| 0A1U Stock | | | USD 68.40 0.85 1.23% |
Altman Z Score is one of the simplest fundamental models to determine how likely your company is to fail. The module uses available fundamental data of a given equity to approximate the Altman Z score. Altman Z Score is determined by evaluating five fundamental price points available from the company's current public disclosure documents. Check out
Trending Equities to better understand how to build diversified portfolios, which includes a position in Uber Technologies. Also, note that the market value of any company could be closely tied with the direction of predictive economic indicators such as
signals in board of governors.
For more information on how to buy Uber Stock please use our
How to buy in Uber Stock guide.
Uber Technologies Company Z Score Analysis
Uber Technologies' Z-Score is a simple linear, multi-factor model that measures the financial health and economic stability of a company. The score is used to predict the probability of a firm going into bankruptcy within next 24 months or two fiscal years from the day stated on the accounting statements used to calculate it. The model uses five fundamental business ratios that are weighted according to algorithm of Professor Edward Altman who developed it in the late 1960s at New York University..
| First Factor | = | 1.2 * ( | Working Capital | / | Total Assets ) |
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| Second Factor | = | 1.4 * ( | Retained Earnings | / | Total Assets ) |
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| Thrid Factor | = | 3.3 * ( | EBITAD | / | Total Assets ) |
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| Fouth Factor | = | 0.6 * ( | Market Value of Equity | / | Total Liabilities ) |
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| Fifth Factor | = | 0.99 * ( | Revenue | / | Total Assets ) |
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To calculate a Z-Score, one would need to know a company's current working capital, its total assets and liabilities, and the amount of its latest earnings as well as earnings before interest and tax. Z-Scores can be used to compare the odds of bankruptcy of companies in a similar line of business or firms operating in the same industry. Companies with Z-Scores above 3.1 are generally considered to be stable and healthy with a low probability of bankruptcy. Scores that fall between 1.8 and 3.1 lie in a so-called 'grey area,' with scores of less than 1 indicating the highest probability of distress. Z Score is a used widely measure by financial auditors, accountants, money managers, loan processors, wealth advisers, and day traders. In the last 25 years, many financial models that utilize z-scores proved it to be successful as a predictor of corporate bankruptcy.
In accordance with the company's disclosures, Uber Technologies has a Z Score of 0.0. This is 100.0% lower than that of the Software sector and about the same as
Information Technology (which currently averages 0.0) industry. The z score for all United Kingdom stocks is 100.0% higher than that of the company.
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Uber Fundamentals
About Uber Technologies Fundamental Analysis
The Macroaxis Fundamental Analysis modules help investors analyze Uber Technologies's financials across various querterly and yearly statements, indicators and fundamental ratios. We help investors to determine the real value of Uber Technologies using virtually all public information available. We use both quantitative as well as qualitative analysis to arrive at
the intrinsic value of Uber Technologies based on its fundamental data. In general, a quantitative approach, as applied to this company, focuses on analyzing
financial statements comparatively, whereas a qaualitative method uses data that is important to a company's growth but cannot be measured and presented in a numerical way.
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Trending Equities to better understand how to build diversified portfolios, which includes a position in Uber Technologies. Also, note that the market value of any company could be closely tied with the direction of predictive economic indicators such as
signals in board of governors.
For more information on how to buy Uber Stock please use our
How to buy in Uber Stock guide.
You can also try the
Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.