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Article
An Empirical Methodology for Developing Stockmarket Trading Systems using Artificial Neural Networks
Expert Systems with Applications (2009)
  • Bruce J Vanstone, Bond University
  • Gavin Finnie, Bond University
Abstract

A great deal of work has been published over the past decade on the application of neural networks to stockmarket trading. Individual researchers have developed their own techniques for designing and testing these neural networks, and this presents a difficulty when trying to learn lessons and compare results. This paper aims to present a methodology for designing robust mechanical trading systems using soft computing technologies, such as artificial neural networks. This methodology describes the key steps involved in creating a neural network for use in stockmarket trading, and places particular emphasis on designing these steps to suit the real-world constraints the neural network will eventually operate in. Such a common methodology brings with it a transparency and clarity that should ensure that previously published results are both reliable and reusable

Keywords
  • neural networks,
  • articial neural networks,
  • stockmarket trading systems
Publication Date
January 1, 2009
Publisher Statement
Pre-publication print

Paper subsequently published as:
Vanstone, B., & Finnie, G. (2009). An empirical methodology for developing stockmarket trading systems using artificial neural networks. Expert Systems with Applications, 36(3), 6668-6680.

Access the publisher's website.

2009 HERDC submission. FoR Code: 0801

© Copyright Elsevier Ltd., 2009
Citation Information
Bruce J Vanstone and Gavin Finnie. "An Empirical Methodology for Developing Stockmarket Trading Systems using Artificial Neural Networks" Expert Systems with Applications Vol. 36 Iss. 3 (2009)
Available at: http://works.bepress.com/bruce_vanstone/4/