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A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

Author

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  • Sarat Chandra Nayak

    (CMR College of Engineering & Technology)

  • Bijan Bihari Misra

    (Silicon Institute of Technology)

Abstract

Accurate prediction of stock market behavior is a challenging issue for financial forecasting. Artificial neural networks, such as multilayer perceptron have been established as better approximation and classification models for this domain. This study proposes a chemical reaction optimization (CRO) based neuro-fuzzy network model for prediction of stock indices. The input vectors to the model are fuzzified by applying a Gaussian membership function, and each input is associated with a degree of membership to different classes. A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model. CRO was chosen because it requires fewer control parameters and has a faster convergence rate. Five statistical parameters are used to evaluate the performance of the model, and the model is validated by forecasting the daily closing indices for five major stock markets. The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior. We conducted the Deibold-Mariano test to check the statistical significance of the proposed model, and it was found to be significant. This model can be used as a promising tool for financial forecasting.

Suggested Citation

  • Sarat Chandra Nayak & Bijan Bihari Misra, 2019. "A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-34, December.
    • Handle: RePEc:spr:fininn:v:5:y:2019:i:1:d:10.1186_s40854-019-0153-1
    DOI: 10.1186/s40854-019-0153-1
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    References listed on IDEAS

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    2. Muhammad Aslam & Rehan Ahmad Khan Sherwani & Muhammad Saleem, 2021. "Vague data analysis using neutrosophic Jarque–Bera test," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-9, December.
    3. Xiao, Hui & Cao, Minhao, 2020. "Balancing the demand and supply of a power grid system via reliability modeling and maintenance optimization," Energy, Elsevier, vol. 210(C).
    4. Salil Madhav Dubey & Hari Mohan Dubey & Surender Reddy Salkuti, 2022. "Modified Quasi-Opposition-Based Grey Wolf Optimization for Mathematical and Electrical Benchmark Problems," Energies, MDPI, vol. 15(15), pages 1-29, August.
    5. Sudersan Behera & Sarat Chandra Nayak & A. V. S. Pavan Kumar, 2024. "Evaluating the Performance of Metaheuristic Based Artificial Neural Networks for Cryptocurrency Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1219-1258, August.
    6. Sanjib Kumar Nayak & Sarat Chandra Nayak & Subhranginee Das, 2021. "Modeling and Forecasting Cryptocurrency Closing Prices with Rao Algorithm-Based Artificial Neural Networks: A Machine Learning Approach," FinTech, MDPI, vol. 1(1), pages 1-16, December.

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