Research Portal

An empirical methodology for developing stockmarket trading systems using artificial neural networks

Research output: Contribution to journal › Article › peer-review

Standard Standard

An empirical methodology for developing stockmarket trading systems using artificial neural networks. / Vanstone, Bruce; Finnie, Gavin.
In: Expert Systems with Applications, Vol. 36, No. 3 PART 2, 01.04.2009, p. 6668-6680.

Research output: Contribution to journal › Article › peer-review

HarvardHarvard

Vanstone, B & Finnie, G 2009, 'An empirical methodology for developing stockmarket trading systems using artificial neural networks', Expert Systems with Applications, vol. 36, no. 3 PART 2, pp. 6668-6680. https://doi.org/10.1016/j.eswa.2008.08.019

APA

Vanstone, B., & Finnie, G. (2009). An empirical methodology for developing stockmarket trading systems using artificial neural networks. Expert Systems with Applications, 36(3 PART 2), 6668-6680. https://doi.org/10.1016/j.eswa.2008.08.019

CBE

Vanstone B, Finnie G. 2009. An empirical methodology for developing stockmarket trading systems using artificial neural networks. Expert Systems with Applications. 36(3 PART 2):6668-6680. https://doi.org/10.1016/j.eswa.2008.08.019

MLA

Vanstone, Bruce and Gavin Finnie. "An empirical methodology for developing stockmarket trading systems using artificial neural networks". Expert Systems with Applications. 2009, 36(3 PART 2). 6668-6680. https://doi.org/10.1016/j.eswa.2008.08.019

VancouverVancouver

Vanstone B, Finnie G. An empirical methodology for developing stockmarket trading systems using artificial neural networks. Expert Systems with Applications. 2009 Apr 1;36(3 PART 2):6668-6680. doi: 10.1016/j.eswa.2008.08.019

Author

Vanstone, Bruce ; Finnie, Gavin. / An empirical methodology for developing stockmarket trading systems using artificial neural networks. In: Expert Systems with Applications. 2009 ; Vol. 36, No. 3 PART 2. pp. 6668-6680.

RIS

TY - JOUR

T1 - An empirical methodology for developing stockmarket trading systems using artificial neural networks

AU - Vanstone, Bruce

AU - Finnie, Gavin

PY - 2009/4/1

Y1 - 2009/4/1

N2 - A great deal of work has been published over the past decade on the application of neural networks to stockmarket trading. Individual researchers have developed their own techniques for designing and testing these neural networks, and this presents a difficulty when trying to learn lessons and compare results. This paper aims to present a methodology for designing robust mechanical trading systems using soft computing technologies, such as artificial neural networks. This paper describes the key steps involved in creating a neural network for use in stockmarket trading, and places particular emphasis on designing these steps to suit the real-world constraints the neural network will eventually operate in. Such a common methodology brings with it a transparency and clarity that should ensure that previously published results are both reliable and reusable.

AB - A great deal of work has been published over the past decade on the application of neural networks to stockmarket trading. Individual researchers have developed their own techniques for designing and testing these neural networks, and this presents a difficulty when trying to learn lessons and compare results. This paper aims to present a methodology for designing robust mechanical trading systems using soft computing technologies, such as artificial neural networks. This paper describes the key steps involved in creating a neural network for use in stockmarket trading, and places particular emphasis on designing these steps to suit the real-world constraints the neural network will eventually operate in. Such a common methodology brings with it a transparency and clarity that should ensure that previously published results are both reliable and reusable.

U2 - 10.1016/j.eswa.2008.08.019

DO - 10.1016/j.eswa.2008.08.019

M3 - Article

VL - 36

SP - 6668

EP - 6680

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 3 PART 2

ER -