openai/comps

Build a trading comparables analysis for the company named in the user's request. If no ticker or company is provided, ask for one before proceeding.

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.

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

2. Identify Peer Group

Based on the company's business model, sector, size, and competitive landscape, identify 5-10 comparable companies. Consider:

• Direct competitors in the same market
• Business model peers (similar revenue model even if different sector)
• Size peers (similar market cap range)
• Growth profile peers (similar growth rate)

Prioritize relevance over size matching. A direct competitor at a different scale is more useful than a similar-sized company in a different industry.

List the peer tickers and briefly justify each selection (1 sentence).

3. Target Company Fundamentals

Calculate 4 quarters backward from latest_calendar_quarter. Pull from Daloopa for the target company:

• Revenue (compute trailing 4Q total)
• EBITDA (compute trailing 4Q; if not available, use Op Income + D&A, label "(calc.)")
• Net Income (trailing 4Q)
• Diluted EPS (trailing 4Q sum)
• Free Cash Flow (trailing 4Q; compute as OCF - CapEx, label "(calc.)")
• Revenue YoY growth (most recent quarter)
• Operating Margin (most recent quarter)
• Net Margin (most recent quarter)

4. Stock Prices & Valuation Multiples

Use get_stock_prices (see ../data-access.md Section 1.7) to pull current prices for the target AND all peers in a single batch call — pass all company_ids together with dates = 3 most recent calendar days.

Compute valuation multiples by combining stock prices with the fundamentals pulled in Sections 3 and 5:

• Market Cap = Close price × Diluted shares outstanding
• Enterprise Value = Market Cap + Total Debt - Cash (from Daloopa balance sheet if available)
• P/E (trailing) = Market Cap / Net Income (trailing 4Q)
• EV/EBITDA = EV / EBITDA (trailing 4Q)
• P/S = Market Cap / Revenue (trailing 4Q)
• P/B = Market Cap / Total Equity
• FCF Yield = FCF (trailing 4Q) / Market Cap
• Dividend Yield = Dividends Paid (trailing 4Q) / Market Cap

For beta, PEG ratio, and forward multiples, use infra scripts, consensus data, or web search (see ../data-access.md Sections 2-3).

If a peer isn't in Daloopa (no company_id), fall back to ../data-access.md Section 2 resolution order for market data. If a peer ticker fails (delisted, no data), drop it and note why.

5. Peer Fundamentals from Daloopa

For each peer that is available in Daloopa:

• Look up the company
• Calculate 4 quarters backward from latest_calendar_quarter. Pull revenue, operating income, net income for those periods.
• Compute revenue growth YoY, operating margin, net margin

For peers not in Daloopa, rely on market data multiples only (see ../data-access.md Section 2) and note the data source limitation.

5.5. Peer Operational KPIs

For each company (target + all peers available in Daloopa), discover and pull company-specific operational KPIs. Use the sector taxonomy below to know what to search for:

• SaaS/Cloud: ARR, net revenue retention, RPO/cRPO, customers >$100K, cloud gross margin
• Consumer Tech: DAU/MAU, ARPU, engagement metrics, installed base, paid subscribers
• E-commerce/Marketplace: GMV, take rate, active buyers/sellers, order frequency
• Retail: same-store sales, store count, average ticket, transactions
• Telecom/Media: subscribers, churn, ARPU, content spend
• Hardware: units shipped, ASP, attach rate, installed base
• Financial Services: AUM, NIM, loan growth, credit quality metrics, fee income ratio
• Pharma/Biotech: pipeline stage, patient starts, scripts, market share
• Industrials/Energy: backlog, book-to-bill, utilization, production volumes, reserves

Pull the same 4 calendar quarters for each peer. Not all peers will have the same KPIs — build a sparse matrix and note which are comparable across the group vs company-specific.

Add KPI columns to the comps table in Section 6 where comparable metrics exist (e.g., subscriber growth, ARPU, units alongside P/E and EV/EBITDA). This shows whether valuation premiums are supported by operational outperformance.

