Close to 50% deviation discovered, leading to a major review and reassessment of operational processes, and incentive structures.
Automatically interpret research notes and report on divergence of analyst ratings from the sentiment expressed in the research note
- Divergence of analyst ratings from the sentiment expressed in a research note was a serious concern from a performance and compliance perspective at a large Hedge Fund.
- While deviation was expected to be significant, there was no practical way of assessing 100,000 research notes, written in cryptic fashion.
- RAGE-AI deep learning applied to interpret all 100,000 research notes with >95% accuracy in a short period of time.
- Built in machine learning, both autonomous and assisted
Intelligent Machine that can assess both cryptic analyst notes and more conventional long text in the research notes.
- Close to 50% deviation discovered leading to a major review and reassessment of operational processes, incentive structures, etc.
- Drill down to differences in analyst ratings and sentiments in research notes at a highly granular level.
Intuitive, comprehensive and actionable view of dependencies and relationships between people, geography , entity as discovered from the corpus of research notes.