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Adaptive Evolutionary Neural Networks for Forecasting and Trading without a Data‐Snooping Bias

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  • Georgios Sermpinis
  • Thanos Verousis
  • Konstantinos Theofilatos

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.

Suggested Citation

  • Georgios Sermpinis & Thanos Verousis & Konstantinos Theofilatos, 2016. "Adaptive Evolutionary Neural Networks for Forecasting and Trading without a Data‐Snooping Bias," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(1), pages 1-12, January.
    • Handle: RePEc:wly:jforec:v:35:y:2016:i:1:p:1-12
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    Cited by:

    1. Flavio Barboza & Geraldo Nunes Silva & José Augusto Fiorucci, 2023. "A review of artificial intelligence quality in forecasting asset prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1708-1728, November.
    2. Joanna Bruzda, 2020. "The wavelet scaling approach to forecasting: Verification on a large set of Noisy data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 353-367, April.
    3. Joseph Zhi Bin Ling & Albert K. Tsui & Zhaoyong Zhang, 2021. "Trading Macro-Cycles of Foreign Exchange Markets Using Hybrid Models," Sustainability, MDPI, vol. 13(17), pages 1-20, September.
    4. Sermpinis, Georgios & Stasinakis, Charalampos & Rosillo, Rafael & de la Fuente, David, 2017. "European Exchange Trading Funds Trading with Locally Weighted Support Vector Regression," European Journal of Operational Research, Elsevier, vol. 258(1), pages 372-384.
    5. Kang, Haijun & Zong, Xiangyu & Wang, Jianyong & Chen, Haonan, 2023. "Binary gravity search algorithm and support vector machine for forecasting and trading stock indices," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 507-526.

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