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Towards Crafting Optimal Functional Link Artificial Neural Networks with Rao Algorithms for Stock Closing Prices Prediction

Author

Listed:
  • Subhranginee Das

    (KIIT University)

  • Sarat Chandra Nayak

    (CMR College of Engineering &Technology)

  • Biswajit Sahoo

    (KIIT University)

Abstract

Quite a good number of population-based meta-heuristics based on mimicking natural phenomena are observed in the literature in resolving varieties of complex optimization problems. They are widely used in search of the optimal model parameters of artificial neural networks (ANNs). However, efficiencies of these are mostly dependent on fine tuning algorithm-specific parameters. Rao algorithms are metaphor-less meta-heuristics which do not need any algorithm-specific parameters. Functional link artificial neural network (FLANN) is a flat network and possesses the ability of mapping input–output nonlinear relationships by using amplification in input vector dimension. This article attempts to observe the efficacy of Rao algorithms on searching the most favorable parameters of FLANN, thus forming hybrid models termed as Rao algorithm-based FLANNs (RAFLANNs). The models are evaluated on forecasting five stock markets such as NASDAQ, BSE, DJIA, HSI, and NIKKEI. The RAFLANNs performances are compared with that of variations of FLANN (i.e., FLANN based on gradient descent, multi-verse optimizer, monarch butterfly optimization and genetic algorithm) and conventional models (i.e., MLP, SVM and ARIMA). The proposed models are found better in terms of prediction accuracy, computation time and statistical significance test.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:compec:v:60:y:2022:i:1:d:10.1007_s10614-021-10130-9
    DOI: 10.1007/s10614-021-10130-9
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    References listed on IDEAS

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    1. Marcucci Juri, 2005. "Forecasting Stock Market Volatility with Regime-Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-55, December.
    2. 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.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Awartani, Basel M.A. & Corradi, Valentina, 2005. "Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries," International Journal of Forecasting, Elsevier, vol. 21(1), pages 167-183.
    5. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-7, March.
    6. 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.
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