Different Scores to Filter Companies

We can filter companies using various scores such as Piotroski F-score, Altman Z-score, Beneish M-score and Montier C-score. In this post, we will look into the scores to identify certain companies.

Piotroski F-score

F-score was developed by Joseph Piotroski. It is used to spot turnaround companies fast and also to identify the healthiest company among a basket of stocks. It involves nine variables from a company’s financial statements. In a research paper by Piotroski entitled “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers“, it was shown that by investing in the top performers over a 20-year test period from 1976 through 1996 that “the mean return earned by a high book-to-market investor can be increased by at least 7.5% annually”. On top of that, he found that buying the top stocks in the market and shorting those that got the worst scores would have resulted in 23% annualized gains. Also, weak stocks, scoring two points or less, were five times more likely to either go bankrupt or delist due to financial problems.

There are nine variables in the F-score, split into three groups. Points are awarded in a simple binary fashion, 1 for yes, 0 for no. The variables are as follows:

A. Profitability Signals

1.     Net Income – Score 1 if there is positive net income in the current year.

2.     Operating Cash Flow – Score 1 if there is positive cashflow from operations in the current year.

3.     Return on Assets – Score 1 if the ROA is higher in the current period compared to  the previous year.

4.     Quality of Earnings – Score 1 if the cash flow from operations exceeds net income before extraordinary items.

B. Leverage, Liquidity and Source of Funds

5.     Decrease in leverage – Score 1 if there is a lower ratio of long-term debt to in the current period compared value in the previous year .

6.     Increase in liquidity – Score 1 if there is a higher current ratio this year compared to the previous year.

7.     Absence of Dilution – Score 1 if the Firm did not issue new shares/equity in the preceding year.

C. Operating Efficiency

8.     Score 1 if there is a higher gross margin compared to the previous year.

9.     Asset Turnover – Score 1 if there is a higher asset turnover ratio year on year (as a measure of productivity).

Altman Z-score

The Z-score is used to identify risky stocks. It was developed by Edward Altman. Any Z-score above 2.99 is considered to be a safe company and any companies with a score less than 1.8 is shown to have a significant risk of financial distress within two years. This formula can also predict bankruptcy of a company.

The original Z-score formula is as follows:

Z = 0.012T1 + 0.014T2 + 0.033T3 + 0.006T4 + 0.009T5.

T1 = Working Capital / Total Assets. Measures liquid assets in relation to the size of the company.

T2 = Retained Earnings / Total Assets. Measures profitability that reflects the company’s age and earning power.

T3 = Earnings Before Interest and Taxes / Total Assets. Measures operating efficiency apart from tax and leveraging factors. It recognizes operating earnings as being important to long-term viability.

T4 = Market Value of Equity / Book Value of Total Liabilities. Adds market dimension that can show up security price fluctuation as a possible red flag.

T5 = Sales/ Total Assets. Standard measure for total asset turnover (varies greatly from industry to industry).

Beneish M-score

The M-score was developed by Messod D. Beneish and it helps to uncover companies that are manipulating its earnings. An M-score above 2.22 highlights companies that may be inflating their earnings artificially. This increases the probability that they will have to report lower earnings in the future. In his research paper entitled “The Detection of Earnings Manipulation“, Beneish has stated that the percentage of correctly classified manipulators ranges from 58 to 76%, while the percentage of incorrectly classified non-manipulators ranges from 7.6% to 17.5%. An interesting note is that students from Cornell University used the M-score to identify Enron as an earnings manipulator before its collapse.

M Score = -4.840 + 0.920 x DSRI + 0.528 x GMI + 0.404 x AQ + 0.892 x SGI + 0.115 x DEPI – 0.172 x SGAI – 0.327 x LVGI + 4.697 x TATA


Days Receivable Index (DSRI) is:
DSRI = (Net Receivablest / Salest) / Net Receivablest-1 / Salest-1)

Gross Margin Index (GMI) is:
GMI = [(Salest-1 – COGSt-1) / Salest-1] / [(Salest – COGSt) / Salest]

Asset Quality Index (AQI) is:
AQI = [1 – (Current Assetst + PP&Et + Securitiest) / Total Assetst] / [1 – ((Current Assetst-1 + PP&Et-1 + Securitiest-1) / Total Assetst-1)]

Sales Growth Index (SGI) is:
SGI = Salest / Salest-1

Depreciation Index (DEPI) is:
DEPI = (Depreciationt-1/ (PP&Et-1 + Depreciationt-1)) / (Depreciationt / (PP&Et + Depreciationt))

SG&A Expense Index (SGAI) is:
SGAI = (SG&A Expenset / Salest) / (SG&A Expenset-1 / Salest-1)

Leverage index (LVGI) is:
LVGI = [(Current Liabilitiest + Total Long Term Debtt) / Total Assetst] / [(Current Liabilitiest-1 + Total Long Term Debtt-1) / Total Assetst-1]

Total Accruals to Total Assets (TATA) is:
TATA = (Income from Continuing Operationst – Cash Flows from Operationst) / Total Assetst

Montier C-score

The C-Score was developed by James Montier and is used to identify companies that cook the books and then short them in the process. It is almost similar to the M-score. A point system of 1 is given for yes and 0 for no. These scores are then summed up to give a final C-score ranging from 0 (no evidence of earnings manipulation) to 6 (all red flags are present). The areas tested are:

  1. Is there a growing divergence between net income and operating cash-flow?
  2. Are Days Sales Outstanding (DSO) increasing? This means that the “accounts receivable” are growing faster than sales and this may be a sign of channel stuffing.
  3. Are days sales of inventory (DSI) increasing? If so, this may suggest slowing sales. This certainly is a bearish sign.
  4. Are other current assets increasing vs revenues? The DSO and/or DSI are usually closely watched so some Chief Financial Officers may use this catch-all line item to help hide things
  5. Are there declines in depreciation relative to gross property plant and equipment? This protects firms altering their estimate of useful asset life to beat earnings estimates.
  6. Is total asset growth high? Some firms are serial acquirers and use their acquisitions to distort their earnings.







4 thoughts on “Different Scores to Filter Companies

  1. A lot of such things are statistically correct, but in reality, how many people can buy all these to achieve statistically correctness?

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