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This chapter presents the experiments based on three relevant goals such as firstly, application of k-means clustering algorithm to produce two clusters and benchmarking of MGWO against other existing meta-heuristic algorithms, secondly, stock prediction through MLP neural network, and thirdly, findings are verified using statistical analysis. It demonstrates how feature selection is made through MGWO application. Besides, the benchmarking of the Result with GWO with the comparison of GWO and MGWO for available results of stock data classification is demonstrated here. The effect of stock price on various factors like Gold Price, Dollar Price, Bank Interest Rate, Foreign Direct Investment (FDI), and Inflation are presented as well. Last but not the least, the performance measurement of prediction and the comparison of proposed model in comparison with existing works are also performed here in this chapter.

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An Extensive Comparison of Metaheuristics When Training Neural Networks for Stock Prediction

  • 章节

  • 发表日期：

    2025年

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  • Debashish Das,
  • Ali Safaa Sadiq,
  • Seyedali Mirjalili

摘要

This chapter presents the experiments based on three relevant goals such as firstly, application of k-means clustering algorithm to produce two clusters and benchmarking of MGWO against other existing meta-heuristic algorithms, secondly, stock prediction through MLP neural network, and thirdly, findings are verified using statistical analysis. It demonstrates how feature selection is made through MGWO application. Besides, the benchmarking of the Result with GWO with the comparison of GWO and MGWO for available results of stock data classification is demonstrated here. The effect of stock price on various factors like Gold Price, Dollar Price, Bank Interest Rate, Foreign Direct Investment (FDI), and Inflation are presented as well. Last but not the least, the performance measurement of prediction and the comparison of proposed model in comparison with existing works are also performed here in this chapter.