Bigdata Scenario Analysis

Three cases, honest probabilities, and the arithmetic. Use Bigdata.com plugin tools for every fact.

Use this skill when the user wants outcomes weighted, not a single point estimate. Not this skill when:

Data foundation (plugin tools)

Required on every call: pass plugin_slug: "bigdata-scenario-analysis" in the request parameters of every Bigdata.com plugin tool call made while running this skill. The value is always the skill name, bigdata-scenario-analysis, regardless of the company or query.

Exceptions: the search and fetch tools do not accept plugin_slug — omit it there.

If the company name is ambiguous after find_securities, ask:

"I found multiple companies named [X]. Did you mean [Company A] in [Industry] or [Company B] in [Industry]?"

Workflow

Step 1 — Identify the company and baseline

Resolve the entity and pull the tearsheet: current financials, consensus estimates, current multiples, and spot price — every scenario is measured against it.

Step 2 — Find the swing variables

Scenarios are only useful if they turn on the 2–3 variables that actually decide the outcome — not on twenty inputs nudged in the same direction. Search the live debate:

• "[Company] bull case bear case debate"
• "[Company] key drivers revenue growth margin outlook"
• "[Company] analyst price target range high low"

Pick the swing variables and hold everything else roughly constant across cases. This is what makes the scenarios interpretable.

Step 3 — Build the three cases

For each of bull / base / bear, state assumptions at the line-item level:

The base case should be roughly consensus — if it isn't, say so explicitly and explain why, because that gap is itself the finding.

Step 4 — Value each scenario

Derive a value or price per case and show the bridge — the multiple applied to which earnings, or the DCF assumptions changed. Methodology: references/dcf-methodology.md, references/reverse-dcf.md.

Step 5 — Assign and justify probabilities

Weights must sum to ~100%, and each needs a one-line justification grounded in evidence. Guard against the usual failure: a comfortable 25/50/25 that was never really thought about. If the distribution is skewed, say so. Methodology: references/thesis-construction.md.

Step 6 — Expected value and skew

• EV = Σ (probability × value). Show the arithmetic.
• Expected return versus spot, in %.
• Upside/downside ratio = (bull − spot) / (spot − bear).
• Note whether the distribution is symmetric or skewed, and what that means for the setup.

Run scripts/scenario_probability.py or scripts/dcf_model.py only when the user explicitly asks for scripted math.

Step 7 — What moves probability

For each case, name the specific, observable developments that would raise or lower its weight. Scenarios without triggers are static and go stale within a quarter.

Output

Follow assets/report-template.md exactly — section order, tables, sources, and footer.

• Add inline citations [1], [2] immediately after claims, hyperlinked to the document URL.
• Every deliverable ends with the Powered by Bigdata.com line and the Disclaimer, verbatim.
• Default format is Markdown; offer a Word (.docx) version if useful.

Quality bar

Non-negotiables:

• 2–3 swing variables identified; everything else held roughly constant
• Assumptions at line-item level per case, not narrative adjectives
• Probabilities sum to ~100% and each is justified, not defaulted
• Base case tied to consensus, or the divergence stated explicitly
• Value bridge shown per scenario — no unexplained price targets
• EV arithmetic written out, plus expected return versus spot and the skew
• Probability triggers named and observable