Fuzzy logic framework for financial distress prediction: Enhancing corporate decision-making under uncertainty
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Abstract
This research aims to develop an enhanced Fuzzy Logic Framework for Financial Distress Prediction to improve corporate decision-making under uncertainty. The primary objective is to address limitations in traditional fuzzy logic models, such as static rule bases and lack of adaptability to dynamic financial conditions. To achieve this, a time-dependent fuzzy logic system is proposed, incorporating real-time financial data and adaptive learning mechanisms to improve predictive accuracy over time. The research design involves creating a dynamic fuzzy rule base, assigning weights to rules based on predictive performance, and optimizing membership functions and rule weights using real-time data. The methodology applies the proposed framework to financial indicators such as liquidity, profitability, and leverage, with a numerical example demonstrating the system's effectiveness in predicting financial distress. The results show that the model can accurately predict financial distress levels, with a predicted distress value of 0.588 compared to an actual value of 0.6. The model’s ability to update rule weights and optimize predictions over time represents a significant improvement over static fuzzy logic models. This research fills a critical gap in financial distress prediction by introducing a dynamic, adaptive fuzzy logic framework that evolves with real-time data. The model offers significant implications for both academics and industry, providing a tool for more accurate risk assessment in volatile financial environments. However, further research is needed to refine the model’s computational efficiency and test its long-term predictive capabilities across different industries
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How to Cite
Judijanto, L., & Riandari, F. (2024). Fuzzy logic framework for financial distress prediction: Enhancing corporate decision-making under uncertainty. International Journal of Basic and Applied Science, 13(1), 1–13. https://doi.org/10.35335/ijobas.v13i1.474
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M. K. Saggi and S. Jain, “A survey towards an integration of big data analytics to big insights for value-creation,” Inf. Process. Manag., vol. 54, no. 5, pp. 758–790, 2018, doi: https://doi.org/10.1016/j.ipm.2018.01.010.
A. Q. Mamo, “Applicability of Altman (1968) model in predicting financial distress of commercial banks in Kenya,” 2011. [Online]. Available: http://erepository.uonbi.ac.ke/handle/11295/12760
C. Tsai, “Financial decision support using neural networks and support vector machines,” Expert Syst., vol. 25, no. 4, pp. 380–393, 2008, doi: https://doi.org/10.1111/j.1468-0394.2008.00449.x.
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