Douglas Politi - Automatic Data President - Added Value Services

ADP Stock  USD 248.33  3.26  1.33%   

President

Mr. Douglas Politi is President Added Value Services of Automatic Data Processing Inc. Prior to his appointment as President, Added Value Services in February 2013, he served as Senior Vice President, CFO Suite from October 2011 to January 2013, and as Senior Vice President, Retirement Services from September 2006 to September 2011. since 2013.
Age 54
Tenure 11 years
Address One ADP Boulevard, Roseland, NJ, United States, 07068
Phone973 974 5000
Webhttps://www.adp.com

Automatic Data Management Efficiency

The company has Return on Asset of 0.0532 % which means that on every $100 spent on assets, it made $0.0532 of profit. This is way below average. In the same way, it shows a return on shareholders' equity (ROE) of 0.9738 %, implying that it generated $0.9738 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 03/28/2024, Return On Capital Employed is likely to grow to 0.52. Also, Return On Assets is likely to grow to 0.08. At this time, Automatic Data's Total Current Liabilities is relatively stable compared to the past year. As of 03/28/2024, Liabilities And Stockholders Equity is likely to grow to about 61.5 B, while Non Current Liabilities Total is likely to drop slightly above 4.8 B.
The company has 3.34 B in debt with debt to equity (D/E) ratio of 1.4, which is OK given its current industry classification. Automatic Data Processing has a current ratio of 0.95, suggesting that it has not enough short term capital to pay financial commitments when the payables are due. Debt can assist Automatic Data until it has trouble settling it off, either with new capital or with free cash flow. So, Automatic Data's shareholders could walk away with nothing if the company can't fulfill its legal obligations to repay debt. However, a more frequent occurrence is when companies like Automatic Data Processing sell additional shares at bargain prices, diluting existing shareholders. Debt, in this case, can be an excellent and much better tool for Automatic to invest in growth at high rates of return. When we think about Automatic Data's use of debt, we should always consider it together with cash and equity.

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Automatic Data Processing, Inc. provides cloud-based human capital management solutions worldwide. The company was founded in 1949 and is headquartered in Roseland, New Jersey. Automatic Data operates under Staffing Employment Services classification in the United States and is traded on NASDAQ Exchange. It employs 60000 people. Automatic Data Processing (ADP) is traded on NASDAQ Exchange in USA. It is located in One ADP Boulevard, Roseland, NJ, United States, 07068 and employs 63,000 people. Automatic Data is listed under Human Resource & Employment Services category by Fama And French industry classification.

Management Performance

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.
David Kwon, Chief VP
Edward Flynn, Vice President - Employer Services, Sales and Marketing
Douglas Politi, President - Added Value Services
Max Li, Global Officer
Maria Black, President - Employer Services - TotalSource
Brock Albinson, Principal Accounting Officer and Corporate Controller
Dermot OBrien, Chief HR Officer
John Jones, Independent Director
Joseph Timko, Chief Strategy Officer
Richard Clark, Independent Director
William Ready, Director
Michael Gregoire, Director
Michael Eberhard, Vice President Treasurer
Michael Bonarti, VP, General Counsel and Secretary
Vipul Nagrath, Global Officer
Donald Weinstein, Chief Strategy Officer
Linda Gooden, Independent Director
Thomas Perrotti, President - Major Account Services and ADP Canada
Ellen Alemany, Independent Director
Christian Greyenbuhl, Vice President Investor Relations
Kathleen Winters, CFO, Corporate Vice President
Joseph DeSilva, President Sales
Scott Powers, Director
Robert Hubbard, Independent Director
Thomas Lynch, Director
Deborah Dyson, Vice President - Client Experience and Continuous Improvement
Francine Katsoudas, Director
Mark Benjamin, President of Global Enterprise Solutions
Don McGuire, Chief Officer
Peter Bisson, Director
Allyce Hackmann, Vice Communications
Eric Fast, Independent Director
Sandra Wijnberg, Director
Dorothy Wisniowski, Vice President Assistant Corporate Secretary
Carlos Rodriguez, CEO and President and Director
Gus Blanchard, Chief Officer
Jan Siegmund, CFO
John Ayala, Vice President - Client Experience and Continuous Improvement
Stuart Sackman, VP of Global Product and Technology
Glenn Hubbard, Independent Director

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.

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.

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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.
Pair CorrelationCorrelation Matching
When determining whether Automatic Data Processing is a good investment, qualitative aspects like company management, corporate governance, and ethical practices play a significant role. A comparison with peer companies also provides context and helps to understand if Automatic Stock is undervalued or overvalued. This multi-faceted approach, blending both quantitative and qualitative analysis, forms a solid foundation for making an informed investment decision about Automatic Data Processing Stock. Highlighted below are key reports to facilitate an investment decision about Automatic Data Processing Stock:
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 unemployment.
Note that the Automatic Data Processing information on this page should be used as a complementary analysis to other Automatic Data's statistical models used to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Technical Analysis module to check basic technical indicators and analysis based on most latest market data.

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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Is Automatic Data's industry expected to grow? Or is there an opportunity to expand the business' product line in the future? Factors like these will boost the valuation of Automatic Data. If investors know Automatic will grow in the future, the company's valuation will be higher. The financial industry is built on trying to define current growth potential and future valuation accurately. All the valuation information about Automatic Data listed above have to be considered, but the key to understanding future value is determining which factors weigh more heavily than others.
Quarterly Earnings Growth
0.092
Dividend Share
5.15
Earnings Share
8.6
Revenue Per Share
45.09
Quarterly Revenue Growth
0.063
The market value of Automatic Data Processing is measured differently than its book value, which is the value of Automatic that is recorded on the company's balance sheet. Investors also form their own opinion of Automatic Data's value that differs from its market value or its book value, called intrinsic value, which is Automatic Data's true underlying value. Investors use various methods to calculate intrinsic value and buy a stock when its market value falls below its intrinsic value. Because Automatic Data's market value can be influenced by many factors that don't directly affect Automatic Data's underlying business (such as a pandemic or basic market pessimism), market value can vary widely from intrinsic value.
Please note, there is a significant difference between Automatic Data's value and its price as these two are different measures arrived at by different means. Investors typically determine if Automatic Data is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Automatic Data's price is the amount at which it trades on the open market and represents the number that a seller and buyer find agreeable to each party.