Anomaly Discovery Pipeline

  • manager research
  • sales intelligence
  • eVestment
  • automation

Purpose

Research on manager universes often stops at the paper. In a data-driven sales architecture, the same dataset should continuously surface commercial opportunities: pain points, peer underperformance, and allocator-relevant anomalies.

How it works

  • Frozen eVestment manager-month panels across six universes (quintiles, persistence, skill evolution, regime cuts)
  • YAML-driven auto-research loop that ranks marketable anomalies
  • Output: candidate topics for insight notes, sales alerts, and targeted consultant outreach

Example use case

Fundamental small-cap pain points surfaced as ranked candidates, connecting quantitative manager-universe research to a specific distribution action rather than a static research archive.

Strategic value

Shared data foundation for three major papers and the sales targeting layer. Research and distribution read from the same observable opportunity set.