Ecolab Beneish M-Score Historical Data
The historical data trend for Ecolab's Beneish M-Score can be seen below:
* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.
Ecolab Beneish M-Score Chart
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Ecolab Annual Data
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Trend |
Dec14 |
Dec15 |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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-2.73 |
-3.33 |
-2.53 |
-2.52 |
-2.67 |
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Ecolab Quarterly Data
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Jun19 |
Sep19 |
Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
Mar24 |
| Beneish M-Score |
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-2.54 |
-2.60 |
-2.69 |
-2.67 |
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Competitive Comparison of Ecolab's Beneish M-Score
For the Specialty Chemicals subindustry, Ecolab's Beneish M-Score, along with its competitors' market caps and Beneish M-Score data, can be viewed below:
* Competitive companies are chosen from companies within the same industry, with headquarter located in same country, with closest market capitalization; x-axis shows the market cap, and y-axis shows the term value; the bigger the dot, the larger the market cap. Note that "N/A" values will not show up in the chart.
Ecolab's Beneish M-Score Distribution in the Chemicals Industry
For the Chemicals industry and Basic Materials sector, Ecolab's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Ecolab's Beneish M-Score falls into.
Ecolab Beneish M-Score Calculation
The M-score was created by Professor Messod Beneish. Instead of measuring the bankruptcy risk (Altman Z-Score) or business trend (Piotroski F-Score), M-score can be used to detect the risk of earnings manipulation. This is the original research paper on M-score.
The M-Score Variables:
The M-score of Ecolab for today is based on a combination of the following eight different indices:
| M | = | -4.84 | + | 0.92 * DSRI | + | 0.528 * GMI | + | 0.404 * AQI | + | 0.892 * SGI | + | 0.115 * DEPI |
| = | -4.84 | + | 0.92 * 0.9365 | + | 0.528 * 0.939 | + | 0.404 * 0.9782 | + | 0.892 * 1.0456 | + | 0.115 * 1.0428 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.0314 | + | 4.679 * -0.045119 | - | 0.327 * 0.9591 |
| = | -2.74 |
* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.
| This Year (Dec23) TTM: | Last Year (Dec22) TTM: |
Total Receivables was R$14,803 Mil.
Revenue was 19297.372 + 19547.868 + 18696.938 + 18603.036 = R$76,145 Mil.
Gross Profit was 8104.269 + 8038.228 + 7364.519 + 7117.031 = R$30,624 Mil.
Total Current Assets was R$27,655 Mil.
Total Assets was R$107,044 Mil.
Property, Plant and Equipment(Net PPE) was R$19,737 Mil.
Depreciation, Depletion and Amortization(DDA) was R$4,596 Mil.
Selling, General, & Admin. Expense(SGA) was R$20,200 Mil.
Total Current Liabilities was R$21,294 Mil.
Long-Term Debt & Capital Lease Obligation was R$39,085 Mil.
Net Income was 1985.399 + 1995.235 + 1600.265 + 1215.687 = R$6,797 Mil.
Non Operating Income was -55.368 + -109.639 + -32.034 + -59.378 = R$-256 Mil.
Cash Flow from Operations was 4177.079 + 3890.214 + 2783.112 + 1032.345 = R$11,883 Mil.
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Total Receivables was R$15,118 Mil.
Revenue was 19256.912 + 19240.341 + 18073.795 + 16256.406 = R$72,827 Mil.
Gross Profit was 7429.06 + 7224.108 + 6912.825 + 5938.338 = R$27,504 Mil.
Total Current Assets was R$28,819 Mil.
Total Assets was R$112,589 Mil.
Property, Plant and Equipment(Net PPE) was R$19,626 Mil.
Depreciation, Depletion and Amortization(DDA) was R$4,814 Mil.
Selling, General, & Admin. Expense(SGA) was R$18,732 Mil.
Total Current Liabilities was R$22,085 Mil.
Long-Term Debt & Capital Lease Obligation was R$44,130 Mil.
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1. DSRI = Days Sales in Receivables Index
Measured as the ratio of Revenue in Total Receivables in year t to year t-1.
A large increase in DSR could be indicative of revenue inflation.
| DSRI | = | (Receivables_t / Revenue_t) | / | (Receivables_t-1 / Revenue_t-1) |
| = | (14802.786 / 76145.214) | / | (15118.292 / 72827.454) |
| = | 0.194402 | / | 0.207591 |
| | = | 0.9365 |
2. GMI = Gross Margin Index
Measured as the ratio of gross margin in year t-1 to gross margin in year t.
