KappaSignal AI
*version: v2.2
| Capability | Benchmark | % Value | Explanation |
|---|---|---|---|
| Forecasting* | Time Series (Math) |
82.4% | The model exhibits high accuracy in predicting future stock prices, with 82% of predictions falling within expected ranges. |
| Feature Engineering | Financial Ratios (Math) |
75.2% | The model effectively identifies and utilizes 75% of relevant financial ratios to improve prediction accuracy. |
| Sentiment Analysis | News & Social Media (MathVista) |
65.3% | The model accurately interprets 65% of news and social media sentiment, capturing market sentiment and its impact on stock prices. |
| Risk Management | VaR & CVaR (Math) (FinMath*) |
78.6% | The model provides 78% effective risk estimation using Value at Risk (VaR) and Conditional Value at Risk (CVaR), accurately quantifying potential losses. |
| Anomaly Detection | Outlier Detection (Math) (FinMath*) |
72.4% | The model detects 72% of market anomalies, such as sudden price spikes or drops, which could indicate potential trading opportunities or risks. |
| Explainability | SHAP Values GPQA (FinMath*) |
80.3% | The model provides clear explanations for 80% of its predictions, identifying the most important features and their contribution to the final outcome. |
| Scalability | Real-time Processing (Natural2Code) |
82.4% | The model can handle 82% of real-time data processing demands, enabling fast and efficient predictions for high-frequency trading. |
| Robustness | Backtesting & Stress Testing (FinMath*) |
77.1% | The model demonstrates 77% robustness across different market conditions and stress tests, indicating its resilience to various market scenarios. |
Notes
*Delisting does not necessarily equate to a company's demise. These companies often transition to over-the-counter (OTC) markets, where they continue to trade. However, OTC markets typically have less stringent reporting requirements compared to major exchanges. This can lead to a decrease in data availability and, consequently, a potential reduction in the accuracy of stock valuations for these delisted companies.
*FinMath is a benchmark that evaluates AI prediction models in financial mathematics using financial instruments, market scenarios, and key financial metrics to assess their performance, risk-adjusted returns, and robustness in real-world conditions.