Teradata Beneish M-Score Historical Data
The historical data trend for Teradata'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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Teradata Annual Data
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Trend |
Dec14 |
Dec15 |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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-3.13 |
-2.77 |
-3.13 |
-3.41 |
-3.25 |
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Teradata 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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-3.41 |
-3.17 |
-3.00 |
-2.92 |
-3.25 |
Competitive Comparison
For the Software - Infrastructure subindustry, Teradata'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.
Teradata Beneish M-Score Distribution
For the Software industry and Technology sector, Teradata's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Teradata's Beneish M-Score falls into.
Teradata 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 Teradata 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 * 0.7767 | + | 0.528 * 0.99 | + | 0.404 * 1.1074 | + | 0.892 * 1.0212 | + | 0.115 * 1.0754 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.9567 | + | 4.679 * -0.132942 | - | 0.327 * 1.063 |
| = | -3.25 |
* 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 $299 Mil.
Revenue was 457 + 438 + 462 + 476 = $1,833 Mil.
Gross Profit was 278 + 259 + 276 + 302 = $1,115 Mil.
Total Current Assets was $869 Mil.
Total Assets was $1,873 Mil.
Property, Plant and Equipment(Net PPE) was $248 Mil.
Depreciation, Depletion and Amortization(DDA) was $116 Mil.
Selling, General, & Admin. Expense(SGA) was $635 Mil.
Total Current Liabilities was $996 Mil.
Long-Term Debt & Capital Lease Obligation was $549 Mil.
Net Income was -7 + 12 + 17 + 40 = $62 Mil.
Non Operating Income was -20 + -13 + -10 + -21 = $-64 Mil.
Cash Flow from Operations was 176 + 41 + 49 + 109 = $375 Mil.
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Total Receivables was $377 Mil.
Revenue was 452 + 417 + 430 + 496 = $1,795 Mil.
Gross Profit was 263 + 259 + 258 + 301 = $1,081 Mil.
Total Current Assets was $1,028 Mil.
Total Assets was $2,022 Mil.
Property, Plant and Equipment(Net PPE) was $257 Mil.
Depreciation, Depletion and Amortization(DDA) was $134 Mil.
Selling, General, & Admin. Expense(SGA) was $650 Mil.
Total Current Liabilities was $1,007 Mil.
Long-Term Debt & Capital Lease Obligation was $562 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) |
| = | (299 / 1833) | / | (377 / 1795) |
| = | 0.163121 | / | 0.210028 |
| | = | 0.7767 |
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) |
| = | (1081 / 1795) | / | (1115 / 1833) |
| = | 0.602228 | / | 0.608292 |
| | = | 0.99 |
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 - (869 + 248) / 1873) | / | (1 - (1028 + 257) / 2022) |
| = | 0.403631 | / | 0.364491 |
| | = | 1.1074 |
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 |
| = | 1833 | / | 1795 |
| | = | 1.0212 |
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)) |
| = | (134 / (134 + 257)) | / | (116 / (116 + 248)) |
| = | 0.342711 | / | 0.318681 |
| | = | 1.0754 |
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) |
| = | (635 / 1833) | / | (650 / 1795) |
| = | 0.346427 | / | 0.362117 |
| | = | 0.9567 |
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) |
| = | ((549 + 996) / 1873) | / | ((562 + 1007) / 2022) |
| = | 0.82488 | / | 0.775964 |
| | = | 1.063 |
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 |
| = | (62 - -64 | - | 375) | / | 1873 |
| | = | -0.132942 |
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.
Teradata has a M-score of -3.25 suggests that the company is unlikely to be a manipulator.
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