Date of this Version

11-1-2007

Document Type

Conference Paper

Publication Details

Accepted Version.

Vanstone, B. J. & Finnie, G. (2008). Enhancing existing stockmarket trading strategies using artificial neural networks: A case study. In M. Ishikawa, K. Doya, H. Miyamoto & T. Yamakawa (Eds.), Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, revised selected papers, part II (pp. 478-487). Berlin, Germany: Springer.

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2008 HERDC submission. FoR Code: 0199

© Copyright Springer-Verlag Berlin Heidelberg, 2008

Abstract

Developing financially viable stockmarket trading systems is a difficult, yet rea-sonably 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 ap-proach, using artificial neural networks, and following their previously published methodology, they demonstrate their approach using an existing medium-term trading strategy as an example.

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This document has been peer reviewed.