# Returns Analysis

> IRR/MOIC scenario analysis for private investments — entry valuation, hold period, exit assumptions, and sensitivity to key drivers.

- Skill: `hvkshetry/returns-analysis` (Agent Skill)
- Install (CLI): `npx skillmds@latest add hvkshetry/returns-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/hvkshetry/returns-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: hvkshetry (https://skillmd.com/u/hvkshetry)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/hvkshetry/returns-analysis

---


# /returns-analysis — IRR/MOIC Scenario Analysis

Model investor-outcome scenarios for private or illiquid investments.

## MCP Tool Map

- Illiquid asset context: `finance-graph.list_assets`, `finance-graph.list_valuation_observations`, `finance-graph.get_ownership_graph`
- Market/rates context: `market-intel-direct.get_market_snapshot`, `market-intel-direct.get_fred_series`
- Comparable exit context: `sec-edgar.sec_edgar_financial`, `sec-edgar.sec_edgar_filing`

## Workflow

### Step 1: Investment Parameters

- Entry valuation and investment amount.
- Ownership percentage, instrument type, and rights.
- Expected hold period and liquidity constraints.

### Step 2: Base Case Modeling

- Project operating performance over hold period.
- Determine exit valuation using comparable transactions or multiple-based approach.
- Calculate gross IRR and MOIC for the base case.

### Step 3: Scenario Analysis

- **Bull case**: Accelerated growth, multiple expansion, early exit.
- **Base case**: Plan-line performance, stable multiples.
- **Bear case**: Slower growth, margin compression, delayed exit or down-round.
- Calculate IRR and MOIC for each scenario.

### Step 4: Sensitivity Analysis

- Key driver sensitivities: revenue growth, exit multiple, hold period, dilution.
- Identify break-even assumptions for minimum acceptable return threshold.

### Step 5: Output

- Scenario summary table (IRR, MOIC, exit value per scenario).
- Sensitivity matrix on top two drivers.
- Key risks and mitigants.
- Comparison to portfolio return requirements.
- Data gaps and assumption risks.