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Ratios and health scores translate raw financial statement data into normalized metrics that make companies comparable. Ratios answer “how does this company look on a given dimension?”; health scores answer “is this company financially sound?” using established academic and practitioner models.

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Endpoints

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Financial Ratios

Endpoint: GET /stocks/{symbol}/ratios Returns pre-computed financial ratios organized into groups. Use the group parameter to fetch one category at a time, or supply name to retrieve a single specific ratio by key (overrides group). Omit both to receive all available ratios. Ratio groups When to use
  • Screening stocks by valuation (e.g. filtering for P/E below a threshold)
  • Comparing profitability metrics between companies in the same sector
  • Monitoring leverage trends across quarterly ttm data
  • Understanding why a company’s ROE changed using DuPont decomposition (was it margin expansion, or increased leverage?)
Example

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Health Scores

Endpoint: GET /stocks/{symbol}/scores Computes one or more quantitative scoring models derived from financial statement data. Use the name parameter to request a specific model. Omit name (or pass all) to receive all three scores in a single response.

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Piotroski F-Score

A nine-point scoring system that measures earnings quality and overall financial strength. Each of nine binary criteria contributes 1 point if met. The criteria span three dimensions:
  • Profitability: Positive ROA, positive operating cash flow, improving ROA, cash-based earnings exceeding accruals
  • Leverage & liquidity: Decreasing long-term debt ratio, improving current ratio, no new share dilution
  • Operating efficiency: Improving gross margin, improving asset turnover
Interpretation

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Altman Z-Score

Predicts the probability of corporate bankruptcy within two years using a weighted combination of five financial ratios. Originally calibrated on public manufacturing companies; results for financial companies, utilities, or firms outside the US may be less reliable. Interpretation

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Beneish M-Score

Detects the likelihood of earnings manipulation using eight financial statement variables. Developed by Messod Beneish (1999) based on SEC enforcement actions. Interpretation

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Notes on using health scores

  • Scores are computed from the most recently available financial statement data. Compare scores across multiple reporting periods to identify trends rather than relying on a single data point.
  • These models are screening tools, not definitive judgments. A safe-zone Altman Z-Score does not guarantee financial health; a low Beneish M-Score does not clear a company of manipulation.
  • Scores are most meaningful when benchmarked against peers in the same industry and business cycle stage.
Example