Frontline Beneish M-Score Historical Data
The historical data trend for Frontline'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.
Frontline Beneish M-Score Chart
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Frontline Annual Data
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Trend |
Dec13 |
Dec14 |
Dec15 |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
| Beneish M-Score |
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-2.35 |
-2.63 |
-3.17 |
3.60 |
-2.20 |
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Frontline Quarterly Data
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Dec18 |
Mar19 |
Jun19 |
Sep19 |
Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
| Beneish M-Score |
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-3.44 |
-2.20 |
-2.96 |
-2.12 |
-2.42 |
Competitive Comparison of Frontline's Beneish M-Score
For the Oil & Gas Midstream subindustry, Frontline'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.
Frontline's Beneish M-Score Distribution in the Oil & Gas Industry
For the Oil & Gas industry and Energy sector, Frontline's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Frontline's Beneish M-Score falls into.
Frontline 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 Frontline 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 | + | 0.528 * 0.4762 | + | 0.404 * 0.948 | + | 0.892 * 1.8738 | + | 0.115 * 0.8946 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.9559 | + | 4.679 * -0.063875 | - | 0.327 * 0.9296 |
| = | -2.28 |
* 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 (Sep23) TTM: | Last Year (Sep22) TTM: |
Total Receivables was kr1,903 Mil.
Revenue was 4044.124 + 5535.994 + 5238.05 + 5237.846 = kr20,056 Mil.
Gross Profit was 1390.457 + 2756.159 + 2519.758 + 2599.573 = kr9,266 Mil.
Total Current Assets was kr9,178 Mil.
Total Assets was kr49,334 Mil.
Property, Plant and Equipment(Net PPE) was kr38,253 Mil.
Depreciation, Depletion and Amortization(DDA) was kr2,070 Mil.
Selling, General, & Admin. Expense(SGA) was kr603 Mil.
Total Current Liabilities was kr5,115 Mil.
Long-Term Debt & Capital Lease Obligation was kr20,289 Mil.
Net Income was 1155.511 + 2490.449 + 2102.521 + 2361.868 = kr8,110 Mil.
Non Operating Income was 292.838 + 403.192 + 11.501 + 284.31 = kr992 Mil.
Cash Flow from Operations was 2172.095 + 3135.685 + 2767.004 + 2194.933 = kr10,270 Mil.
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Total Receivables was kr0 Mil.
Revenue was 3935.866 + 2928.629 + 1920.874 + 1917.887 = kr10,703 Mil.
Gross Profit was 1275.856 + 737.08 + 191.813 + 149.984 = kr2,355 Mil.
Total Current Assets was kr7,138 Mil.
Total Assets was kr46,843 Mil.
Property, Plant and Equipment(Net PPE) was kr37,800 Mil.
Depreciation, Depletion and Amortization(DDA) was kr1,820 Mil.
Selling, General, & Admin. Expense(SGA) was kr337 Mil.
Total Current Liabilities was kr4,373 Mil.
Long-Term Debt & Capital Lease Obligation was kr21,575 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) |
| = | (1903.098 / 20056.014) | / | (0 / 10703.256) |
| = | 0.094889 | / | 0 |
| | = | 1 |
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) |
| = | (2354.733 / 10703.256) | / | (9265.947 / 20056.014) |
| = | 0.220002 | / | 0.462003 |
| | = | 0.4762 |
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 - (9178.252 + 38252.635) / 49333.781) | / | (1 - (7137.947 + 37799.56) / 46843.476) |
| = | 0.038572 | / | 0.040688 |
| | = | 0.948 |
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 |
| = | 20056.014 | / | 10703.256 |
| | = | 1.8738 |
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)) |
| = | (1819.741 / (1819.741 + 37799.56)) | / | (2070.297 / (2070.297 + 38252.635)) |
| = | 0.045931 | / | 0.051343 |
| | = | 0.8946 |
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) |
| = | (602.885 / 20056.014) | / | (336.583 / 10703.256) |
| = | 0.03006 | / | 0.031447 |
| | = | 0.9559 |
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) |
| = | ((20289.159 + 5115.049) / 49333.781) | / | ((21575.011 + 4372.782) / 46843.476) |
| = | 0.514945 | / | 0.553925 |
| | = | 0.9296 |
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 |
| = | (8110.349 - 991.841 | - | 10269.717) | / | 49333.781 |
| | = | -0.063875 |
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.
Frontline has a M-score of -2.28 suggests that the company is unlikely to be a manipulator.
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