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Designing Short Term Trading Systems with Artificial Neural Networks

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 39))

There is a long established history of applying Artificial Neural Networks (ANNs) to financial data sets. In this paper, the authors demonstrate the use of this methodology to develop a financially viable, short-term trading system. When developing short-term systems, the authors typically site the neural network within an already existing non-neural trading system. This paper briefly reviews an existing medium-term long-only trading system, and then works through the Vanstone and Finnie methodology to create a short-term focused ANN which will enhance this trading strategy. The initial trading strategy and the ANN enhanced trading strategy are comprehensively benchmarked both in-sample and out-of-sample, and the superiority of the resulting ANN enhanced system is demonstrated.

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References

  1. Vanstone, B. and Finnie, G. (2007). “An Empirical Methodology for developing Stockmarket Trading Systems using Artificial Neural Networks.” Expert Systems with Applications. In-Press (DOI: http://dx.doi.org/10.1016/j.eswa.2008.08.019).

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© 2009 Springer Science+Business Media B.V

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Vanstone, B., Finnie, G., Hahn, T. (2009). Designing Short Term Trading Systems with Artificial Neural Networks. In: Ao, SI., Gelman, L. (eds) Advances in Electrical Engineering and Computational Science. Lecture Notes in Electrical Engineering, vol 39. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2311-7_34

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  • DOI: https://doi.org/10.1007/978-90-481-2311-7_34

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-2310-0

  • Online ISBN: 978-90-481-2311-7

  • eBook Packages: EngineeringEngineering (R0)

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