Canfor Beneish M-Score Historical Data
The historical data trend for Canfor'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.
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Canfor Annual Data
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Trend |
Dec14 |
Dec15 |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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|
|
|
|
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-2.25 |
-3.02 |
-2.60 |
-2.45 |
-2.10 |
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Canfor Quarterly Data
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Mar19 |
Jun19 |
Sep19 |
Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
| Beneish M-Score |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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-2.45 |
-2.37 |
-1.90 |
-1.95 |
-2.10 |
Competitive Comparison
For the Lumber & Wood Production subindustry, Canfor'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.
Canfor Beneish M-Score Distribution
For the Forest Products industry and Basic Materials sector, Canfor's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Canfor's Beneish M-Score falls into.
Canfor 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 Canfor 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 * 1.5409 | + | 0.528 * 2.1368 | + | 0.404 * 1.0705 | + | 0.892 * 0.7057 | + | 0.115 * 1.0705 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.225 | + | 4.679 * -0.081301 | - | 0.327 * 1.0448 |
| = | -2.04 |
* 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 (Dec23) TTM: | Last Year (Dec22) TTM: |
Total Receivables was $382 Mil.
Revenue was 956.246 + 969.847 + 1088.364 + 1012.497 = $4,027 Mil.
Gross Profit was 160.629 + 174.858 + 231.447 + 107.871 = $675 Mil.
Total Current Assets was $1,683 Mil.
Total Assets was $4,570 Mil.
Property, Plant and Equipment(Net PPE) was $1,903 Mil.
Depreciation, Depletion and Amortization(DDA) was $312 Mil.
Selling, General, & Admin. Expense(SGA) was $638 Mil.
Total Current Liabilities was $675 Mil.
Long-Term Debt & Capital Lease Obligation was $159 Mil.
Net Income was -87.284 + -17.072 + -33.042 + -103.778 = $-241 Mil.
Non Operating Income was 9.914 + 8.499 + -3.011 + -3.654 = $12 Mil.
Cash Flow from Operations was -6.261 + 151.947 + 138.341 + -165.388 = $119 Mil.
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Total Receivables was $351 Mil.
Revenue was 1010.894 + 1249.269 + 1697.204 + 1748.736 = $5,706 Mil.
Gross Profit was 169.01 + 279.406 + 725.476 + 869.273 = $2,043 Mil.
Total Current Assets was $2,256 Mil.
Total Assets was $4,961 Mil.
Property, Plant and Equipment(Net PPE) was $1,706 Mil.
Depreciation, Depletion and Amortization(DDA) was $303 Mil.
Selling, General, & Admin. Expense(SGA) was $738 Mil.
Total Current Liabilities was $650 Mil.
Long-Term Debt & Capital Lease Obligation was $216 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) |
| = | (382.23 / 4026.954) | / | (351.491 / 5706.103) |
| = | 0.094918 | / | 0.061599 |
| | = | 1.5409 |
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) |
| = | (2043.165 / 5706.103) | / | (674.805 / 4026.954) |
| = | 0.358067 | / | 0.167572 |
| | = | 2.1368 |
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 - (1682.841 + 1902.877) / 4570.215) | / | (1 - (2256.091 + 1706.441) / 4960.766) |
| = | 0.215416 | / | 0.201226 |
| | = | 1.0705 |
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 |
| = | 4026.954 | / | 5706.103 |
| | = | 0.7057 |
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)) |
| = | (302.985 / (302.985 + 1706.441)) | / | (311.966 / (311.966 + 1902.877)) |
| = | 0.150782 | / | 0.140852 |
| | = | 1.0705 |
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) |
| = | (637.77 / 4026.954) | / | (737.722 / 5706.103) |
| = | 0.158375 | / | 0.129286 |
| | = | 1.225 |
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) |
| = | ((158.989 + 674.717) / 4570.215) | / | ((215.753 + 650.423) / 4960.766) |
| = | 0.182422 | / | 0.174605 |
| | = | 1.0448 |
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 |
| = | (-241.176 - 11.748 | - | 118.639) | / | 4570.215 |
| | = | -0.081301 |
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.
Canfor has a M-score of -2.04 suggests that the company is unlikely to be a manipulator.
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