Wooyang Co Beneish M-Score Historical Data
The historical data trend for Wooyang Co'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.
Wooyang Co Beneish M-Score Chart
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Wooyang Co Annual Data
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Trend |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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- |
-2.71 |
-1.51 |
-2.31 |
-2.80 |
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Wooyang Co 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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-2.63 |
-2.51 |
-2.65 |
-2.80 |
-2.93 |
Competitive Comparison of Wooyang Co's Beneish M-Score
For the Packaged Foods subindustry, Wooyang Co'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.
Wooyang Co's Beneish M-Score Distribution in the Consumer Packaged Goods Industry
For the Consumer Packaged Goods industry and Consumer Defensive sector, Wooyang Co's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Wooyang Co's Beneish M-Score falls into.
Wooyang Co 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 Wooyang Co 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 * 0.9101 | + | 0.528 * 1.0748 | + | 0.404 * 0.874 | + | 0.892 * 1.1566 | + | 0.115 * 0.9466 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.0171 | + | 4.679 * -0.105057 | - | 0.327 * 0.9949 |
| = | -2.93 |
* 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 ₩17,506 Mil.
Revenue was 43012.746 + 50558.241 + 49583.56 + 48682.076 = ₩191,837 Mil.
Gross Profit was 4347.521 + 3542.41 + 4190.772 + 5741.207 = ₩17,822 Mil.
Total Current Assets was ₩64,592 Mil.
Total Assets was ₩146,933 Mil.
Property, Plant and Equipment(Net PPE) was ₩79,060 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩8,976 Mil.
Selling, General, & Admin. Expense(SGA) was ₩11,336 Mil.
Total Current Liabilities was ₩88,638 Mil.
Long-Term Debt & Capital Lease Obligation was ₩16,433 Mil.
Net Income was -706.321 + -1372.15 + -2582.349 + 17.794 = ₩-4,643 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ₩0 Mil.
Cash Flow from Operations was 3653.773 + 786.53 + 5215.383 + 1137.617 = ₩10,793 Mil.
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Total Receivables was ₩16,631 Mil.
Revenue was 42470.938 + 44150.236 + 39064.037 + 40175.106 = ₩165,860 Mil.
Gross Profit was 4938.385 + 4736.518 + 3281.477 + 3604.519 = ₩16,561 Mil.
Total Current Assets was ₩64,545 Mil.
Total Assets was ₩149,906 Mil.
Property, Plant and Equipment(Net PPE) was ₩81,529 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩8,709 Mil.
Selling, General, & Admin. Expense(SGA) was ₩9,636 Mil.
Total Current Liabilities was ₩78,581 Mil.
Long-Term Debt & Capital Lease Obligation was ₩29,161 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) |
| = | (17505.873 / 191836.623) | / | (16631.264 / 165860.317) |
| = | 0.091254 | / | 0.100273 |
| | = | 0.9101 |
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) |
| = | (16560.899 / 165860.317) | / | (17821.91 / 191836.623) |
| = | 0.099848 | / | 0.092901 |
| | = | 1.0748 |
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 - (64591.977 + 79059.509) / 146933.475) | / | (1 - (64545.328 + 81529.482) / 149905.764) |
| = | 0.022337 | / | 0.025556 |
| | = | 0.874 |
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 |
| = | 191836.623 | / | 165860.317 |
| | = | 1.1566 |
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)) |
| = | (8709.151 / (8709.151 + 81529.482)) | / | (8976.145 / (8976.145 + 79059.509)) |
| = | 0.096512 | / | 0.10196 |
| | = | 0.9466 |
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) |
| = | (11335.981 / 191836.623) | / | (9635.85 / 165860.317) |
| = | 0.059092 | / | 0.058096 |
| | = | 1.0171 |
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) |
| = | ((16433.248 + 88638.451) / 146933.475) | / | ((29160.745 + 78581.303) / 149905.764) |
| = | 0.715097 | / | 0.718732 |
| | = | 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 |
| = | (-4643.026 - 0 | - | 10793.303) | / | 146933.475 |
| | = | -0.105057 |
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.
Wooyang Co has a M-score of -2.93 suggests that the company is unlikely to be a manipulator.
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