Everybot Beneish M-Score Historical Data
The historical data trend for Everybot'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.
Everybot Beneish M-Score Chart
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Everybot Annual Data
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Trend |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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- |
- |
- |
-2.72 |
-2.90 |
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Everybot Quarterly Data
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Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
| Beneish M-Score |
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-2.72 |
-3.02 |
-2.86 |
-3.56 |
-2.90 |
Competitive Comparison of Everybot's Beneish M-Score
For the Consumer Electronics subindustry, Everybot'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.
Everybot's Beneish M-Score Distribution in the Hardware Industry
For the Hardware industry and Technology sector, Everybot's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Everybot's Beneish M-Score falls into.
Everybot 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 Everybot 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 * 1.1178 | + | 0.528 * 0.9543 | + | 0.404 * 1.0177 | + | 0.892 * 0.598 | + | 0.115 * 0.9435 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.0175 | + | 4.679 * -0.037218 | - | 0.327 * 0.899 |
| = | -2.90 |
* 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 ₩2,302 Mil.
Revenue was 7016.193 + 9083.809 + 8126.643 + 7470.769 = ₩31,697 Mil.
Gross Profit was 2841.355 + 4847.358 + 4141.285 + 3623.417 = ₩15,453 Mil.
Total Current Assets was ₩36,628 Mil.
Total Assets was ₩87,390 Mil.
Property, Plant and Equipment(Net PPE) was ₩32,913 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩1,537 Mil.
Selling, General, & Admin. Expense(SGA) was ₩7,827 Mil.
Total Current Liabilities was ₩14,197 Mil.
Long-Term Debt & Capital Lease Obligation was ₩7,351 Mil.
Net Income was 240.632 + 969.714 + 1247.212 + 355.434 = ₩2,813 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ₩0 Mil.
Cash Flow from Operations was -937.773 + 5463.912 + 1096.598 + 442.721 = ₩6,065 Mil.
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Total Receivables was ₩3,444 Mil.
Revenue was 10456.695 + 14952.388 + 14868.113 + 12729.309 = ₩53,007 Mil.
Gross Profit was 4754.836 + 6537.571 + 6714.992 + 6652.819 = ₩24,660 Mil.
Total Current Assets was ₩40,865 Mil.
Total Assets was ₩86,864 Mil.
Property, Plant and Equipment(Net PPE) was ₩28,566 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩1,256 Mil.
Selling, General, & Admin. Expense(SGA) was ₩12,863 Mil.
Total Current Liabilities was ₩5,456 Mil.
Long-Term Debt & Capital Lease Obligation was ₩18,369 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) |
| = | (2302.099 / 31697.414) | / | (3444.147 / 53006.505) |
| = | 0.072627 | / | 0.064976 |
| | = | 1.1178 |
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) |
| = | (24660.218 / 53006.505) | / | (15453.415 / 31697.414) |
| = | 0.46523 | / | 0.487529 |
| | = | 0.9543 |
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 - (36628.204 + 32912.669) / 87389.768) | / | (1 - (40864.806 + 28566.295) / 86864.211) |
| = | 0.204245 | / | 0.200694 |
| | = | 1.0177 |
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 |
| = | 31697.414 | / | 53006.505 |
| | = | 0.598 |
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)) |
| = | (1255.524 / (1255.524 + 28566.295)) | / | (1537.266 / (1537.266 + 32912.669)) |
| = | 0.042101 | / | 0.044623 |
| | = | 0.9435 |
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) |
| = | (7827.156 / 31697.414) | / | (12863.452 / 53006.505) |
| = | 0.246934 | / | 0.242677 |
| | = | 1.0175 |
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) |
| = | ((7351.333 + 14196.749) / 87389.768) | / | ((18369.204 + 5455.775) / 86864.211) |
| = | 0.246574 | / | 0.274278 |
| | = | 0.899 |
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 |
| = | (2812.992 - 0 | - | 6065.458) | / | 87389.768 |
| | = | -0.037218 |
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.
Everybot has a M-score of -2.90 suggests that the company is unlikely to be a manipulator.
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