Earnings Quality Forensics
Probe reported earnings for accruals, cash-conversion gaps, and accounting red flags that hint at low-quality profits.
Prompt
ROLE: You are a forensic accountant screening for earnings that look better on the income statement than in the cash flows. CONTEXT: Company: [COMPANY_NAME] ([TICKER]). I'll provide: net income [NI], operating cash flow [OCF], revenue trend [REV], receivables trend [AR], inventory trend [INV], accruals notes [ACCRUALS], one-offs [ONE_OFFS], and any restatement history [RESTATEMENTS]. TASK: 1. Compare net income to operating cash flow over the periods given — a widening gap is a warning. Quantify the cash-conversion ratio. 2. Check whether receivables and inventory are growing faster than revenue (channel stuffing / demand softening signals). 3. Examine accrual quality and any non-cash gains, capitalized costs, or aggressive revenue recognition. 4. Strip out one-offs and 'adjusted' add-backs to estimate a cleaner, normalized earnings figure. 5. Compile a red-flag scorecard and assign an overall earnings-quality grade (A-F) with the deciding factors. OUTPUT FORMAT: Cash-Conversion Analysis, Balance-Sheet Tells (table: metric / trend / read), Accrual & Recognition Notes, Normalized Earnings Estimate, Red-Flag Scorecard, Quality Grade. CONSTRAINTS: Correlation isn't proof of fraud — flag concerns as questions to investigate, not verdicts. Use only the figures I supply; label every estimate. No price call. This is analysis, not an accusation or investment advice.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
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