Enterprise AI Analysis
Metaheuristic-optimized ANFIS and ANN models for stock price forecasting: evidence from the Borsa Istanbul 100 index
This study presents a comparative analysis of metaheuristic-enhanced Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models for predicting stock prices in the Borsa Istanbul 100 index. Using a comprehensive dataset of weekly data (2010–2023), the ANFIS model, improved with Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), and the ANN model demonstrated superior accuracy compared to traditional regression methods. Our findings highlight that AI-based modeling significantly improves stock price forecasts due to its adaptability to dynamic market conditions, offering crucial insights for investment strategies and risk management.
Executive Impact & Key Performance Metrics
Leveraging metaheuristic-optimized AI models significantly enhances predictive accuracy and operational efficiency in financial forecasting.
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Highest R² Score Achieved (ANN)
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MSE Reduction (ANN vs. Regression)
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MAE Reduction (ANN vs. Regression)
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ANFIS-PSO Training Time
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology Insights
Key Findings
Strategic Implications
80% RMSE Reduction
Achieved by ANFIS-PSO over standalone ANFIS, demonstrating superior optimization efficiency.
Enterprise Process Flow (PSO for ANFIS)
Initialise parameters of PSO
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Evaluate the fitness value
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Update personal best and global best
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Update velocity and position of each particle
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Termination criterion satisfied?
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Optimal result
| Model | MSE (Mean Squared Error) | MAE (Mean Absolute Error) | R² (R-squared) |
|---|---|---|---|
| Regression | 0.08233 | 0.2237 | 0.8847 |
| ANN | 0.0254 | 0.1195 | 0.9248 |
| ANFIS-PSO | 0.0261 | 0.1162 | 0.9226 |
| ANFIS-ACO | 0.0464 | 0.1684 | 0.8655 |
0.9248 R²
Achieved by ANN for BIST 100 forecasting, significantly outperforming traditional regression models.
AI Adaptability in Dynamic Markets
The study demonstrated that artificial intelligence-based modeling significantly improves stock price forecasts for the Borsa Istanbul 100 index due to its adaptability to dynamic market conditions. By integrating fuzzy logic and neural networks, these models can capture intricate, non-linear relationships between financial indicators (Gold/TL, VIX, Government Bond Yield) and stock market movements, which traditional linear models struggle to address. This enhanced adaptability is particularly crucial in emerging markets characterized by high volatility and information asymmetry.
Empowering Investment Strategies and Risk Management
For investors and portfolio managers, the AI-based models provide more accurate predictions, enabling the development of more effective investment strategies. Understanding the significant impact of factors like gold prices, bond interest rates, and the VIX index allows for better portfolio diversification and risk mitigation, especially in volatile periods. The ability to forecast stock prices with higher precision directly translates into potential for optimized returns and reduced exposure to market downturns.
Strategic Policy Making and Early Warning Systems
The models developed in this research offer critical tools for policymakers. By utilizing these AI models, governmental bodies can evaluate the potential repercussions of monetary and fiscal policy decisions at an earlier stage. This allows for proactive measures to stabilize markets and manage foreign investor sentiment, providing an early warning mechanism against market shocks and facilitating more comprehensive policy implications.
Calculate Your Potential AI ROI
Estimate the financial impact of implementing AI-driven forecasting and optimization in your enterprise.
Annual Savings Potential
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
Based on the research, here's a strategic timeline for integrating advanced AI models into your financial forecasting operations, leading to enhanced predictability and efficiency.
Phase 1: Market Expansion Validation
Test the applicability of metaheuristic-optimized ANFIS and ANN models in other emerging or developed markets to validate generalizability and robustness.
Phase 2: Enhanced Feature Integration
Incorporate additional macroeconomic factors and alternative variables (e.g., cryptoassets, sectoral indices) into the models to capture a broader range of market influences.
Phase 3: Algorithmic Diversity Exploration
Integrate and compare different meta-heuristic algorithms beyond PSO and ACO to further optimize ANFIS and ANN parameters for improved performance.
Phase 4: Next-Gen AI Adoption
Explore cutting-edge meta-heuristic algorithms like Grey Wolf Optimizer, Firefly Algorithm, Differential Evolution, and Whale Optimization for faster convergence and complex data adaptation.
Phase 5: Deep Learning Hybridization
Develop and implement advanced deep learning-based hybrid models to further enhance financial forecasting accuracy, particularly for highly volatile and complex market scenarios.
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