Data Patterns Total Debt vs. Market Capitalization
| DATAPATTNS | 2,584 12.80 0.50% |
For Data Patterns profitability analysis, we use financial ratios and fundamental drivers that measure the ability of Data Patterns to generate income relative to revenue, assets, operating costs, and current equity. These fundamental indicators attest to how well Data Patterns Limited utilizes its assets to generate profit and value for its shareholders. The profitability module also shows relationships between Data Patterns's most relevant fundamental drivers. It provides multiple suggestions of what could affect the performance of Data Patterns Limited over time as well as its relative position and ranking within its peers.
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Data Patterns Limited Market Capitalization vs. Total Debt Fundamental Analysis
Comparative valuation techniques use various fundamental indicators to help in determining Data Patterns's current stock value. Our valuation model uses many indicators to compare Data Patterns value to that of its competitors to determine the firm's financial worth. Data Patterns Limited is rated as one of the top companies in total debt category among its peers. It also is one of the top stocks in market capitalization category among its peers creating about 2,650 of Market Capitalization per Total Debt. The reason why the comparable model can be used in almost all circumstances is due to the vast number of multiples that can be utilized, such as the price-to-earnings (P/E), price-to-book (P/B), price-to-sales (P/S), price-to-cash flow (P/CF), and many others. The P/E ratio is the most commonly used of these ratios because it focuses on the Data Patterns' earnings, one of the primary drivers of an investment's value.Data Total Debt vs. Competition
Data Patterns Limited is rated as one of the top companies in total debt category among its peers. Total debt of Industrials industry is currently estimated at about 4.75 Billion. Data Patterns claims roughly 61.7 Million in total debt contributing just under 2% to equities under Industrials industry.
Data Market Capitalization 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.
Data Patterns |
| = | 61.7 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.
Market Capitalization is the total market value of a company's equity. It is one of many ways to value a company and is calculated by multiplying the price of the stock by the number of shares issued. If a firm has one type of stock its market capitalization will be the current market share price multiplied by the number of shares. However, if a company has multiple types of equities then the market cap will be the total of the market caps of the different types of shares.
Data Patterns |
| = | 163.49 B |
In most publications or references market cap is broken down into the mega-cap, large-cap, mid-cap, small-cap, micro-cap, and nano-cap. Market Cap is a measurement of business as total market value of all of the outstanding shares at a given time, and can be used to compare different companies based on their size.
Data Market Capitalization vs Competition
Data Patterns Limited is one of the top stocks in market capitalization category among its peers. Market capitalization of Industrials industry is currently estimated at about 506.12 Billion. Data Patterns totals roughly 163.49 Billion in market capitalization claiming about 32% of equities under Industrials industry.
Data Patterns Profitability Projections
The most important aspect of a successful company is its ability to generate a profit. For investors in Data Patterns, profitability is also one of the essential criteria for including it into their portfolios because, without profit, Data Patterns will eventually generate negative long term returns. The profitability progress is the general direction of Data Patterns' change in net profit over the period of time. It can combine multiple indicators of Data Patterns, 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.
| Last Reported | Projected for Next Year | ||
| Accumulated Other Comprehensive Income | 15 B | 15.7 B | |
| Net Interest Income | -120.8 M | -126.8 M | |
| Interest Income | 340.3 M | 357.3 M | |
| Operating Income | 3.2 B | 1.6 B | |
| Net Income From Continuing Ops | 2.2 B | 1.1 B | |
| Income Before Tax | 3 B | 1.5 B | |
| Total Other Income Expense Net | -228.5 M | -217.1 M | |
| Net Income Applicable To Common Shares | 1.1 B | 1.1 B | |
| Net Income | 2.2 B | 1.3 B | |
| Income Tax Expense | 735.3 M | 372.9 M | |
| Change To Netincome | 138.6 M | 138.1 M |
Data Profitability Driver Comparison
Profitability drivers are factors that can directly affect your investment outlook on Data Patterns. 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 Data Patterns 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 Data Patterns' important profitability drivers and their relationship over time.
Use Data Patterns 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 Data Patterns 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 Data Patterns will appreciate offsetting losses from the drop in the long position's value.Data Patterns Pair Trading
Data Patterns Limited Pair Trading Analysis
The ability to find closely correlated positions to Data Patterns could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Data Patterns 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 Data Patterns - 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 Data Patterns Limited to buy it.
The correlation of Data Patterns 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 Data Patterns moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Data Patterns Limited 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 Data Patterns 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 Data Patterns position
In addition to having Data Patterns 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?
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Chemicals Makers
Companies developing chemicals for crops, soil as well as human, and animals. The Chemicals Makers theme has 45 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 Chemicals Makers Theme or any other thematic opportunities.
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Additional Tools for Data Stock Analysis
When running Data Patterns' price analysis, check to measure Data Patterns' 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 Patterns is operating at the current time. Most of Data Patterns' value examination focuses on studying past and present price action to predict the probability of Data Patterns' future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Data Patterns' price. Additionally, you may evaluate how the addition of Data Patterns to your portfolios can decrease your overall portfolio volatility.