Datadog Beneish M-Score Historical Data
The historical data trend for Datadog'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.
|
Datadog Annual Data
|
|
Trend |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
|
|
|
- |
-2.68 |
-1.17 |
-2.74 |
-3.01 |
|
Datadog Quarterly Data
|
|
Mar19 |
Jun19 |
Sep19 |
Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
| Beneish M-Score |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
-2.74 |
-2.87 |
-3.17 |
-3.13 |
-3.01 |
Competitive Comparison
For the Software - Application subindustry, Datadog'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.
Datadog Beneish M-Score Distribution
For the Software industry and Technology sector, Datadog's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Datadog's Beneish M-Score falls into.
Datadog 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 Datadog 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 * 1.0032 | + | 0.528 * 0.9822 | + | 0.404 * 0.7836 | + | 0.892 * 1.2706 | + | 0.115 * 1.0785 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.979 | + | 4.679 * -0.15445 | - | 0.327 * 0.9128 |
| = | -3.01 |
* 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 $509 Mil.
Revenue was 589.649 + 547.536 + 509.46 + 481.714 = $2,128 Mil.
Gross Profit was 484.82 + 444.217 + 407.614 + 381.8 = $1,718 Mil.
Total Current Assets was $3,178 Mil.
Total Assets was $3,936 Mil.
Property, Plant and Equipment(Net PPE) was $298 Mil.
Depreciation, Depletion and Amortization(DDA) was $44 Mil.
Selling, General, & Admin. Expense(SGA) was $789 Mil.
Total Current Liabilities was $1,003 Mil.
Long-Term Debt & Capital Lease Obligation was $880 Mil.
Net Income was 53.993 + 22.63 + -3.969 + -24.086 = $49 Mil.
Non Operating Income was -1.966 + 1.032 + -0.731 + -1.793 = $-3 Mil.
Cash Flow from Operations was 220.226 + 152.783 + 153.158 + 133.787 = $660 Mil.
|
Total Receivables was $400 Mil.
Revenue was 469.399 + 436.533 + 406.138 + 363.03 = $1,675 Mil.
Gross Profit was 372.642 + 342.934 + 324.213 + 288.568 = $1,328 Mil.
Total Current Assets was $2,344 Mil.
Total Assets was $3,005 Mil.
Property, Plant and Equipment(Net PPE) was $213 Mil.
Depreciation, Depletion and Amortization(DDA) was $35 Mil.
Selling, General, & Admin. Expense(SGA) was $635 Mil.
Total Current Liabilities was $760 Mil.
Long-Term Debt & Capital Lease Obligation was $815 Mil.
|
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) |
| = | (509.279 / 2128.359) | / | (399.551 / 1675.1) |
| = | 0.239282 | / | 0.238524 |
| | = | 1.0032 |
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) |
| = | (1328.357 / 1675.1) | / | (1718.451 / 2128.359) |
| = | 0.793002 | / | 0.807407 |
| | = | 0.9822 |
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 - (3178.137 + 298.434) / 3936.072) | / | (1 - (2344.234 + 212.975) / 3004.852) |
| = | 0.116741 | / | 0.148973 |
| | = | 0.7836 |
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 |
| = | 2128.359 | / | 1675.1 |
| | = | 1.2706 |
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)) |
| = | (34.629 / (34.629 + 212.975)) | / | (44.465 / (44.465 + 298.434)) |
| = | 0.139856 | / | 0.129674 |
| | = | 1.0785 |
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) |
| = | (789.468 / 2128.359) | / | (634.701 / 1675.1) |
| = | 0.370928 | / | 0.378903 |
| | = | 0.979 |
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) |
| = | ((880.363 + 1003.052) / 3936.072) | / | ((815.429 + 759.748) / 3004.852) |
| = | 0.478501 | / | 0.524211 |
| | = | 0.9128 |
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 |
| = | (48.568 - -3.458 | - | 659.954) | / | 3936.072 |
| | = | -0.15445 |
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.
Datadog has a M-score of -3.01 suggests that the company is unlikely to be a manipulator.
Quick Browse
Personalize
Premium Gurus 
Create New Portfolio
Portfolio Contest
DDOG has been successfully added to your