Commodity Trading Using Neural Networks: Models for the Gold Market

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dc.contributor.authorBrauner, Eriken_US
dc.contributor.authorDayhoff, Judith E.en_US
dc.contributor.authorSun, Xiaoyunen_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T10:04:05Z
dc.date.available2007-05-23T10:04:05Z
dc.date.issued1997en_US
dc.description.abstractEssential to building a good financial forecasting model is having a realistic trading model to evaluate forecasting performance. Using gold trading as a platform for testing we present a profit based model which we use to evaluate a number of different approaches to forecasting. Using novel training techniques we show that neural network forecasting systems are capable of generating returns for above those of classical regression models.en_US
dc.format.extent403896 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/5868
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1997-41en_US
dc.subjectneural networksen_US
dc.subjectneural systemsen_US
dc.subjectrobust information processingen_US
dc.subjectsignal processingen_US
dc.subjecttime series predictionen_US
dc.subjectforecastingen_US
dc.subjectfinancial engineeringen_US
dc.subjectIntelligent Control Systemsen_US
dc.titleCommodity Trading Using Neural Networks: Models for the Gold Marketen_US
dc.typeTechnical Reporten_US

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