Adaptive evolutionary neural networks for forecasting and trading without a data-snooping bias
Sermpinis, Georgios Verousis, Thanos Theofilatos, Konstantinos
Sermpinis, Georgios
Verousis, Thanos
Theofilatos, Konstantinos
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Journal article with impact factor
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Supervisor
Publication Year
2016
Journal
Journal of Forecasting
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Publication Volume
35
Publication Issue
1
Publication Begin page
1
Publication End page
12
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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. Copyright © 2015 John Wiley & Sons, Ltd.
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Keywords
Artificial Intelligence, Financial Forecasting, Data Snooping, Trading