Empirical validation of ELM trained neural networks for financial modelling

Volodymyr Novykov*, Christopeher M Bilson, Adrian Gepp, Geoffrey Harris, Bruce J Vanstone

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)
106 Downloads (Pure)


The purpose of this work is to compare predictive performance of neural networks trained using the relatively novel technique of training single hidden layer feedforward neural networks (SFNN), called Extreme Learning Machine (ELM), with commonly used backpropagation-trained recurrent neural networks (RNN) as applied to the task of financial market prediction. Evaluated on a set of large capitalisation stocks on the Australian market, specifically the components of the ASX20, ELM-trained SFNNs showed superior performance over RNNs for individual stock price prediction. While this conclusion of efficacy holds generally, long short-term memory (LSTM) RNNs were found to outperform for a small subset of stocks. Subsequent analysis identified several areas of performance deviations which we highlight as potentially fruitful areas for further research and performance improvement.
Original languageEnglish
Pages (from-to)1581-1605
Number of pages25
JournalNeural Computing and Applications
Issue number2
Early online date1 Oct 2022
Publication statusPublished - Jan 2023


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