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Reconcile conglomerate valuation perimeters
Align segment earnings, consolidated minority interests and separately held equity stakes before valuation.
Reconcile conglomerate valuation perimeters is a free AI workflow for the job "Handle money and investing". It guides your AI through several skills in order, passing each step's output to the next.
Free · No account · Made by AI Blocks · Level: advanced · Thinking: high
What it does
A linked perimeter ledger for segment profit, consolidated EV and nonoperating holdings. Public-market financial modelling. The governing file preserves the component methods, input coverage and acceptance checks through each handoff.
You give it
- Segment reconciliations, consolidation perimeter, market equity, subsidiary ownership, parent-only balances and dated holding values.
- Reporting date, currency and scale, fiscal calendar, accounting basis and authorized scope.
- Available spreadsheet tools and required native output format.
You get
- A linked perimeter ledger for segment profit, consolidated EV and nonoperating holdings.
- Original step files, financial assumptions, source coverage and a governing handoff summary.
- Reconciliation results, unresolved inputs and actual checks completed.
Use it when
- You need a linked perimeter ledger for segment profit, consolidated EV and nonoperating holdings.
- You work on public-market financial modelling and need traceable calculations.
- A reviewer needs to trace the result back to inputs and assumptions.
Steps
- Segment profit elimination bridge: Tie segment profit to consolidated operating profit before using segment values.
- Minority-interest multiple reconciliation: Match consolidated earnings with an enterprise-value numerator that includes outside subsidiary ownership.
- Listed holding look-through NAV: Reconcile quoted ownership stakes and holding-company costs to look-through value per share.
How to use it
- Download the complete pack and read its governing SKILL.md.
- Share the actual sources and the financial question you need answered.
- Ask your AI to follow the methods, preserve original files and stop affected work on missing evidence.
- Review assumptions, reconciliations and CHECKS MISSED before relying on the result.