MongoDB Beneish M-Score Historical Data
The historical data trend for MongoDB'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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MongoDB Annual Data
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Trend |
Jan16 |
Jan17 |
Jan18 |
Jan19 |
Jan20 |
Jan21 |
Jan22 |
Jan23 |
Jan24 |
| Beneish M-Score |
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-2.98 |
-2.72 |
-2.66 |
-2.67 |
-2.75 |
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MongoDB Quarterly Data
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Apr19 |
Jul19 |
Oct19 |
Jan20 |
Apr20 |
Jul20 |
Oct20 |
Jan21 |
Apr21 |
Jul21 |
Oct21 |
Jan22 |
Apr22 |
Jul22 |
Oct22 |
Jan23 |
Apr23 |
Jul23 |
Oct23 |
Jan24 |
| Beneish M-Score |
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-2.67 |
-2.84 |
-2.71 |
-2.73 |
-2.75 |
Competitive Comparison
For the Software - Infrastructure subindustry, MongoDB'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.
MongoDB Beneish M-Score Distribution
For the Software industry and Technology sector, MongoDB's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where MongoDB's Beneish M-Score falls into.
MongoDB 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 MongoDB 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 * 0.8711 | + | 0.528 * 0.9735 | + | 0.404 * 1.0529 | + | 0.892 * 1.3107 | + | 0.115 * 0.8681 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.8664 | + | 4.679 * -0.103651 | - | 0.327 * 0.8887 |
| = | -2.75 |
* 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 (Jan24) TTM: | Last Year (Jan23) TTM: |
Total Receivables was $326 Mil.
Revenue was 458.002 + 432.938 + 423.791 + 368.28 = $1,683 Mil.
Gross Profit was 343.361 + 325.88 + 318.454 + 270.831 = $1,259 Mil.
Total Current Assets was $2,484 Mil.
Total Assets was $2,870 Mil.
Property, Plant and Equipment(Net PPE) was $90 Mil.
Depreciation, Depletion and Amortization(DDA) was $32 Mil.
Selling, General, & Admin. Expense(SGA) was $976 Mil.
Total Current Liabilities was $564 Mil.
Long-Term Debt & Capital Lease Obligation was $1,174 Mil.
Net Income was -55.46 + -29.297 + -37.597 + -54.246 = $-177 Mil.
Non Operating Income was -1.074 + 1.16 + -1.865 + 1.144 = $-1 Mil.
Cash Flow from Operations was 54.627 + 38.422 + -25.302 + 53.73 = $121 Mil.
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Total Receivables was $285 Mil.
Revenue was 361.312 + 333.621 + 303.66 + 285.447 = $1,284 Mil.
Gross Profit was 272.152 + 239.969 + 215.383 + 207.232 = $935 Mil.
Total Current Assets was $2,237 Mil.
Total Assets was $2,589 Mil.
Property, Plant and Equipment(Net PPE) was $99 Mil.
Depreciation, Depletion and Amortization(DDA) was $29 Mil.
Selling, General, & Admin. Expense(SGA) was $860 Mil.
Total Current Liabilities was $589 Mil.
Long-Term Debt & Capital Lease Obligation was $1,176 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) |
| = | (325.61 / 1683.011) | / | (285.192 / 1284.04) |
| = | 0.193469 | / | 0.222105 |
| | = | 0.8711 |
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) |
| = | (934.736 / 1284.04) | / | (1258.526 / 1683.011) |
| = | 0.727965 | / | 0.747782 |
| | = | 0.9735 |
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 - (2483.636 + 90.407) / 2869.642) | / | (1 - (2236.584 + 99.035) / 2588.893) |
| = | 0.103009 | / | 0.097831 |
| | = | 1.0529 |
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 |
| = | 1683.011 | / | 1284.04 |
| | = | 1.3107 |
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)) |
| = | (29.182 / (29.182 + 99.035)) | / | (32.125 / (32.125 + 90.407)) |
| = | 0.227599 | / | 0.262176 |
| | = | 0.8681 |
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) |
| = | (976.318 / 1683.011) | / | (859.699 / 1284.04) |
| = | 0.580102 | / | 0.669527 |
| | = | 0.8664 |
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) |
| = | ((1174.191 + 564.22) / 2869.642) | / | ((1176.144 + 588.512) / 2588.893) |
| = | 0.605794 | / | 0.681626 |
| | = | 0.8887 |
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 |
| = | (-176.6 - -0.635 | - | 121.477) | / | 2869.642 |
| | = | -0.103651 |
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.
MongoDB has a M-score of -2.75 suggests that the company is unlikely to be a manipulator.
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