Adaptive evolutionary neural networks for forecasting and trading without a data-snooping bias

Sermpinis, G. , Verousis, T. and Theofilatos, K. (2016) Adaptive evolutionary neural networks for forecasting and trading without a data-snooping bias. Journal of Forecasting, 35(1), pp. 1-12. (doi: 10.1002/for.2338)

Abstract

In this paper, we present two neural-network-based techniques: an adaptive evolutionary multilayer perceptron (aDEMLP) and an adaptive evolutionary wavelet neural network (aDEWNN). The two models are applied to the task of forecasting and trading the SPDR Dow Jones Industrial Average (DIA), the iShares NYSE Composite Index Fund (NYC) and the SPDR S&P 500 (SPY) exchange-traded funds (ETFs). We benchmark their performance against two traditional MLP and WNN architectures, a smooth transition autoregressive model (STAR), a moving average convergence/divergence model (MACD) and a random walk model. We show that the proposed architectures present superior forecasting and trading performance compared to the benchmarks and are free from the limitations of the traditional neural networks such as the data-snooping bias and the time-consuming and biased processes involved in optimizing their parameters.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sermpinis, Professor Georgios
• Authors: Sermpinis, G., Verousis, T., and Theofilatos, K.
• College/School: College of Social Sciences > Adam Smith Business School > Economics
• Journal Name: Journal of Forecasting
• Publisher: John Wiley & Sons Ltd.
• ISSN: 0277-6693
• ISSN (Online): 1099-131X
• Copyright Holders: Copyright © 2015 John Wiley & Sons Ltd.
• First Published: First published in Journal of Forecasting 35(1):1-12
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher.
• Data DOI: 10.1002/for.2338