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American Journal of Business and Operations Research

Volume 13 , Issue 1 , PP: 24-41, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

Financial Sector-Ready Framework for Corporate Performance Forecasting Using Football Optimization

Marwa M. Eid 1 * , Asifa Iqbal 2 , Shahid Mahmood 3 , S. K. Towfek 4

  • 1 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt; Jadara Research Center, Jadara University, Irbid 21110, Jordan - (mmm@ieee.org)
  • 2 School of international languages Zhengzhou University, Henan, China - (asifaiqbal615@gmail.com)
  • 3 School of Finance and Economics, Jiangsu University, Zhenjiang, People’s Republic of China - (shahidnajam786@live.com)
  • 4 Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA; Applied Science Research Center. Applied Science Private University, Amman, Jordan - (sktowfek@jcsis.org)

    • Doi: https://doi.org/10.54216/AJBOR.130102

    Received: February 25, 2025 Revised: June 02, 2025 Accepted: August 04, 2025
    Abstract

    In today’s interconnected global economy, accurate financial forecasting is critical for strengthening corporate decision-making, mitigating investment risks, and maintaining competitive advantage over the long term. Traditional forecasting models often struggle with the complexities of high-dimensional and nonlinear financial data. To address this challenge, we present a hybrid forecasting framework that integrates advanced machine learning techniques with an intelligent optimization algorithm. Specifically, the model combines Long Short- Term Memory (LSTM) networks with the Football Optimization Algorithm (FbOA) to optimize key features and tuning parameters. This approach yields more stable, efficient, and accurate financial predictions using a compact set of influential variables. The proposed framework offers a cost-effective solution for corporate finance applications, enhancing investor confidence and supporting strategic economic development. By bridging cutting-edge AI methodologies and practical financial analytics, this study highlights the transformative potential of hybrid models in reshaping financial forecasting in dynamic markets.

    Keywords :

    Economic and Financial Forecasting , Metaheuristic Optimization in Finance , Football Optimization Algorithm (FbOA) , Deep Learning for Financial Analytics , Corporate Economic Performance

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    Cite This Article As :
    M., Marwa. , Iqbal, Asifa. , Mahmood, Shahid. , K., S.. Financial Sector-Ready Framework for Corporate Performance Forecasting Using Football Optimization. American Journal of Business and Operations Research, vol. , no. , 2025, pp. 24-41. DOI: https://doi.org/10.54216/AJBOR.130102
    M., M. Iqbal, A. Mahmood, S. K., S. (2025). Financial Sector-Ready Framework for Corporate Performance Forecasting Using Football Optimization. American Journal of Business and Operations Research, (), 24-41. DOI: https://doi.org/10.54216/AJBOR.130102
    M., Marwa. Iqbal, Asifa. Mahmood, Shahid. K., S.. Financial Sector-Ready Framework for Corporate Performance Forecasting Using Football Optimization. American Journal of Business and Operations Research , no. (2025): 24-41. DOI: https://doi.org/10.54216/AJBOR.130102
    M., M. , Iqbal, A. , Mahmood, S. , K., S. (2025) . Financial Sector-Ready Framework for Corporate Performance Forecasting Using Football Optimization. American Journal of Business and Operations Research , () , 24-41 . DOI: https://doi.org/10.54216/AJBOR.130102
    M. M. , Iqbal A. , Mahmood S. , K. S. [2025]. Financial Sector-Ready Framework for Corporate Performance Forecasting Using Football Optimization. American Journal of Business and Operations Research. (): 24-41. DOI: https://doi.org/10.54216/AJBOR.130102
    M., M. Iqbal, A. Mahmood, S. K., S. "Financial Sector-Ready Framework for Corporate Performance Forecasting Using Football Optimization," American Journal of Business and Operations Research, vol. , no. , pp. 24-41, 2025. DOI: https://doi.org/10.54216/AJBOR.130102

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