Earnings Prep

Generate a pre-earnings preparation report for the company named in the user's request. If no ticker or company is provided, ask for one before proceeding.

This is the note a L/S equity analyst reads the night before a company reports — it tells them exactly what to focus on when the print drops.

Before starting, read ../data-access.md for data access methods and ../design-system.md for formatting conventions. Follow the data access detection logic and design system throughout this skill.

Source quality (MANDATORY, applies to every web search in this skill): Follow ../data-access.md Section 2.5 — cite only primary sources (SEC filings, IR pages, press releases, transcripts) and Tier-1 financial press (Reuters, Bloomberg, WSJ, FT). Never use or cite Yahoo Finance editorial, Benzinga, Seeking Alpha, Motley Fool, Zacks, TipRanks, StockTwits, Reddit, or similar aggregators/blogs.

Follow these steps:

1. Company Lookup

Look up the company by ticker using discover_companies. Capture:

• company_id
• latest_calendar_quarter — anchor for all period calculations below (see ../data-access.md Section 1.5)
• latest_fiscal_quarter
• Firm name for report attribution (default: "Daloopa") — see ../data-access.md Section 4.5

Determine the upcoming quarter — the one AFTER latest_calendar_quarter. This is the quarter the company is about to report. All analysis is oriented around preparing the analyst for this print.

2. Last Quarter Recap

Pull the most recent quarter's full financials from Daloopa. Calculate 4 quarters backward from latest_calendar_quarter (for YoY context).

Pull:

• Revenue, Gross Profit, Operating Income, EBITDA, Net Income, Diluted EPS
• Operating Cash Flow, CapEx, FCF (calc.)
• Segment/product revenue breakdown
• Company-specific KPIs (use the business-model taxonomy: SaaS → ARR/NRR/RPO; Consumer → DAU/ARPU; E-commerce → GMV/take rate; etc.)

Summarize the story of last quarter in 3-5 bullets:

• What beat expectations (guidance or consensus)?
• What missed or disappointed?
• What was the stock reaction? (use get_stock_prices per ../data-access.md Section 1.7 to get the actual next-day move; supplement with WebSearch for narrative context if needed)
• What narrative emerged from the call? (e.g., "AI monetization acceleration," "margin expansion story intact," "consumer weakness")
• What was the single most debated metric?

This is the baseline everyone on the upcoming call will be anchoring to.

3. Outstanding Guidance for Upcoming Quarter

Search for ALL guidance series using keywords: "guidance", "outlook", "estimate", "forecast", "target". Apply the +1 quarter offset to identify which guidance applies to the upcoming print:

• CRITICAL: Guidance from Q(N) earnings call applies to Q(N+1) results
• The guidance issued during the latest_calendar_quarter earnings call is what applies to the upcoming quarter

Pull and present:

• Revenue guidance (point estimate or range)
• EPS guidance
• Margin guidance (gross, operating, EBITDA)
• CapEx guidance
• Segment-level guidance (if available)
• KPI guidance (subscriber adds, unit volumes, ARPU targets, etc.)

Search filings for directional/qualitative guidance:

• Search documents for: "expect", "anticipate", "similar to", "consistent with"
• Search documents for: "low single digit", "mid single digit", "double digit", "sequential"
• Search documents for: "headwind", "tailwind", "conservatively", "assumes"
• Capture exact management quotes with document citations

Flag any guidance updates between quarters:

• Search for "pre-announce", "update", "revise" in the most recent quarter's filings
• Check if the company issued an 8-K updating guidance after the last earnings call

Present all guidance in a single table: Metric | Guidance Value | Source Quarter | Type (Quantitative/Directional).

4. Guidance Credibility & Whisper Number

This section MUST be built entirely from Daloopa data — guidance series AND actual result series pulled via get_company_fundamentals. Do not use web search or estimates for this analysis.

