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 |
Dec15 |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
Dec24 |
| Beneish M-Score |
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-3.14 |
-2.32 |
-2.78 |
-2.64 |
-2.61 |
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Ecoplastic Quarterly Data
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Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
Mar24 |
Jun24 |
Sep24 |
Dec24 |
| Beneish M-Score |
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-2.64 |
-2.92 |
-2.96 |
-2.77 |
-2.61 |
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 * 0.9939 | + | 0.528 * 1.2395 | + | 0.404 * 0.9328 | + | 0.892 * 1.0437 | + | 0.115 * 1.2694 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.2299 | + | 4.679 * -0.048845 | - | 0.327 * 1.071 |
| = | -2.61 |
* 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 (Dec24) TTM: | Last Year (Dec23) TTM: |
Total Receivables was ₩219,826 Mil.
Revenue was 574846.743 + 556162.618 + 561457.795 + 562652.544 = ₩2,255,120 Mil.
Gross Profit was 10101.907 + 28250.812 + 38185.52 + 39071.928 = ₩115,610 Mil.
Total Current Assets was ₩506,510 Mil.
Total Assets was ₩1,307,175 Mil.
Property, Plant and Equipment(Net PPE) was ₩751,027 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩58,699 Mil.
Selling, General, & Admin. Expense(SGA) was ₩46,991 Mil.
Total Current Liabilities was ₩784,552 Mil.
Long-Term Debt & Capital Lease Obligation was ₩223,678 Mil.
Net Income was 1368.24 + -6880.162 + 9736.695 + 9858.136 = ₩14,083 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ₩0 Mil.
Cash Flow from Operations was 48231.97 + -69707.347 + 40034.374 + 59372.735 = ₩77,932 Mil.
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Total Receivables was ₩211,898 Mil.
Revenue was 650565.135 + 492730.715 + 515395.163 + 501930.901 = ₩2,160,622 Mil.
Gross Profit was 30547.574 + 39322.734 + 35359.858 + 32060.666 = ₩137,291 Mil.
Total Current Assets was ₩427,490 Mil.
Total Assets was ₩988,358 Mil.
Property, Plant and Equipment(Net PPE) was ₩520,634 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩52,764 Mil.
Selling, General, & Admin. Expense(SGA) was ₩36,604 Mil.
Total Current Liabilities was ₩533,776 Mil.
Long-Term Debt & Capital Lease Obligation was ₩177,986 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) |
| = | (219826.129 / 2255119.7) | / | (211898.043 / 2160621.914) |
| = | 0.097479 | / | 0.098073 |
| | = | 0.9939 |
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) |
| = | (137290.832 / 2160621.914) | / | (115610.167 / 2255119.7) |
| = | 0.063542 | / | 0.051266 |
| | = | 1.2395 |
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 - (506510.091 + 751026.598) / 1307174.746) | / | (1 - (427489.706 + 520633.986) / 988358.418) |
| = | 0.037974 | / | 0.040709 |
| | = | 0.9328 |
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 |
| = | 2255119.7 | / | 2160621.914 |
| | = | 1.0437 |
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)) |
| = | (52763.95 / (52763.95 + 520633.986)) | / | (58698.89 / (58698.89 + 751026.598)) |
| = | 0.09202 | / | 0.072492 |
| | = | 1.2694 |
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) |
| = | (46990.954 / 2255119.7) | / | (36604.323 / 2160621.914) |
| = | 0.020837 | / | 0.016942 |
| | = | 1.2299 |
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) |
| = | ((223677.613 + 784552.029) / 1307174.746) | / | ((177986.089 + 533776.104) / 988358.418) |
| = | 0.771304 | / | 0.720146 |
| | = | 1.071 |
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 |
| = | (14082.909 - 0 | - | 77931.732) | / | 1307174.746 |
| | = | -0.048845 |
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 -2.61 suggests that the company is unlikely to be a manipulator.
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