Opasnet Co Beneish M-Score Historical Data
The historical data trend for Opasnet 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.
Opasnet Co Beneish M-Score Chart
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Opasnet Co Annual Data
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Trend |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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-3.25 |
-1.79 |
-2.80 |
-0.39 |
-2.63 |
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Opasnet Co Quarterly Data
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Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
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Mar23 |
Jun23 |
Sep23 |
Dec23 |
Mar24 |
Jun24 |
Sep24 |
| Beneish M-Score |
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-1.10 |
-2.63 |
-2.89 |
-2.28 |
-2.36 |
Competitive Comparison of Opasnet Co's Beneish M-Score
For the Information Technology Services subindustry, Opasnet 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.
Opasnet Co's Beneish M-Score Distribution in the Software Industry
For the Software industry and Technology sector, Opasnet Co's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Opasnet Co's Beneish M-Score falls into.
Opasnet 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 Opasnet 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 * 1.2561 | + | 0.528 * 0.9675 | + | 0.404 * 1.0451 | + | 0.892 * 0.9095 | + | 0.115 * 1.1783 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.1012 | + | 4.679 * -0.015912 | - | 0.327 * 0.8808 |
| = | -2.36 |
* 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 (Sep24) TTM: | Last Year (Sep23) TTM: |
Total Receivables was ₩24,678 Mil.
Revenue was 44412.413 + 38981.746 + 33147.482 + 82873.174 = ₩199,415 Mil.
Gross Profit was 7330.894 + 6828.899 + 5089.037 + 10387.899 = ₩29,637 Mil.
Total Current Assets was ₩89,219 Mil.
Total Assets was ₩113,688 Mil.
Property, Plant and Equipment(Net PPE) was ₩2,750 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩1,326 Mil.
Selling, General, & Admin. Expense(SGA) was ₩3,617 Mil.
Total Current Liabilities was ₩53,386 Mil.
Long-Term Debt & Capital Lease Obligation was ₩2,068 Mil.
Net Income was 1685.201 + 1368.706 + 131.339 + 3200.949 = ₩6,386 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ₩0 Mil.
Cash Flow from Operations was -3936.727 + -3884.548 + -10228.947 + 26245.477 = ₩8,195 Mil.
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Total Receivables was ₩21,601 Mil.
Revenue was 52407.366 + 50224.639 + 39475.382 + 77148.64 = ₩219,256 Mil.
Gross Profit was 7310.502 + 8545.741 + 6624.892 + 9044.731 = ₩31,526 Mil.
Total Current Assets was ₩91,813 Mil.
Total Assets was ₩115,073 Mil.
Property, Plant and Equipment(Net PPE) was ₩2,225 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩1,382 Mil.
Selling, General, & Admin. Expense(SGA) was ₩3,611 Mil.
Total Current Liabilities was ₩61,121 Mil.
Long-Term Debt & Capital Lease Obligation was ₩2,603 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) |
| = | (24677.677 / 199414.815) | / | (21601.369 / 219256.027) |
| = | 0.12375 | / | 0.098521 |
| | = | 1.2561 |
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) |
| = | (31525.866 / 219256.027) | / | (29636.729 / 199414.815) |
| = | 0.143786 | / | 0.148618 |
| | = | 0.9675 |
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 - (89219.062 + 2750.311) / 113688.489) | / | (1 - (91813.287 + 2224.649) / 115073.074) |
| = | 0.191041 | / | 0.182798 |
| | = | 1.0451 |
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 |
| = | 199414.815 | / | 219256.027 |
| | = | 0.9095 |
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)) |
| = | (1382.229 / (1382.229 + 2224.649)) | / | (1325.699 / (1325.699 + 2750.311)) |
| = | 0.38322 | / | 0.325244 |
| | = | 1.1783 |
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) |
| = | (3617.065 / 199414.815) | / | (3611.401 / 219256.027) |
| = | 0.018138 | / | 0.016471 |
| | = | 1.1012 |
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) |
| = | ((2067.806 + 53385.889) / 113688.489) | / | ((2602.84 + 61121.146) / 115073.074) |
| = | 0.487769 | / | 0.55377 |
| | = | 0.8808 |
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 |
| = | (6386.195 - 0 | - | 8195.255) | / | 113688.489 |
| | = | -0.015912 |
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.
Opasnet Co has a M-score of -2.36 suggests that the company is unlikely to be a manipulator.
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