SUPPORTED SCORE RANGE
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out of 100 · higher means fewer observed risk signals
Weighted evidence coverage
NSE + BSE EQUITY RESEARCH
One stock.
The evidence behind it.
Enter a company name or ticker. Frogility calculates financial quality and risk indicators from the records it has, explains every signal, and makes missing evidence visible.
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Search published NSE/BSE equity directories. Financial scoring coverage remains limited to reviewed issuer reports. You do not need to enter any metrics.
3 years audited financials12 months trading history5 years regulatory history100 points transparent weights
HIGHEST-SCORING PENNY STOCKS
Your daily top 10. No minimum score.
Ranked against one another using supported scores. Price ≤ ₹50. Recalculated daily at 6:30 pm IST.
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AUTOMATIC ASSESSMENT
Five pillars, one transparent model
Unknown checks widen the range. An overall point score requires all applicable model evidence; no missing item is assumed clean.
Promoters, directors and legal evidence
Explore dated relationships and original evidence →EVIDENCE BREAKDOWN
What shaped this assessment
These rules are Frogility's screening heuristics, not a SEBI or exchange rating. Data can be incomplete or inaccurate. The report is not a fraud verdict, assurance of safety, or personalised investment advice.
THE METHOD
Evidence first. Every time.
Defaults set the review window and thresholds. They never fill in an unknown financial fact.
Resolve the company
Match its legal name, exchange ticker and ISIN. Select the right entity when a name is ambiguous.
Calculate the signals
Evaluate source-backed financial facts and recorded events using published rules. Keep units and periods consistent.
Show what is missing
Display a supported score range when evidence is incomplete. Source freshness is measured from the underlying period.
Score formula, weights and default thresholds
Known risk points = sum of each observed severity (0, 0.5 or 1) × its weight. The supported range is 100 − known risk − unknown weight to 100 − known risk. At full coverage, both bounds are the same score. This is not a probability of fraud.
Fraud patterns and the limits of public evidence
Accounting manipulation
Cash-profit gaps, accruals, receivables, restatements and audit findings can reveal inconsistencies. They can also have legitimate explanations.
Promoter and related-party abuse
Pledges, ownership changes, dilution and related-party exposures need transaction-level context before alleging misuse.
Market manipulation
Price, volume, circuit and liquidity signals flag unusual trading. Wash trades, spoofing and front-running usually need order and beneficial-owner data.
Misleading claims and enforcement
Match parties and current status in original orders. Allegations, appeals, stays, revocations and final findings are different states.
Shell operations and hidden liabilities
Weak cash generation and inconsistent disclosures can justify further inquiry. Public financial ratios cannot establish fictitious operations.
Impersonation and investment scams
Stock quality cannot authenticate a WhatsApp tip, broker or payment request. Check the intermediary and the offer separately.
No system using public records can detect every fraud. A clean statistical check is not a clean bill of health.
SEBI investor guidance ↗DATA AVAILABILITY
What is connected
Imported records require review and publication permission before they appear in a public report.
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The weighted model is an experimental heuristic. It has not been validated as a predictor of fraud. Read the methodology and data limits.
OWNERSHIP AND GOVERNANCE
Know which relationships are evidenced.
Promoter roles, board appointments, shareholding and accounting parents are separate facts. Browse eligible reference records with their dates, source locators and verification states.
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