Step 1: Pull 8 quarters of guidance data. You already discovered guidance series in Section 3. Now pull ALL of those guidance series for the last 8 quarters (from latest_calendar_quarter backward). These are the guidance values management provided each quarter.

Step 2: Pull 8 quarters of corresponding actuals. For every guided metric, identify the corresponding actual result series (e.g., if there is a "Revenue guidance" series, pull the actual "Revenue" series). Pull these actuals for the same 8-quarter period.

Step 3: Build the complete beat/miss table. Apply the +1 quarter offset: guidance from Q(N) is compared to the actual result in Q(N+1). For EVERY quarter where both a guidance value and a corresponding actual exist, compute:

• Guidance value (midpoint if range)
• Actual value
• Delta (Actual - Guidance midpoint)
• Beat/Miss % ((Actual - Guidance midpoint) / |Guidance midpoint| × 100)
• Classification: Beat / In-line / Miss (use +/-1% threshold for in-line)

Present a FULL detail table — every quarter, every guided metric. This is the core analytical engine of the whisper number. Do not summarize or abbreviate — show all rows. Format:

| Guidance Source Qtr | Metric | Guidance (Mid) | Actual Qtr | Actual | Delta | Beat/Miss % |

If a company provides range guidance (low/high), show the midpoint and note the range width. If a company only provides directional guidance for some metrics (e.g., "revenue growth in low teens"), convert to an implied numeric value for comparison (e.g., 12-13% → midpoint ~12.5% applied to prior year actual).

Step 4: Compute summary statistics from the detail table:

• Beat rate per metric (% of quarters where actual > guidance midpoint)
• Average beat magnitude per metric (in absolute terms and %)
• Beat pattern trend: is the beat getting larger (sandbagging increasing), shrinking (guidance getting more accurate), or volatile? Look at the last 4 vs. prior 4.
• Range width trend: is management tightening or widening guidance ranges?

Step 5: Calculate the implied "whisper number":

• Whisper = Current guidance midpoint + Average historical beat (from the detail table above)
• This is the REAL bar the stock is trading against, not the stated guidance
• If the company beats by 2% on average, the market expects a 2% beat — an in-line result to guidance is effectively a miss
• Calculate whisper for EVERY guided metric, not just revenue

Present the whisper summary: | Metric | Current Guidance (Mid) | Avg Historical Beat | Implied Whisper | Beat Rate (n/N) |

Credibility verdict: Is management's guidance informative (tight, accurate) or performative (always sandbagged, uninformative)? If the beat rate is >90%, say so — it means the guidance number is a floor, not a forecast. If the beat magnitude is increasing, management is becoming MORE conservative over time.

5. Peer & Adjacent Company Read-Throughs

This is the most differentiated section. For companies in the same sector that have ALREADY reported this earnings season, their results contain direct signal about the upcoming print.

Identify the read-through universe (aim for 5-8 companies):

• Competitors: Direct rivals in the same market
• Suppliers: Companies that sell to the target company
• Customers: Companies that buy from the target company
• Industry bellwethers: Large companies whose results signal sector trends

CRITICAL: Always use Daloopa as the primary data source for peer analysis. For each peer:

1. Look up the peer in Daloopa: discover_companies with the peer's ticker. If Daloopa has the company, check latest_calendar_quarter to determine whether they have already reported the relevant quarter.
2. If the peer has data for the current earnings season quarter: Pull their financials from Daloopa (discover_company_series → get_company_fundamentals). Focus on 2-4 metrics most relevant to the read-through (e.g., for a supplier: revenue, segment breakdown, inventory; for a competitor: revenue growth, market share proxies, pricing commentary).
3. Search the peer's filings in Daloopa: search_documents with keywords related to the target company's products, markets, or industry (e.g., for an Apple supplier, search for "Apple", "smartphone", "consumer electronics").
4. Use WebSearch only to supplement Daloopa data — for earnings-season timing confirmation, stock price reactions, or analyst commentary that Daloopa filings don't cover.

