Monte Carlo Fashions Beneish M-Score Historical Data
The historical data trend for Monte Carlo Fashions'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.
Monte Carlo Fashions Beneish M-Score Chart
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Monte Carlo Fashions Annual Data
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Mar24 |
| Beneish M-Score |
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-2.29 |
-2.92 |
-1.71 |
-1.68 |
-2.33 |
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Monte Carlo Fashions Quarterly Data
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Dec19 |
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Sep24 |
| Beneish M-Score |
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-2.33 |
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Competitive Comparison of Monte Carlo Fashions's Beneish M-Score
For the Apparel Manufacturing subindustry, Monte Carlo Fashions'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.
Monte Carlo Fashions's Beneish M-Score Distribution in the Manufacturing - Apparel & Accessories Industry
For the Manufacturing - Apparel & Accessories industry and Consumer Cyclical sector, Monte Carlo Fashions's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Monte Carlo Fashions's Beneish M-Score falls into.
Monte Carlo Fashions 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 Monte Carlo Fashions 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 * 1.0288 | + | 0.528 * 1.114 | + | 0.404 * 1.4302 | + | 0.892 * 0.9503 | + | 0.115 * 0.9302 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.0144 | + | 4.679 * -0.014195 | - | 0.327 * 0.9799 |
| = | -2.33 |
* 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 (Mar24) TTM: | Last Year (Mar23) TTM: |
Total Receivables was ₹3,853 Mil.
Revenue was ₹10,606 Mil.
Gross Profit was ₹4,037 Mil.
Total Current Assets was ₹9,840 Mil.
Total Assets was ₹15,161 Mil.
Property, Plant and Equipment(Net PPE) was ₹3,400 Mil.
Depreciation, Depletion and Amortization(DDA) was ₹512 Mil.
Selling, General, & Admin. Expense(SGA) was ₹1,002 Mil.
Total Current Liabilities was ₹5,243 Mil.
Long-Term Debt & Capital Lease Obligation was ₹1,285 Mil.
Net Income was ₹599 Mil.
Gross Profit was ₹0 Mil.
Cash Flow from Operations was ₹815 Mil.
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Total Receivables was ₹3,940 Mil.
Revenue was ₹11,160 Mil.
Gross Profit was ₹4,733 Mil.
Total Current Assets was ₹10,627 Mil.
Total Assets was ₹14,964 Mil.
Property, Plant and Equipment(Net PPE) was ₹3,012 Mil.
Depreciation, Depletion and Amortization(DDA) was ₹418 Mil.
Selling, General, & Admin. Expense(SGA) was ₹1,040 Mil.
Total Current Liabilities was ₹5,437 Mil.
Long-Term Debt & Capital Lease Obligation was ₹1,139 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) |
| = | (3852.5 / 10605.6) | / | (3940.2 / 11159.7) |
| = | 0.363251 | / | 0.353074 |
| | = | 1.0288 |
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) |
| = | (4732.5 / 11159.7) | / | (4037.4 / 10605.6) |
| = | 0.424071 | / | 0.380686 |
| | = | 1.114 |
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 - (9840.1 + 3400.1) / 15160.7) | / | (1 - (10626.6 + 3012.2) / 14964.2) |
| = | 0.126676 | / | 0.088571 |
| | = | 1.4302 |
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 |
| = | 10605.6 | / | 11159.7 |
| | = | 0.9503 |
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)) |
| = | (417.7 / (417.7 + 3012.2)) | / | (512.2 / (512.2 + 3400.1)) |
| = | 0.121782 | / | 0.13092 |
| | = | 0.9302 |
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) |
| = | (1002.1 / 10605.6) | / | (1039.5 / 11159.7) |
| = | 0.094488 | / | 0.093148 |
| | = | 1.0144 |
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) |
| = | ((1285.2 + 5243) / 15160.7) | / | ((1138.8 + 5437) / 14964.2) |
| = | 0.4306 | / | 0.439435 |
| | = | 0.9799 |
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 |
| = | (599.4 - 0 | - | 814.6) | / | 15160.7 |
| | = | -0.014195 |
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.
Monte Carlo Fashions has a M-score of -2.33 suggests that the company is unlikely to be a manipulator.
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