Calculate risk adjusted npv

What it's for

Gives CEOs, investors, and product owners one inspectable economic hurdle that includes both expected discounted value and represented severe downside.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "cvar_loss_penalty": 0.4,
  "minimum_risk_adjusted_npv": 0,
  "period_discount_rate": 0.025,
  "scenarios": [
    {
      "cash_flows": [
        -500,
        -150,
        100,
        150,
        200
      ],
      "id": "downside",
      "probability": 0.15
    },
    {
      "cash_flows": [
        -500,
        100,
        250,
        300,
        350
      ],
      "id": "base",
      "probability": 0.55
    },
    {
      "cash_flows": [
        -500,
        200,
        400,
        500,
        600
      ],
      "id": "upside",
      "probability": 0.3
    }
  ],
  "tail_probability": 0.15
}

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "assumptions": [
    "Cash flows are incremental, after-tax or pre-tax consistently, in one currency and price basis.",
    "Scenario probabilities and joint cash-flow paths were frozen before the decision.",
    "The CVaR penalty weight is an explicit risk preference, not an empirically estimated fact."
  ],
  "configuration": {
    "confidence_level": 0.9,
    "cvar_loss_penalty": 0.4,
    "linearity_check_absolute_error": 0,
    "period_count": 5,
    "period_discount_rate": 0.025,
    "scenario_count": 3,
    "tail_probability": 0.15
  },
  "decision": "risk_adjusted_npv_clears_hurdle",
  "method": "scenario_cvar_risk_adjusted_npv_v1",
  "scenario_diagnostics": [
    {
      "is_loss_tail": true,
      "npv": -230.68,
      "probability": 0.15,
      "scenario_id": "downside"
    },
    {
      "is_loss_tail": false,
      "npv": 431.1771,
      "probability": 0.55,
      "scenario_id": "base"
    },
    {
      "is_loss_tail": false,
      "npv": 1083.7178,
      "probability": 0.3,
      "scenario_id": "upside"
    }
  ],
  "summary": {
    "cvar_downside_penalty": 92.272,
    "cvar_loss": 230.68,
    "expected_npv": 527.6608,
    "minimum_risk_adjusted_npv": 0,
    "npv_interval_lower": -230.68,
    "npv_interval_upper": 1083.7178,

How it works

Decision analysis — Turn uncertainty, cost and risk appetite into a defensible choice, with the reasoning left inspectable.

1. 1 Freeze incremental cash-flow paths in one currency, tax and price basis, period cadence, scenario probabilities, and discount rate before evaluating the investment.
2. 2 Discount every complete scenario path, verify expected-flow and probability-weighted scenario NPV agree by linearity, and calculate positive-NPV probability plus loss VaR/CVaR.
3. 3 Subtract the explicitly governed CVaR downside penalty from expected NPV and compare the resulting certainty-adjusted value with the accountable owner's hurdle.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "discount aligned scenario cashflow paths expose" }
  → finds "calculate_risk_adjusted_npv"

gitrevio_capability_describe
  { "capability_id": "calculate_risk_adjusted_npv" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "calculate_risk_adjusted_npv", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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