Abstract
Developing financially viable stockmarket trading systems is a difficult, yet reasonably well understood process. Once an initial trading system has been built, the desire usually turns to finding ways to improve the system. Typically, this is done by adding and subtracting if-then style rules, which act as filters to the initial buy/sell signal. Each time a new set of rules are added, the system is retested, and, dependant on the effect of the added rules, they may be included into the system. Naturally, this style of data snooping leads to a curve-fitting approach, and the resultant system may not continue to perform well out-of-sample. The authors promote a different approach, using artificial neural networks, and following their previously published methodology, they demonstrate their approach using an existing medium-term trading strategy as an example.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Vanstone, B., Finnie, G.: An Empirical Methodology for developing Stockmarket Trading Systems using Artficial Neural Networks (2007), http://epublications.bond.edu.au/infotech_pubs/21
Elder, A.: Entries & Exits: visits to sixteen trading rooms. John Wiley and Sons, Hoboken (2006)
Guppy, D.: Trend Trading. Wrightbooks, Milton (2004)
guppytraders.com. Guppy Multiple Moving Average, www.guppytraders.com/gup329.shtml
Vanstone, B.: Trading in the Australian stockmarket using artificial neural networks, Bond University (2006)
Norgate Premium Data (2004), www.premiumdata.net
Wealth-Lab (2005), www.wealth-lab.com
Tharp, V.K.: Trade your way to Financial Freedom. McGraw-Hill, NY (1998)
Sweeney, J.: Maximum Adverse Excursion: analyzing price fluctuations for trading management. J. Wiley, New York (1996)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vanstone, B., Finnie, G. (2008). Enhancing Existing Stockmarket Trading Strategies Using Artificial Neural Networks: A Case Study. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_50
Download citation
DOI: https://doi.org/10.1007/978-3-540-69162-4_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69159-4
Online ISBN: 978-3-540-69162-4
eBook Packages: Computer ScienceComputer Science (R0)