Atlassian Beneish M-Score Historical Data
The historical data trend for Atlassian'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.
Atlassian Beneish M-Score Chart
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Atlassian Annual Data
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Trend |
Jun14 |
Jun15 |
Jun16 |
Jun17 |
Jun18 |
Jun19 |
Jun20 |
Jun21 |
Jun22 |
Jun23 |
| Beneish M-Score |
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-3.08 |
-2.77 |
-3.15 |
-3.13 |
-3.67 |
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Atlassian 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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-3.64 |
-3.98 |
-3.67 |
-3.81 |
-3.45 |
Competitive Comparison of Atlassian's Beneish M-Score
For the Software - Application subindustry, Atlassian'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.
Atlassian's Beneish M-Score Distribution in the Software Industry
For the Software industry and Technology sector, Atlassian's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Atlassian's Beneish M-Score falls into.
Atlassian 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 Atlassian 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 * 1.2121 | + | 0.528 * 1.0151 | + | 0.404 * 1.3806 | + | 0.892 * 1.2239 | + | 0.115 * 0.7174 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.9784 | + | 4.679 * -0.324761 | - | 0.327 * 0.9217 |
| = | -3.45 |
* 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 $526 Mil.
Revenue was 1060.11 + 977.775 + 939.098 + 915.453 = $3,892 Mil.
Gross Profit was 865.574 + 799.746 + 769.322 + 746.801 = $3,181 Mil.
Total Current Assets was $2,272 Mil.
Total Assets was $4,449 Mil.
Property, Plant and Equipment(Net PPE) was $267 Mil.
Depreciation, Depletion and Amortization(DDA) was $63 Mil.
Selling, General, & Admin. Expense(SGA) was $1,443 Mil.
Total Current Liabilities was $2,118 Mil.
Long-Term Debt & Capital Lease Obligation was $1,175 Mil.
Net Income was -84.469 + -31.883 + -58.952 + -209.037 = $-384 Mil.
Non Operating Income was -4.639 + -8.335 + -7.096 + -0.943 = $-21 Mil.
Cash Flow from Operations was 289.594 + 166.956 + 272.775 + 352.369 = $1,082 Mil.
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Total Receivables was $355 Mil.
Revenue was 872.704 + 807.392 + 759.841 + 740.491 = $3,180 Mil.
Gross Profit was 716.759 + 668 + 629.437 + 624.428 = $2,639 Mil.
Total Current Assets was $2,135 Mil.
Total Assets was $3,614 Mil.
Property, Plant and Equipment(Net PPE) was $355 Mil.
Depreciation, Depletion and Amortization(DDA) was $56 Mil.
Selling, General, & Admin. Expense(SGA) was $1,205 Mil.
Total Current Liabilities was $1,645 Mil.
Long-Term Debt & Capital Lease Obligation was $1,257 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) |
| = | (526.379 / 3892.436) | / | (354.844 / 3180.428) |
| = | 0.135231 | / | 0.111571 |
| | = | 1.2121 |
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) |
| = | (2638.624 / 3180.428) | / | (3181.443 / 3892.436) |
| = | 0.829644 | / | 0.81734 |
| | = | 1.0151 |
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 - (2271.541 + 267.29) / 4449.498) | / | (1 - (2134.76 + 355.145) / 3614.015) |
| = | 0.429412 | / | 0.311042 |
| | = | 1.3806 |
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 |
| = | 3892.436 | / | 3180.428 |
| | = | 1.2239 |
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)) |
| = | (56.233 / (56.233 + 355.145)) | / | (62.923 / (62.923 + 267.29)) |
| = | 0.136694 | / | 0.190553 |
| | = | 0.7174 |
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) |
| = | (1442.902 / 3892.436) | / | (1204.97 / 3180.428) |
| = | 0.370694 | / | 0.37887 |
| | = | 0.9784 |
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) |
| = | ((1174.661 + 2118.099) / 4449.498) | / | ((1257.159 + 1644.65) / 3614.015) |
| = | 0.74003 | / | 0.802932 |
| | = | 0.9217 |
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 |
| = | (-384.341 - -21.013 | - | 1081.694) | / | 4449.498 |
| | = | -0.324761 |
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.
Atlassian has a M-score of -3.45 suggests that the company is unlikely to be a manipulator.
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