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Adaptive Evolutionary Neural Networks for Forecasting and Trading without a Data‐Snooping Bias
Author(s) -
Sermpinis Georgios,
Verousis Thanos,
Theofilatos Konstantinos
Publication year - 2016
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2338
Subject(s) - benchmark (surveying) , computer science , autoregressive model , artificial neural network , convergence (economics) , trading strategy , divergence (linguistics) , random walk , perceptron , multilayer perceptron , econometrics , artificial intelligence , machine learning , economics , statistics , mathematics , linguistics , philosophy , geodesy , economic growth , geography
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.