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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Dec17 |
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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Mar19 |
Jun19 |
Sep19 |
Dec19 |
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Dec20 |
Mar21 |
Jun21 |
Sep21 |
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Mar23 |
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Sep23 |
Dec23 |
| Beneish M-Score |
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-2.39 |
-2.58 |
-2.64 |
-2.50 |
-2.60 |
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 * 1.0099 | + | 0.528 * 0.9639 | + | 0.404 * 1.034 | + | 0.892 * 1.1695 | + | 0.115 * 1.063 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.7625 | + | 4.679 * -0.07902 | - | 0.327 * 0.8579 |
| = | -2.60 |
* 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 $4,121 Mil.
Revenue was 9936 + 9292 + 9230 + 8823 = $37,281 Mil.
Gross Profit was 3879 + 3666 + 3715 + 3564 = $14,824 Mil.
Total Current Assets was $11,297 Mil.
Total Assets was $38,699 Mil.
Property, Plant and Equipment(Net PPE) was $3,314 Mil.
Depreciation, Depletion and Amortization(DDA) was $823 Mil.
Selling, General, & Admin. Expense(SGA) was $7,038 Mil.
Total Current Liabilities was $9,454 Mil.
Long-Term Debt & Capital Lease Obligation was $11,009 Mil.
Net Income was 1429 + 221 + 394 + -157 = $1,887 Mil.
Non Operating Income was 1171 + -182 + 166 + 205 = $1,360 Mil.
Cash Flow from Operations was 823 + 966 + 1190 + 606 = $3,585 Mil.
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Total Receivables was $3,489 Mil.
Revenue was 8607 + 8343 + 8073 + 6854 = $31,877 Mil.
Gross Profit was 3300 + 3170 + 2920 + 2828 = $12,218 Mil.
Total Current Assets was $9,249 Mil.
Total Assets was $32,109 Mil.
Property, Plant and Equipment(Net PPE) was $3,531 Mil.
Depreciation, Depletion and Amortization(DDA) was $947 Mil.
Selling, General, & Admin. Expense(SGA) was $7,892 Mil.
Total Current Liabilities was $8,853 Mil.
Long-Term Debt & Capital Lease Obligation was $10,938 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) |
| = | (4121 / 37281) | / | (3489 / 31877) |
| = | 0.110539 | / | 0.109452 |
| | = | 1.0099 |
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) |
| = | (12218 / 31877) | / | (14824 / 37281) |
| = | 0.383286 | / | 0.397629 |
| | = | 0.9639 |
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 - (11297 + 3314) / 38699) | / | (1 - (9249 + 3531) / 32109) |
| = | 0.622445 | / | 0.601981 |
| | = | 1.034 |
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 |
| = | 37281 | / | 31877 |
| | = | 1.1695 |
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)) |
| = | (947 / (947 + 3531)) | / | (823 / (823 + 3314)) |
| = | 0.211478 | / | 0.198936 |
| | = | 1.063 |
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) |
| = | (7038 / 37281) | / | (7892 / 31877) |
| = | 0.188782 | / | 0.247577 |
| | = | 0.7625 |
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) |
| = | ((11009 + 9454) / 38699) | / | ((10938 + 8853) / 32109) |
| = | 0.528773 | / | 0.616369 |
| | = | 0.8579 |
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 |
| = | (1887 - 1360 | - | 3585) | / | 38699 |
| | = | -0.07902 |
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 -2.60 suggests that the company is unlikely to be a manipulator.
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