NFC Beneish M-Score Historical Data
The historical data trend for NFC'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.
NFC Beneish M-Score Chart
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NFC Annual Data
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Trend |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
Dec24 |
| Beneish M-Score |
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-2.98 |
-2.16 |
-2.34 |
-0.23 |
-3.18 |
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NFC 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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-0.23 |
-1.53 |
-1.35 |
-1.96 |
-3.18 |
Competitive Comparison of NFC's Beneish M-Score
For the Household & Personal Products subindustry, NFC'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.
NFC's Beneish M-Score Distribution in the Consumer Packaged Goods Industry
For the Consumer Packaged Goods industry and Consumer Defensive sector, NFC's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where NFC's Beneish M-Score falls into.
NFC 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 NFC 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 * 0.4536 | + | 0.528 * 1.6117 | + | 0.404 * 2.0301 | + | 0.892 * 0.9383 | + | 0.115 * 0.9455 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.118 | + | 4.679 * -0.181752 | - | 0.327 * 1.01 |
| = | -3.18 |
* 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 ₩5,323 Mil.
Revenue was 7809.64 + 9825.324 + 11307.235 + 11283.358 = ₩40,226 Mil.
Gross Profit was 2841.3 + -367.362 + 425.223 + 3156.519 = ₩6,056 Mil.
Total Current Assets was ₩27,241 Mil.
Total Assets was ₩73,631 Mil.
Property, Plant and Equipment(Net PPE) was ₩40,958 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩1,406 Mil.
Selling, General, & Admin. Expense(SGA) was ₩3,301 Mil.
Total Current Liabilities was ₩14,677 Mil.
Long-Term Debt & Capital Lease Obligation was ₩3,278 Mil.
Net Income was -315.291 + 905.924 + -4593.098 + -1237.832 = ₩-5,240 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ₩0 Mil.
Cash Flow from Operations was 3179.51 + 3160.839 + 1293.41 + 508.488 = ₩8,142 Mil.
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Total Receivables was ₩12,504 Mil.
Revenue was 10416.187 + 11542.678 + 11011.367 + 9898.445 = ₩42,869 Mil.
Gross Profit was 2296.284 + 2551.886 + 3112.076 + 2441.065 = ₩10,401 Mil.
Total Current Assets was ₩35,684 Mil.
Total Assets was ₩80,632 Mil.
Property, Plant and Equipment(Net PPE) was ₩42,017 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩1,361 Mil.
Selling, General, & Admin. Expense(SGA) was ₩3,146 Mil.
Total Current Liabilities was ₩19,362 Mil.
Long-Term Debt & Capital Lease Obligation was ₩105 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) |
| = | (5322.564 / 40225.557) | / | (12503.77 / 42868.677) |
| = | 0.132318 | / | 0.291676 |
| | = | 0.4536 |
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) |
| = | (10401.311 / 42868.677) | / | (6055.68 / 40225.557) |
| = | 0.242632 | / | 0.150543 |
| | = | 1.6117 |
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 - (27240.651 + 40957.843) / 73630.63) | / | (1 - (35684.352 + 42017.498) / 80632.076) |
| = | 0.073775 | / | 0.036341 |
| | = | 2.0301 |
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 |
| = | 40225.557 | / | 42868.677 |
| | = | 0.9383 |
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)) |
| = | (1361.103 / (1361.103 + 42017.498)) | / | (1405.802 / (1405.802 + 40957.843)) |
| = | 0.031377 | / | 0.033184 |
| | = | 0.9455 |
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) |
| = | (3300.57 / 40225.557) | / | (3146.237 / 42868.677) |
| = | 0.082052 | / | 0.073392 |
| | = | 1.118 |
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) |
| = | ((3277.83 + 14677.385) / 73630.63) | / | ((105.282 + 19362.258) / 80632.076) |
| = | 0.243855 | / | 0.241437 |
| | = | 1.01 |
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 |
| = | (-5240.297 - 0 | - | 8142.247) | / | 73630.63 |
| | = | -0.181752 |
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.
NFC has a M-score of -3.18 suggests that the company is unlikely to be a manipulator.
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