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Earnings Quality Score

Measures how closely reported earnings track real cash generation. Low earnings quality often precedes earnings disappointments or restatements.

Score: 0–100 (higher = higher quality) Rating: high (≥70) | medium (45–69) | low (20–44) | poor (<20)

Signals

Signal Weight Logic
Accruals Ratio 30% (Net Income − OCF) / Total Assets; lower accruals = more cash-backed earnings (Sloan, 1996)
Cash Earnings Quality 25% OCF / Net Income over time; ratio above 1.0 means cash generation exceeds reported profit
Revenue Recognition Quality 20% Change in receivables vs revenue growth; aggressive receivables growth may signal channel stuffing or premature recognition
Gross Margin Stability 15% Coefficient of variation of gross margin; stable margins indicate genuine pricing, not accounting smoothing
Asset Efficiency Trend 10% Asset turnover trend; improving efficiency signals real operational leverage, not financial engineering

Usage

from fin_ratios.utils.earnings_quality import (
    earnings_quality_score_from_series,
    earnings_quality_score,
)
from fin_ratios.fetchers.edgar import fetch_edgar

annual_data = fetch_edgar('AAPL', num_years=7)
score = earnings_quality_score_from_series(annual_data)

print(score.score)    # 84
print(score.rating)   # 'high'
print(score.table())

# One-liner
score = earnings_quality_score('AAPL', years=7, source='yahoo')

import { earningsQualityScoreFromSeries } from 'fin-ratios'
import { fetchEdgarFlat } from 'fin-ratios/fetchers/edgar'

const annualData = await fetchEdgarFlat('AAPL')
const score = earningsQualityScoreFromSeries(annualData)

console.log(score.score)   // 84
console.log(score.rating)  // 'high'

Interpreting Accruals

The accruals ratio is the most predictive signal:

  • < −0.05 → possibly aggressive cash collection; investigate
  • −0.05 to +0.05 → healthy, earnings closely match cash
  • > +0.10 → earnings significantly exceed cash generation; risk of overstatement

Output Structure

@dataclass
class EarningsQualityScore:
    score: int
    rating: str   # high / medium / low / poor
    components: EarningsQualityComponents
    evidence: list[str]
    interpretation: str

@dataclass
class EarningsQualityComponents:
    accruals_ratio: float          # 0–1
    cash_earnings: float           # 0–1
    revenue_recognition: float     # 0–1
    gross_margin_stability: float  # 0–1
    asset_efficiency: float        # 0–1

Minimum Data Requirements

  • At least 3 years of data
  • net_income, operating_cash_flow, total_assets, capex, depreciation required

References

  • Sloan, R.G. (1996). Do Stock Prices Fully Reflect Information in Accruals and Cash Flows About Future Earnings? The Accounting Review.
  • Richardson, S. et al. (2005). Accrual Reliability, Earnings Persistence and Stock Prices. Journal of Accounting and Economics.
  • Penman, S. (2013). Financial Statement Analysis and Security Valuation (5th ed.).