Pakka Beneish M-Score Historical Data
The historical data trend for Pakka'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.
Pakka Beneish M-Score Chart
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Pakka Annual Data
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Mar14 |
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Mar19 |
Mar20 |
Mar21 |
Mar22 |
Mar23 |
| Beneish M-Score |
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-2.74 |
-3.01 |
-2.65 |
-1.63 |
-2.29 |
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Pakka Quarterly Data
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Mar19 |
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Sep19 |
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Dec23 |
| Beneish M-Score |
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-2.29 |
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Competitive Comparison of Pakka's Beneish M-Score
For the Paper & Paper Products subindustry, Pakka'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.
Pakka's Beneish M-Score Distribution in the Forest Products Industry
For the Forest Products industry and Basic Materials sector, Pakka's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Pakka's Beneish M-Score falls into.
Pakka 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 Pakka 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 * 0.756 | + | 0.528 * 1.1083 | + | 0.404 * 1.0313 | + | 0.892 * 1.4027 | + | 0.115 * 0.9003 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.8 | + | 4.679 * -0.01428 | - | 0.327 * 0.8996 |
| = | -2.29 |
* 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 (Mar23) TTM: | Last Year (Mar22) TTM: |
Total Receivables was ₹257 Mil.
Revenue was ₹4,083 Mil.
Gross Profit was ₹1,786 Mil.
Total Current Assets was ₹1,500 Mil.
Total Assets was ₹3,731 Mil.
Property, Plant and Equipment(Net PPE) was ₹2,121 Mil.
Depreciation, Depletion and Amortization(DDA) was ₹127 Mil.
Selling, General, & Admin. Expense(SGA) was ₹294 Mil.
Total Current Liabilities was ₹1,168 Mil.
Long-Term Debt & Capital Lease Obligation was ₹222 Mil.
Net Income was ₹460 Mil.
Gross Profit was ₹0 Mil.
Cash Flow from Operations was ₹514 Mil.
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Total Receivables was ₹242 Mil.
Revenue was ₹2,911 Mil.
Gross Profit was ₹1,411 Mil.
Total Current Assets was ₹1,252 Mil.
Total Assets was ₹3,245 Mil.
Property, Plant and Equipment(Net PPE) was ₹1,899 Mil.
Depreciation, Depletion and Amortization(DDA) was ₹102 Mil.
Selling, General, & Admin. Expense(SGA) was ₹262 Mil.
Total Current Liabilities was ₹1,053 Mil.
Long-Term Debt & Capital Lease Obligation was ₹291 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) |
| = | (256.893 / 4083.082) | / | (242.239 / 2910.863) |
| = | 0.062916 | / | 0.083219 |
| | = | 0.756 |
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) |
| = | (1411.477 / 2910.863) | / | (1786.356 / 4083.082) |
| = | 0.4849 | / | 0.437502 |
| | = | 1.1083 |
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 - (1499.821 + 2120.61) / 3731.346) | / | (1 - (1252.34 + 1899.108) / 3244.979) |
| = | 0.029725 | / | 0.028823 |
| | = | 1.0313 |
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 |
| = | 4083.082 | / | 2910.863 |
| | = | 1.4027 |
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)) |
| = | (102.154 / (102.154 + 1899.108)) | / | (127.465 / (127.465 + 2120.61)) |
| = | 0.051045 | / | 0.0567 |
| | = | 0.9003 |
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) |
| = | (293.97 / 4083.082) | / | (261.973 / 2910.863) |
| = | 0.071997 | / | 0.089998 |
| | = | 0.8 |
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) |
| = | ((221.914 + 1168.353) / 3731.346) | / | ((290.502 + 1053.466) / 3244.979) |
| = | 0.372591 | / | 0.414168 |
| | = | 0.8996 |
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 |
| = | (460.429 - 0 | - | 513.712) | / | 3731.346 |
| | = | -0.01428 |
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.
Pakka has a M-score of -2.29 suggests that the company is unlikely to be a manipulator.
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