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Estimating stock closing indices using a GA-weighted condensed polynomial neural network

Author

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  • Sarat Chandra Nayak

    (CMR College of Engineering &Technology (Autonomous))

  • Bijan Bihari Misra

    (Silicon Institute of Technology)

Abstract

Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information. However, predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high volatility and nonlinearity. This paper proposes a novel condensed polynomial neural network (CPNN) for the task of forecasting stock closing price indices. We developed a model that uses partial descriptions (PDs) and is limited to only two layers for the PNN architecture. The outputs of these PDs along with the original features are fed to a single output neuron, and the synaptic weight values and biases of the CPNN are optimized by a genetic algorithm. The proposed model was evaluated by predicting the next day’s closing price of five fast-growing stock indices: the BSE, DJIA, NASDAQ, FTSE, and TAIEX. In comparative testing, the proposed model proved its ability to provide closing price predictions with superior accuracy. Further, the Deibold-Mariano test justified the statistical significance of the model, establishing that this approach can be adopted as a competent financial forecasting tool.

Suggested Citation

  • Sarat Chandra Nayak & Bijan Bihari Misra, 2018. "Estimating stock closing indices using a GA-weighted condensed polynomial neural network," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-22, December.
  • Handle: RePEc:spr:fininn:v:4:y:2018:i:1:d:10.1186_s40854-018-0104-2
    DOI: 10.1186/s40854-018-0104-2
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    Cited by:

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    6. Subhranginee Das & Sarat Chandra Nayak & Biswajit Sahoo, 2022. "Towards Crafting Optimal Functional Link Artificial Neural Networks with Rao Algorithms for Stock Closing Prices Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 1-23, June.
    7. Changshi Liu & Gang Kou & Yi Peng & Fawaz E. Alsaadi, 2019. "Location-Routing Problem for Relief Distribution in the Early Post-Earthquake Stage from the Perspective of Fairness," Sustainability, MDPI, vol. 11(12), pages 1-16, June.
    8. 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).
    9. Sarat Chandra Nayak & Bijan Bihari Misra, 2020. "Extreme learning with chemical reaction optimization for stock volatility prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-23, December.
    10. Manik Sharma & Samriti Sharma & Gurvinder Singh, 2018. "Performance Analysis of Statistical and Supervised Learning Techniques in Stock Data Mining," Data, MDPI, vol. 3(4), pages 1-16, November.
    11. Wang, Lei & Liu, Lutao, 2020. "Long-range correlation and predictability of Chinese stock prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    12. Cao, Minhao & Guo, Jianjun & Xiao, Hui & Wu, Liang, 2022. "Reliability analysis and optimal generator allocation and protection strategy of a non-repairable power grid system," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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