Ecoplastic Beneish M-Score Historical Data
The historical data trend for Ecoplastic'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.
Ecoplastic Beneish M-Score Chart
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Ecoplastic Annual Data
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Trend |
Dec14 |
Dec15 |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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-2.65 |
-3.14 |
-2.32 |
-2.78 |
-2.63 |
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Ecoplastic Quarterly Data
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Jun19 |
Sep19 |
Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
Mar24 |
| Beneish M-Score |
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-2.89 |
-2.89 |
-2.64 |
-2.63 |
-2.92 |
Competitive Comparison of Ecoplastic's Beneish M-Score
For the Auto Parts subindustry, Ecoplastic'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.
Ecoplastic's Beneish M-Score Distribution in the Vehicles & Parts Industry
For the Vehicles & Parts industry and Consumer Cyclical sector, Ecoplastic's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Ecoplastic's Beneish M-Score falls into.
Ecoplastic 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 Ecoplastic 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 * 0.7591 | + | 0.528 * 0.9043 | + | 0.404 * 0.8261 | + | 0.892 * 1.1447 | + | 0.115 * 1.2247 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.8615 | + | 4.679 * -0.060215 | - | 0.327 * 0.9949 |
| = | -2.92 |
* 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 (Mar24) TTM: | Last Year (Mar23) TTM: |
Total Receivables was ₩211,271 Mil.
Revenue was 562652.544 + 650565.135 + 492730.715 + 515395.163 = ₩2,221,344 Mil.
Gross Profit was 39071.928 + 30547.574 + 39322.734 + 35359.858 = ₩144,302 Mil.
Total Current Assets was ₩480,302 Mil.
Total Assets was ₩1,100,784 Mil.
Property, Plant and Equipment(Net PPE) was ₩578,937 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩55,116 Mil.
Selling, General, & Admin. Expense(SGA) was ₩37,025 Mil.
Total Current Liabilities was ₩610,134 Mil.
Long-Term Debt & Capital Lease Obligation was ₩201,609 Mil.
Net Income was 9858.136 + 9575.239 + 12030.162 + 10760.133 = ₩42,224 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ₩0 Mil.
Cash Flow from Operations was 59372.735 + 55788.431 + -19433.479 + 12779.594 = ₩108,507 Mil.
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Total Receivables was ₩243,142 Mil.
Revenue was 501930.901 + 527684.8 + 432850.033 + 478129.724 = ₩1,940,595 Mil.
Gross Profit was 32060.666 + 33227.583 + 20625.291 + 28080.628 = ₩113,994 Mil.
Total Current Assets was ₩411,118 Mil.
Total Assets was ₩859,927 Mil.
Property, Plant and Equipment(Net PPE) was ₩409,525 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩48,790 Mil.
Selling, General, & Admin. Expense(SGA) was ₩37,544 Mil.
Total Current Liabilities was ₩553,404 Mil.
Long-Term Debt & Capital Lease Obligation was ₩84,005 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) |
| = | (211271.145 / 2221343.557) | / | (243142.339 / 1940595.458) |
| = | 0.09511 | / | 0.125293 |
| | = | 0.7591 |
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) |
| = | (113994.168 / 1940595.458) | / | (144302.094 / 2221343.557) |
| = | 0.058742 | / | 0.064962 |
| | = | 0.9043 |
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 - (480302.029 + 578936.756) / 1100783.958) | / | (1 - (411117.842 + 409525.174) / 859927.139) |
| = | 0.037741 | / | 0.045683 |
| | = | 0.8261 |
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 |
| = | 2221343.557 | / | 1940595.458 |
| | = | 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)) |
| = | (48790.241 / (48790.241 + 409525.174)) | / | (55116.143 / (55116.143 + 578936.756)) |
| = | 0.106456 | / | 0.086927 |
| | = | 1.2247 |
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) |
| = | (37025.208 / 2221343.557) | / | (37543.828 / 1940595.458) |
| = | 0.016668 | / | 0.019347 |
| | = | 0.8615 |
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) |
| = | ((201609.281 + 610134.091) / 1100783.958) | / | ((84004.852 + 553403.85) / 859927.139) |
| = | 0.737423 | / | 0.741236 |
| | = | 0.9949 |
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 |
| = | (42223.67 - 0 | - | 108507.281) | / | 1100783.958 |
| | = | -0.060215 |
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.
Ecoplastic has a M-score of -2.92 suggests that the company is unlikely to be a manipulator.
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