| DTSTW Stock | | | USD 0.39 0.07 15.22% |
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
Data Storage Piotroski F Score and
Data Storage Valuation analysis.
For more information on how to buy Data Stock please use our
How to Invest in Data Storage guide.
At this time, Data Storage's
Net Working Capital is fairly stable compared to the past year.
Change In Working Capital is likely to climb to about 1.9
M in 2024, whereas
Capital Lease Obligations is likely to drop slightly above 571.4
K in 2024. At this time, Data Storage's
Net Income is fairly stable compared to the past year.
Interest Income is likely to climb to about 569.3
K in 2024, whereas
Interest Expense is likely to drop slightly above 70.8
K in 2024.
Data Storage Company Z Score Analysis
Data Storage's 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 ) |
|
| Second Factor | = | 1.4 * ( | Retained Earnings | / | Total Assets ) |
|
| Thrid Factor | = | 3.3 * ( | EBITAD | / | Total Assets ) |
|
| Fouth Factor | = | 0.6 * ( | Market Value of Equity | / | Total Liabilities ) |
|
| Fifth Factor | = | 0.99 * ( | Revenue | / | Total Assets ) |
|
Data Z Score Driver Correlations
Understanding the fundamental principles of building solid financial models for Data Storage is extremely important. It helps to project a fair market value of Data Stock properly, considering its historical
fundamentals such as Z Score. Since Data Storage's main accounts across its financial reports are all linked and dependent on each other, it is essential to analyze all possible correlations between related accounts. However, instead of reviewing all of Data Storage's historical
financial statements, investors can examine the correlated drivers to determine its overall health. This can be effectively done using a conventional correlation matrix of Data Storage's interrelated accounts and indicators.
Click cells to compare fundamentals
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.
According to the company's disclosures, Data Storage has a Z Score of 0.0. This indicator is about the same for the IT Services average (which is currently at 0.0) sector and about the same as
Information Technology (which currently averages 0.0) industry. This indicator is about the same for all United States stocks average (which is currently at 0.0).
Data Storage Current Valuation Drivers
We derive many important indicators used in calculating different scores of Data Storage from analyzing Data Storage's financial statements. These drivers represent accounts that assess Data Storage's ability to generate profits relative to its revenue, operating costs, and shareholders' equity. Below are some of Data Storage's important valuation drivers and their relationship over time.
Data Fundamentals
About Data Storage Fundamental Analysis
The Macroaxis Fundamental Analysis modules help investors analyze Data Storage's financials across various querterly and yearly statements, indicators and fundamental ratios. We help investors to determine the real value of Data Storage using virtually all public information available. We use both quantitative as well as qualitative analysis to arrive at
the intrinsic value of Data Storage 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.
Please read more on our
fundamental analysis page.
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Additional Tools for Data Stock Analysis
When running Data Storage's price analysis, check to
measure Data Storage's market volatility, profitability, liquidity, solvency, efficiency, growth potential, financial leverage, and other vital indicators. We have many different tools that can be utilized to determine how healthy Data Storage is operating at the current time. Most of Data Storage's value examination focuses on studying past and present price action to
predict the probability of Data Storage's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Data Storage's price. Additionally, you may evaluate how the addition of Data Storage to your portfolios can decrease your overall portfolio volatility.