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Learning Trading Strategies for Imperfect Markets

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Neural Networks and the Financial Markets

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

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Abstract

In the last decade much interest has been shown in the possibility of using sophisticated forecasting techniques as the basis of trading systems that will beat the market. Recent empirical evidence has indicated that financial markets can exhibit some degree of predictable behaviour (as described in Chapter 3). These results are justified on the basis that markets are only truly efficient, or unpredictable, with respect to information or modelling techniques that are commonly available to other market participants. Sophisticated modelling techniques in effect generate new information with respect to which markets are not necessarily efficient. In principle, this effect is encapsulated in the relative efficient market hypothesis (Lo and MacKinlay, 1999).

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© 2002 Springer-Verlag London

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Towers, N., Burgess, A.N. (2002). Learning Trading Strategies for Imperfect Markets. In: Shadbolt, J., Taylor, J.G. (eds) Neural Networks and the Financial Markets. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0151-2_12

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  • DOI: https://doi.org/10.1007/978-1-4471-0151-2_12

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-531-1

  • Online ISBN: 978-1-4471-0151-2

  • eBook Packages: Springer Book Archive

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