Forcs Co Beneish M-Score Historical Data
The historical data trend for Forcs 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.
Forcs Co Beneish M-Score Chart
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Forcs Co Annual Data
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Trend |
Jun14 |
Jun15 |
Jun16 |
Jun17 |
Jun18 |
Jun19 |
Jun20 |
Jun21 |
Jun22 |
Jun23 |
| Beneish M-Score |
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-0.74 |
-2.99 |
-2.24 |
-2.24 |
-2.18 |
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Forcs 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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-1.99 |
-2.18 |
-2.64 |
-2.87 |
-3.06 |
Competitive Comparison of Forcs Co's Beneish M-Score
For the Software - Application subindustry, Forcs 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.
Forcs Co's Beneish M-Score Distribution in the Software Industry
For the Software industry and Technology sector, Forcs Co's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Forcs Co's Beneish M-Score falls into.
Forcs 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 Forcs 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 * 0.8173 | + | 0.528 * 0.896 | + | 0.404 * 0.9975 | + | 0.892 * 0.8724 | + | 0.115 * 0.7461 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.0409 | + | 4.679 * -0.035045 | - | 0.327 * 1.1215 |
| = | -3.06 |
* 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 ₩4,042 Mil.
Revenue was 5876.728 + 11043.607 + 7215.16 + 6512.478 = ₩30,648 Mil.
Gross Profit was 3330.051 + 8175.15 + 4672.42 + 3838.181 = ₩20,016 Mil.
Total Current Assets was ₩28,609 Mil.
Total Assets was ₩78,152 Mil.
Property, Plant and Equipment(Net PPE) was ₩35,098 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩579 Mil.
Selling, General, & Admin. Expense(SGA) was ₩3,289 Mil.
Total Current Liabilities was ₩3,587 Mil.
Long-Term Debt & Capital Lease Obligation was ₩0 Mil.
Net Income was -282.881 + 3611.561 + 1096.289 + 1110.819 = ₩5,536 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ₩0 Mil.
Cash Flow from Operations was -294.534 + 4801.429 + 2163.864 + 1603.842 = ₩8,275 Mil.
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Total Receivables was ₩5,668 Mil.
Revenue was 6209.414 + 12787.58 + 7737.912 + 8395.276 = ₩35,130 Mil.
Gross Profit was 3555.167 + 8092.337 + 3339.729 + 5569.635 = ₩20,557 Mil.
Total Current Assets was ₩24,133 Mil.
Total Assets was ₩73,005 Mil.
Property, Plant and Equipment(Net PPE) was ₩35,345 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩433 Mil.
Selling, General, & Admin. Expense(SGA) was ₩3,621 Mil.
Total Current Liabilities was ₩2,987 Mil.
Long-Term Debt & Capital Lease Obligation was ₩0 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) |
| = | (4041.687 / 30647.973) | / | (5668.256 / 35130.182) |
| = | 0.131875 | / | 0.16135 |
| | = | 0.8173 |
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) |
| = | (20556.868 / 35130.182) | / | (20015.802 / 30647.973) |
| = | 0.585163 | / | 0.653087 |
| | = | 0.896 |
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 - (28609.182 + 35098.322) / 78151.754) | / | (1 - (24133.021 + 35344.784) / 73004.516) |
| = | 0.184823 | / | 0.185286 |
| | = | 0.9975 |
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 |
| = | 30647.973 | / | 35130.182 |
| | = | 0.8724 |
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)) |
| = | (432.907 / (432.907 + 35344.784)) | / | (578.572 / (578.572 + 35098.322)) |
| = | 0.0121 | / | 0.016217 |
| | = | 0.7461 |
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) |
| = | (3288.677 / 30647.973) | / | (3621.432 / 35130.182) |
| = | 0.107305 | / | 0.103086 |
| | = | 1.0409 |
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) |
| = | ((0 + 3586.534) / 78151.754) | / | ((0 + 2987.298) / 73004.516) |
| = | 0.045892 | / | 0.040919 |
| | = | 1.1215 |
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 |
| = | (5535.788 - 0 | - | 8274.601) | / | 78151.754 |
| | = | -0.035045 |
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.
Forcs Co has a M-score of -3.06 suggests that the company is unlikely to be a manipulator.
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