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Comparative study of stock market forecasting using different functional link artificial neural networks

Author

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  • Dwiti Krishna Bebarta
  • Birendra Biswal
  • P.K. Dash

Abstract

This paper presents different forecasting functional link artificial neural network (FLANN) models to investigate and compare various time series stock data. The architecture of several FLANN models like CFLANN, LFLANN, LeF-LANN, and CEFLANN are discussed. The processing technique and experimental results are provided to investigate the prediction of stocks. This piece of work presents the training and testing of all the models by analysing and forecasting different Indian stocks like IBM, RIL and DWSG. All the forecasting models have been tested using same duration time of time series data. The experimental results illustrate that the trigonometric polynomial-based CEFLANN model outperforms the forecasting time series stock data in terms of percentage average error than the polynomial-based FLANN models. Lastly, the percentage of average error is further improved by optimising the free parameters of the trigonometric polynomial-based CEFLANN model with differential evolution algorithm (DEA).

Suggested Citation

  • Dwiti Krishna Bebarta & Birendra Biswal & P.K. Dash, 2012. "Comparative study of stock market forecasting using different functional link artificial neural networks," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(4), pages 398-427.
    • Handle: RePEc:ids:injdan:v:4:y:2012:i:4:p:398-427
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    Citations

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    Cited by:

    1. Manel Hamdi & Chaker Aloui & Santosh kumar Nanda, 2016. "Comparing Functional Link Artificial Neural Network And Multilayer Feedforward Neural Network Model To Forecast Crude Oil Prices," Economics Bulletin, AccessEcon, vol. 36(4), pages 2430-2442.
    2. Manolis Maragoudakis & Dimitrios Serpanos, 2016. "Exploiting Financial News and Social Media Opinions for Stock Market Analysis using MCMC Bayesian Inference," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 589-622, April.
    3. Jasleen Kaur & Khushdeep Dharni, 2022. "Application and performance of data mining techniques in stock market: A review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 219-241, October.

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