Performance Fees
Independently recalculate performance fees and expose how they are built up.
Performance Fees independently recalculates performance fees and compares them against the administrator's figures, while exposing the building blocks behind the calculation. It handles common models such as High Water Mark and fictive-asset (benchmark or spread) approaches, including caps, floors, and crystallization on redemptions. It answers: is the performance fee charged to this share class correct, and how was it built up?
How it works
Using data such as gross asset value, shares outstanding, subscriptions, redemptions, dividends, and FX rates, it reproduces the performance fee as a percentage of overperformance, the amount by which the fund's value exceeds a moving lower bound. That lower bound can be defined in several ways, such as a High Water Mark or a fictive asset that tracks a benchmark and spread, and the model also handles flows, crystallization on redemptions, caps and floors, and configurable reset calendars. It flags an anomaly when the administrator's daily or cumulative figures look abnormal against this independent calculation.
What you get
- A comparison of daily and cumulative performance fees (administrator versus independent calculation).
- A comparison of crystallized performance-fee amounts.
- The intermediary metrics behind the fee, such as the High Water Mark, benchmark return, and gross asset value.
- The evolution of subscriptions, redemptions, and dividends over time, plus event alerts on abnormal daily or cumulative deviations.
How to use it in Spark
The recalculated fees and their inputs arrive as data in your workspace. Review them in a data view, chart the build-up on a canvas, and let a workflow or the Agent flag deviations for review.