Importadora Y Exportadora De La Patagonia Beneish M-Score Historical Data
The historical data trend for Importadora Y Exportadora De La Patagonia'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.
Importadora Y Exportadora De La Patagonia Beneish M-Score Chart
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Importadora Y Exportadora De La Patagonia Annual Data
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Trend |
Jun14 |
Jun15 |
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Jun17 |
Jun18 |
Jun19 |
Jun20 |
Jun21 |
Jun22 |
Jun23 |
| Beneish M-Score |
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-2.57 |
-2.06 |
-2.15 |
-0.92 |
-3.39 |
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Importadora Y Exportadora De La Patagonia Quarterly Data
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Jun19 |
Sep19 |
Dec19 |
Mar20 |
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Dec20 |
Mar21 |
Jun21 |
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Dec21 |
Mar22 |
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Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
Mar24 |
| Beneish M-Score |
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-2.18 |
-3.39 |
4.35 |
17.17 |
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Competitive Comparison of Importadora Y Exportadora De La Patagonia's Beneish M-Score
For the Department Stores subindustry, Importadora Y Exportadora De La Patagonia'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.
Importadora Y Exportadora De La Patagonia's Beneish M-Score Distribution in the Retail - Cyclical Industry
For the Retail - Cyclical industry and Consumer Cyclical sector, Importadora Y Exportadora De La Patagonia's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Importadora Y Exportadora De La Patagonia's Beneish M-Score falls into.
Importadora Y Exportadora De La Patagonia 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 Importadora Y Exportadora De La Patagonia 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 * | + | 0.528 * | + | 0.404 * | + | 0.892 * | + | 0.115 * |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * | + | 4.679 * | - | 0.327 * |
| = | |
* 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 ARS107,393 Mil.
Revenue was 386920.177 + 284767.544 + 170387.269 + -711023.089 = ARS131,052 Mil.
Gross Profit was 111524.181 + 94076.711 + 51279.895 + -237902.409 = ARS18,978 Mil.
Total Current Assets was ARS333,011 Mil.
Total Assets was ARS629,170 Mil.
Property, Plant and Equipment(Net PPE) was ARS285,199 Mil.
Depreciation, Depletion and Amortization(DDA) was ARS9,758 Mil.
Selling, General, & Admin. Expense(SGA) was ARS1,440 Mil.
Total Current Liabilities was ARS209,842 Mil.
Long-Term Debt & Capital Lease Obligation was ARS14,850 Mil.
Net Income was 11214.308 + 17871.057 + 7131.943 + -46606.377 = ARS-10,389 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ARS0 Mil.
Cash Flow from Operations was 12300.696 + 18548.909 + 6234.299 + -47622.796 = ARS-10,539 Mil.
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Total Receivables was ARS0 Mil.
Revenue was 390345.153 + 271693.101 + 167198.417 + 354362.239 = ARS1,183,599 Mil.
Gross Profit was 105851.892 + 72321.514 + 45780.818 + 96243.988 = ARS320,198 Mil.
Total Current Assets was ARS0 Mil.
Total Assets was ARS0 Mil.
Property, Plant and Equipment(Net PPE) was ARS0 Mil.
Depreciation, Depletion and Amortization(DDA) was ARS22,435 Mil.
Selling, General, & Admin. Expense(SGA) was ARS15,372 Mil.
Total Current Liabilities was ARS0 Mil.
Long-Term Debt & Capital Lease Obligation was ARS0 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) |
| = | (107392.871 / 131051.901) | / | (0 / 1183598.91) |
| = | 0.819468 | / | 0 |
| | = | |
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) |
| = | (320198.212 / 1183598.91) | / | (18978.378 / 131051.901) |
| = | 0.270529 | / | 0.144816 |
| | = | |
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 - (333010.847 + 285199.072) / 629169.951) | / | (1 - (0 + 0) / 0) |
| = | 0.01742 | / | |
| | = | |
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 |
| = | 131051.901 | / | 1183598.91 |
| | = | |
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)) |
| = | (22434.642 / (22434.642 + 0)) | / | (9757.54 / (9757.54 + 285199.072)) |
| = | 1 | / | 0.033081 |
| | = | |
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) |
| = | (1440.224 / 131051.901) | / | (15371.741 / 1183598.91) |
| = | 0.01099 | / | 0.012987 |
| | = | |
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) |
| = | ((14849.529 + 209842.057) / 629169.951) | / | ((0 + 0) / 0) |
| = | 0.357124 | / | |
| | = | |
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 |
| = | (-10389.069 - 0 | - | -10538.892) | / | 629169.951 |
| | = | 0.000238 |
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.
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