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    Commodity Trading Using Neural Networks: Models for the Gold Market

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    TR_97-41.pdf (394.4Kb)
    No. of downloads: 828

    Date
    1997
    Author
    Brauner, Erik
    Dayhoff, Judith E.
    Sun, Xiaoyun
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    Abstract
    Essential 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.
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    http://hdl.handle.net/1903/5868
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    • Institute for Systems Research Technical Reports

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    DRUM is brought to you by the University of Maryland Libraries
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