Paul Boland - Automatic Data Chief Officer
| ADP Stock | USD 304.67 0.48 0.16% |
Executive
Paul Boland is Chief Officer of Automatic Data Processing
| Age | 60 |
| Address | One ADP Boulevard, Roseland, NJ, United States, 07068 |
| Phone | 973 974 5000 |
| Web | https://www.adp.com |
Automatic Data Management Efficiency
The company has Return on Asset of 0.0654 % which means that on every $100 spent on assets, it made $0.0654 of profit. This is way below average. In the same way, it shows a return on shareholders' equity (ROE) of 0.8726 %, implying that it generated $0.8726 on every 100 dollars invested. Automatic Data's management efficiency ratios could be used to measure how well Automatic Data manages its routine affairs as well as how well it operates its assets and liabilities. As of 11/26/2024, Return On Tangible Assets is likely to grow to 0.1. Also, Return On Capital Employed is likely to grow to 0.56. At this time, Automatic Data's Total Current Liabilities is relatively stable compared to the past year. As of 11/26/2024, Liabilities And Stockholders Equity is likely to grow to about 57.1 B, while Non Current Liabilities Other is likely to drop slightly above 926.2 M.Similar Executives
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Management Performance
| Return On Equity | 0.87 | ||||
| Return On Asset | 0.0654 |
Automatic Data Processing Leadership Team
Elected by the shareholders, the Automatic Data's board of directors comprises two types of representatives: Automatic Data inside directors who are chosen from within the company, and outside directors, selected externally and held independent of Automatic. The board's role is to monitor Automatic Data's management team and ensure that shareholders' interests are well served. Automatic Data's inside directors are responsible for reviewing and approving budgets prepared by upper management to implement core corporate initiatives and projects. On the other hand, Automatic Data's outside directors are responsible for providing unbiased perspectives on the board's policies.
| M Heron, Managing Operations | ||
| Christian Greyenbuhl, Vice President Investor Relations | ||
| Jonathan Lehberger, Corporate Officer | ||
| David Kwon, Chief VP | ||
| Joseph DeSilva, President Sales | ||
| Don McGuire, Chief Officer | ||
| Max Li, Global Officer | ||
| Maria Black, President - Employer Services - TotalSource | ||
| Brock Albinson, Principal Accounting Officer and Corporate Controller | ||
| Allyce Hackmann, Vice Communications | ||
| Carlos Rodriguez, CEO and President and Director | ||
| Gus Blanchard, Chief Officer | ||
| Paul Boland, Chief Officer | ||
| Michael JD, Chief Officer | ||
| Michael Bonarti, VP, General Counsel and Secretary | ||
| Vipul Nagrath, Global Officer | ||
| Sreenivasa Kutam, President Innovation | ||
| David JD, Chief VP | ||
| Donald Weinstein, Chief Strategy Officer | ||
| John Ayala, Vice President - Client Experience and Continuous Improvement |
Automatic Stock Performance Indicators
The ability to make a profit is the ultimate goal of any investor. But to identify the right stock is not an easy task. Is Automatic Data a good investment? Although profit is still the single most important financial element of any organization, multiple performance indicators can help investors identify the equity that they will appreciate over time.
| 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 |
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
| 0.79 | WHLM | Wilhelmina | PairCorr |
| 0.69 | CVEO | Civeo Corp | PairCorr |
| 0.62 | MLKN | MillerKnoll | PairCorr |
| 0.51 | MMS | Maximus | PairCorr |
| 0.48 | AP | Ampco Pittsburgh | 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.