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 * 1.0393 | + | 0.528 * 0.9979 | + | 0.404 * 0.9635 | + | 0.892 * 1.099 | + | 0.115 * 0.8719 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.0068 | + | 4.679 * -0.028714 | - | 0.327 * 0.9586 |
| = | -2.51 |
* 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 $1,140 Mil.
Revenue was 805.108 + 458.795 + 496.599 + 509.447 = $2,270 Mil.
Gross Profit was 735.059 + 393.538 + 428.259 + 441.795 = $1,999 Mil.
Total Current Assets was $2,050 Mil.
Total Assets was $7,323 Mil.
Property, Plant and Equipment(Net PPE) was $195 Mil.
Depreciation, Depletion and Amortization(DDA) was $133 Mil.
Selling, General, & Admin. Expense(SGA) was $855 Mil.
Total Current Liabilities was $889 Mil.
Long-Term Debt & Capital Lease Obligation was $854 Mil.
Net Income was 274.762 + 55.502 + 69.526 + 100.622 = $500 Mil.
Non Operating Income was -2.876 + 0.096 + -3.483 + -0.177 = $-6 Mil.
Cash Flow from Operations was 232.722 + 160.768 + 62.866 + 260.766 = $717 Mil.
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Total Receivables was $998 Mil.
Revenue was 694.115 + 472.511 + 473.85 + 425.077 = $2,066 Mil.
Gross Profit was 632.688 + 410.544 + 411.363 + 360.317 = $1,815 Mil.
Total Current Assets was $1,664 Mil.
Total Assets was $6,688 Mil.
Property, Plant and Equipment(Net PPE) was $210 Mil.
Depreciation, Depletion and Amortization(DDA) was $115 Mil.
Selling, General, & Admin. Expense(SGA) was $773 Mil.
Total Current Liabilities was $795 Mil.
Long-Term Debt & Capital Lease Obligation was $866 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) |
| = | (1140.276 / 2269.949) | / | (998.389 / 2065.553) |
| = | 0.502336 | / | 0.483352 |
| | = | 1.0393 |
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) |
| = | (1814.912 / 2065.553) | / | (1998.651 / 2269.949) |
| = | 0.878657 | / | 0.880483 |
| | = | 0.9979 |
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 - (2049.567 + 194.76) / 7322.875) | / | (1 - (1664.122 + 209.978) / 6687.945) |
| = | 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 |
| = | 2269.949 | / | 2065.553 |
| | = | 1.099 |
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)) |
| = | (114.563 / (114.563 + 209.978)) | / | (132.504 / (132.504 + 194.76)) |
| = | 0.353 | / | 0.404884 |
| | = | 0.8719 |
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) |
| = | (855.135 / 2269.949) | / | (772.871 / 2065.553) |
| = | 0.37672 | / | 0.374171 |
| | = | 1.0068 |
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) |
| = | ((854.396 + 889.294) / 7322.875) | / | ((866.376 + 794.836) / 6687.945) |
| = | 0.238115 | / | 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 |
| = | (500.412 - -6.44 | - | 717.122) | / | 7322.875 |
| | = | -0.028714 |
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 -2.51 suggests that the company is unlikely to be a manipulator.
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