Enhancing existing stockmarket trading strategies using artificial neural networks: A case study

Bruce Vanstone*, Gavin Finnie

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationNeural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
Pages478-487
Number of pages10
Volume4985 LNCS
EditionPART 2
DOIs
Publication statusPublished - 2008
Event14th International Conference on Neural Information Processing, ICONIP 2007 - Kitakyushu, Japan
Duration: 13 Nov 200716 Nov 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4985 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference14th International Conference on Neural Information Processing, ICONIP 2007
CountryJapan
CityKitakyushu
Period13/11/0716/11/07

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    Vanstone, B., & Finnie, G. (2008). Enhancing existing stockmarket trading strategies using artificial neural networks: A case study. In Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers (PART 2 ed., Vol. 4985 LNCS, pp. 478-487). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4985 LNCS, No. PART 2). https://doi.org/10.1007/978-3-540-69162-4_50