| DLE Stock | | | EUR 0.35 0.01 2.78% |
This module uses fundamental data of Datalex to approximate its Piotroski F score. Datalex F Score is determined by combining nine binary scores representing 3 distinct fundamental categories of Datalex. 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 Datalex financial position does not get reflected in the current market share price suggesting a possibility of arbitrage. Check out
Investing Opportunities to better understand how to build diversified portfolios, which includes a position in Datalex. 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.
At this time, it appears that Datalex's Piotroski F Score is Inapplicable. 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..
0.0
Piotroski F Score - Inapplicable
| Change in Return on Assets | |
| Cash Flow Return on Assets | |
| Current Quality of Earnings (accrual) | |
| Long Term Debt Over Assets Change | |
| Change In Outstending Shares | |
Datalex Piotroski F Score Drivers
The critical factor to consider when applying the Piotroski F Score to Datalex is to make sure Datalex is not a subject of accounting manipulations and runs a healthy internal audit department. So, if Datalex'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 Datalex's financial numbers are properly reported.
About Datalex 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.
About Datalex Fundamental Analysis
The Macroaxis Fundamental Analysis modules help investors analyze Datalex's financials across various querterly and yearly statements, indicators and fundamental ratios. We help investors to determine the real value of Datalex using virtually all public information available. We use both quantitative as well as qualitative analysis to arrive at
the intrinsic value of Datalex 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.
Pair Trading with Datalex
One of the main advantages of trading using pair correlations is that every trade hedges away some risk. Because there are two separate transactions required, even if Datalex position performs unexpectedly, the other equity can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Datalex will appreciate offsetting losses from the drop in the long position's value.
The ability to find closely correlated positions to Datalex could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Datalex when you sell it. If you don't do this, your portfolio allocation will be skewed against your target asset allocation. So, investors can't just sell and buy back Datalex - that would be a violation of the tax code under the "wash sale" rule, and this is why you need to find a similar enough asset and use the proceeds from selling Datalex to buy it.
The correlation of Datalex is a statistical measure of how it moves in relation to other instruments. This measure is expressed in what is known as the correlation coefficient, which ranges between -1 and +1. A perfect positive correlation (i.e., a correlation coefficient of +1) implies that as Datalex moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Datalex moves in either direction, the perfectly negatively correlated security will move in the opposite direction. If the correlation is 0, the equities are not correlated; they are entirely random. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally considered weak.
Correlation analysis and pair trading evaluation for Datalex can also be used as hedging techniques within a particular sector or industry or even over random equities to generate a better risk-adjusted return on your portfolios.
Additional Tools for Datalex Stock Analysis
When running Datalex's price analysis, check to
measure Datalex'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 Datalex is operating at the current time. Most of Datalex's value examination focuses on studying past and present price action to
predict the probability of Datalex's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Datalex's price. Additionally, you may evaluate how the addition of Datalex to your portfolios can decrease your overall portfolio volatility.