Creating short-term stockmarket trading strategies using artificial neural networks: A case study

Bruce Vanstone*, Tobias Hahn

*Corresponding author for this work

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

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Abstract

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.

Original languageEnglish
Title of host publicationWORLD CONGRESS ON ENGINEERING 2008, VOLS I-II
EditorsSI Ao, L Gelman, DWL Hukins, A Hunter, AM Korsunsky
PublisherINT ASSOC ENGINEERS-IAENG
Pages80-84
Number of pages5
ISBN (Print)978-988-98671-9-5
Publication statusPublished - 2008
EventWorld Congress on Engineering 2008 - London, United Kingdom
Duration: 2 Jul 20084 Jul 2008
http://www.iaeng.org/WCE2008/index.html

Publication series

NameLecture Notes in Engineering and Computer Science
PublisherINT ASSOC ENGINEERS-IAENG

Conference

ConferenceWorld Congress on Engineering 2008
Abbreviated titleWCE 2008
CountryUnited Kingdom
CityLondon
Period2/07/084/07/08
Internet address

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