Workday Beneish M-Score Historical Data
The historical data trend for Workday'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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Workday Annual Data
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Trend |
Jan14 |
Jan15 |
Jan16 |
Jan17 |
Jan18 |
Jan19 |
Jan20 |
Jan21 |
Jan22 |
Jan23 |
| Beneish M-Score |
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-1.84 |
-3.19 |
-3.24 |
-2.96 |
-3.05 |
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Workday Quarterly Data
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Jul18 |
Oct18 |
Jan19 |
Apr19 |
Jul19 |
Oct19 |
Jan20 |
Apr20 |
Jul20 |
Oct20 |
Jan21 |
Apr21 |
Jul21 |
Oct21 |
Jan22 |
Apr22 |
Jul22 |
Oct22 |
Jan23 |
Apr23 |
| Beneish M-Score |
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-3.07 |
-2.97 |
-3.07 |
-3.05 |
-2.74 |
Competitive Comparison
For the Software - Application subindustry, Workday'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.
Workday Beneish M-Score Distribution
For the Software industry and Technology sector, Workday's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Workday's Beneish M-Score falls in comparison to its industry or sector. The grey bar indicates the Beneish M-Score's extreme value range as defined by GuruFocus.
Workday 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 Workday 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 * 1.1689 | + | 0.528 * 0.9865 | + | 0.404 * 0.9631 | + | 0.892 * 1.1977 | + | 0.115 * 1.034 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.0375 | + | 4.679 * -0.130451 | - | 0.327 * 0.8766 |
| = | -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 (Apr23) TTM: | Last Year (Apr22) TTM: |
Total Receivables was $1,089 Mil.
Revenue was 1684.312 + 1646.26 + 1599.103 + 1535.798 = $6,465 Mil.
Gross Profit was 1266.868 + 1192.781 + 1163.31 + 1112.713 = $4,736 Mil.
Total Current Assets was $7,873 Mil.
Total Assets was $13,253 Mil.
Property, Plant and Equipment(Net PPE) was $1,473 Mil.
Depreciation, Depletion and Amortization(DDA) was $345 Mil.
Selling, General, & Admin. Expense(SGA) was $2,575 Mil.
Total Current Liabilities was $4,044 Mil.
Long-Term Debt & Capital Lease Obligation was $3,160 Mil.
Net Income was 0.136 + -125.697 + -74.721 + -64.158 = $-264 Mil.
Non Operating Income was -7.44 + -7.648 + 2.537 + -17.843 = $-30 Mil.
Cash Flow from Operations was 277.316 + 694.452 + 408.668 + 114.358 = $1,495 Mil.
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Total Receivables was $778 Mil.
Revenue was 1434.657 + 1376.141 + 1327.263 + 1260.361 = $5,398 Mil.
Gross Profit was 1031.836 + 986.344 + 967.539 + 914.84 = $3,901 Mil.
Total Current Assets was $7,443 Mil.
Total Assets was $12,799 Mil.
Property, Plant and Equipment(Net PPE) was $1,438 Mil.
Depreciation, Depletion and Amortization(DDA) was $351 Mil.
Selling, General, & Admin. Expense(SGA) was $2,072 Mil.
Total Current Liabilities was $4,781 Mil.
Long-Term Debt & Capital Lease Obligation was $3,155 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) |
| = | (1089.286 / 6465.473) | / | (778.076 / 5398.422) |
| = | 0.168477 | / | 0.14413 |
| | = | 1.1689 |
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) |
| = | (3900.559 / 5398.422) | / | (4735.672 / 6465.473) |
| = | 0.722537 | / | 0.732456 |
| | = | 0.9865 |
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 - (7873.093 + 1472.776) / 13252.796) | / | (1 - (7443.226 + 1438.24) / 12799.106) |
| = | 0.2948 | / | 0.306087 |
| | = | 0.9631 |
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 |
| = | 6465.473 | / | 5398.422 |
| | = | 1.1977 |
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)) |
| = | (351.106 / (351.106 + 1438.24)) | / | (344.952 / (344.952 + 1472.776)) |
| = | 0.19622 | / | 0.189771 |
| | = | 1.034 |
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) |
| = | (2575.221 / 6465.473) | / | (2072.426 / 5398.422) |
| = | 0.398304 | / | 0.383895 |
| | = | 1.0375 |
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) |
| = | ((3159.904 + 4044.089) / 13252.796) | / | ((3155.305 + 4781.387) / 12799.106) |
| = | 0.543583 | / | 0.620097 |
| | = | 0.8766 |
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 |
| = | (-264.44 - -30.394 | - | 1494.794) | / | 13252.796 |
| | = | -0.130451 |
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.
Workday has a M-score of -2.74 suggests that the company is unlikely to be a manipulator.
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