Grizzly Bulls publishes financial research, market and company analysis, wealth estimates, reference material, public data, and systematic-trading products. These formats are different, but they share one editorial rule: the strength of a claim should not exceed the strength of the evidence behind it.
This page describes the sitewide editorial standard. Individual research products may use stricter, source-specific methodology and publication rules.
Named authors and researchers are not decorative bylines. Their profile should provide a stable identity and enough background or proof of work for readers to understand who is behind the material.
Lee Baileyis the founder and primary researcher currently identified across Grizzly Bulls' reviewed research and much of its editorial material. His canonical profile includes background, research principles, and a maintained portfolio of published work.
A confident sentence is not evidence. Financial claims should trace to sources appropriate to the question, preserve material dates and definitions, and leave gaps visible when the available evidence does not support a stronger conclusion.
Reported values, sourced observations, derived calculations, modeled estimates, and editorial interpretation are different kinds of claims. Grizzly Bulls should label those differences rather than presenting every number with the same certainty.
SEC filings, company disclosures, government data, court records, official datasets, and other first-party records are preferred when they directly answer the question. Secondary sources can add context, but should not silently replace stronger available evidence.
Publication dates, meaningful update dates, data snapshots, and source dates answer different questions. A routine deployment, formatting change, or shared-site edit is not a reason to make an old page look newly researched.
Grizzly Bulls uses software automation extensively, and AI-assisted tools may help with tasks such as organizing research, analyzing structured information, writing or reviewing code, drafting or editing prose, and producing media or other publication assets.
Those tools do not create independent factual authority. A generated sentence, summary, classification, or calculation is not treated as true merely because a model produced it. Material factual claims should remain grounded in the applicable source, dataset, calculation, or reviewed research record, and uncertainty should remain visible when the evidence is incomplete.
We do not add an AI label simply because software assisted somewhere in a workflow. When automation materially affects how a reader should interpret a specific result, the relevant methodology or limitation should explain that process directly.
Some Grizzly Bulls products necessarily involve estimation or modeling. Net-worth estimates can depend on private assets or incomplete ownership evidence. Trading models depend on historical data and explicit rules. Valuation or ownership calculations can combine observations from different dates.
Where those distinctions matter, pages should identify the estimate or model as such, show the material assumptions or scenario range, and avoid converting missing evidence into a precise-looking fact.
Reviewed flagship research follows the more detailed Grizzly Bulls Research standards, including methodology, limitations, public versions, data dates, and correction semantics.
Grizzly Bulls operates commercial products, including premium systematic-trading services and data-related products. Commercial value does not make a factual claim stronger.
Search demand, subscriptions, affiliate or licensing opportunities, promotion, and press interest should not determine whether unsupported evidence is promoted into a fact. Product pages can explain benefits and features, but performance, risk, financial, and research claims should remain bounded by the evidence and applicable disclosures.
Readers can report a factual error, questionable source, methodology concern, or stale claim through the public correction path. Include the exact page, claim, date, or source so the issue can be reviewed against the underlying evidence.
Material corrections or substantive reader-facing revisions should update the public record and, where applicable, the page's meaningful update date. Routine deployments, styling changes, or unrelated shared-site edits should not manufacture freshness.