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.
*The expected return formula, E(R) = Σ (Ri * Pi), calculates the anticipated average return of an investment by summing the products of each possible return (Ri) and its corresponding probability (Pi). This formula helps investors estimate potential profits or losses and assess the risk-reward trade-off before making investment decisions.