6. Build Comps Table

Create the main comparables table with these columns: | Company | Ticker | Mkt Cap | EV | P/E | Fwd P/E | EV/EBITDA | P/S | Rev Growth | Op Margin | Net Margin | FCF Yield |

Sort by market cap descending. Include:

• Peer median row
• Peer mean row
• Target company row (highlighted / separated)
• Target's percentile rank within the peer group for each metric

7. Implied Valuation

Apply peer group median and mean multiples to the target's fundamentals:

| Methodology | Peer Median Multiple | Target Metric | Implied Value | |---|---|---|---| | P/E | XX.Xx | $X.XX EPS | $XXX | | EV/EBITDA | XX.Xx | $XXX EBITDA | $XXX | | P/S | XX.Xx | $XXX Revenue | $XXX | | FCF Yield | X.X% | $XXX FCF | $XXX |

For each:

• Implied Enterprise Value = Multiple × Target's Metric
• Implied Equity Value = EV - Net Debt (for EV-based multiples) or direct (for equity multiples)
• Implied Share Price = Equity Value / Shares Outstanding

Compute range (min to max implied price) and central tendency.

8. Consensus Forward Estimates (if available)

If consensus estimates are available (see ../data-access.md Section 3):

• Add NTM (next twelve months) revenue and EPS estimates for target and each peer
• Compute forward P/E and forward EV/EBITDA using consensus NTM estimates
• Note where the target's forward multiples sit vs the peer group
• Flag any peers with significant estimate revision trends

If consensus data is not available, use trailing multiples only and note the limitation.

9. Premium/Discount Analysis

Assess whether the target trades at a premium or discount to peers:

• For each multiple, show target vs peer median as a % premium/discount
• Consider whether a premium/discount is justified based on:
  • Growth differential (higher growth = deserves premium)
  • Margin differential (higher margins = deserves premium)
  • Market position (leader vs challenger)
  • Risk profile

Be honest about whether the premium is truly justified:

• A company can deserve a premium and still be overvalued if the premium has stretched too far beyond fundamentals. Quantify: how much growth differential is needed to justify the current premium? Is the company delivering that?
• If the stock trades at a significant premium but growth is decelerating toward peer levels, flag the derating risk explicitly.
• Don't default to "premium is justified because it's the market leader" — that's already in the price. What justifies the premium expanding or sustaining from here?
• Reference KPI outperformance as justification (or lack thereof). Example: "AAPL trades at 34x P/E vs peer median 28x — premium partly justified by +14% Services growth vs peer median +8%, but Wearables decline (-2.2% YoY) is a drag peers don't have." If the target's KPIs are in line with or worse than peers, the premium is harder to defend.

10. Save Report

Save to reports/{TICKER}_comps.html using the HTML report template from ../design-system.md. Write the full analysis as styled HTML with the design system CSS inlined. This is the final deliverable — no intermediate markdown step needed.

Structure the report with these sections:

<h1>{Company Name} ({TICKER}) — Comparable Companies Analysis</h1>
<p>Generated: {date}</p>

<h2>Summary</h2>
{2-3 sentences: Where does the company trade relative to peers? Is it cheap or expensive and why?}

<h2>Peer Group Selection</h2>
<table>
| Peer | Ticker | Rationale |
{table with justification for each peer}
</table>

<h2>Comparables Table</h2>
<table>
| Company | Ticker | Mkt Cap | P/E | Fwd P/E | EV/EBITDA | P/S | Rev Growth | Op Margin |
{full comps table with target highlighted}
| **Peer Median** | | | XX.Xx | XX.Xx | XX.Xx | XX.Xx | X.X% | X.X% |
| **Peer Mean** | | | XX.Xx | XX.Xx | XX.Xx | XX.Xx | X.X% | X.X% |
| **{TICKER}** | | | **XX.Xx** | **XX.Xx** | **XX.Xx** | **XX.Xx** | **X.X%** | **X.X%** |
</table>

<h2>Target vs Peer Premium/Discount</h2>
<table>
| Multiple | Target | Peer Median | Premium/Discount |
{table showing where target is rich/cheap}
</table>

<h2>Implied Valuation</h2>
<table>
| Methodology | Multiple | Target Metric | Implied Price | vs Current |
{table with implied values}
</table>

<table>
| **Valuation Range** | **Low** | **Median** | **High** |
| Implied Price | $XXX | $XXX | $XXX |
| vs Current Price | -X% | +X% | +X% |
</table>

<h2>Premium/Discount Justification</h2>
{Analysis of whether current premium/discount is warranted}

<h2>Peer Operational KPIs</h2>
<table>
| KPI | {TICKER} | Peer 1 | Peer 2 | ... | Peer Median |
{KPI comparison table — sparse where data unavailable, footnoted}
</table>

<h2>Key Observations</h2>
<ul>{3-5 bullet points on relative valuation, standout metrics, peer group dynamics, KPI differentiation}</ul>

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

Tell the user where the HTML report was saved.

Highlight: where the stock trades relative to peers (premium/discount), the implied valuation range, and the most relevant multiple for this company.