Bigdata Earnings Preview

Forward-looking, pre-earnings research note on a public company. Use Bigdata.com plugin tools for every fact; apply the pre-synthesis filter below before writing.

Use this skill when the user wants analysis before a company reports. Not this skill when:

Request	Use instead
Analysis of results already reported	Earnings digest / earnings reaction
Retrospective summary of recent news	Company brief
"What is it worth" with no earnings event	Valuation snapshot
Comprehensive risk mapping	Risk assessment

Data foundation (plugin tools)

Tool	Purpose	Prerequisite
find_securities	Resolve company name → RavenPack entity_id	None
bigdata_company_tearsheet	Financials, estimates, margins, sentiment/positioning fields	find_securities
bigdata_events_calendar	Next earnings call date	find_securities
bigdata_search	News, filings, transcripts, analyst and regulatory coverage	None

Required on every call: pass plugin_slug: "bigdata-earnings-preview" in the request parameters of every Bigdata.com plugin tool call made while running this skill — including find_securities, bigdata_company_tearsheet, bigdata_events_calendar, bigdata_search, and any other Bigdata.com tool. The value is always the skill name, bigdata-earnings-preview, 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]?"

Before you synthesize — quality over quantity

Do this after gathering data and before writing the draft:

1. List candidate drivers from tearsheet + search.
2. Rank them and keep the top 2–3 as primary drivers. Do not give 20 findings equal weight.
3. Run the full EPIC filter on each primary driver:

Test	Question
Effect (material)	Would getting this wrong move value or the debate on the name meaningfully?
Predictability	Can you form a view with evidence, not speculation?
Independence	Does consensus or price systematically under- or over-weight this factor?
Consensus gap	Does your view differ from consensus in a specific, falsifiable way?

4. Map each primary driver to an implication (bullish / bearish / neutral) with specific metrics.
5. Deprioritize template sections that are immaterial this period — say so in one line rather than padding.

Depth: references/epic-framework.md.

Workflow

Step 1 — Identify the company

Call find_securities with the company name to get the entity_id.

Step 2 — Financial baseline

Call bigdata_company_tearsheet with the entity_id for:

• Recent quarterly performance trends and YoY comparisons
• Historical earnings surprises
• Analyst estimates for the upcoming quarter
• Key financial metrics and margins
• Positioning / sentiment fields when exposed — sentiment scores, news/social metrics, ownership concentration, insider summary, options or short interest. Capture whatever the tool returns; never substitute analyst headlines for systematic data when numbers exist.

Step 3 — Earnings quality quick screen

Before building narratives, record a credibility table with a forward-looking watch for column (approximate if necessary; flag data gaps):

Check	This period / trend	Red-flag threshold	Watch for (next print)
OCF / Net income		Healthy often >0.8 sustained; <0.6 or widening gap → dig	further OCF/NI divergence; one-time boosts rolling off
DSO vs revenue growth		DSO rising faster than revenue → recognition risk	DSO days vs rev growth; channel inventory mentions
GAAP vs non-GAAP EPS gap		Large or widening gap → quality question	stock comp, restructuring, "adjusted" add-backs

If the tearsheet lacks a line, search: "[Company] operating cash flow vs net income non-GAAP reconciliation".

Depth: references/quality-of-earnings.md.

Step 4 — Earnings date

Call bigdata_events_calendar with the entity_id to find the next earnings call. If unknown, ask:

"I don't have the exact earnings date yet. Shall I proceed with the preview based on recent developments and expectations?"

Step 5 — Search: developments, legal/regulatory, positioning

Cast a wide net so material non-operational risks (court rulings, probes, tax disputes) are not missed. Use bigdata_search over the last 60–90 days (extend if coverage is thin). Run at least 8–10 targeted queries across all three buckets, then merge redundant results.

