Integrating bioinformatics optimization techniques, support vector machines, and deep learning models for minimizing risks of financial data analysis

The increasing complexity of financial markets requires forecasting models that can capture nonlinear patterns and rapidly changing dynamics. This study integrates bioinformatics-inspired optimization techniques — genetic algorithms (GA) and artificial ant colonies (AAC) — with support vector machines (SVM) and deep learning models to enhance financial data analysis. Using BIST-100 index data spanning 2000–2023 (plus 2024 Q1), GA and AAC were optimized through parameter tuning and combined with advanced machine learning (ML) architectures. Comparative experiments demonstrate that deep learning and AAC models achieved the lowest error rates (root mean square error (RMSE) ≈ 59.16 and 67.08), outperforming GA, SVM, and autoregressive integrated moving average (ARIMA) benchmarks. Incorporating macroeconomic indicators such as exchange rates, interest rates, and oil prices further improved predictive accuracy. The findings indicate that bioinformatics optimization methods significantly improve forecast robustness, offering more precise predictions and reduced volatility sensitivity. These results highlight the transferability of bioinformatics approaches to finance, supporting their use for portfolio management, risk assessment, and strategic decision-making. The study’s conclusions underscore the potential of hybrid, bio-inspired models to reshape financial analytics and provide actionable insights for practitioners and policymakers in volatile markets.