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

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  • Vanstone, Bruce
  • Finnie, Gavin
  • Hahn, Tobias

Abstract

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.

Suggested Citation

  • Vanstone, Bruce & Finnie, Gavin & Hahn, Tobias, 2012. "Creating trading systems with fundamental variables and neural networks: The Aby case study," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 86(C), pages 78-91.
    • Handle: RePEc:eee:matcom:v:86:y:2012:i:c:p:78-91
    DOI: 10.1016/j.matcom.2011.01.002
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    Cited by:

    1. Na, Haejung & Kim, Soonho, 2021. "Predicting stock prices based on informed traders’ activities using deep neural networks," Economics Letters, Elsevier, vol. 204(C).

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