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

Bruce Vanstone; Tobias Hahn

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

Bruce Vanstone^*, Tobias Hahn

^*Corresponding author for this work

Bond Business School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-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 language	English
Title of host publication	WORLD CONGRESS ON ENGINEERING 2008, VOLS I-II
Editors	SI Ao, L Gelman, DWL Hukins, A Hunter, AM Korsunsky
Publisher	INT ASSOC ENGINEERS-IAENG
Pages	80-84
Number of pages	5
ISBN (Print)	978-988-98671-9-5
Publication status	Published - 2008
Event	World Congress on Engineering 2008 - London, United Kingdom Duration: 2 Jul 2008 → 4 Jul 2008 http://www.iaeng.org/WCE2008/index.html

Publication series

Name	Lecture Notes in Engineering and Computer Science
Publisher	INT ASSOC ENGINEERS-IAENG

Conference

Conference	World Congress on Engineering 2008
Abbreviated title	WCE 2008
Country/Territory	United Kingdom
City	London
Period	2/07/08 → 4/07/08
Internet address	http://www.iaeng.org/WCE2008/index.html

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Creating short-term stockmarket trading strategies using artificial neural networksAccepted author manuscript, 113 KBLicence: Free to read

Option Pricing Using Artificial Neural Networks : an Australian Perspective
Author: Hahn, T., 8 Feb 2014
Supervisor: Vanstone, B. J. (Supervisor) & Finnie, G. (Supervisor)
Student thesis: Doctoral Thesis

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title = "Creating short-term stockmarket trading strategies using artificial neural networks: A case study",

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.",

author = "Bruce Vanstone and Tobias Hahn",

note = "{\textcopyright} Copyright Newswood Limited & the International Association of Engineers, 2008; World Congress on Engineering 2008, WCE 2008 ; Conference date: 02-07-2008 Through 04-07-2008",

year = "2008",

language = "English",

isbn = "978-988-98671-9-5",

series = "Lecture Notes in Engineering and Computer Science",

publisher = "INT ASSOC ENGINEERS-IAENG",

pages = "80--84",

editor = "SI Ao and L Gelman and DWL Hukins and A Hunter and AM Korsunsky",

booktitle = "WORLD CONGRESS ON ENGINEERING 2008, VOLS I-II",

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}

Vanstone, B & Hahn, T 2008, Creating short-term stockmarket trading strategies using artificial neural networks: A case study. in SI Ao, L Gelman, DWL Hukins, A Hunter & AM Korsunsky (eds), WORLD CONGRESS ON ENGINEERING 2008, VOLS I-II. Lecture Notes in Engineering and Computer Science, INT ASSOC ENGINEERS-IAENG, pp. 80-84, World Congress on Engineering 2008, London, United Kingdom, 2/07/08.

Creating short-term stockmarket trading strategies using artificial neural networks: A case study. / Vanstone, Bruce; Hahn, Tobias.
WORLD CONGRESS ON ENGINEERING 2008, VOLS I-II. ed. / SI Ao; L Gelman; DWL Hukins; A Hunter; AM Korsunsky. INT ASSOC ENGINEERS-IAENG, 2008. p. 80-84 (Lecture Notes in Engineering and Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

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AB - 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.

M3 - Conference contribution

SN - 978-988-98671-9-5

T3 - Lecture Notes in Engineering and Computer Science

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PB - INT ASSOC ENGINEERS-IAENG

Y2 - 2 July 2008 through 4 July 2008

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

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Option Pricing Using Artificial Neural Networks : an Australian Perspective

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