| name | parallax-earnings-quality |
| description | Forensic earnings quality analysis: revenue recognition patterns, accruals, quality sub-scores, manipulation risk, and hidden risk detection via Parallax MCP tools. Symbol in RIC format. NOT for general stock analysis (use /parallax-deep-dive), not for full due diligence (use /parallax-due-diligence). |
| negative-triggers | ["General stock analysis → use /parallax-deep-dive","Full due diligence → use /parallax-due-diligence","Quick stock check → use /parallax-should-i-buy"] |
| gotchas | ["JIT-load _parallax/parallax-conventions.md for RIC resolution, parallel execution, and fallback patterns","get_financial_analysis (Palepu framework) is async ~2-5 min — this is the core of the forensic analysis","Quality score of 10 doesn't mean no risk — dig into sub-components","Cross-reference news for context on any red flags found","Focus output on actionable risk flags, not comprehensive financial review"] |
Earnings Quality Analysis
Forensic-focused earnings quality and hidden risk detection.
Usage
/parallax-earnings-quality AAPL.O
/parallax-earnings-quality 7203.T "concerned about revenue recognition"
Workflow
Execute using mcp__claude_ai_Parallax__* tools. JIT-load _parallax/parallax-conventions.md for execution mode, RIC resolution, and fallback patterns.
Batch 0 — Tool Loading
Call ToolSearch with query "+Parallax" to load the deferred MCP tool schemas before the first mcp__claude_ai_Parallax__* call.
Batch A — Data gathering (parallel)
Fire all simultaneously:
| Tool | Parameters | Notes |
|---|
get_score_analysis | symbol | Quality score trajectory (default 52-week lookback) |
get_financials | symbol, statement="income" | Revenue/margin trends (default 4 periods) |
get_financials | symbol, statement="cash_flow" | Cash conversion (default 4 periods) |
get_financials | symbol, statement="ratios" | Accrual ratios |
get_financial_analysis | symbol | Async ~2-5 min — Palepu forensic analysis |
get_news_synthesis | symbol | Async — accounting news, auditor changes |
Batch B — AI synthesis (after Batch A)
Call get_assessment with a prompt focused on: earnings quality concerns, revenue recognition patterns, accrual anomalies, cash flow vs. earnings divergence, and any specific concerns the user raised. Feed in all findings from Batch A.
Output Format
- Risk Summary (red/yellow/green traffic light for overall earnings quality)
- Quality Score Trend (52-week trajectory with inflection points flagged)
- Forensic Findings (from Palepu analysis — accruals, revenue quality, cash conversion)
- Red Flags (specific items that warrant investigation)
- News Context (any accounting-related developments)
- AI Assessment (synthesized risk opinion)
- Recommended Actions (what to monitor, what warrants deeper investigation)
"This is informational analysis based on Parallax factor scores, not investment advice. All outputs should be reviewed by qualified professionals before any investment decisions."