Gross margin has deteriorated when this index is above 1. A firm with poorer prospects is more likely to manipulate earnings.
| GMI | = | GrossMargin_t-1 | / | GrossMargin_t |
| = | (GrossProfit_t-1 / Revenue_t-1) | / | (GrossProfit_t / Revenue_t) |
| = | (27504.331 / 72827.454) | / | (30624.047 / 76145.214) |
| = | 0.377664 | / | 0.40218 |
| | = | 0.939 |
3. AQI = Asset Quality Index
AQI is the ratio of asset quality in year t to year t-1.
Asset quality is measured as the ratio of non-current assets other than Property, Plant and Equipment to Total Assets.
| AQI | = | (1 - (CurrentAssets_t + PPE_t) / TotalAssets_t) | / | (1 - (CurrentAssets_t-1 + PPE_t-1) / TotalAssets_t-1) |
| = | (1 - (27654.961 + 19736.884) / 107043.971) | / | (1 - (28819.277 + 19626.189) / 112588.839) |
| = | 0.557267 | / | 0.569713 |
| | = | 0.9782 |
4. SGI = Sales Growth Index
Ratio of Revenue in year t to sales in year t-1.
Sales growth is not itself a measure of manipulation. However, growth companies are likely to find themselves under pressure to manipulate in order to keep up appearances.
| SGI | = | Sales_t | / | Sales_t-1 |
| = | Revenue_t | / | Revenue_t-1 |
| = | 76145.214 | / | 72827.454 |
| | = | 1.0456 |
5. DEPI = Depreciation Index
Measured as the ratio of the rate of Depreciation, Depletion and Amortization in year t-1 to the corresponding rate in year t.
DEPI greater than 1 indicates that assets are being depreciated at a slower rate. This suggests that the firm might be revising useful asset life assumptions upwards, or adopting a new method that is income friendly.
| DEPI | = | (Depreciation_t-1 / (Depreciaton_t-1 + PPE_t-1)) | / | (Depreciation_t / (Depreciaton_t + PPE_t)) |
| = | (4813.903 / (4813.903 + 19626.189)) | / | (4596.156 / (4596.156 + 19736.884)) |
| = | 0.196967 | / | 0.188885 |
| | = | 1.0428 |
Note: If the Depreciation, Depletion and Amortization data is not available, we assume that the depreciation rate is constant and set the Depreciation Index to 1.
6. SGAI = Sales, General and Administrative expenses Index
The ratio of Selling, General, & Admin. Expense(SGA) to Sales in year t relative to year t-1.
SGA expenses index > 1 means that the company is becoming less efficient in generate sales.
| SGAI | = | (SGA_t / Sales_t) | / | (SGA_t-1 /Sales_t-1) |
| = | (20200.067 / 76145.214) | / | (18732.152 / 72827.454) |
| = | 0.265283 | / | 0.257213 |
| | = | 1.0314 |
7. LVGI = Leverage Index
The ratio of total debt to Total Assets in year t relative to yeat t-1.
An LVGI > 1 indicates an increase in leverage
| LVGI | = | ((LTD_t + CurrentLiabilities_t) / TotalAssets_t) | / | ((LTD_t-1 + CurrentLiabilities_t-1) / TotalAssets_t-1) |
| = | ((39085.215 + 21293.551) / 107043.971) | / | ((44130.075 + 22085.232) / 112588.839) |
| = | 0.564056 | / | 0.588116 |
| | = | 0.9591 |
8. TATA = Total Accruals to Total Assets
Total accruals calculated as the change in working capital accounts other than cash less depreciation.
| TATA | = | (IncomefromContinuingOperations_t | - | CashFlowsfromOperations_t) | / | TotalAssets_t |
| = | (NetIncome_t - NonOperatingIncome_t | - | CashFlowsfromOperations_t) | / | TotalAssets_t |
| = | (6796.586 - -256.419 | - | 11882.75) | / | 107043.971 |
| | = | -0.045119 |
An M-Score of equal or less than -1.78 suggests that the company is unlikely to be a manipulator. An M-Score of greater than -1.78 signals that the company is likely to be a manipulator.
Ecolab has a M-score of -2.74 suggests that the company is unlikely to be a manipulator.
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