| DTSTW Stock | | | USD 0.37 0.03 7.50% |
This module uses fundamental data of Data Storage to approximate its Piotroski F score. Data Storage F Score is determined by combining nine binary scores representing 3 distinct fundamental categories of Data Storage. These three categories are profitability, efficiency, and funding. Some research analysts and sophisticated value traders use Piotroski F Score to find opportunities outside of the conventional market and financial statement analysis.They believe that some of the new information about Data Storage financial position does not get reflected in the current market share price suggesting a possibility of arbitrage. Check out
Data Storage Altman Z Score,
Data Storage Correlation,
Data Storage Valuation, as well as analyze
Data Storage Alpha and Beta and
Data Storage Hype 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
Short Term Debt is fairly stable compared to the past year.
Long Term Debt is likely to climb to about 105.9
K in 2024, whereas
Short and Long Term Debt Total is likely to drop slightly above 571.4
K in 2024. At this time, Data Storage's
Average Inventory is fairly stable compared to the past year.
Cash Per Share is likely to climb to 1.96 in 2024, whereas
PTB Ratio is likely to drop 0.66 in 2024.
At this time, it appears that Data Storage's Piotroski F Score is Healthy. Although some professional money managers and academia have recently criticized
Piotroski F-Score model, we still consider it an effective method of
predicting the state of the financial strength of any organization that is not predisposed to accounting gimmicks and manipulations. Using this score on the criteria to originate an efficient long-term portfolio can help investors filter out the purely speculative stocks or equities playing fundamental games by manipulating their earnings..
5.0
Piotroski F Score - Healthy
| Change in Return on Assets | |
| Cash Flow Return on Assets | |
| Current Quality of Earnings (accrual) | |
| Long Term Debt Over Assets Change | Higher Leverage| Focus | |
| Change In Outstending Shares | |
Data Storage Piotroski F Score Drivers
The critical factor to consider when applying the Piotroski F Score to Data Storage is to make sure Data is not a subject of accounting manipulations and runs a healthy internal audit department. So, if Data Storage's auditors report directly to the board (not management), the managers will be reluctant to manipulate simply due to the fear of punishment. On the other hand, the auditors will be free to investigate the ledgers properly because they know that the board has their back. Below are the main accounts that are used in the Piotroski F Score model. By analyzing the historical trends of the mains drivers, investors can determine if Data Storage's financial numbers are properly reported.
Data Storage F Score Driver Matrix
One of the toughest challenges investors face today is learning how to quickly synthesize historical
financial statements and information provided by the company, SEC reporting, and various external parties in order to project the various growth rates. Understanding the correlation between Data Storage's different financial indicators related to revenue, expenses, operating profit, and net earnings helps investors identify and prioritize their investing strategies towards Data Storage in a much-optimized way.
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About Data Storage Piotroski F Score
F-Score is one of many stock grading techniques developed by Joseph Piotroski, a professor of accounting at the Stanford University Graduate School of Business. It was published in 2002 under the paper titled
Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers. Piotroski F Score is based on binary analysis strategy in which stocks are given one point for passing 9 very simple fundamental tests, and zero point otherwise. According to Mr. Piotroski's analysis, his F-Score binary model can help to predict the performance of low price-to-book stocks.
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.
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.