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Challenge

Tokenized Private Market Fund Asset Valuation Engine

MAS initiatives to strengthen Singapore's asset management hub place heavy emphasis on private asset tokenization and automated NAV calculation. Build an autonomous valuation agent that continuously ingests illiquid asset performance metrics and updates fund NAVs. Achieve reliable valuation outputs with verifiable mathematical consistency.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build an automated private market tokenized asset valuation and NAV pipeline for Singapore fund managers.

Delivery guide

How work is evaluated

Evaluation

Evaluates NAV calculation precision and adherence to VCC reporting schema.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

nav_math_check

Verifies that token price exactly matches formula output.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

valuation_precision

Absolute precision error on token NAV calculation. • target: 0.0001 • range: 0-0.01

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Implement automated Net Asset Value (NAV) pricing functions for illiquid tokenized assets

  • Ingest private equity quarterly cash flow files and apply discounted cash flow (DCF) models

  • Validate schema outputs against Singapore Variable Capital Company (VCC) reporting standards

  • Deploy automated audit trail logging for real-time fund manager inspection

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic financial holding statements and cash flow projections for Singapore Variable Capital Companies (VCCs).

Challenge setup

Evaluates NAV calculation precision and adherence to VCC reporting schema.

Challenge input

JSON portfolio holding summary

Pydantic

Standard validation library for structural financial models.

Evaluated output

JSON computed NAV and token price

Evaluation checks
  • Verifies that token price exactly matches formula output.
  • Absolute precision error on token NAV calculation. • target: 0.0001 • range: 0-0.01
Configured evaluation evidence
  • Benchmark: FundVal-Math-Bench
  • Valuation Precision target: 0.0001
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Zed · Required
  • Ray Tune · Optional
  • Google ADK · Optional

Evaluation contract

  • nav_math_check · Weight 1
  • valuation_precision · Weight 1

Metrics

  • valuation_precision · Target 0.0001

Benchmarks

  • FundVal-Math-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON portfolio holding summary

    Output: JSON computed NAV and token price

    Execution harness: Python

Evaluation tools

  • Pydantic · Data Schema Enforcement

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Tokenized Private Market Fund Asset Valuation Engine