For each read-through, extract (with Daloopa citations):

1. The specific data point — the peer's metric that creates signal. Cite the Daloopa fundamental_id.
2. The implication — bullish or bearish for the target company, and why
3. Confidence level — High (direct disclosed relationship), Moderate (inferred from industry), Low (circumstantial)

For peers that haven't reported yet: Note them as "reports after {TICKER}" — their results will be a read-through in the opposite direction.

Group read-throughs by:

• Competitors — share shift signals, pricing environment, demand trends
• Suppliers — order book signals, inventory levels, capacity commentary
• Customers — demand signals, inventory destocking/restocking, spending priorities
• Industry Bellwethers — macro/sector health, end-market demand

Web research for sector context (supplementary only — after Daloopa pulls):

• Search: "{TICKER} sector earnings season {year} read through" — analyst commentary on cross-company signals
• Search: "{TICKER} competitors results {upcoming_quarter_label} {year}" — what peers have already signaled

6. Key Metrics to Watch

Identify the 5-7 metrics the analyst should focus on when the print drops. For each metric:

| Metric | Current Level | Guidance/Expected | Bullish Threshold | Bearish Threshold | Why It Matters |

Be specific with thresholds — not "revenue growth" but "revenue above $95B signals iPhone cycle acceleration; below $92B confirms China weakness." Not "margins" but "gross margin above 47% confirms services mix shift; below 45% signals hardware pricing pressure."

Prioritize by information value:

1. Metrics where guidance has been vague or directional (highest uncertainty)
2. Metrics where peer read-throughs are conflicting (the print will resolve the debate)
3. Metrics that drive the forward multiple (the ones the market will re-rate on)
4. KPIs that lead revenue by 1-2 quarters (predictive of next quarter's financials)

7. Consensus & Positioning

Gather available consensus context:

From data sources (consensus estimates if available per ../data-access.md Section 3):

• Consensus revenue and EPS for the upcoming quarter
• Number of analysts at Buy / Hold / Sell
• Consensus price target (median and range)
• Recent estimate revision trends (last 30/60/90 days — moving up or down?)

From web search (supplement or replace if consensus data unavailable):

• Search: "{TICKER} earnings preview consensus estimates {upcoming_quarter_label} {year}" — sell-side previews
• Search: "{TICKER} analyst expectations {year}" — positioning and sentiment

Note limitations if consensus data is not directly available. Even directional context ("estimates have been revised up 3% over the last 90 days") is valuable.

8. Historical Earnings Reaction

Stock price data (from Daloopa): Use get_stock_prices (see ../data-access.md Section 1.7) to get actual post-earnings price moves for the last 4-6 earnings prints. For each historical earnings date, pull prices for a window: start_date = 1 trading day before earnings, end_date = 3-5 trading days after. Compute:

• Next-day move (pre-earnings close → post-earnings close)
• 3-day drift (post-earnings close → 3 days later)

To estimate historical earnings dates, use the quarter-end date + ~30-45 days as an approximation, or use WebSearch to confirm exact dates if needed.

Also pull the current stock price (3 most recent calendar days) for the report header.

Supplement with web search for options context:

• Search: "{TICKER} options implied move earnings {upcoming_quarter_label}" — current implied volatility

Present as a table: | Quarter | Revenue Beat/Miss | EPS Beat/Miss | Next-Day Move | 3-Day Drift | Notes |

Populate the Revenue/EPS Beat/Miss columns from the guidance credibility analysis in Section 4. The price move columns come from get_stock_prices.

Pattern identification:

• Does the stock tend to sell off on beats? (buy-the-rumor, sell-the-news pattern)
• Does it rally on in-line results? (low expectations already embedded)
• Is there a pattern of post-earnings drift (continued move in the days after)?
• What's the current implied move from the options market? If it's elevated vs. history, the market expects a big move.

