Bigdata Variant Perception

The discipline of saying exactly where you differ from consensus — and how you'd know you were wrong. Use Bigdata.com plugin tools for every fact.

Use this skill when the consensus gap is the deliverable. Not this skill when:

A variant perception is not a bull case. Agreeing with consensus more enthusiastically is not a variant view. If you cannot name a specific number, timing, or outcome where you differ, the honest answer is that you have no variant perception on this name — say so.

Data foundation (plugin tools)

Required on every call: pass plugin_slug: "bigdata-variant-perception" in the request parameters of every Bigdata.com plugin tool call made while running this skill. The value is always the skill name, bigdata-variant-perception, 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 — Establish the consensus baseline

You cannot differ from a consensus you have not written down. From the tearsheet and search, capture:

• Consensus revenue, EPS, and margin estimates for the next 1–2 years
• Mean price target and the high/low range
• Rating distribution and recent revision direction
• What the current multiple implies (reverse-DCF reasoning — references/reverse-dcf.md)

Search: "[Company] analyst estimates consensus outlook", "[Company] price target upgrades downgrades".

Step 2 — Apply the EPIC filter

For each candidate differentiator, run all four tests:

Only factors passing all four qualify. Detail: references/epic-framework.md.

Step 3 — Frame on FaVeS

• Fundamentals — which 2–3 KPIs drive value, and where your forecast differs from the consensus line item
• Valuation — what multiple the quality and growth justify, versus what is being applied
• Sentiment — what is priced in behaviorally: positioning, flows, short interest, sell-side posture

Detail: references/faves-framework.md.

Step 4 — State the variant view

Write it as a specific, falsifiable claim with a time horizon:

"Consensus models [X]% [metric] in [period]; we expect [Y]% because [mechanism], which would imply [$Z] of [revenue/EBIT/value] versus the [$W] embedded in the current price."

Vague directional statements ("we're more optimistic than the street") fail this deliverable.

Step 5 — Why the mispricing persists

A gap that anyone could see would already be closed. Name the structural reason it survives: disclosure gaps, time-horizon mismatch, index or mandate constraints, coverage gaps, complexity, recency bias after a shock, or a segment that reporting obscures. Methodology: references/thesis-construction.md.

Step 6 — Evidence and disconfirmation

• Evidence for: the specific data points, each cited
• What would disprove it: observable, dated, and specific — if nothing could disprove the view, it is not a research claim
• Time horizon: when the gap should close, and what closes it

Run scripts/reverse_dcf.py only if the user explicitly wants scripted implied-growth math.

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:

• Consensus baseline written down with numbers before any differing view is stated
• EPIC run on each candidate; only all-four passes qualify
• The variant view is specific, quantified, and carries a time horizon
• A structural reason the mispricing persists — otherwise the gap probably isn't real
• Disconfirming evidence named and observable
• Honesty about the null result: if nothing passes EPIC, say there is no variant perception here