Ansys Beneish M-Score Historical Data
The historical data trend for Ansys'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.
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Ansys Annual Data
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Trend |
Dec14 |
Dec15 |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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-2.33 |
-2.42 |
-2.37 |
-2.47 |
-2.51 |
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Ansys Quarterly Data
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Mar19 |
Jun19 |
Sep19 |
Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
| Beneish M-Score |
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-2.47 |
-2.44 |
-2.38 |
-2.50 |
-2.51 |
Competitive Comparison
For the Software - Application subindustry, Ansys'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.
Ansys Beneish M-Score Distribution
For the Software industry and Technology sector, Ansys's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Ansys's Beneish M-Score falls into.
Ansys 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 Ansys 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 * 1.0379 | + | 0.528 * 0.9982 | + | 0.404 * 0.9635 | + | 0.892 * 1.069 | + | 0.115 * 0.8704 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.0081 | + | 4.679 * -0.029424 | - | 0.327 * 0.9586 |
| = | -2.54 |
* 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 €1,046 Mil.
Revenue was 738.284 + 429.891 + 458.361 + 475.823 = €2,102 Mil.
Gross Profit was 674.049 + 368.745 + 395.283 + 412.637 = €1,851 Mil.
Total Current Assets was €1,879 Mil.
Total Assets was €6,715 Mil.
Property, Plant and Equipment(Net PPE) was €179 Mil.
Depreciation, Depletion and Amortization(DDA) was €123 Mil.
Selling, General, & Admin. Expense(SGA) was €792 Mil.
Total Current Liabilities was €815 Mil.
Long-Term Debt & Capital Lease Obligation was €783 Mil.
Net Income was 251.957 + 52.005 + 64.172 + 93.981 = €462 Mil.
Non Operating Income was -2.637 + 0.09 + -3.215 + -0.165 = €-6 Mil.
Cash Flow from Operations was 213.406 + 150.64 + 58.025 + 243.555 = €666 Mil.
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Total Receivables was €942 Mil.
Revenue was 655.245 + 477.236 + 448.262 + 385.97 = €1,967 Mil.
Gross Profit was 597.257 + 414.649 + 389.149 + 327.168 = €1,728 Mil.
Total Current Assets was €1,571 Mil.
Total Assets was €6,313 Mil.
Property, Plant and Equipment(Net PPE) was €198 Mil.
Depreciation, Depletion and Amortization(DDA) was €109 Mil.
Selling, General, & Admin. Expense(SGA) was €735 Mil.
Total Current Liabilities was €750 Mil.
Long-Term Debt & Capital Lease Obligation was €818 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) |
| = | (1045.633 / 2102.359) | / | (942.479 / 1966.713) |
| = | 0.497362 | / | 0.479215 |
| | = | 1.0379 |
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) |
| = | (1728.223 / 1966.713) | / | (1850.714 / 2102.359) |
| = | 0.878737 | / | 0.880304 |
| | = | 0.9982 |
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 - (1879.453 + 178.595) / 6715.076) | / | (1 - (1570.931 + 198.219) / 6313.42) |
| = | 0.693518 | / | 0.719779 |
| | = | 0.9635 |
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 |
| = | 2102.359 | / | 1966.713 |
| | = | 1.069 |
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)) |
| = | (109.022 / (109.022 + 198.219)) | / | (122.927 / (122.927 + 178.595)) |
| = | 0.354842 | / | 0.407688 |
| | = | 0.8704 |
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) |
| = | (792.468 / 2102.359) | / | (735.389 / 1966.713) |
| = | 0.376942 | / | 0.373918 |
| | = | 1.0081 |
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) |
| = | ((783.481 + 815.483) / 6715.076) | / | ((817.859 + 750.325) / 6313.42) |
| = | 0.238116 | / | 0.248389 |
| | = | 0.9586 |
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 |
| = | (462.115 - -5.927 | - | 665.626) | / | 6715.076 |
| | = | -0.029424 |
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.
Ansys has a M-score of -2.54 suggests that the company is unlikely to be a manipulator.
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