Automatic Data Processing Net Income
| ADP Stock | USD 245.97 4.72 1.88% |
As of the 30th of January, Automatic Data shows the Standard Deviation of 1.32, mean deviation of 0.9217, and Risk Adjusted Performance of (0.08). 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 Total Revenue |
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Gross Profit | Profit Margin | Market Capitalization | Enterprise Value Revenue 4.8496 | Revenue |
| Last Reported | Projected for Next Year | ||
| Net Income | 4.7 B | 4.9 B | |
| Net Income Applicable To Common Shares | 3.9 B | 4.1 B | |
| Net Income From Continuing Ops | 4.7 B | 2.4 B | |
| Net Income Per Share | 9.02 | 9.47 | |
| Net Income Per E B T | 0.69 | 0.56 |
Automatic | Net Income | Build AI portfolio with Automatic Stock |
Evaluating Automatic Data's Net Income across multiple reporting periods reveals the company's ability to sustain growth and manage resources effectively. This longitudinal analysis highlights inflection points, cyclical patterns, and structural changes that short-term snapshots might miss, offering deeper insight into Automatic Data Processing's fundamental strength.
Latest Automatic Data's Net Income Growth Pattern
Below is the plot of the Net Income of Automatic Data Processing over the last few years. Net income is one of the most important fundamental items in finance. It plays a large role in Automatic Data Processing financial statement analysis. It represents the amount of money remaining after all of Automatic Data Processing operating expenses, interest, taxes and preferred stock dividends have been deducted from a company total revenue. It is Automatic Data's Net Income historical data analysis aims to capture in quantitative terms the overall pattern of either growth or decline in Automatic Data's overall financial position and show how it may be relating to other accounts over time.
| View | Last Reported 4.08 B | 10 Years Trend |
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Net Income |
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Automatic Net Income Regression Statistics
| Arithmetic Mean | 2,463,746,632 | |
| Geometric Mean | 1,968,333,098 | |
| Coefficient Of Variation | 54.10 | |
| Mean Deviation | 1,078,296,434 | |
| Median | 2,292,800,000 | |
| Standard Deviation | 1,332,830,191 | |
| Sample Variance | 1776436.3T | |
| Range | 4.8B | |
| R-Value | 0.96 | |
| Mean Square Error | 138031.9T | |
| R-Squared | 0.93 | |
| Slope | 254,144,576 | |
| Total Sum of Squares | 28422981.1T |
Automatic Net Income History
Other Fundumenentals of Automatic Data Processing
| Net Income Applicable To Common Shares | ||
| Net Income From Continuing Ops | ||
| Net Income Per Share | ||
| Net Income Per E B T |
Automatic Data Net Income component correlations
Automatic Net Income 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 Net Income. 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.
Click cells to compare fundamentals
Can Human Resource & Employment Services industry sustain growth momentum? Does Automatic have expansion opportunities? 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. Determining accurate worth demands scrutiny of both present operating results and projected expansion capacity. Evaluating Automatic Data demands reviewing these metrics collectively while recognizing certain factors exert disproportionate influence.
Quarterly Earnings Growth 0.115 | Dividend Share 3.08 | Earnings Share 10.4 | Revenue Per Share | Quarterly Revenue Growth 0.062 |
Understanding Automatic Data Processing requires distinguishing between market price and book value, where the latter reflects Automatic's accounting equity. The concept of intrinsic value—what Automatic Data's is actually worth based on fundamentals—guides informed investors toward better entry and exit points. Market participants employ diverse analytical approaches to determine fair value and identify buying opportunities when prices dip below calculated worth. Market sentiment, economic cycles, and investor behavior can push Automatic Data's price substantially above or below its fundamental value.
Understanding that Automatic Data's value differs from its trading price is crucial, as each reflects different aspects of the company. Evaluating whether Automatic Data represents a sound investment requires analyzing earnings trends, revenue growth, technical signals, industry dynamics, and expert forecasts. In contrast, Automatic Data's trading price reflects the actual exchange value where willing buyers and sellers reach mutual agreement.
Automatic Data 'What if' Analysis
In the world of financial modeling, what-if analysis is part of sensitivity analysis performed to test how changes in assumptions impact individual outputs in a model. When applied to Automatic Data's stock what-if analysis refers to the analyzing how the change in your past investing horizon will affect the profitability against the current market value of Automatic Data.
| 11/01/2025 |
| 01/30/2026 |
If you would invest 0.00 in Automatic Data on November 1, 2025 and sell it all today you would earn a total of 0.00 from holding Automatic Data Processing or generate 0.0% return on investment in Automatic Data over 90 days. Automatic Data is related to or competes with Paychex, Parker Hannifin, Lockheed Martin, General Dynamics, Trane Technologies, 3M, and Northrop Grumman. Automatic Data Processing, Inc. provides cloud-based human capital management solutions worldwide More
Automatic Data Upside/Downside Indicators
Understanding different market momentum indicators often help investors to time their next move. Potential upside and downside technical ratios enable traders to measure Automatic Data's stock current market value against overall market sentiment and can be a good tool during both bulling and bearish trends. Here we outline some of the essential indicators to assess Automatic Data Processing upside and downside potential and time the market with a certain degree of confidence.
