Meta Data Total Debt vs. Shares Owned By Institutions
Based on the key profitability measurements obtained from Meta Data's financial statements, Meta Data may not be well positioned to generate adequate gross income at the moment. It has a very high risk of underperforming in January. Profitability indicators assess Meta Data's ability to earn profits and add value for shareholders.
Check out Trending Equities to better understand how to build diversified portfolios. Also, note that the market value of any company could be closely tied with the direction of predictive economic indicators such as signals in gross domestic product.
For Meta Data profitability analysis, we use financial ratios and fundamental drivers that measure the ability of Meta Data to generate income relative to revenue, assets, operating costs, and current equity. These fundamental indicators attest to how well Meta Data utilizes its assets to generate profit and value for its shareholders. The profitability module also shows relationships between Meta Data's most relevant fundamental drivers. It provides multiple suggestions of what could affect the performance of Meta Data over time as well as its relative position and ranking within its peers.
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Meta Data Shares Owned By Institutions vs. Total Debt Fundamental Analysis
Comparative valuation techniques use various fundamental indicators to help in determining Meta Data's current stock value. Our valuation model uses many indicators to compare Meta Data value to that of its competitors to determine the firm's financial worth. Meta Data is rated below average in total debt category among its peers. It is rated below average in shares owned by institutions category among its peers . The ratio of Total Debt to Shares Owned By Institutions for Meta Data is about 515,905,000 . Comparative valuation analysis is a catch-all technique that is used if you cannot value Meta Data by discounting back its dividends or cash flows. It compares the stock's price multiples to nearest competition to determine if the stock is relatively undervalued or overvalued.Meta Total Debt vs. Competition
Meta Data is rated below average in total debt category among its peers. Total debt of Consumer Discretionary industry is presently estimated at about 7.46 Billion. Meta Data claims roughly 103.18 Million in total debt contributing just under 2% to stocks in Consumer Discretionary industry.
Meta Shares Owned By Institutions vs. Total Debt
Total Debt refers to the amount of long term interest-bearing liabilities that a company carries on its balance sheet. That may include bonds sold to the public, notes written to banks or capital leases. Typically, debt can help a company magnify its earnings, but the burden of interest and principal payments will eventually prevent the firm from borrow excessively.
Meta Data |
| = | 103.18 M |
In most industries, total debt may also include the current portion of long-term debt. Since debt terms vary widely from one company to another, simply comparing outstanding debt obligations between different companies may not be adequate. It is usually meant to compare total debt amounts between companies that operate within the same sector.
Shares Owned by Institutions show the percentage of the outstanding shares of stock issued by a company that is currently owned by other institutions such as asset management firms, hedge funds, or investment banks. Many investors like investing in companies with a large percentage of the firm owned by institutions because they believe that larger firms such as banks, pension funds, and mutual funds, will invest when they think that good things are going to happen.
Meta Data |
| = | 0.20 % |
Since Institution investors conduct a lot of independent research they tend to be more involved and usually more knowledgeable about entities they invest as compared to amateur investors.
Meta Shares Owned By Institutions Comparison
Meta Data is currently under evaluation in shares owned by institutions category among its peers.
Meta Data Profitability Projections
The most important aspect of a successful company is its ability to generate a profit. For investors in Meta Data, profitability is also one of the essential criteria for including it into their portfolios because, without profit, Meta Data will eventually generate negative long term returns. The profitability progress is the general direction of Meta Data's change in net profit over the period of time. It can combine multiple indicators of Meta Data, where stable trends show no significant progress. An accelerating trend is seen as positive, while a decreasing one is unfavorable. A rising trend means that profits are rising, and operational efficiency may be rising as well. A decreasing trend is a sign of poor performance and may indicate upcoming losses.
Meta Data Limited provides tutoring services for the students of kindergarten and primary, middle, and high schools in the Peoples Republic of China. Meta Data Limited was founded in 2007 and is headquartered in Shanghai, the Peoples Republic of China. Meta Data operates under Education Training Services classification in the United States and is traded on New York Stock Exchange. It employs 13497 people.
Meta Profitability Driver Comparison
Profitability drivers are factors that can directly affect your investment outlook on Meta Data. Investors often realize that things won't turn out the way they predict. There are maybe way too many unforeseen events and contingencies during the holding period of Meta Data position where the market behavior may be hard to predict, tax policy changes, gold or oil price hikes, calamities change, and many others. The question is, are you prepared for these unexpected events? Although some of these situations are obviously beyond your control, you can still follow the important profit indicators to know where you should focus on when things like this occur. Below are some of the Meta Data's important profitability drivers and their relationship over time.
Use Meta Data in pair-trading
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 Meta Data 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 Meta Data will appreciate offsetting losses from the drop in the long position's value.Meta Data Pair Trading
Meta Data Pair Trading Analysis
The ability to find closely correlated positions to Meta Data could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Meta Data 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 Meta Data - 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 Meta Data to buy it.
The correlation of Meta Data 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 Meta Data moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Meta Data 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 Meta Data 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.Use Investing Themes to Complement your Meta Data position
In addition to having Meta Data in your portfolios, you can quickly add positions using our predefined set of ideas and optimize them against your very unique investing style. A single investing idea is a collection of funds, stocks, ETFs, or cryptocurrencies that are programmatically selected from a pull of investment themes. After you determine your investment opportunity, you can then find an optimal portfolio that will maximize potential returns on the chosen idea or minimize its exposure to market volatility.Did You Try This Idea?
Run Natural Foods Thematic Idea Now
Natural Foods
Companies producing natural foods including dairy products and different types of meets. The Natural Foods theme has 37 constituents at this time.
You can either use a buy-and-hold strategy to lock in the entire theme or actively trade it to take advantage of the short-term price volatility of individual constituents. Macroaxis can help you discover thousands of investment opportunities in different asset classes. In addition, you can partner with us for reliable portfolio optimization as you plan to utilize Natural Foods Theme or any other thematic opportunities.
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Check out Trending Equities to better understand how to build diversified portfolios. Also, note that the market value of any company could be closely tied with the direction of predictive economic indicators such as signals in gross domestic product. You can also try the Technical Analysis module to check basic technical indicators and analysis based on most latest market data.
Other Consideration for investing in Meta Stock
If you are still planning to invest in Meta Data check if it may still be traded through OTC markets such as Pink Sheets or OTC Bulletin Board. You may also purchase it directly from the company, but this is not always possible and may require contacting the company directly. Please note that delisted stocks are often considered to be more risky investments, as they are no longer subject to the same regulatory and reporting requirements as listed stocks. Therefore, it is essential to carefully research the Meta Data's history and understand the potential risks before investing.
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