Kortek Beneish M-Score Historical Data
The historical data trend for Kortek'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.
Kortek Beneish M-Score Chart
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Kortek Annual Data
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Trend |
Dec14 |
Dec15 |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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-3.32 |
-2.37 |
-1.34 |
-1.99 |
-3.78 |
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Kortek Quarterly Data
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Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
Mar24 |
Jun24 |
Sep24 |
| Beneish M-Score |
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-3.57 |
-3.78 |
-3.25 |
-3.13 |
-2.97 |
Competitive Comparison of Kortek's Beneish M-Score
For the Computer Hardware subindustry, Kortek'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.
Kortek's Beneish M-Score Distribution in the Hardware Industry
For the Hardware industry and Technology sector, Kortek's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Kortek's Beneish M-Score falls into.
Kortek 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 Kortek 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 * 1.0211 | + | 0.528 * 0.9398 | + | 0.404 * 1.0977 | + | 0.892 * 0.8636 | + | 0.115 * 0.9502 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.3091 | + | 4.679 * -0.085804 | - | 0.327 * 0.8144 |
| = | -2.97 |
* 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 ₩72,887 Mil.
Revenue was 85302.668 + 71805.247 + 71872.662 + 79519.667 = ₩308,500 Mil.
Gross Profit was 20920.108 + 17791.768 + 15427.891 + 9193.066 = ₩63,333 Mil.
Total Current Assets was ₩317,943 Mil.
Total Assets was ₩442,547 Mil.
Property, Plant and Equipment(Net PPE) was ₩87,292 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩5,006 Mil.
Selling, General, & Admin. Expense(SGA) was ₩19,826 Mil.
Total Current Liabilities was ₩95,396 Mil.
Long-Term Debt & Capital Lease Obligation was ₩5,878 Mil.
Net Income was 2330.29 + 7013.624 + 6292.079 + -3288.574 = ₩12,347 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ₩0 Mil.
Cash Flow from Operations was 1691.292 + 2088.847 + 3015.597 + 43524.069 = ₩50,320 Mil.
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Total Receivables was ₩82,655 Mil.
Revenue was 70880.103 + 91087.626 + 85620.095 + 109620.045 = ₩357,208 Mil.
Gross Profit was 13874.327 + 17379.03 + 14274.944 + 23386.035 = ₩68,914 Mil.
Total Current Assets was ₩342,769 Mil.
Total Assets was ₩478,496 Mil.
Property, Plant and Equipment(Net PPE) was ₩98,974 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩5,378 Mil.
Selling, General, & Admin. Expense(SGA) was ₩17,536 Mil.
Total Current Liabilities was ₩132,496 Mil.
Long-Term Debt & Capital Lease Obligation was ₩1,962 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) |
| = | (72887.108 / 308500.244) | / | (82654.51 / 357207.869) |
| = | 0.236263 | / | 0.231391 |
| | = | 1.0211 |
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) |
| = | (68914.336 / 357207.869) | / | (63332.833 / 308500.244) |
| = | 0.192925 | / | 0.205293 |
| | = | 0.9398 |
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 - (317943.215 + 87292.461) / 442546.635) | / | (1 - (342768.606 + 98974.465) / 478495.826) |
| = | 0.08431 | / | 0.076809 |
| | = | 1.0977 |
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 |
| = | 308500.244 | / | 357207.869 |
| | = | 0.8636 |
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)) |
| = | (5378.348 / (5378.348 + 98974.465)) | / | (5006.291 / (5006.291 + 87292.461)) |
| = | 0.05154 | / | 0.05424 |
| | = | 0.9502 |
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) |
| = | (19826.057 / 308500.244) | / | (17536.068 / 357207.869) |
| = | 0.064266 | / | 0.049092 |
| | = | 1.3091 |
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) |
| = | ((5878.12 + 95396.238) / 442546.635) | / | ((1962.005 + 132496.358) / 478495.826) |
| = | 0.228844 | / | 0.281002 |
| | = | 0.8144 |
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 |
| = | (12347.419 - 0 | - | 50319.805) | / | 442546.635 |
| | = | -0.085804 |
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.
Kortek has a M-score of -2.97 suggests that the company is unlikely to be a manipulator.
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