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Stock trend forecasting in turbulent market periods using neuro-fuzzy systems

Author

Listed:
  • George S. Atsalakis

    (Technical University of Crete)

  • Eftychios E. Protopapadakis

    (Technical University of Crete)

  • Kimon P. Valavanis

    (University of Denver)

Abstract

This paper presents a neuro-fuzzy based methodology to forecast short-term stock trends during turbulent stock market periods. The methodology uses two adaptive neuro-fuzzy inference systems; the controller and the stock market process. The model is based on inverse control theory that simulates the stock market dynamics; enabling 1 day ahead forecasting. The proposed methodology is tested and evaluated using real stock shares data of the New York Stock Exchange. Data demonstrates transactions that occurred during four turbulent market periods: the Black Monday of October 19, 1987, the Russian crisis of 1998, the 11th of September 2001 crisis and the credit crisis of 2008.

Suggested Citation

  • George S. Atsalakis & Eftychios E. Protopapadakis & Kimon P. Valavanis, 2016. "Stock trend forecasting in turbulent market periods using neuro-fuzzy systems," Operational Research, Springer, vol. 16(2), pages 245-269, July.
    • Handle: RePEc:spr:operea:v:16:y:2016:i:2:d:10.1007_s12351-015-0197-6
    DOI: 10.1007/s12351-015-0197-6
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    Cited by:

    1. Mehmet Sahiner & David G. McMillan & Dimos Kambouroudis, 2023. "Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(3), pages 723-762, September.
    2. Muhammad Zubair Mumtaz, 2021. "Predicting Stock Indices Trends using Neuro-fuzzy Systems in COVID-19," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 26(2), pages 1-18, July-Dec.
    3. Zuzana JANKOVÁ & Petr DOSTÁL, 2022. "Evaluation of the Degree of Uncertainty in the Type-2 Fuzzy Logic System for Forecasting Stock Index," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 41-57, December.
    4. Zongxin Zhang & Ying Chen, 2022. "Tail Risk Early Warning System for Capital Markets Based on Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 901-923, October.
    5. Tai Vo-Van & Ha Che-Ngoc & Nghiep Le-Dai & Thao Nguyen-Trang, 2022. "A New Strategy for Short-Term Stock Investment Using Bayesian Approach," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 887-911, February.

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