in the Information and Mathematical Sciences, 6, 15-29en
dc.identifier.issn1175-2777
dc.identifier.urihttp://hdl.handle.net/10179/4427
dc.description.abstractThis paper presents results of neural network based trend prediction for equity markets. Raw equity exchange data is pre-processed before being fed into a series of neural networks. The use of Self Organising Maps (SOM) is investigated as a data classification method to limit neural network inputs and training data requirements. The resulting primary simulation is a neural network that can prediction whether the next trading period will be, on average, higher or lower than the current. Combinations of pre-processing and feature extracting SOM’s are investigated to determine the more optimal system configuration.en
dc.language.isoenen
dc.publisherMassey Universityen
dc.subjectNeural networksen
dc.subjectData classificationen
dc.subjectSelf Organising Mapsen
dc.titleEquity trend prediction with neural networksen
dc.typeArticleen


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