Bigdata.com

Write a full institutional investment memo on a public company using Bigdata.com data — thesis, variant perception versus consensus, valuation, risks, catalysts, and an explicit recommendation with conviction. Runs the complete workflow: EPIC-filtered primary drivers, FaVeS variant perception, earnings quality and moat assessment, valuation by the method that fits the business with a secondary cross-check, bull/base/bear scenarios with probabilities and a probability-weighted value, key risks and what would change the view. Triggers: "investment memo for X", "full analysis of X", "should I buy X", "build the bull case for X", "write up X as an investment", "deep dive on X", "thesis on X", "DCF thesis for X".

bigdata-investment-memo

Assess how durable a public company's competitive advantage is and whether management can be trusted with the capital, using Bigdata.com data. Covers moat identification by type with evidence, moat strength via ROIC versus WACC, pricing power and share trend, a competitive advantage period estimate with erosion signals, industry structure via five forces, the capital allocation track record across M&A, buybacks, dividends and reinvestment, and governance — board independence, dual roles, compensation design, related-party exposure, insider activity. Triggers: "does X have a moat", "moat review for X", "how durable is X's advantage", "is X's management any good", "capital allocation at X", "governance review of X", "competitive advantage of X".

bigdata-moat-governance-review

Compare a public company against its peer set using Bigdata.com data — valuation multiples, growth, profitability, returns, leverage, and sentiment — to judge relative attractiveness. Builds the peer set with an explicit rationale for inclusion and exclusion, tabulates like-for-like metrics with peer median and quartile positioning, decomposes any premium or discount into what fundamentals justify versus what they do not, and closes with a relative verdict. Triggers: "compare X to its peers", "peer comparables for X", "how does X screen vs competitors", "is X cheap relative to peers", "comps table for X", "relative valuation of X", "who are X's peers".

bigdata-peer-comparables

Write a first-trading-day post-IPO reaction note for a newly listed company using Bigdata.com data plus web market data. Anchors the deal (offer price vs range, shares, greenshoe, implied market cap), reconstructs day 1 (open, intraday range, close, volume, first-day return), reads demand and float mechanics including stabilization, resets valuation against peers at the close, notes the quiet-period coverage gap, and maps the dated post-IPO timeline. Balanced, no buy/avoid call. Triggers: "post-IPO day 1", "first day trading reaction for X", "how did X's IPO debut", "X IPO pop", "X first day of trading", "IPO debut analysis".

bigdata-post-ipo-day1

Write a day-14 post-IPO note on potential NASDAQ-100 fast-track index inclusion for a recently listed large-cap, using Bigdata.com data plus web search for index methodology and market data. Covers two-week trading status, an eligibility check against Nasdaq's current published rules (cited, never assumed), a float-adjusted index weight and implied passive-demand estimate with the math shown, days-to-cover versus ADV, the historical index effect and reversal risk, and dated watch points. Balanced, no buy/avoid call. Triggers: "NASDAQ-100 fast track for X", "index inclusion impact on X", "post-IPO day 14", "will X be added to the Nasdaq-100", "passive flows from index inclusion".

bigdata-post-ipo-day14

Write a day-179 post-IPO note on the 180-day lock-up expiry using Bigdata.com data plus filings and market data. Covers lock-up terms from the prospectus (expiry date, covered holders, share count, early-release provisions), float and overhang math (post-expiry float, days-to-trade versus ADV), insider and VC selling-intention signals, positioning into the event (short interest, borrow, options skew), the historical lock-up-expiry effect with analogs, and a two-sided read. Balanced, no buy/avoid call. Triggers: "180-day lock-up expiry for X", "lockup expiration impact", "post-IPO day 179", "shares unlocking for X", "insider selling after lockup", "float expansion at lockup".

bigdata-post-ipo-day179

Write a day-365 post-IPO note on the 366-day founder and significant-investor lock-up expiry and float expansion toward 15-20%, using Bigdata.com data plus filings and market data. Covers the staggered lock-up structure from the prospectus, float expansion math and days-to-trade, the offsetting float-adjusted index reweight demand netted against new supply, a realistic read on whether founders actually sell, the dual-class governance angle, and a two-sided setup. Balanced, no buy/avoid call. Triggers: "366-day lock-up for X", "founder lock-up expiry", "float expansion for X", "post-IPO one year lockup", "index reweight after float increase", "founder selling after IPO".

bigdata-post-ipo-day365

Produce a balanced pre-IPO research note on an upcoming, not-yet-listed company using its S-1/F-1 plus Bigdata.com data. Covers deal structure (price range, shares, greenshoe, implied valuation, underwriters, lock-ups, share classes), two years plus interim financials, business model and funding history, TAM and listed comparables, IPO-window conditions, and 90-day sentiment — closing with bull and bear debates and watch points, never a participate/avoid call. Triggers: "analyze the IPO of X", "S-1 analysis", "upcoming listing for X", "IPO report on X", "should I look at X's IPO", "pre-IPO research on X", "X IPO valuation".

