Automatic Data Processing Stock Price To Book
| ADP Stock | EUR 227.05 3.40 1.48% |
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.
Automatic |
Automatic Data Processing Company Price To Book Analysis
Automatic Data's Price to Book (P/B) ratio is used to relate a company book value to its current market price. A high P/B ratio indicates that investors expect executives to generate more returns on their investments from a given set of assets. Book value is the accounting value of assets minus liabilities.
Current Automatic Data Price To Book | 41.97 X |
Most of Automatic Data's fundamental indicators, such as Price To Book, 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.
Price to Book ratio is mostly used in financial services industries where assets and liabilities are typically represented by dollars. Although low Price to Book ratio generally implies that the firm is undervalued, it is often a good indicator that the company may be in financial or managerial distress and should be investigated more carefully.
| Competition |
Based on the latest financial disclosure, Automatic Data Processing has a Price To Book of 41.9664 times. This is much higher than that of the Industrials sector and 64.87% lower than that of the Business Services industry. The price to book for all Germany stocks is notably lower than that of the firm.
Automatic Price To Book 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 Price To Book 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 price to book category among related companies.
Automatic Fundamentals
| Return On Equity | 0.78 | |||
| Return On Asset | 0.0458 | |||
| Profit Margin | 0.18 % | |||
| Operating Margin | 0.24 % | |||
| Current Valuation | 104.54 B | |||
| Shares Outstanding | 414.35 M | |||
| Shares Owned By Insiders | 0.11 % | |||
| Shares Owned By Institutions | 83.70 % | |||
| Price To Earning | 28.24 X | |||
| Price To Book | 41.97 X | |||
| Price To Sales | 5.97 X | |||
| Revenue | 16.05 B | |||
| Gross Profit | 7.45 B | |||
| EBITDA | 4.4 B | |||
| Net Income | 2.95 B | |||
| Cash And Equivalents | 1.95 B | |||
| Cash Per Share | 4.49 X | |||
| Total Debt | 2.99 B | |||
| Debt To Equity | 41.90 % | |||
| Current Ratio | 1.05 X | |||
| Book Value Per Share | 7.21 X | |||
| Cash Flow From Operations | 3.1 B | |||
| Earnings Per Share | 7.08 X | |||
| Price To Earnings To Growth | 2.95 X | |||
| Target Price | 210.81 | |||
| Number Of Employees | 60 K | |||
| Beta | 0.83 | |||
| Market Capitalization | 85.75 B | |||
| Total Asset | 63.07 B | |||
| Z Score | 16.8 | |||
| Annual Yield | 0.02 % | |||
| Five Year Return | 1.86 % | |||
| Net Asset | 63.07 B | |||
| Last Dividend Paid | 4.37 |
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
| 0.65 | PCX | Paychex | 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.Check out Trending Equities to better understand how to build diversified portfolios, which includes a position in Automatic Data Processing. Also, note that the market value of any company could be tightly coupled with the direction of predictive economic indicators such as signals in board of governors. You can also try the Price Exposure Probability module to analyze equity upside and downside potential for a given time horizon across multiple markets.
Complementary 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.
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