Financial Sector-Ready Framework for Corporate Performance
Forecasting Using Football Optimization
Marwa M. Eid1,2,∗, Asifa Iqbal3, Shahid Mahmood4, S. K. Towfek5,6
1Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt
2Jadara Research Center, Jadara University, Irbid 21110, Jordan
3School of international languages Zhengzhou University, Henan, China
4School of Finance and Economics, Jiangsu University, Zhenjiang, People’s Republic of China
5Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
6Applied Science Research Center. Applied Science Private University, Amman, Jordan
Emails: mmm@ieee.org; asifaiqbal615@gmail.com; shahidnajam786@live.com; sktowfek@jcsis.org
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. Tra-
ditional 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 bridg-
ing 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 Optimiza-
tion Algorithm (FbOA); Deep Learning for Financial Analytics; Corporate Economic Performance