Automatic Data Processing Stock Total Debt
| ADP Stock | USD 304.67 0.48 0.16% |
Automatic Data Processing fundamentals help investors to digest information that contributes to Automatic Data's financial success or failures. It also enables traders to predict the movement of Automatic Stock. The fundamental analysis module provides a way to measure Automatic Data's intrinsic value by examining its available economic and financial indicators, including the cash flow records, the balance sheet account changes, the income statement patterns, and various microeconomic indicators and financial ratios related to Automatic Data stock.
As of 11/25/2024, Total Debt To Capitalization is likely to grow to 0.45. Automatic | Total Debt |
Automatic Data Processing Company Total Debt Analysis
Automatic Data's 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.
Current Automatic Data Total Debt | 3.8 B |
Most of Automatic Data's fundamental indicators, such as Total Debt, are part of a valuation analysis module that helps investors searching for stocks that are currently trading at higher or lower prices than their real value. If the real value is higher than the market price, Automatic Data Processing is considered to be undervalued, and we provide a buy recommendation. Otherwise, we render a sell signal.
Automatic Total Debt Driver Correlations
Understanding the fundamental principles of building solid financial models for Automatic Data is extremely important. It helps to project a fair market value of Automatic Stock properly, considering its historical fundamentals such as Total Debt. Since Automatic Data's main accounts across its financial reports are all linked and dependent on each other, it is essential to analyze all possible correlations between related accounts. However, instead of reviewing all of Automatic Data's historical financial statements, investors can examine the correlated drivers to determine its overall health. This can be effectively done using a conventional correlation matrix of Automatic Data's interrelated accounts and indicators.
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Automatic Total Debt Historical Pattern
Today, most investors in Automatic Data Stock are looking for potential investment opportunities by analyzing not only static indicators but also various Automatic Data's growth ratios. Consistent increases or drops in fundamental ratios usually indicate a possible pattern that can be successfully translated into profits. However, when comparing two companies, knowing each company's total debt growth rates may not be enough to decide which company is a better investment. That's why investors frequently use a static breakdown of Automatic Data total debt as a starting point in their analysis.
Automatic Data Total Debt |
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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.
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Automatic Short Long Term Debt Total
Short Long Term Debt Total |
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Based on the latest financial disclosure, Automatic Data Processing has a Total Debt of 3.8 B. This is 95.65% higher than that of the Professional Services sector and significantly higher than that of the Industrials industry. The total debt for all United States stocks is 28.56% higher than that of the company.
Automatic Total Debt Peer Comparison
Stock peer comparison is one of the most widely used and accepted methods of equity analyses. It analyses Automatic Data's direct or indirect competition against its Total Debt to detect undervalued stocks with similar characteristics or determine the stocks which would be a good addition to a portfolio. Peer analysis of Automatic Data could also be used in its relative valuation, which is a method of valuing Automatic Data by comparing valuation metrics of similar companies.Automatic Data is currently under evaluation in total debt category among its peers.
