Uangel Beneish M-Score Historical Data
The historical data trend for Uangel'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.
Uangel Beneish M-Score Chart
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Uangel Annual Data
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Trend |
Dec14 |
Dec15 |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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-1.99 |
-3.47 |
-1.45 |
-1.18 |
-2.38 |
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Uangel 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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-1.18 |
-1.37 |
-1.78 |
-1.99 |
-2.38 |
Competitive Comparison of Uangel's Beneish M-Score
For the Telecom Services subindustry, Uangel'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.
Uangel's Beneish M-Score Distribution in the Telecommunication Services Industry
For the Telecommunication Services industry and Communication Services sector, Uangel's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Uangel's Beneish M-Score falls into.
Uangel 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 Uangel 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 * 0.5528 | + | 0.528 * 1.255 | + | 0.404 * 1.5513 | + | 0.892 * 1.2195 | + | 0.115 * 0.8018 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.0592 | + | 4.679 * 0.004368 | - | 0.327 * 1.0807 |
| = | -2.38 |
* 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 ₩6,323 Mil.
Revenue was 12037.309 + 9126.78 + 8434.11 + 7040.84 = ₩36,639 Mil.
Gross Profit was 5383.42 + 3308.667 + 3427.802 + 2403.069 = ₩14,523 Mil.
Total Current Assets was ₩31,101 Mil.
Total Assets was ₩62,949 Mil.
Property, Plant and Equipment(Net PPE) was ₩5,766 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩1,782 Mil.
Selling, General, & Admin. Expense(SGA) was ₩3,775 Mil.
Total Current Liabilities was ₩5,993 Mil.
Long-Term Debt & Capital Lease Obligation was ₩565 Mil.
Net Income was 1416.856 + -322.965 + 872.072 + -528.248 = ₩1,438 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ₩0 Mil.
Cash Flow from Operations was 2934.557 + -944.707 + 2306.405 + -3133.511 = ₩1,163 Mil.
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Total Receivables was ₩9,381 Mil.
Revenue was 10339.017 + 7937.092 + 6316.857 + 5451.937 = ₩30,045 Mil.
Gross Profit was 5616.44 + 4087.451 + 3149.981 + 2091.705 = ₩14,946 Mil.
Total Current Assets was ₩38,864 Mil.
Total Assets was ₩61,024 Mil.
Property, Plant and Equipment(Net PPE) was ₩5,860 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩1,368 Mil.
Selling, General, & Admin. Expense(SGA) was ₩2,923 Mil.
Total Current Liabilities was ₩5,220 Mil.
Long-Term Debt & Capital Lease Obligation was ₩663 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) |
| = | (6323.416 / 36639.039) | / | (9381.013 / 30044.903) |
| = | 0.172587 | / | 0.312233 |
| | = | 0.5528 |
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) |
| = | (14945.577 / 30044.903) | / | (14522.958 / 36639.039) |
| = | 0.497441 | / | 0.396379 |
| | = | 1.255 |
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 - (31100.885 + 5765.571) / 62948.969) | / | (1 - (38864.441 + 5859.618) / 61023.632) |
| = | 0.414344 | / | 0.267103 |
| | = | 1.5513 |
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 |
| = | 36639.039 | / | 30044.903 |
| | = | 1.2195 |
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)) |
| = | (1368.494 / (1368.494 + 5859.618)) | / | (1782.148 / (1782.148 + 5765.571)) |
| = | 0.189329 | / | 0.236117 |
| | = | 0.8018 |
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) |
| = | (3775.335 / 36639.039) | / | (2922.779 / 30044.903) |
| = | 0.103041 | / | 0.09728 |
| | = | 1.0592 |
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) |
| = | ((564.988 + 5993.188) / 62948.969) | / | ((662.868 + 5219.751) / 61023.632) |
| = | 0.104182 | / | 0.096399 |
| | = | 1.0807 |
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 |
| = | (1437.715 - 0 | - | 1162.744) | / | 62948.969 |
| | = | 0.004368 |
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.
Uangel has a M-score of -2.38 suggests that the company is unlikely to be a manipulator.
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