| DDR Stock | | | 8.39 0.39 4.44% |
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
Investing Opportunities to better understand how to build diversified portfolios, which includes a position in Dicker Data. 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.
Change In Working Capital is likely to gain to about 88.6
M in 2024, whereas
Net Invested Capital is likely to drop slightly above 370.2
M in 2024. At this time, Dicker Data's
EBITDA is comparatively stable compared to the past year.
Total Operating Expenses is likely to gain to about 149
M in 2024, whereas
Other Operating Expenses is likely to drop slightly above 1.3
B in 2024.
Dicker Data Company Z Score Analysis
Dicker Data'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 ) |
|
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, Dicker Data has a Z Score of 0.0. This indicator is about the same for the Electronic Equipment, Instruments & Components 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 Australia stocks average (which is currently at 0.0).
Dicker Z Score Peer Comparison
Stock peer comparison is one of the most widely used and accepted methods of equity analyses. It analyses Dicker Data's direct or indirect competition against its Z Score to detect undervalued stocks with similar characteristics or determine the stocks which would be a good addition to a portfolio. Peer analysis of Dicker Data could also be used in its relative valuation, which is a method of valuing Dicker Data by comparing valuation metrics of similar companies.
Dicker Data is currently under evaluation in z score category among its peers.
Dicker Fundamentals
About Dicker Data Fundamental Analysis
The Macroaxis Fundamental Analysis modules help investors analyze Dicker Data's financials across various querterly and yearly statements, indicators and fundamental ratios. We help investors to determine the real value of Dicker Data using virtually all public information available. We use both quantitative as well as qualitative analysis to arrive at
the intrinsic value of Dicker Data 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.
Build portfolios using Macroaxis predefined set of investing ideas. Many of Macroaxis investing ideas can easily outperform a given market. Ideas can also be optimized per your risk profile before portfolio origination is invoked. Macroaxis thematic optimization helps investors identify companies most likely to benefit from changes or shifts in various micro-economic or local macro-level trends. Originating optimal thematic portfolios involves aligning investors' personal views, ideas, and beliefs with their actual investments.
Additional Tools for Dicker Stock Analysis
When running Dicker Data's price analysis, check to
measure Dicker Data'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 Dicker Data is operating at the current time. Most of Dicker Data's value examination focuses on studying past and present price action to
predict the probability of Dicker Data's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Dicker Data's price. Additionally, you may evaluate how the addition of Dicker Data to your portfolios can decrease your overall portfolio volatility.