A Spiking Neural Network for Financial Prediction

Reid, David and Hussain, Abir Jaafar and Tawfik, Hissam (2013) A Spiking Neural Network for Financial Prediction. Proceedings of International Joint Conference on Neural Networks. pp. 3111-3118. ISSN 2161-4393 (Accepted for Publication)

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In this paper a Polychronous Spiking Network was applied to financial time series prediction with the aim of exploiting the inherent temporal capabilities of the spiking neural model. The performance of this network was benchmarked against two “traditional”, rate-encoded, neural networks; a Multi-Layer Perceptron network and a Functional Link Neural Network. Three non-stationary datasets were used to test these simulations: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil.
The experiments demonstrated favourable prediction results for the spiking neural network in terms of Annualised Return, for both 1-Step and 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown, Signal-To-Noise ratio, and Normalised Mean Square Error. The results suggest that the inherent temporal characteristics of the polychronous spiking network make it a more suited architecture than traditional neural networks for use in non-stationary financial data prediction environments.

Item Type: Article
Additional Information and Comments: Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Publisher version available here: http://ieeexplore.ieee.org/document/6707140/ DOI: 10.1109/IJCNN.2013.6707140
Keywords: piking neural network, polychronisation, financial time series, non-stationary data
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: David REID
Date Deposited: 08 Mar 2017 16:38
Last Modified: 08 Mar 2017 16:38
URI: https://hira.hope.ac.uk/id/eprint/1785

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