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A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting

Author

Listed:
  • Faizal Hafiz
  • Jan Broekaert
  • Davide La Torre
  • Akshya Swain

Abstract

This study proposes a new framework to evolve efficacious yet parsimonious neural architectures for the movement prediction of stock market indices using technical indicators as inputs. In the light of a sparse signal-to-noise ratio under the Efficient Market hypothesis, developing machine learning methods to predict the movement of a financial market using technical indicators has shown to be a challenging problem. To this end, the neural architecture search is posed as a multi-criteria optimization problem to balance the efficacy with the complexity of architectures. In addition, the implications of different dominant trading tendencies which may be present in the pre-COVID and within-COVID time periods are investigated. An $\epsilon-$ constraint framework is proposed as a remedy to extract any concordant information underlying the possibly conflicting pre-COVID data. Further, a new search paradigm, Two-Dimensional Swarms (2DS) is proposed for the multi-criteria neural architecture search, which explicitly integrates sparsity as an additional search dimension in particle swarms. A detailed comparative evaluation of the proposed approach is carried out by considering genetic algorithm and several combinations of empirical neural design rules with a filter-based feature selection method (mRMR) as baseline approaches. The results of this study convincingly demonstrate that the proposed approach can evolve parsimonious networks with better generalization capabilities.

Suggested Citation

  • Faizal Hafiz & Jan Broekaert & Davide La Torre & Akshya Swain, 2021. "A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting," Papers 2111.08060, arXiv.org.
    • Handle: RePEc:arx:papers:2111.08060
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    File URL: http://arxiv.org/pdf/2111.08060
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    References listed on IDEAS

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    1. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    2. Urquhart, Andrew & Hudson, Robert, 2013. "Efficient or adaptive markets? Evidence from major stock markets using very long run historic data," International Review of Financial Analysis, Elsevier, vol. 28(C), pages 130-142.
    3. Michał Buszko & Witold Orzeszko & Marcin Stawarz, 2021. "COVID-19 pandemic and stability of stock market—A sectoral approach," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-26, May.
    4. Rajagopal, 2015. "Market Trend Analysis," Palgrave Macmillan Books, in: The Butterfly Effect in Competitive Markets, chapter 4, pages 95-118, Palgrave Macmillan.
    5. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    6. Rohitash Chandra & Yixuan He, 2021. "Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-32, July.
    7. Oberlechner, Thomas, 2001. "Importance of Technical and Fundamental Analysis in the European Foreign Exchange Market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(1), pages 81-93, January.
    8. Sabri Boughorbel & Fethi Jarray & Mohammed El-Anbari, 2017. "Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-17, June.
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    Cited by:

    1. Davide La Torre & Danilo Liuzzi & Marco Repetto & Matteo Rocca, 2024. "Enhancing deep learning algorithm accuracy and stability using multicriteria optimization: an application to distributed learning with MNIST digits," Annals of Operations Research, Springer, vol. 339(1), pages 455-475, August.

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