Wipro Beneish M-Score Historical Data
The historical data trend for Wipro'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.
Wipro Beneish M-Score Chart
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Wipro Annual Data
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Trend |
Mar14 |
Mar15 |
Mar16 |
Mar17 |
Mar18 |
Mar19 |
Mar20 |
Mar21 |
Mar22 |
Mar23 |
| Beneish M-Score |
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-2.62 |
-2.37 |
-2.88 |
-2.02 |
-2.42 |
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Wipro 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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-2.16 |
-2.30 |
-2.42 |
-2.86 |
-2.57 |
Competitive Comparison of Wipro's Beneish M-Score
For the Information Technology Services subindustry, Wipro'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.
Wipro's Beneish M-Score Distribution in the Software Industry
For the Software industry and Technology sector, Wipro's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Wipro's Beneish M-Score falls into.
Wipro 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 Wipro 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 * 0.876 | + | 0.528 * 0.9623 | + | 0.404 * 1.0217 | + | 0.892 * 1.0114 | + | 0.115 * 1.1019 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.0836 | + | 4.679 * -0.006403 | - | 0.327 * 0.9139 |
| = | -2.60 |
* 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 $2,606 Mil.
Revenue was 2710.473 + 2776.643 + 2818.924 + 2816.531 = $11,123 Mil.
Gross Profit was 794.125 + 815.431 + 840.743 + 836.835 = $3,287 Mil.
Total Current Assets was $7,043 Mil.
Total Assets was $13,077 Mil.
Property, Plant and Equipment(Net PPE) was $1,205 Mil.
Depreciation, Depletion and Amortization(DDA) was $305 Mil.
Selling, General, & Admin. Expense(SGA) was $1,469 Mil.
Total Current Liabilities was $3,079 Mil.
Long-Term Debt & Capital Lease Obligation was $924 Mil.
Net Income was 318.563 + 349.054 + 373.724 + 370.166 = $1,412 Mil.
Non Operating Income was 2.865 + 15.068 + 17.844 + 22.298 = $58 Mil.
Cash Flow from Operations was 0 + 456.223 + 453.38 + 527.561 = $1,437 Mil.
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Total Receivables was $2,941 Mil.
Revenue was 2808.552 + 2756.896 + 2737.906 + 2694.297 = $10,998 Mil.
Gross Profit was 767.091 + 764.323 + 795.844 + 800.559 = $3,128 Mil.
Total Current Assets was $7,708 Mil.
Total Assets was $14,228 Mil.
Property, Plant and Equipment(Net PPE) was $1,378 Mil.
Depreciation, Depletion and Amortization(DDA) was $394 Mil.
Selling, General, & Admin. Expense(SGA) was $1,340 Mil.
Total Current Liabilities was $3,818 Mil.
Long-Term Debt & Capital Lease Obligation was $949 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) |
| = | (2605.935 / 11122.571) | / | (2941.406 / 10997.651) |
| = | 0.234293 | / | 0.267458 |
| | = | 0.876 |
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) |
| = | (3127.817 / 10997.651) | / | (3287.134 / 11122.571) |
| = | 0.284408 | / | 0.295537 |
| | = | 0.9623 |
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 - (7042.771 + 1205.357) / 13077.284) | / | (1 - (7708.034 + 1377.642) / 14228.236) |
| = | 0.369278 | / | 0.361433 |
| | = | 1.0217 |
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 |
| = | 11122.571 | / | 10997.651 |
| | = | 1.0114 |
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)) |
| = | (393.725 / (393.725 + 1377.642)) | / | (304.565 / (304.565 + 1205.357)) |
| = | 0.222272 | / | 0.201709 |
| | = | 1.1019 |
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) |
| = | (1468.821 / 11122.571) | / | (1340.23 / 10997.651) |
| = | 0.132058 | / | 0.121865 |
| | = | 1.0836 |
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) |
| = | ((924.124 + 3079.234) / 13077.284) | / | ((948.503 + 3817.713) / 14228.236) |
| = | 0.306131 | / | 0.334983 |
| | = | 0.9139 |
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 |
| = | (1411.507 - 58.075 | - | 1437.164) | / | 13077.284 |
| | = | -0.006403 |
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.
Wipro has a M-score of -2.60 suggests that the company is unlikely to be a manipulator.
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