Akademik Məlumat İdarəetmə Sistemi
JOURNAL OF INTELLIGENT AND FUZZY SYSTEMS, vol.1530, pp.283-290, 2025 (SCI-Expanded)
In today’s volatile and unpredictable financial markets, accurately assessing risk is both a crucial and challenging task. Traditional forecasting models often fall short when dealing with the inherent uncertainty, rapid fluctuations, and nonlinear patterns that define modern financial environments. These conventional approaches rely heavily on precise numerical inputs and deterministic methodologies, making them less effective in capturing the complexities of dynamic market behavior. This article explores the application of fuzzy logic as an innovative and adaptive approach to financial forecasting, aiming to enhance risk assessment under uncertain conditions. By leveraging fuzzy sets, membership functions, and rule-based systems, the proposed model effectively manages ambiguous, imprecise, and incomplete data. This flexibility allows for a more nuanced and context-aware analysis of market trends, offering a significant advantage over rigid statistical models. The study highlights how fuzzy logic improves both the precision and adaptability of financial predictions, leading to better-informed decision-making for investors, analysts, and financial institutions. Results indicate that this methodology outperforms conventional techniques in identifying potential risks and market anomalies, making it a valuable tool for navigating today’s complex and fast-changing financial landscape.