AI Blocks › Handle money and investing
Build a clean-surplus residual income model
Link common equity movements to residual income and a separate stable price-to-book scenario.
Build a clean-surplus residual income model 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 common-equity and residual-income workbook with explicit clean-surplus exceptions. Public-market financial modelling. The governing file preserves the component methods, input coverage and acceptance checks through each handoff.
You give it
- Common equity history, ordinary earnings, distributions, OCI, capital movements, forward ROE and cost-of-equity assumptions.
- Reporting date, currency and scale, fiscal calendar, accounting basis and authorized scope.
- Available spreadsheet tools and required native output format.
You get
- A linked common-equity and residual-income workbook with explicit clean-surplus exceptions.
- 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 common-equity and residual-income workbook with explicit clean-surplus exceptions.
- You work on public-market financial modelling and need traceable calculations.
- A reviewer needs to trace the result back to inputs and assumptions.
Steps
- Book value per share roll-forward: Reconcile retained earnings, capital movements and closing shares to book value per share.
- One-period residual income bridge: Measure one period’s earnings above the required return on opening common equity.
- Justified price-to-book scenario: Link a stable price-to-book scenario to forward ROE, retention and required equity return.
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.