The short version: automated ingestion keeps the statement, market and estimate data current. A machine-learning layer predicts forward financial-statement line items such as operating margins, produces the factor signals the models consume, and adapts how that evidence is weighted. An automated routing layer sends every company to the valuation method its balance sheet actually justifies. Evidence checks then decide what is publishable. The workflow brings data preparation, model routing and publication checks into a common process.
Consistency Across Markets
Applying a valuation framework across markets requires consistent treatment of data, accounting conventions and business models. Method availability depends on the evidence available for each issuer.- Alignment. Price, statements, estimates, share count and exchange rates must describe the same economic moment for every company, not just the one being examined.
- Comparability. Accounting bases, reporting perimeters and fiscal calendars differ across markets. A peer multiple is meaningless when numerator and denominator sit on different bases.
- Method selection. A bank, a REIT, a mine and a software company do not share a valuation model. Choosing correctly, every time, is a routing problem.
- Evidence availability. Publication checks assess whether the available inputs support each method.
The Data Foundation
Financial statements
Aligned annual and interim periods across income statement, balance sheet and cash flow, reconstructed onto a common operating and financing basis.
Market data
Daily prices, volumes, returns and volatility with multi-decade history, plus market-value capital weights and exchange rates.
Rates and risk inputs
Dated sovereign curves, credit spreads, equity risk premiums and country risk evidence, carried with the date they were observed.
Estimates and consensus
Published analyst estimates and summary statistics, used as a market baseline and as one clearly labelled evidence source among several.
Where Machine Learning Enters
Machine learning does specific jobs in this framework. It does not replace the valuation identities and it does not choose the answer.Machine-learning estimates reflect their training data and objectives. In this framework, they provide forecast baselines, signal weights and review flags. The operating assumptions and valuation calculations remain available for inspection.
Automated Method Routing
Every company is routed to an economic archetype before any model runs. The archetype determines the primary intrinsic lens, the peer-metric family and the companion variable the reader should check. A bank is valued on equity economics because deposits and borrowings are operating raw material. A resource company is valued on a finite reserve life rather than a perpetual annuity. A holding company is valued look-through rather than on consolidated revenue. Automated routing selects metrics appropriate to the business model. Where the available evidence supports a proxy, the output labels it accordingly. See Archetype Conventions for what each archetype means.Publication Checks
Before any valuation output is published, its components must survive automated evidence checks. These verify that the security, currency and date are consistent, that a multiple and its target denominator sit on the same accounting and time basis, that the enterprise-to-equity bridge reconciles, and that enough usable observations exist to support the statistic being displayed. The checks determine which components support the published result.1
Each component is assessed independently
Each evidence source is checked on its own. One defective input changes which evidence can determine the result, and its failure reason stays in the audit trail.
2
Raw values are never clipped or overwritten
Every calculated figure is retained even when it is excluded from the published result. Readers can inspect the original calculation alongside the publication decision.
3
Method availability is explained
When the evidence cannot support a method, the output says the method is unavailable and names what is missing. It does not substitute an economically unrelated metric to fill a panel.
4
Exceptional cases receive evidence review
Where two qualified sources corroborate an extreme result, or remain irreconcilable, the case goes to human evidence review. The reviewer chooses among existing evidence or declines publication. The reviewer cannot enter a bespoke number.
The Reconstruction Test
The pipeline is built so that a reader can reproduce the result. Given revenue, margin, reinvestment, WACC, terminal growth, net debt and diluted shares, a reader should be able to reproduce the order of magnitude of intrinsic value with a calculator. Given the peer evidence and consensus statistics, a reader should be able to reproduce the market-evidence result and identify every judgment that separates the raw calculation from the published one. Calculations retain full precision internally. Displayed client figures are rounded for readability.The calculation trail helps readers assess the inputs, assumptions and publication decisions behind each result.
Analytical Roles
- The forward forecast sets out the operating case. The reverse DCF provides a separate comparison with price-implied expectations.
- Intrinsic valuation and market-evidence targets have distinct roles. A target that combines compatible sources identifies its inputs, weights and exclusions.
- Technical interpretation is grounded in the supplied price and volume metrics.
- Peer outliers are flagged and explained, with the observations retained in the evidence.
Related Pages
Scoring Methodology
How factor scores are calculated and integrated into an overall recommendation.
Financial Statements
The statement reconstruction that the pipeline performs before any forecast begins.
Archetype Conventions
How a company’s balance sheet determines which valuation method applies.
Interpreting Outputs
What the pipeline finally publishes, and how to read it.