Build a Hybrid Exit and Continuing-Company Allocation Planner

A valuation specialist assessing companies with both near-term exit and continued-operation possibilities.

Combine explicit exit scenarios with a continuing-company option-pricing branch. Make conditional probabilities and class allocations visible so the two approaches do not count value twice.

Key: branch_id + parent_branch_id + class_id.

Fields: conditional_probability, branch_value_asof, allocation_method, class_units, discount_state.

Keep branch probabilities conditional. A branch value must state whether it is already discounted.

Build a mutually exclusive scenario tree.

Apply the appropriate allocation method within each branch.

Aggregate present-valued class outcomes using branch probabilities.

Export a tree-level reconciliation and method comparison.

An editable scenario tree with class distributions, probability checks, and method-change comparisons.

A typed scenario tree and hybrid allocation calculator.

A report explaining why each branch uses its selected method.

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.

Leaf probabilities reconcile to one.

Do not add an unconditional company value to a probability-weighted branch.

Branch class allocations reconcile before aggregation.

Moving all probability to one branch reproduces that branch exactly.

Eliminating the explicit exit branch must leave the continuing-company result, not a blended residual.

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

Full target: Create fictional near-term sale branches and a continuing-company branch. Supply a rights-consistent option-pricing result for the continuing branch.

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

NumPy, SciPy, 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.

A hybrid model requires a documented rationale. Its complexity alone does not improve reliability.

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.