Creating short-term stockmarket trading strategies using Artificial Neural Networks: A Case Study
Vanstone, B. J., & Hahn, T. (2008). Creating short-term stockmarket trading strategies using artificial neural networks: A case study. Paper presented at the World Congress on Engineering 2008 (WCE 2008), Imperial College London, London, United Kingdom.
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2008 HERDC submission. FoR Code: 0199
© Copyright Newswood Limited & the International Association of Engineers, 2008
This paper won the Merit Award for the ICIIS 2008 conference.
Developing short-term stockmarket trading systems is a complex process, as there is a great deal of random noise present in the time series data of individual securities. The primary difficulty in training neural networks to identify return expectations is to find variables to help identify the signal present in the data. In this paper, the authors follow the previously published Vanstone and Finnie methodology. They develop a successful neural network, and demonstrate its effectiveness as the core element of a financially viable trading system.
Bruce J. Vanstone and Tobias Hahn. "Creating short-term stockmarket trading strategies using Artificial Neural Networks: A Case Study" Information Technology papers.. Sep. 2008.
Available at: http://works.bepress.com/tobias_hahn/2