Enhancing stockmarket trading performance with ANNs

Artificial neural networks (ANNs) have been repeatedly and consistently applied to the domain of trading financial time series, with mixed results. Many researchers have developed their own techniques for both building and testing such ANNs, and this presents a difficulty when trying to learn lessons and compare results. In a previous paper, Vanstone and Finnie have outlined an empirical methodology for creating and testing ANNs for use within stockmarket trading systems. This paper demonstrates the use of their methodology, and creates and benchmarks a financially viable ANN-based trading system. Many researchers appear to fail at the final hurdles in their endeavour to create ANN-based trading systems, most likely due to their lack of understanding of the constraints of real-world trading. This paper also attempts to address this issue.