bigdata-pre-ipo-analysis

Give a fast, PM-style quick take on a stock using Bigdata.com data — a one-line current view, the 2-3 drivers that actually matter right now, the key risks and what would change the view, and the near-term setup with the next catalyst. Deliberately short: one page, no full thesis, no model. Triggers: "quick take on X", "what do you think of X", "give me a fast view on X", "thoughts on X", "X in a nutshell", "one-liner on X", "is X interesting right now".

bigdata-quick-take

Compare regions or blocs using Bigdata.com data — economic indicators, market performance, and cross-asset views — and turn that into an allocation recommendation. Covers growth, inflation, policy and labor per region, comparative developed-versus-emerging analysis, regional equity valuations, and fixed income and currency views for each. Triggers: "compare US vs Europe vs Asia", "which regions look attractive", "regional allocation", "developed vs emerging markets", "Europe vs US equities", "global allocation view".

bigdata-regional-comparison

Produce a comprehensive risk assessment for a public company using Bigdata.com data (10-K risk factors, 8-K material events, news, tearsheet financials). Covers six categories — regulatory and legal, competitive and moat erosion, operational, financial and balance sheet, macro, and management and governance — each rated by likelihood and impact, with a distress screen when leverage is stretched, mitigation status, a priority matrix, and a scenario bridge to value drivers. Triggers: "risk assessment for X", "assess risks for X", "what are the risks with X", "what could go wrong at X", "risk factors for X", "how risky is X", "downside risks for X".

bigdata-risk-assessment

Build bull, base, and bear cases for a public company using Bigdata.com data — with explicit line-item assumptions, justified probability weights summing to 100%, a value or price per scenario with the bridge shown, a probability-weighted expected value and expected return versus spot, the upside/downside skew and risk-reward ratio, and what would move probability between the cases. Triggers: "scenario analysis for X", "bull base bear for X", "what's the upside and downside on X", "expected value for X", "probability-weighted view on X", "risk reward on X", "model out the cases for X".

bigdata-scenario-analysis

Analyze a market sector using Bigdata.com data — performance, valuations, themes, sub-industries, and upcoming catalysts. Maps the sector to its own operating and valuation KPIs rather than generic P/E, reads cycle and profitability positioning as early, mid, or late versus history, aggregates bellwether tearsheet metrics, and closes with a positioning call plus top picks and areas to avoid. Triggers: "analyze the X sector", "what's happening in X sector", "X sector outlook", "how is the X industry doing", "X sector valuations", "is the X sector attractive", "semiconductor/energy/healthcare sector view".

bigdata-sector-analysis

Build an actionable investment playbook for a sector using Bigdata.com data and sector-specific frameworks — the KPIs that actually matter in that sector, how to value companies in it and why, the live debates and where consensus sits on each, valuation context against the sector's own history, a sub-industry map with cycle position, screening criteria and sector-specific red flags, and an actionable setup of what to own, avoid, and watch. More operational than a sector analysis: it teaches how to invest the sector, not just how it is doing. Triggers: "sector playbook for X", "how do I analyze X companies", "what KPIs matter in X", "how to value X sector companies", "investing framework for X sector", "X sector cheat sheet".

bigdata-sector-playbook

Research a macro investment theme using Bigdata.com data — scope and sub-themes, investment implications, sector impact, named beneficiaries and vulnerable losers with tearsheet fundamentals, the policy and regulatory dimension, geographic impact, and concrete implementation ideas. Covers themes such as AI, energy transition, inflation and rates, deglobalization and reshoring, demographics, geopolitical risk, and fiscal policy. Triggers: "research the X theme", "X investment implications", "who benefits from X", "how do I play X", "AI / energy transition / deglobalization theme", "thematic view on X".

bigdata-thematic-research

Answer what a public company is worth and whether it is cheap or expensive, using Bigdata.com data (tearsheet multiples, estimates, margins, peer context). Produces a multiples cross-check against the company's own history and peer median, an implied-expectations read on what the current price already embeds (reverse-DCF reasoning, no model build required), the 2-3 value drivers that dominate, and a cheap / fair / rich verdict. Triggers: "what is X worth", "is X expensive", "valuation snapshot for X", "what's priced in for X", "is X cheap vs peers", "how is X valued", "fair value for X".

bigdata-valuation-snapshot

State explicitly where your view on a public company differs from consensus, using Bigdata.com data. Establishes the consensus baseline from estimates and sell-side posture, applies the EPIC filter to candidate differentiators, frames the view on FaVeS (fundamentals, valuation, sentiment), and states the variant view as a specific, falsifiable claim with a time horizon — plus what the market is missing, why the mispricing persists, the evidence for the view, and what would disprove it. Triggers: "variant perception on X", "where do I differ from consensus on X", "what is the market missing on X", "non-consensus view on X", "what's priced in versus reality for X", "contrarian case for X".

bigdata-variant-perception