Creating trading systems with fundamental variables and neural networks: The Aby case study
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid
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Yn: Mathematics and Computers in Simulation, Cyfrol 86, 12.2012, t. 78-91.
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid
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TY - JOUR
T1 - Creating trading systems with fundamental variables and neural networks: The Aby case study
AU - Vanstone, Bruce
AU - Finnie, Gavin
AU - Hahn, Tobias
PY - 2012/12
Y1 - 2012/12
N2 - The development of the Financial Crisis throughout 2008 and 2009 has made many investors and fund managers question whether growth-based investment approaches have had their day. Value-based approaches built on fundamental analysis have resurfaced again. Typically, these value-based models use fundamental variables to decide between investment opportunities. In a previous work, Vanstone et al. studied a set of filters published by Aby et al. during the dot-com crash of 2000 and subsequent aftermath, and tested and benchmarked these filters in the Australian market. The Aby filters rely on 4 different fundamental variables, and use rules with specific cut-off values to determine when to enter and exit trades. These cut-off values were found to be too restrictive for the Australian markets. This paper uses a neural network methodology by Vanstone and Finnie to develop a stockmarket trading system based on these same 4 fundamental variables, and demonstrates the important role neural networks have to play within complex and noisy environments, such as that provided by the stockmarket.
AB - The development of the Financial Crisis throughout 2008 and 2009 has made many investors and fund managers question whether growth-based investment approaches have had their day. Value-based approaches built on fundamental analysis have resurfaced again. Typically, these value-based models use fundamental variables to decide between investment opportunities. In a previous work, Vanstone et al. studied a set of filters published by Aby et al. during the dot-com crash of 2000 and subsequent aftermath, and tested and benchmarked these filters in the Australian market. The Aby filters rely on 4 different fundamental variables, and use rules with specific cut-off values to determine when to enter and exit trades. These cut-off values were found to be too restrictive for the Australian markets. This paper uses a neural network methodology by Vanstone and Finnie to develop a stockmarket trading system based on these same 4 fundamental variables, and demonstrates the important role neural networks have to play within complex and noisy environments, such as that provided by the stockmarket.
U2 - 10.1016/j.matcom.2011.01.002
DO - 10.1016/j.matcom.2011.01.002
M3 - Article
VL - 86
SP - 78
EP - 91
JO - Mathematics and Computers in Simulation
JF - Mathematics and Computers in Simulation
SN - 0378-4754
ER -