Lyft Beneish M-Score Historical Data
The historical data trend for Lyft'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.
Lyft Beneish M-Score Chart
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Lyft Annual Data
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Trend |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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-4.02 |
-3.28 |
-3.32 |
-3.47 |
-2.61 |
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Lyft Quarterly Data
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Mar19 |
Jun19 |
Sep19 |
Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
| Beneish M-Score |
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-3.47 |
-3.48 |
-3.45 |
-3.16 |
-2.61 |
Competitive Comparison of Lyft's Beneish M-Score
For the Software - Application subindustry, Lyft'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.
Lyft's Beneish M-Score Distribution in the Software Industry
For the Software industry and Technology sector, Lyft's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Lyft's Beneish M-Score falls into.
Lyft 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 Lyft 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 | + | 0.528 * 0.9595 | + | 0.404 * 0.9315 | + | 0.892 * 1.0753 | + | 0.115 * 1.4984 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.6917 | + | 4.679 * -0.058379 | - | 0.327 * 0.9552 |
| = | -2.61 |
* 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 (Dec23) TTM: | Last Year (Dec22) TTM: |
Total Receivables was $0 Mil.
Revenue was 1224.585 + 1157.55 + 1020.906 + 1000.548 = $4,404 Mil.
Gross Profit was 480.722 + 513.05 + 414.307 + 451.556 = $1,860 Mil.
Total Current Assets was $2,577 Mil.
Total Assets was $4,564 Mil.
Property, Plant and Equipment(Net PPE) was $564 Mil.
Depreciation, Depletion and Amortization(DDA) was $117 Mil.
Selling, General, & Admin. Expense(SGA) was $1,352 Mil.
Total Current Liabilities was $2,962 Mil.
Long-Term Debt & Capital Lease Obligation was $973 Mil.
Net Income was -26.309 + -12.1 + -114.262 + -187.649 = $-340 Mil.
Non Operating Income was 5.135 + -2.47 + 18.606 + 3.124 = $24 Mil.
Cash Flow from Operations was 43.508 + 2.261 + -69.973 + -74.04 = $-98 Mil.
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Total Receivables was $0 Mil.
Revenue was 1174.992 + 1053.82 + 990.748 + 875.575 = $4,095 Mil.
Gross Profit was 400.609 + 483.117 + 340.392 + 435.281 = $1,659 Mil.
Total Current Assets was $2,583 Mil.
Total Assets was $4,556 Mil.
Property, Plant and Equipment(Net PPE) was $449 Mil.
Depreciation, Depletion and Amortization(DDA) was $155 Mil.
Selling, General, & Admin. Expense(SGA) was $1,818 Mil.
Total Current Liabilities was $3,133 Mil.
Long-Term Debt & Capital Lease Obligation was $980 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) |
| = | (0 / 4403.589) | / | (0 / 4095.135) |
| = | 0 | / | 0 |
| | = | 1 |
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) |
| = | (1659.399 / 4095.135) | / | (1859.635 / 4403.589) |
| = | 0.405212 | / | 0.4223 |
| | = | 0.9595 |
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 - (2577.419 + 564.046) / 4564.467) | / | (1 - (2582.859 + 448.615) / 4556.431) |
| = | 0.311756 | / | 0.334682 |
| | = | 0.9315 |
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 |
| = | 4403.589 | / | 4095.135 |
| | = | 1.0753 |
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)) |
| = | (154.798 / (154.798 + 448.615)) | / | (116.513 / (116.513 + 564.046)) |
| = | 0.256537 | / | 0.171202 |
| | = | 1.4984 |
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) |
| = | (1352.084 / 4403.589) | / | (1817.692 / 4095.135) |
| = | 0.307041 | / | 0.443866 |
| | = | 0.6917 |
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) |
| = | ((973.464 + 2961.561) / 4564.467) | / | ((979.563 + 3132.563) / 4556.431) |
| = | 0.8621 | / | 0.902488 |
| | = | 0.9552 |
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 |
| = | (-340.32 - 24.395 | - | -98.244) | / | 4564.467 |
| | = | -0.058379 |
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.
Lyft has a M-score of -2.61 suggests that the company is unlikely to be a manipulator.
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