Uber Technologies Beneish M-Score Historical Data
The historical data trend for Uber Technologies'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.
Uber Technologies Beneish M-Score Chart
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Uber Technologies Annual Data
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Dec16 |
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Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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-2.96 |
-2.79 |
-2.02 |
-2.39 |
-2.60 |
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Uber Technologies Quarterly Data
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Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
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Sep21 |
Dec21 |
Mar22 |
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Mar23 |
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Sep23 |
Dec23 |
Mar24 |
Jun24 |
Sep24 |
| Beneish M-Score |
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-2.50 |
-2.60 |
-2.50 |
-2.47 |
-2.68 |
Competitive Comparison of Uber Technologies's Beneish M-Score
For the Software - Application subindustry, Uber Technologies'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.
Uber Technologies's Beneish M-Score Distribution in the Software Industry
For the Software industry and Technology sector, Uber Technologies's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Uber Technologies's Beneish M-Score falls into.
Uber Technologies 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 Uber Technologies 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 * 0.9586 | + | 0.528 * 1.0085 | + | 0.404 * 0.9964 | + | 0.892 * 1.1447 | + | 0.115 * 1.0172 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.8367 | + | 4.679 * -0.087243 | - | 0.327 * 0.8797 |
| = | -2.72 |
* 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 (Sep24) TTM: | Last Year (Sep23) TTM: |
Total Receivables was €3,772 Mil.
Revenue was 10080.388 + 9940.3 + 9320.52 + 9111.312 = €38,453 Mil.
Gross Profit was 3988.727 + 3912.948 + 3645.96 + 3557.043 = €15,105 Mil.
Total Current Assets was €13,813 Mil.
Total Assets was €42,452 Mil.
Property, Plant and Equipment(Net PPE) was €2,858 Mil.
Depreciation, Depletion and Amortization(DDA) was €700 Mil.
Selling, General, & Admin. Expense(SGA) was €6,595 Mil.
Total Current Liabilities was €9,762 Mil.
Long-Term Debt & Capital Lease Obligation was €11,246 Mil.
Net Income was 2353.412 + 942.935 + -601.68 + 1310.393 = €4,005 Mil.
Non Operating Income was 1492.056 + 226.676 + -770.04 + 1073.807 = €2,022 Mil.
Cash Flow from Operations was 1938.051 + 1690.78 + 1302.72 + 754.691 = €5,686 Mil.
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Total Receivables was €3,438 Mil.
Revenue was 8706.604 + 8519.29 + 8240.682 + 8125.008 = €33,592 Mil.
Gross Profit was 3435.042 + 3428.945 + 3328.776 + 3115.2 = €13,308 Mil.
Total Current Assets was €10,006 Mil.
Total Assets was €33,684 Mil.
Property, Plant and Equipment(Net PPE) was €3,147 Mil.
Depreciation, Depletion and Amortization(DDA) was €788 Mil.
Selling, General, & Admin. Expense(SGA) was €6,885 Mil.
Total Current Liabilities was €8,812 Mil.
Long-Term Debt & Capital Lease Obligation was €10,136 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) |
| = | (3772.487 / 38452.52) | / | (3437.853 / 33591.584) |
| = | 0.098108 | / | 0.102343 |
| | = | 0.9586 |
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) |
| = | (13307.963 / 33591.584) | / | (15104.678 / 38452.52) |
| = | 0.39617 | / | 0.392814 |
| | = | 1.0085 |
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 - (13813.231 + 2857.972) / 42452.417) | / | (1 - (10006.223 + 3147.383) / 33684.213) |
| = | 0.607297 | / | 0.609502 |
| | = | 0.9964 |
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 |
| = | 38452.52 | / | 33591.584 |
| | = | 1.1447 |
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)) |
| = | (787.919 / (787.919 + 3147.383)) | / | (700.366 / (700.366 + 2857.972)) |
| = | 0.200218 | / | 0.196824 |
| | = | 1.0172 |
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) |
| = | (6594.521 / 38452.52) | / | (6885.41 / 33591.584) |
| = | 0.171498 | / | 0.204974 |
| | = | 0.8367 |
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) |
| = | ((11246.282 + 9762.335) / 42452.417) | / | ((10135.529 + 8812.485) / 33684.213) |
| = | 0.494874 | / | 0.562519 |
| | = | 0.8797 |
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 |
| = | (4005.06 - 2022.499 | - | 5686.242) | / | 42452.417 |
| | = | -0.087243 |
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.
Uber Technologies has a M-score of -2.72 suggests that the company is unlikely to be a manipulator.
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