Monitoring Deviation from Investment Policy


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

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Business Problem

  • 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.

 

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Solution

  • 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.

 

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Benefits

  • 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.