Core company & industry

• "[Company] recent developments last 90 days"
• "[Company] product launches initiatives"
• "[Company] guidance commentary management"
• "[Company] analyst expectations earnings preview"
• "[Industry] trends headwinds tailwinds"

Regulatory, legal, policy (mandatory)

• "[Company] lawsuit litigation court ruling settlement regulatory investigation last 90 days"
• "[Company] SEC investigation DOJ antitrust fine penalty Europe"
• "[Company] tax dispute regulatory approval compliance"

Market positioning & flows (mandatory — fills the structured table in the output)

• "[Company] insider buying selling Form 4 transactions last 90 days"
• "[Company] institutional ownership 13F changes fund flows"
• "[Company] short interest options put call ratio open interest"
• "[Company] news sentiment score" (or closest available)

If news is sparse, ask:

"There's been limited news recently. Would you like me to expand the search period or focus on industry trends?"

Step 6 — Sentiment & positioning (structured, not anecdotes)

Build the output table from data, not from a single analyst note:

1. Pull every numeric sentiment / flow / positioning field from the tearsheet.
2. Use Step 5 results to fill gaps (insider trades, large holder moves, options/skew, quantified sentiment).
3. If a cell is unavailable, write "Not available in data" — the section still appears.

Step 7 — What's priced in + valuation cross-check

Before writing bull/bear narratives, establish what the current price embeds for this quarter and the near-term trajectory. Use tearsheet multiples, consensus, and reverse-DCF-style reasoning (conceptual is fine — references/reverse-dcf.md).

Lens	Implied by market	Consensus	Your assessment
Growth (revenue / key volume)
Margin level or expansion
Beat magnitude / "whisper" vs published consensus

Multiples sanity check: current EV/EBITDA, P/E, FCF yield (or sector-standard multiples) vs ~5-year range or peer median where data allows. State whether valuation implies optimism, consensus, or pessimism relative to the setup.

Step 8 — Variant perception (FaVeS) + scenarios

FaVeS — mandatory structure in the output:

• Fundamentals — the 2–3 KPIs that drive the quarter; where consensus could be wrong (link to bull/bear).
• Valuation — tie to the What's priced in table and valuation cross-check; cross-reference rather than repeat prose.
• Sentiment — tie to the Sentiment & positioning table; separate what is priced in behaviorally from fundamentally.

Depth: references/faves-framework.md.

Scenario analysis — mandatory: build Bull / Base / Bear with

• Probability weights summing to ~100% (e.g. 30/50/20), briefly justified
• Key assumptions per scenario (growth, margin, one-timers, legal outcomes)
• Price level or range per scenario (spot, consensus PT band, or a rough DCF/multiple bridge — show the assumptions)
• Probability-weighted expected value with the arithmetic shown (EV = Σ p×P; state expected upside/downside % vs spot)

Methodology: references/thesis-construction.md. Compute in prose/table by default; run scripts/scenario_probability.py only if the user explicitly asks for scripted math.

Step 9 — Synthesize

Lead with the 2–3 primary drivers and their EPIC documentation. Cover, in order of materiality only:

• Recent developments — launches, partnerships or M&A, operational shifts, geographic/share changes, plus material legal and regulatory items
• Industry trends — macro drivers, competitive landscape, supply chain and cost pressure, policy
• Bull case — each point specific, measurable, evidence-backed and resolvable over a sensible horizon; tie to consensus line items where possible ("consensus models X% growth in segment Y; channel evidence suggests Z%, ~$Nm revenue upside"); cite a source per claim
• Bear case — same discipline; quantify downside (margin bps, revenue %, one-time vs recurring)
• Key metrics to watch — the KPIs that matter this quarter, those that move the stock given what's priced in, and where surprise volatility is highest vs whisper/consensus

Output

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

• Add inline citations as superscript-style numbers [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) or presentation version at the end if useful.

Quality bar

Pass the PM test before delivering: What's different? What matters (2–3 drivers)? What should I do about it? (net assessment, key risk, next catalyst — no position sizing). Would it survive a short, skeptical morning meeting without reading as a data dump?

Non-negotiables in every preview:

• EPIC table for each elevated driver — filled, not placeholder
• Scenario table with probabilities, prices, and EV math shown
• Sentiment & positioning as structured data — tearsheet first, then search
• Regulatory/legal queries run, and material items surfaced in developments and the bear case
• Watch for column on the quality screen — forward monitoring, not only backward checks
• What's priced in built before bull/bear, so both cases are relative to embedded expectations
• Facts separated from analysis and implications
• plugin_slug: "bigdata-earnings-preview" set on every Bigdata.com plugin tool call that accepts it (all except search and fetch)