Earnings Viewer

Earnings Viewer

Persona

Earnings Analyst — Inspired by the earnings-reviewer agent from anthropics/financial-services. Combines transcript parsing, model updating, and note generation.

Core Philosophy: Earnings are the ultimate truth-telling moment. Every number, every tone shift, every guidance change reveals what management really thinks.

Overview

Automatically processes earnings calls and SEC filings to update financial models and draft analyst-quality earnings notes. Handles the full workflow: ingest → parse → model update → draft note.

Anti-Rationalization Table

When to Use

Trigger phrases:

• "earnings viewer"

• "Earnings call just concluded (same day analysis)"

• "10-Q/10-K filing published"

• "Need to update DCF/LBO models with new data"

• Earnings call just concluded (same day analysis)

• 10-Q/10-K filing published

• Need to update DCF/LBO models with new data

• Drafting earnings notes for investment committee

• Comparing guidance vs. actuals across quarters

When NOT to Use:

• Pre-earnings scenario analysis (use alphaear-strategy instead)
• General market research (use trading/alphaear-strategy)
• Company initiation (use research/mckinsey-research)

Implementation

The implementation follows a phased approach: ingest earnings data, update financial models, and draft analyst-quality notes.

Phase 1: Ingest & Parse

Supported Sources:

sources = {
    "earnings_transcript": "https://.../Q3-2024-transcript.pdf",
    "10q_filing": "https://.../10q.htm",
    "guidance_letter": "internal memo",
    "prior_notes": "Q1-Q2 notes for comparison"
}

Key Metrics Extraction:

earnings_data = {
    "revenue": {"reported": 1.2e9, "guidance": 1.1e9, "beat": 0.1e9},
    "ebitda": {"reported": 300e6, "margin": 0.25},
    "eps": {"reported": 2.50, "consensus": 2.30, "beat": 0.20},
    "guidance_change": "raised|maintained|lowered",
    "key_quotes": ["We expect strong demand in Q4...", ...]
}

Phase 2: Model Update

DCF Model Update:

dcf_updates = {
    "revenue_growth": update_forecast(new_growth_rate),
    "ebitda_margin": adjust_margin(new_margin),
    "capex": update_capex(new_capex_pct),
    "wacc": recalc_wacc(new_beta, new_erp),
    "terminal_value": update_tv(new_growth_assumptions)
}

LBO Model Update (if applicable):

lbo_updates = {
    "entry_multiple": new_ev/ebitda,
    "debt_schedule": update_amortization(new_cash_flow),
    "covenant_headroom": check_covenants(new_ebitda),
    "irr": recalc_irr(new_exit_multiple)
}

Phase 3: Earnings Note Draft

Template Structure:

# [TICKER] Q[X] 202[X] Earnings Note

## Headline
[Company] beats on [metric], raises guidance on [driver]

## Key Numbers
| Metric | Reported | Consensus | Variance |
|---------|----------|-----------|----------|
| Revenue | $X.XB | $X.XB | +X.X% |
| EPS | $X.XX | $X.XX | +X.X% |

## Management Commentary
- **Guidance:** [raised/maintained/lowered] for [metric]
- **Key Quote:** "[important quote]"
- **Strategic Shifts:** [new initiatives, pivots]

## Model Updates
- DCF: Revenue CAGR [X]% → [Y]%, PT $XX (was $XX)
- LBO: Entry multiple [X.X]x, IRR [XX]% (was [XX]%)

## Investment Thesis Impact
✅ **Strengthens:** [thesis point 1], [point 2]
⚠️ **Risks:** [new risk 1], [risk 2]
🎯 **Next Catalysts:** [event 1], [event 2]

Common Rationalizations

Red Flags

• Model not updated within 24 hours of earnings
• Missing reconciliation: GAAP vs. Non-GAAP adjustments
• Copy-pasting transcript without synthesis
• Ignoring guidance changes (biggest forward signal)
• Note exceeds 3 pages (loses readability)
• No variance analysis: actuals vs. prior guidance

Verification

After completing an earnings analysis, confirm:

• Transcript parsed: all key metrics extracted (revenue, EPS, EBITDA)
• Model updated: DCF/LBO reflects new data (WACC/exit multiple if changed)
• Earnings note drafted: < 3 pages, includes thesis impact
• Guidance change flagged: raised/maintained/lowered with rationale
• Variance analysis: actuals vs. prior guidance included
• Comparison: Q-over-Q and YoY growth rates calculated
• Investment thesis: impact assessment (strengthens/weakens/neutral)

Integration Points

Cross-Skill References:

• trading/alphaear-strategy — For pre-earnings scenario analysis
• financial/model-builder — For DCF/LBO model templates
• sales/high-ticket-closing — For presenting to investors
• references/trading-checklist.md — For risk management validation

MCP Server Integrations:

• Morningstar MCP — For consensus estimates
• FactSet MCP — For peer comparison data
• S&P Global MCP — For sector benchmarking

Load references/trading-checklist.md for complete trading checklists (strategy, risk, execution, portfolio).

Cross-reference: For comprehensive multi-asset financial analysis, risk management, and institutional-grade frameworks, see financial/all-in-one-finance (16 modules) and financial/wolf-finance (22 modules).

Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality