**Frank Voisin is the author of the popular value focused website Frankly Speaking, found at http://www.FrankVoisin.com.**

Earlier this week, we discussed the Altman Z-Score, which is a measure designed to predict the likelihood that a firm will go bankrupt within two years. Today, we will discuss Piotroski’s F-Score, which is in many ways the polar opposite. Rather than identifying the weakest companies, the F-Score identifies the healthiest companies.

Piotroski noticed that, among apparent value stocks (firms with high book-to-market ratios, or low P/B), some ultimately were strong performers while others collapsed. He sought out a method of separating out the value traps. He did so by also considering accounting ratios (as in the Z-Score), but rather than analyzing these ratios at one point in time, he placed the emphasis on the change in these ratios over time.

Here is the Piotroski’s F-Score Equation:

F-Score = ROA + ?ROA + CFO + ACCRUAL + ?MARGIN + ?TURN + ?LEVER + ?LIQUID + EQ_OFFER

Piotroski’s nine factors are split into three groups:

Factor |
Ratio |
Scoring |

Profitability Signals |
||

ROA | Net Income Before Extraordinary Items | +1 if positive this year |

?ROA | Change in ROA from last year | +1 if increased YoY |

CFO | Cash Flow from Operations | +1 if positive this year |

ACCRUAL | Cash Flow from Operations – Net Income Before Extraordinary Items | +1 if positive this year |

Operating Efficiency Signals |
||

?MARGIN | Change in Gross Margin from last year | +1 if increased YoY |

?TURN | Change in Asset Turnover from last year | +1 if increased YoY |

Leverage, Liquidity and Source of Funds Signals |
||

?LEVER | Change in Debt / Assets from last year | +1 if decreased YoY |

?LIQUID | Change in Current Ratio from last year | +1 if increased YoY |

EQ_OFFER | Did the firm issue any common equity last year? | +1 if no |

Note that if you look at the original paper, you will see that ROA, CFO and ACCRUAL are defined slightly differently. This is because Piotroski scaled these factors by Total Assets. This is unnecessary for our purposes because we are only looking at whether these are positive or negative, whereas Piotroski presumably scaled them for his statistical analysis.

At the end, you score each factor as explained in this table and the sum of these is your F-Score, which ranges from zero to nine. The higher the score, the more financially sound the company. Here were Piotroski’s results:

I show that the mean return earned by a high book-to-market investor can be increased by at least 7.5% annually through the selection of financially strong high BM firms while the entire distribution of realized returns is shifted to the right. In addition, an investment strategy that buys expected winners and shorts expected losers generates a 23% annual return between 1976 and 1996, and the strategy appears to be robust across time and to controls for alternative investment strategies. Within the portfolio of high BM firms, the benefits to financial statement analysis are concentrated in small and medium-sized firms, companies with low share turnover, and firms with no analyst following, yet this superior performance is not dependent on purchasing firms with low share prices.

You can read Piotroski’s paper from 2002, *Value Investing: The use of historical financial statement information to separate winners from losers*, here [PDF].

So how should you use this? Like the Altman Z-Score, I recommend incorporating the F-Score into your investment analysis spreadsheet and calculating the F-Score for any firm you are analyzing. Given how easy it is to calculate and its results over time, there really is no excuse for not considering it.

How do you incorporate the Piotroski F-Score into your analysis?