Automatic Data Processing Stock Analysis

Automatic Stock Analysis Notes

About 84.0% of the company shares are held by institutions such as insurance companies. The company has Price/Earnings To Growth (PEG) ratio of 2.69. Automatic Data Processing recorded earning per share (EPS) of 9.36. The entity last dividend was issued on the 13th of December 2024. The firm had 1139:1000 split on the 1st of October 2014. 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. For more info on Automatic Data Processing please contact Carlos Rodriguez at 973 974 5000 or go to https://www.adp.com.

Automatic Data Processing Investment Alerts

Automatic Data Processing Upcoming and Recent Events

Automatic Largest EPS Surprises

View All Earnings Estimates

Automatic Data Environmental, Social, and Governance (ESG) Scores

Automatic Stock Institutional Investors

Note, although Automatic Data's institutional investors appear to be way more sophisticated than retail investors, it remains unclear if professional active investment managers can reliably enhance risk-adjusted returns by an amount that exceeds fees and expenses.

Automatic Market Capitalization

The company currently falls under 'Mega-Cap' category with a total capitalization of 125.06 B.

Automatic Profitablity

The company has Net Profit Margin of 0.2 %, which implies that it may need a different competitive strategy as even a very small decline in it revenue may erase profits and result in a net loss. This is way below average. In the same way, it shows Net Operating Margin of 0.26 %, which entails that for every 100 dollars of revenue, it generated $0.26 of operating income.

Management Efficiency

Automatic Data Processing 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 11/30/2024, 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 11/30/2024, 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.

Technical Drivers

As of the 30th of November, Automatic Data shows the Downside Deviation of 0.7519, mean deviation of 0.6703, and Risk Adjusted Performance of 0.1508. Automatic Data Processing technical analysis gives you the methodology to make use of historical prices and volume patterns to determine a pattern that approximates the direction of the firm's future prices.

Automatic Data Processing Price Movement Analysis

Automatic Data Processing Insider Trading Activities

Automatic Data Outstanding Bonds

Automatic Data Predictive Daily Indicators

Automatic Data Corporate Filings

Automatic Data Forecast Models

Automatic Data's time-series forecasting models are one of many Automatic Data's stock analysis techniques aimed at predicting future share value based on previously observed values. Time-series forecasting models ae widely used for non-stationary data. Non-stationary data are called the data whose statistical properties e.g. the mean and standard deviation are not constant over time but instead, these metrics vary over time. These non-stationary Automatic Data's historical data is usually called time-series. Some empirical experimentation suggests that the statistical forecasting models outperform the models based exclusively on fundamental analysis to predict the direction of the market movement and maximize returns from investment trading.

Stock analysis is the technique used by a trader or investor to examine and evaluate how Automatic Data prices is reacting to, or reflecting on a current market direction and economic conditions. It can be used to make informed decisions about market timing, and when buying or selling Automatic shares will generate the highest return on investment. We also built our stock analysis module to help investors to gain an insight into the world economy as a whole, the stock market, thematic ideas. a specific sector, or an individual Stock such as Automatic Data. By using and applying Automatic Stock analysis, traders can create a robust methodology for identifying Automatic entry and exit points for their positions.

We track the performance of the top 100 financial experts across various large and mid-size financial boutiques. Automatic analyst recommendations are determined by taking all analyst recommendations and averaging them as Strong Buy, Buy, Hold, Strong Sell or Sell. There is no one specific way to measure analysis performance other than comparing it to the past results via a very sophisticated attribution analysis. Automatic analyst consensus and target price projections should be used in combination with other traditional techniques such as stock price forecasting, technical analysis, earnings estimate, and various momentum models.

Automatic Data Processing stock analysis indicators help investors evaluate how Automatic Data stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing and determine when trading Automatic Data shares will generate the highest return on investment. By understating and applying Automatic Data stock analysis, traders can identify Automatic Data position entry and exit signals to maximize returns.

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.