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Disclaimer
Artificial intelligence and machine learning are rapidly evolving fields of study. We are constantly working to improve our Services to make them more accurate, reliable, safe, and beneficial. However, due to the probabilistic nature of machine learning, there is always the possibility that our Services may produce incorrect output. As such, it is important to evaluate the accuracy of any output from our Services as appropriate for your use case, including by using human review. Read more...
Dataset
This analysis dives deep into a comprehensive collection of financial and macroeconomic data, armed with diverse machine learning features to unlock actionable insights in stock market modeling. Researchers, analysts, and enthusiasts will find it an invaluable resource for exploring the potential of this powerful technology in predicting market behavior.
Algorithm
In this project, Artificial neural networks* examine all scholarly research reports on stock predictions in the literature, determine the most appropriate method for the stock being studied, and publish a new forecast report with the results and references. Read more...
Accuracy
The model's performance is actively tracked in 15-minute intervals, with adjustments made in response to new data streams.
KappaSignal®
*KappaSignal is a leading financial technology firm specializing in AI-driven stock analysis and forecasting.
AUC Score
In machine learning, the area under the curve (AUC) score is a measure of the performance of a binary classifier. AUC score is calculated by plotting the true positive rate (TPR) against the false positive rate (FPR) at different classification thresholds. The AUC score is the area under the ROC curve. Read more...
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Return: --%
Based on: --
*Expected Return = E(R) = Σ (Ri * Pi)
*The accuracy of the model is being monitored on a regular basis.(15-minute period)
*These predictions are designed for a long-term investment timeframe.