Enterprise AI Analysis
IKNet: Interpretable Stock Price Prediction via Keyword-Guided Integration of News and Technical Indicators
IKNet proposes an explainable forecasting framework that models the semantic association between individual news keywords and stock price movements, integrating FinBERT-based contextual analysis with technical indicators to forecast next-day closing prices with enhanced transparency and predictive accuracy.
Executive Impact Summary
IKNet significantly boosts predictive performance and offers transparent insights into market drivers, critical for financial decision-making.
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RMSE Reduction vs. Baselines
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Cumulative Returns Improvement
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Rank in Sharpe Ratio
Deep Analysis & Enterprise Applications
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32.9%
Reduction in RMSE compared to baselines, demonstrating superior predictive accuracy across diverse market conditions.
18.5%
Improvement in cumulative returns, highlighting stable investment performance even under high market volatility.
IKNet vs. Baseline Models (2024 Performance)
| Model | RMSE | SMAPE | Key Advantages |
|---|---|---|---|
| IKNet (Ours) | 58.006 | 0.850 |
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| FinBERT-Attention-LSTM | 118.971 | 1.679 |
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| Transformer | 142.280 | 2.113 |
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| Ridge | 150.386 | 2.474 |
|
Source: Table 2 from research paper. Note: Lower RMSE and SMAPE indicate better performance.
Enterprise Process Flow
FinBERT-based Keyword Extraction
→
Keyword Encoding Module
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Technical Indicator Encoder
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Feature Fusion and Prediction
The IKNet architecture integrates keyword-level features from financial news with structured technical indicators. FinBERT extracts contextual embeddings, followed by nonlinear projection for efficiency. A GRU captures sequential keyword dependencies, while a Bi-LSTM processes technical indicators. These are then fused for final price prediction, enabling SHAP-based attribution at the keyword level.
Interpretable Volatility Event Analysis (August 2, 2024)
On August 2, 2024, a U.S. employment report triggered a 3.0% decline in S&P 500. IKNet's SHAP analysis highlighted negative keywords like 'tumbled', 'plunged', 'layoffs', and 'hurt' as strong indicators of this downturn, demonstrating its ability to provide contextualized explanations of market movements driven by public sentiment. Conversely, positive terms such as 'boosted' and 'expansion' had less influence.
Refers to Figure 4 from the research paper (SHAP-based interpretation of keyword contributions).
SHAP-based analysis reveals that news keywords consistently rank as top contributors, often exceeding major technical indicators in importance, acting as primary decision drivers. This capability provides fine-grained, contextual understanding of sentiment dynamics, enhancing model transparency and reliability for critical financial decisions.
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Estimated Annual Savings
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Hours Reclaimed Annually
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Your AI Transformation Roadmap
A typical implementation journey, tailored to your enterprise's unique needs and existing infrastructure.
Phase 1: Discovery & Strategy
Comprehensive assessment of your current data workflows, identification of key integration points, and strategic alignment with business objectives.
Phase 2: Data Engineering & Model Training
Establish secure data pipelines, cleanse and transform historical data, and train custom AI models like IKNet on your specific financial data and news sources.
Phase 3: Integration & Pilot Deployment
Seamless integration of the AI forecasting system into existing trading platforms or decision-support tools. Pilot deployment with real-time data for initial validation.
Phase 4: Monitoring, Optimization & Scaling
Continuous monitoring of model performance, adaptive recalibration, and scaling the solution across various asset classes or market segments.
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