Azul Beneish M-Score Historical Data
The historical data trend for Azul'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.
Azul Beneish M-Score Chart
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Azul Annual Data
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Trend |
Dec13 |
Dec14 |
Dec15 |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
| Beneish M-Score |
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-2.74 |
-3.05 |
-6.24 |
-4.18 |
-3.26 |
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Azul 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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-4.14 |
-3.26 |
-4.31 |
-4.30 |
-3.95 |
Competitive Comparison of Azul's Beneish M-Score
For the Airlines subindustry, Azul'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.
Azul's Beneish M-Score Distribution in the Transportation Industry
For the Transportation industry and Industrials sector, Azul's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Azul's Beneish M-Score falls into.
Azul 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 Azul 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 * 0.6861 | + | 0.528 * 0.7626 | + | 0.404 * 0.7825 | + | 0.892 * 1.189 | + | 0.115 * 0.9746 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.3149 | + | 4.679 * -0.268174 | - | 0.327 * 0.991 |
| = | -3.95 |
* 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 R$2,018 Mil.
Revenue was 4916.4 + 4254.195 + 4478.328 + 4453.452 = R$18,102 Mil.
Gross Profit was 1701.4 + 666.194 + 909.951 + 289.981 = R$3,568 Mil.
Total Current Assets was R$5,931 Mil.
Total Assets was R$19,605 Mil.
Property, Plant and Equipment(Net PPE) was R$9,772 Mil.
Depreciation, Depletion and Amortization(DDA) was R$2,393 Mil.
Selling, General, & Admin. Expense(SGA) was R$204 Mil.
Total Current Liabilities was R$14,064 Mil.
Long-Term Debt & Capital Lease Obligation was R$21,622 Mil.
Net Income was -1075 + 23.908 + -736.641 + 1106.053 = R$-682 Mil.
Non Operating Income was -747 + 885.376 + 269.42 + 1793.85 = R$2,202 Mil.
Cash Flow from Operations was -95.5 + 593.27 + 1043.153 + 833.327 = R$2,374 Mil.
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Total Receivables was R$2,473 Mil.
Revenue was 4376.811 + 3924.763 + 3193.041 + 3729.729 = R$15,224 Mil.
Gross Profit was 742.736 + 424.135 + 428.62 + 692.712 = R$2,288 Mil.
Total Current Assets was R$5,129 Mil.
Total Assets was R$18,319 Mil.
Property, Plant and Equipment(Net PPE) was R$8,531 Mil.
Depreciation, Depletion and Amortization(DDA) was R$2,024 Mil.
Selling, General, & Admin. Expense(SGA) was R$546 Mil.
Total Current Liabilities was R$14,858 Mil.
Long-Term Debt & Capital Lease Obligation was R$18,790 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) |
| = | (2017.742 / 18102.375) | / | (2473.2 / 15224.344) |
| = | 0.111463 | / | 0.16245 |
| | = | 0.6861 |
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) |
| = | (2288.203 / 15224.344) | / | (3567.526 / 18102.375) |
| = | 0.150299 | / | 0.197075 |
| | = | 0.7626 |
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 - (5931.435 + 9772.011) / 19605.095) | / | (1 - (5129.4 + 8530.7) / 18318.9) |
| = | 0.199012 | / | 0.254317 |
| | = | 0.7825 |
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 |
| = | 18102.375 | / | 15224.344 |
| | = | 1.189 |
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)) |
| = | (2023.571 / (2023.571 + 8530.7)) | / | (2393.31 / (2393.31 + 9772.011)) |
| = | 0.19173 | / | 0.196732 |
| | = | 0.9746 |
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) |
| = | (204.318 / 18102.375) | / | (545.703 / 15224.344) |
| = | 0.011287 | / | 0.035844 |
| | = | 0.3149 |
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) |
| = | ((21622.424 + 14064.059) / 19605.095) | / | ((18790.1 + 14857.6) / 18318.9) |
| = | 1.820266 | / | 1.836775 |
| | = | 0.991 |
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 |
| = | (-681.68 - 2201.646 | - | 2374.25) | / | 19605.095 |
| | = | -0.268174 |
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.
Azul has a M-score of -3.95 suggests that the company is unlikely to be a manipulator.
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