Piotroski F-Score

The Piotroski F-Score is a financial scoring system developed by accounting professor Joseph Piotroski in 2000 to identify financially strong companies among low price-to-book value stocks. It uses nine binary criteria across profitability, leverage, and efficiency to assign each company a score from 0 to 9.

What Is the Piotroski F-Score?

The F-Score assigns 1 point for each criterion met and 0 otherwise:

**Profitability signals (4 criteria):**
– F1: Positive return on assets (ROA > 0)
– F2: Positive operating cash flow
– F3: ROA increasing year over year
– F4: Cash flow from operations > Net income (accruals are low)

**Leverage and liquidity signals (3 criteria):**
– F5: Debt ratio decreasing (leverage improved)
– F6: Current ratio improving (liquidity better)
– F7: No new equity shares issued (no dilution)

**Operating efficiency signals (2 criteria):**
– F8: Gross margin improving
– F9: Asset turnover ratio improving

Interpreting the F-Score

| F-Score | Interpretation |
|———|—————|
| 8-9 | Strong; financially improving company |
| 5-7 | Average; watch for trends |
| 0-2 | Weak; financially deteriorating |

Piotroski’s Original Research

Piotroski found that buying high F-Score value stocks (8-9) and shorting low F-Score stocks (0-2) from the universe of low price-to-book stocks generated a mean annual return of 23% in his US study. The F-Score was designed to filter out financially deteriorating “value traps.”

Practical Example

A value investor screens for Indian small-cap stocks trading below book value. She finds 40 candidates. After applying the F-Score, only 8 score 8 or above. She investigates these 8 further for qualitative factors. The F-Score filtering reduced time spent on financially weakening companies.

Key Takeaways

– Piotroski F-Score scores companies 0-9 on nine profitability, leverage, and efficiency criteria
– Higher scores (8-9) indicate financially improving companies; lower scores (0-2) signal deterioration
– Designed to identify quality within the universe of low price-to-book value stocks
– Each criterion is binary (yes/no); only companies genuinely improving across multiple dimensions score high
– Useful as a quantitative screen, but always combine with qualitative analysis and industry context