Enhancing Financial Decision-Making in SMEs: Improving
Forecasting Accuracy for Sustainable Growth
Sayed Elkenawy1,2,*
1School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT),
Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain
2Jadara Research Center, Jadara University, Irbid 21110, Jordan
Email: sayed.elkenawy@polytechnic.bh
Abstract
The growing complexity of financial decision-making in Small and Medium-Sized Enterprises (SMEs)
necessitates advanced predictive models capable of accurately forecasting financial outcomes such as revenue,
profit margins, and cash flow. Despite the availability of various machine learning models, there remains
a need for optimization techniques that enhance model accuracy, generalization, and efficiency. This paper
addresses this gap by applying metaheuristic optimization strategies to improve the performance of baseline
financial forecasting models, particularly the Logarithmic Transformation (LogTrans) model. We propose
the integration of several state-of-the-art metaheuristic algorithms, including Simulated Simulated Annealing
(SSO), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WAO), and others, to optimize
hyperparameters and perform feature selection. Our results demonstrate that the optimized SSO + LogTrans
configuration outperforms all other models, achieving a remarkable Mean Squared Error (MSE) of 1.95E-07,
a Root Mean Squared Error (RMSE) of 4.42E-04, and a high R-squared (R²) value of 0.966. These findings
indicate that metaheuristic-driven optimization significantly improves predictive accuracy and generalization
capability in SME financial decision-making models. The implications of this study extend beyond SMEs,
offering potential applications in industries such as banking, investment, and insurance, where precise financial
forecasting is critical. Furthermore, our approach highlights the importance of metaheuristics in the automated
optimization of machine learning models, paving the way for further advancements in real-time decision
support systems for dynamic financial environments.
Keywords: Financial Forecasting; Metaheuristic Optimization; Small and Medium-Sized Enterprises (SMEs);
Machine Learning Models; Inancial Decision-Making