Automatic Data ESG Sustainability
Some studies have found that companies with high sustainability scores are getting higher valuations than competitors with lower social-engagement activities. While most ESG disclosures are voluntary and do not directly affect the long term financial condition, Automatic Data's sustainability indicators can be used to identify proper investment strategies using environmental, social, and governance scores that are crucial to Automatic Data's managers, analysts, and investors.Environmental | Governance | Social |
Automatic Data Institutional Holders
Institutional Holdings refers to the ownership stake in Automatic Data that is held by large financial organizations, pension funds or endowments. Institutions may purchase large blocks of Automatic Data's outstanding shares and can exert considerable influence upon its management. Institutional holders may also work to push the share price higher once they own the stock. Extensive social media coverage, TV shows, articles in high-profile magazines, and presentations at investor conferences help move the stock higher, increasing Automatic Data's value.| Shares | Northern Trust Corp | 2024-09-30 | 5.3 M | Capital Research Global Investors | 2024-09-30 | 4.8 M | Ameriprise Financial Inc | 2024-06-30 | 4.1 M | Amvescap Plc. | 2024-06-30 | 4 M | State Farm Mutual Automobile Ins Co | 2024-09-30 | 3.7 M | Legal & General Group Plc | 2024-06-30 | 3.5 M | Ubs Asset Mgmt Americas Inc | 2024-09-30 | 3.2 M | Deutsche Bank Ag | 2024-06-30 | 2.9 M | Ninety One Uk Limited | 2024-09-30 | 2.6 M | Vanguard Group Inc | 2024-09-30 | 40.5 M | Blackrock Inc | 2024-06-30 | 34.1 M |
Automatic Fundamentals
| Return On Equity | 0.87 | ||||
| Return On Asset | 0.0654 | ||||
| Profit Margin | 0.20 % | ||||
| Operating Margin | 0.26 % | ||||
| Current Valuation | 126.34 B | ||||
| Shares Outstanding | 407.46 M | ||||
| Shares Owned By Insiders | 0.12 % | ||||
| Shares Owned By Institutions | 84.43 % | ||||
| Number Of Shares Shorted | 6.51 M | ||||
| Price To Earning | 35.60 X | ||||
| Price To Book | 23.25 X | ||||
| Price To Sales | 6.37 X | ||||
| Revenue | 19.2 B | ||||
| Gross Profit | 8.51 B | ||||
| EBITDA | 5.8 B | ||||
| Net Income | 3.75 B | ||||
| Cash And Equivalents | 1.23 B | ||||
| Cash Per Share | 2.97 X | ||||
| Total Debt | 3.8 B | ||||
| Debt To Equity | 1.40 % | ||||
| Current Ratio | 0.97 X | ||||
| Book Value Per Share | 13.12 X | ||||
| Cash Flow From Operations | 4.16 B | ||||
| Short Ratio | 4.24 X | ||||
| Earnings Per Share | 9.37 X | ||||
| Price To Earnings To Growth | 2.69 X | ||||
| Target Price | 298.67 | ||||
| Number Of Employees | 64 K | ||||
| Beta | 0.8 | ||||
| Market Capitalization | 124.34 B | ||||
| Total Asset | 54.36 B | ||||
| Retained Earnings | 23.62 B | ||||
| Working Capital | 462.5 M | ||||
| Current Asset | 3.68 B | ||||
| Current Liabilities | 2 B | ||||
| Annual Yield | 0.02 % | ||||
| Five Year Return | 1.95 % | ||||
| Net Asset | 54.36 B | ||||
| Last Dividend Paid | 5.6 |
About Automatic Data Fundamental Analysis
The Macroaxis Fundamental Analysis modules help investors analyze Automatic Data Processing's financials across various querterly and yearly statements, indicators and fundamental ratios. We help investors to determine the real value of Automatic Data using virtually all public information available. We use both quantitative as well as qualitative analysis to arrive at the intrinsic value of Automatic Data Processing 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 Automatic Data
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 Automatic 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 Automatic Data will appreciate offsetting losses from the drop in the long position's value.Moving together with Automatic Stock
Moving against Automatic Stock
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| 0.49 | AP | Ampco Pittsburgh | PairCorr |
| 0.48 | MMS | Maximus | PairCorr |
The ability to find closely correlated positions to Automatic 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 Automatic 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 Automatic 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 Automatic Data Processing to buy it.
The correlation of Automatic 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 Automatic Data moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Automatic Data Processing 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 Automatic 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.Additional Tools for Automatic Stock Analysis
When running Automatic Data's price analysis, check to measure Automatic 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 Automatic Data is operating at the current time. Most of Automatic Data's value examination focuses on studying past and present price action to predict the probability of Automatic 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 Automatic Data's price. Additionally, you may evaluate how the addition of Automatic Data to your portfolios can decrease your overall portfolio volatility.