GitLab Beneish M-Score Historical Data
The historical data trend for GitLab'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.
GitLab Beneish M-Score Chart
|
GitLab Annual Data
|
|
Trend |
Jan20 |
Jan21 |
Jan22 |
Jan23 |
Jan24 |
| Beneish M-Score |
|
- |
- |
-1.93 |
-2.36 |
-4.21 |
|
GitLab Quarterly Data
|
|
Jan20 |
Apr20 |
Jul20 |
Oct20 |
Jan21 |
Apr21 |
Jul21 |
Oct21 |
Jan22 |
Apr22 |
Jul22 |
Oct22 |
Jan23 |
Apr23 |
Jul23 |
Oct23 |
Jan24 |
Apr24 |
| Beneish M-Score |
Get a 7-Day Free Trial
|
|
|
|
|
|
|
|
|
|
|
-2.52 |
-3.12 |
-4.13 |
-4.21 |
-4.41 |
Competitive Comparison of GitLab's Beneish M-Score
For the Software - Infrastructure subindustry, GitLab'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.
GitLab's Beneish M-Score Distribution in the Software Industry
For the Software industry and Technology sector, GitLab's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where GitLab's Beneish M-Score falls into.
GitLab 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 GitLab 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 * 0.8122 | + | 0.528 * 0.9811 | + | 0.404 * 1.1404 | + | 0.892 * 1.2073 | + | 0.115 * 0.7586 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.8648 | + | 4.679 * -0.38804 | - | 0.327 * 1.9068 |
| = | -4.54 |
* 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 (Apr24) TTM: | Last Year (Apr23) TTM: |
Total Receivables was MXN2,307 Mil.
Revenue was 2887.041 + 2813.101 + 2701.956 + 2333.557 = MXN10,736 Mil.
Gross Profit was 2566.643 + 2538.471 + 2429.645 + 2087.865 = MXN9,623 Mil.
Total Current Assets was MXN21,553 Mil.
Total Assets was MXN22,553 Mil.
Property, Plant and Equipment(Net PPE) was MXN57 Mil.
Depreciation, Depletion and Amortization(DDA) was MXN119 Mil.
Selling, General, & Admin. Expense(SGA) was MXN9,239 Mil.
Total Current Liabilities was MXN11,510 Mil.
Long-Term Debt & Capital Lease Obligation was MXN0 Mil.
Net Income was -932.456 + -626.364 + -5147.957 + -837.252 = MXN-7,544 Mil.
Non Operating Income was -9.675 + -194.4 + 10.272 + -22.235 = MXN-216 Mil.
Cash Flow from Operations was 650.794 + 426.88 + -107.614 + 453.216 = MXN1,423 Mil.
|
Total Receivables was MXN2,353 Mil.
Revenue was 2282.218 + 2310.406 + 2242.447 + 2057.498 = MXN8,893 Mil.
Gross Profit was 2031.49 + 2042.76 + 1953.381 + 1792.168 = MXN7,820 Mil.
Total Current Assets was MXN20,060 Mil.
Total Assets was MXN20,939 Mil.
Property, Plant and Equipment(Net PPE) was MXN112 Mil.
Depreciation, Depletion and Amortization(DDA) was MXN117 Mil.
Selling, General, & Admin. Expense(SGA) was MXN8,849 Mil.
Total Current Liabilities was MXN5,595 Mil.
Long-Term Debt & Capital Lease Obligation was MXN10 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) |
| = | (2306.995 / 10735.655) | / | (2352.729 / 8892.569) |
| = | 0.214891 | / | 0.264572 |
| | = | 0.8122 |
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) |
| = | (7819.799 / 8892.569) | / | (9622.624 / 10735.655) |
| = | 0.879363 | / | 0.896324 |
| | = | 0.9811 |
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 - (21552.818 + 57.387) / 22552.507) | / | (1 - (20060.02 + 112.17) / 20939.393) |
| = | 0.041783 | / | 0.036639 |
| | = | 1.1404 |
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 |
| = | 10735.655 | / | 8892.569 |
| | = | 1.2073 |
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)) |
| = | (117.432 / (117.432 + 112.17)) | / | (118.764 / (118.764 + 57.387)) |
| = | 0.511459 | / | 0.674217 |
| | = | 0.7586 |
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) |
| = | (9239.145 / 10735.655) | / | (8849.261 / 8892.569) |
| = | 0.860604 | / | 0.99513 |
| | = | 0.8648 |
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) |
| = | ((0 + 11510.434) / 22552.507) | / | ((9.551 + 5595.066) / 20939.393) |
| = | 0.510384 | / | 0.267659 |
| | = | 1.9068 |
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 |
| = | (-7544.029 - -216.038 | - | 1423.276) | / | 22552.507 |
| | = | -0.38804 |
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.
GitLab has a M-score of -4.54 suggests that the company is unlikely to be a manipulator.
Quick Browse
Personalize
Premium Gurus 
Create New Portfolio
Portfolio Contest
MEX:GTLB has been successfully added to your