Creating trading systems with fundamental variables and neural networks: The Aby case study

Bruce Vanstone*, Gavin Finnie, Tobias Hahn

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

18 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)78-91
Number of pages14
JournalMathematics and Computers in Simulation
Publication statusPublished - 2012


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