Financial Forecasting
Research on CNN-LSTM Stock Prediction Model Based on Adaptive Dynamic Constrained Optimization and Bidirectional EVT Correction
This paper presents an advanced CNN-LSTM-EVT hybrid model for stock price prediction that integrates dynamic parameter optimization and extreme value theory (EVT) for enhanced accuracy, stability, and risk control. The model improves upon traditional methods by addressing challenges like incomplete feature extraction and extreme event prediction, offering a robust solution for financial market analysis.
The Core Problem: Traditional stock prediction models struggle with the inherent randomness and non-linearity of financial markets, failing to simultaneously achieve high prediction accuracy, robust training stability, and effective quantification of extreme risks. This often leads to inaccurate forecasts and inadequate risk management, especially during market turn-downs.
Executive Impact & Quantified Value
Our AI-driven analysis of the CNN-LSTM-EVT model reveals significant potential for financial institutions and investors to achieve superior forecasting accuracy and proactive risk management. By integrating advanced deep learning with extreme value theory, the model provides a quantitative edge, enabling better investment decisions and mitigating potential losses during volatile market conditions. This translates into millions in potential savings and enhanced portfolio performance.
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Improved Prediction Accuracy
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Reduced Extreme Risk Deviation
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MAE Reduction vs. LSTM
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Enhanced Model Stability
Deep Analysis & Enterprise Applications
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Enterprise Process Flow
Data Acquisition & Preprocessing
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CNN-LSTM Core Model
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Adaptive Dynamic Optimization
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EVT Risk Quantification & Correction
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Final Prediction & VaR Output
32.46 CNY
Average Absolute Error (MAE) Improvement
The CNN-LSTM-EVT model achieved an MAE of 32.46 CNY, demonstrating a significant improvement over traditional models (e.g., Single LSTM: 37.03 CNY, Traditional CNN-LSTM: 36.34 CNY).
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Real-world Application: Shanghai & Shenzhen 300 Index
The model was tested using daily trading data from the Shanghai and Shenzhen 300 Index (sh.000300) from 2015 to 2024. This dataset, known for its strong market representation, allowed for a robust validation of the model's capabilities in real-world volatile market conditions.
Outcome: The experiment demonstrated that the CNN-LSTM-EVT model effectively addressed challenges like weak local feature extraction and low prediction accuracy in extreme scenarios, particularly in identifying extreme decline risks, leading to more robust and accurate forecasts.
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Your AI Implementation Roadmap
A structured approach to integrating advanced AI financial forecasting into your enterprise operations.
Phase 1: Discovery & Strategy (2-4 Weeks)
Comprehensive assessment of your current data infrastructure, financial processes, and specific forecasting needs. Define clear objectives and a tailored AI strategy, including data governance and integration planning.
Phase 2: Model Development & Customization (6-12 Weeks)
Design and develop the CNN-LSTM-EVT model, customizing it with your historical data. This includes feature engineering, hyperparameter tuning, and initial model training to align with your unique market conditions.
Phase 3: Validation & Refinement (4-8 Weeks)
Rigorous backtesting and validation of the model's performance against historical data, including stress-testing for extreme events. Iterative refinement based on performance metrics and stakeholder feedback to ensure robust accuracy and stability.
Phase 4: Integration & Deployment (3-6 Weeks)
Seamless integration of the AI forecasting system into your existing trading platforms, risk management tools, and reporting infrastructure. Full deployment and configuration for real-time operation and continuous monitoring.
Phase 5: Performance Monitoring & Optimization (Ongoing)
Continuous monitoring of model performance, data drift, and market changes. Regular updates and re-training of the model to maintain optimal accuracy and adapt to evolving financial landscapes, ensuring long-term value.
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