Build a Segment Valuation and Intercompany Elimination Workspace

A sum-of-the-parts valuation analyst.

Value a multi-business company without counting internal revenue, assets, or corporate costs twice. Maintain a clear bridge from segment estimates to consolidated equity.

Key: segment_id + fiscal_period + valuation_scenario.

Fields: external_revenue, internal_revenue, segment_value, corporate_cost_value, elimination_id, net_claims.

Segment totals and consolidated totals require an explicit reconciliation.

Map segments and intercompany relationships.

Separate external activity from internal transactions.

Apply documented segment valuations and corporate adjustments.

Export the consolidated bridge and unresolved allocation decisions.

A segment matrix with elimination links, allocation controls, and a consolidated value waterfall.

A segment-to-consolidation mapping model.

A valuation workbook with elimination provenance and allocation sensitivities.

Runnable local application, setup commands, and a dependency lockfile.

Versioned input data, data dictionary, acquisition instructions, and rights notes.

Calculation traces, source references, assumption records, and exported review artifacts.

A deterministic baseline and tests for normal, incomplete, contradictory, and failure-recovery cases.

A recorded complete workflow with a reproducible manifest and explicit limitations.

Internal transactions are eliminated exactly once.

Corporate costs are not omitted from all segments and the consolidated bridge.

Segment definition changes remain visible across years.

Unallocated values carry explicit uncertainty rather than fabricated precision.

Keep segment estimates fixed and introduce a duplicated asset value. The corrected consolidated value falls by that duplicate.

The attached starter files are prototype inputs. Expected starter answers are visible. They are not independent holdouts.

Full target: Use public segment disclosures with fictional standalone valuation estimates and explicit corporate adjustments. Preserve unallocated items instead of distributing them silently.

Complete target contracts and expert review remain pending. Public observations are not historical release vintages.

DuckDB, pandas, openpyxl

Use an available model appropriate to the task. Record provider, model identifier, version or retrieval date, settings, cost, and latency. Use deterministic code for calculations. Models may extract, retrieve, or explain evidence. They must not invent missing records, override approval rules, or claim professional authority.

The application does not infer hidden segment profitability from incomplete disclosures.

No fabricated records, source citations, professional endorsements, or expert-adjudication claims.

No future-data leakage into dated decisions, model fitting, or historical valuation inputs.

No unauthorized payments, account restrictions, regulatory filings, or production changes.

No automated appraisal certification, tax-compliance determination, or unsupported fraud accusation.

No concealed uncertainty or silently invented inputs required for a numerical conclusion.

Correct domain calculations and justified methods with documented assumptions. Weight: 30.

Source lineage, temporal integrity, rights checks, and explicit synthetic-data labels. Weight: 25.

A working application with review, export, error handling, and reproducible state. Weight: 20.

Adversarial cases, uncertainty, missing-data behavior, and sensitivity analysis. Weight: 15.

Evidence-linked conclusions that distinguish facts, assumptions, and unresolved judgments. Weight: 10. Score each dimension from zero to two. Weighted score = sum(weight * score / 2). A failed safety gate blocks acceptance.