Build a Marketability Adjustment Evidence and Sensitivity Workbench

An appraisal reviewer checking common-stock marketability assumptions.

Organize evidence for a proposed marketability adjustment and expose its valuation impact. Prevent unexplained percentage haircuts from becoming accepted appraisal assumptions.

Key: adjustment_id + security_class + evidence_version.

Fields: pre_adjustment_value, proposed_discount, restriction_term, liquidity_horizon, source_locator, reviewer_state.

Distinguish observed restrictions from analyst-proposed percentages.

Collect restriction evidence and unresolved applicability questions.

Record the rationale for each proposed adjustment.

Calculate scenario sensitivities without approving a percentage.

Export a reviewer decision log and evidence packet.

A restriction evidence table, adjustment slider, value bridge, and explicit approval states.

A marketability evidence register.

A sensitivity workbook that preserves rejected and unapproved assumptions.

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.

A proposed discount remains unapproved without reviewer evidence.

Reject discounts outside zero to one.

Do not apply the same restriction adjustment twice.

The report explains that its numerical examples are not recommended discount rates.

The same pre-adjustment value produces different outputs under hypothetical discounts. Neither output establishes a defensible discount.

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

Full target: Use fictional transfer restrictions, liquidity horizons, and evidence abstracts. Supply authorized research inputs separately. No proprietary study statistics are included.

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

pandas, Pydantic, Plotly

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 20% and 35% fixtures are arbitrary arithmetic examples, not appraisal guidance.

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.