Build a Reverse Valuation Expectations Explorer

An equity research analyst investigating implied expectations.

Solve for operating assumptions consistent with a supplied market value. Show multiple feasible explanations and cases where the inverse problem has no defensible solution.

Key: scenario_id + free_parameter + fixed_assumption_set.

Fields: target_value, parameter_bounds, cashflow_model_version, discount_convention, solver_tolerance.

Identify which parameters are fixed. One price cannot identify many unconstrained assumptions.

Define a bounded forward valuation model.

Solve one declared inverse parameter at a time.

Test alternative fixed assumptions and boundary solutions.

Export feasible sets, residuals, and non-identification warnings.

An implied-assumption chart with fixed-input controls, solver diagnostics, and alternative scenario comparisons.

A forward and inverse valuation engine.

A report separating mathematical solutions from plausible business 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.

Forward evaluation of a solution reproduces the target within tolerance.

Out-of-bounds solutions return an explicit failure.

Multiple feasible solutions remain visible.

Solver convergence is not labeled proof of a market expectation.

The same terminal value implies different cash flow when the discount-growth spread changes.

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

Full target: Use a fictional target enterprise value, public historical operating context, and bounded analyst-supplied forecast parameters. No live trading data is required.

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

SciPy, NumPy, 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.

Implied assumptions are conditional model outputs, not observed investor beliefs.

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.