9. Macro & Sector Backdrop

Web search for developments since last quarter that could affect results:

• Search: "{TICKER} {industry} outlook {current_year}" — sector developments
• Search: "{TICKER} headwinds tailwinds {current_year}" — company-specific macro factors

Distill into 5-8 bullets, each with a directional tag (Positive / Negative / Uncertain):

• Industry-specific: new regulations, competitor product launches, market share shifts
• Macro: FX moves (specify currencies and direction), commodity prices, interest rates
• Policy: tariffs, trade restrictions, tax changes
• Channel: inventory levels in the channel, distributor commentary, supply chain status
• Company-specific: product launches since last quarter, management changes, M&A

Keep each bullet to one sentence. The analyst needs context, not a macro essay.

10. Potential Surprises & Call Catalysts

Beyond the numbers, what could management announce that would move the stock? Search filings and news for signals:

• Search documents: "restructuring", "acquisition", "buyback", "dividend" in recent filings
• Search: "{TICKER} potential announcement catalyst {year}" — speculative but grounded

Categories:

• Capital allocation: New buyback authorization, dividend change (hike/cut/initiation), M&A announcement, asset sale/spinoff
• Operational: Restructuring/layoffs, new product launch, partnership/contract win, segment reporting changes
• Strategic: New guidance metrics, long-term targets update, management changes, investor day announcement
• Accounting/Disclosure: Guidance methodology change, segment redefinition, one-time charge pre-announcement

For each potential surprise, note the signal strength (rumored / speculated / no signal) and the likely stock impact direction.

11. Pre-Earnings Checklist

A concise, actionable summary that fits on a single card. This is what the analyst tapes to their monitor:

The Numbers:

• Revenue whisper: $X.XX (guidance: $X.XX, avg beat: +X.X%)
• EPS whisper: $X.XX (guidance: $X.XX, avg beat: +X.X%)

Top 3 Metrics to Watch:

1. [Metric] — current: X, bull: >Y, bear: <Z
2. [Metric] — current: X, bull: >Y, bear: <Z
3. [Metric] — current: X, bull: >Y, bear: <Z

The Bull Catalyst: What would make this stock go up 5%+ after the print? (one sentence)

The Bear Risk: What would make this stock go down 5%+ after the print? (one sentence)

Read-Through Signal: After this company reports, what does it mean for [2-3 other names]?

Historical Pattern: Last 4 prints averaged +/-X% next-day move; options imply +/-X% this time.

12. Save Report

Save to reports/{TICKER}_earnings_prep_{UPCOMING_CQ}.html (e.g., AAPL_earnings_prep_2026Q1.html) using the HTML report template from ../design-system.md. The period in the filename is the upcoming calendar quarter being prepped for — the one AFTER latest_calendar_quarter. Write the full analysis as styled HTML with the design system CSS inlined. This is the final deliverable — no intermediate markdown step needed.

The report should include:

• Executive summary (2-3 sentences: what quarter is coming, what the key debate is, what the whisper number implies)
• Last quarter recap (story + key metrics table)
• Outstanding guidance table with source citations
• Whisper number calculation with historical beat/miss detail table
• Peer read-throughs (grouped by Competitors / Suppliers / Customers / Industry, with Daloopa citations on peer data)
• Key metrics to watch (table with specific thresholds)
• Consensus & positioning summary
• Historical earnings reaction table
• Macro & sector backdrop (bulleted list with directional tags)
• Potential surprises & call catalysts
• Pre-earnings checklist (prominently styled — this is the payoff of the whole report)

All financial figures must use Daloopa citation format: <a href="https://daloopa.com/src/{fundamental_id}">$X.XX million</a>

Tell the user where the HTML report was saved.

Highlight what makes this print particularly interesting: Is the whisper number meaningfully above guidance (setting up for disappointment even on a beat)? Are peer read-throughs conflicting (creating genuine uncertainty)? Is there a potential surprise catalyst that could overshadow the numbers? Give the analyst the single most important thing to watch.