Q1 Accounting Quality Screen: Twelve Names That Failed
Our forty-check forensic screen run across the Nifty 500. Cash conversion and receivable ageing did most of the flagging.
Our forty-check forensic screen was run across the Nifty 500 following Q1 results. Twelve names failed on two or more checks. We publish the check list, the failure pattern and what each flag does and does not imply.
- Universe
- Nifty 500
- Checks run
- 40
- Names flagged
- 12
- Threshold
- 2+ fails
Key findings
- Cash conversion did most of the flagging
The widening gap between reported profit and operating cash flow accounted for the largest share of individual check failures in this run.
- Receivable ageing was the second signal
A lengthening receivable cycle without a corresponding change in customer mix is the pattern the screen is designed to surface.
- A flag is a question, not a verdict
Most flagged names have plausible explanations. The screen exists to direct attention, not to conclude, and we treat it that way.
Number of individual check failures across the Nifty 500. Sample data.
What the screen tests
The forty checks cover cash conversion, working-capital drift, related-party exposure, auditor tenure and changes, contingent liabilities, promoter pledge movement, segment margin consistency and several accrual-quality measures.
Each check is binary with a documented threshold. The full list and thresholds are published in the attached workbook.
How we use the output
A name failing two or more checks is removed from consideration for new coverage until the pattern is explained. An existing coverage name failing two or more triggers a review note rather than an automatic downgrade.
We do not publish the names of flagged companies outside the subscriber archive, because a screen failure is not an accusation and should not be circulated as one.
Limitations we accept
The screen produces false positives, particularly in businesses with seasonal working capital or genuine one-off items. It is calibrated to over-flag rather than under-flag, which is the correct error to make for this purpose.
It also cannot detect fraud that is properly disguised in the accounts. It detects patterns that warrant a closer look.
Download the full report and model
20-page PDF plus the three-statement model as an editable spreadsheet. Change an assumption and see what the answer becomes.
Downloads are available to subscribers on the Professional and Premium tiers. Demo links on this build are inactive.
Head of Research · SkyGrowthWealth Research
Maintains the model behind this note and publishes every revision to it.
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