TY - GEN

T1 - Creating short-term stockmarket trading strategies using artificial neural networks: A case study

AU - Vanstone, Bruce

AU - Hahn, Tobias

N1 - © 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

PY - 2008

Y1 - 2008

N2 - 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.

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

SP - 80

EP - 84

BT - WORLD CONGRESS ON ENGINEERING 2008, VOLS I-II

A2 - Ao, SI

A2 - Gelman, L

A2 - Hukins, DWL

A2 - Hunter, A

A2 - Korsunsky, AM

PB - INT ASSOC ENGINEERS-IAENG

ER -