| Information Ratio | (0.17) | |||
| Maximum Drawdown | 6.58 | |||
| Value At Risk | (2.05) | |||
| Potential Upside | 1.69 |
Automatic Data Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Automatic Data's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Automatic Data's standard deviation. In reality, there are many statistical measures that can use Automatic Data historical prices to predict the future Automatic Data's volatility.| Risk Adjusted Performance | (0.08) | |||
| Jensen Alpha | (0.17) | |||
| Total Risk Alpha | (0.26) | |||
| Treynor Ratio | 12.96 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Automatic Data's price to converge to an average value over time is called mean reversion. However, historically, high market prices usually discourage investors that believe in mean reversion to invest, while low prices are viewed as an opportunity to buy.
Automatic Data January 30, 2026 Technical Indicators
| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
| Risk Adjusted Performance | (0.08) | |||
| Market Risk Adjusted Performance | 12.97 | |||
| Mean Deviation | 0.9217 | |||
| Coefficient Of Variation | (841.29) | |||
| Standard Deviation | 1.32 | |||
| Variance | 1.75 | |||
| Information Ratio | (0.17) | |||
| Jensen Alpha | (0.17) | |||
| Total Risk Alpha | (0.26) | |||
| Treynor Ratio | 12.96 | |||
| Maximum Drawdown | 6.58 | |||
| Value At Risk | (2.05) | |||
| Potential Upside | 1.69 | |||
| Skewness | (1.71) | |||
| Kurtosis | 7.55 |
Automatic Data Processing Backtested Returns
Automatic Data Processing secures Sharpe Ratio (or Efficiency) of -0.0509, which signifies that the company had a -0.0509 % return per unit of risk over the last 3 months. Automatic Data Processing exposes twenty-four different technical indicators, which can help you to evaluate volatility embedded in its price movement. Please confirm Automatic Data's Mean Deviation of 0.9217, risk adjusted performance of (0.08), and Standard Deviation of 1.32 to double-check the risk estimate we provide. The firm shows a Beta (market volatility) of -0.0129, which signifies not very significant fluctuations relative to the market. As returns on the market increase, returns on owning Automatic Data are expected to decrease at a much lower rate. During the bear market, Automatic Data is likely to outperform the market. At this point, Automatic Data Processing has a negative expected return of -0.0564%. Please make sure to confirm Automatic Data's skewness, as well as the relationship between the rate of daily change and price action indicator , to decide if Automatic Data Processing performance from the past will be repeated at some point in the near future.
Auto-correlation | -0.51 |
Good reverse predictability
Automatic Data Processing has good reverse predictability. Overlapping area represents the amount of predictability between Automatic Data time series from 1st of November 2025 to 16th of December 2025 and 16th of December 2025 to 30th of January 2026. The more autocorrelation exist between current time interval and its lagged values, the more accurately you can make projection about the future pattern of Automatic Data Processing price movement. The serial correlation of -0.51 indicates that about 51.0% of current Automatic Data price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.51 | |
| Spearman Rank Test | -0.16 | |
| Residual Average | 0.0 | |
| Price Variance | 21.41 |
Because income is reported on the Income Statement of a company and is measured in dollars some investors prefer to use Profit Margin, which measures income as a percentage of sales.
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Automatic Accumulated Other Comprehensive Income
Accumulated Other Comprehensive Income |
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Based on the recorded statements, Automatic Data Processing reported net income of 4.08 B. This is much higher than that of the Professional Services sector and significantly higher than that of the Industrials industry. The net income for all United States stocks is significantly lower than that of the firm.
Automatic Net Income 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 Net Income 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 net income 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 | Laurel Wealth Advisors, Llc | 2025-06-30 | 4.7 M | Wellington Management Company Llp | 2025-06-30 | 4.6 M | Amvescap Plc. | 2025-06-30 | 4.6 M | Fundsmith Llp | 2025-06-30 | 4.4 M | Ubs Asset Mgmt Americas Inc | 2025-06-30 | 4.2 M | Ameriprise Financial Inc | 2025-06-30 | 4.2 M | State Farm Mutual Automobile Ins Co | 2025-06-30 | 3.7 M | Amundi | 2025-06-30 | 3.1 M | Goldman Sachs Group Inc | 2025-06-30 | 2.9 M | Vanguard Group Inc | 2025-06-30 | 41.3 M | Blackrock Inc | 2025-06-30 | 35.5 M |
Automatic Fundamentals
| Return On Equity | 0.74 | |||
| Return On Asset | 0.0471 | |||
| Profit Margin | 0.20 % | |||
| Operating Margin | 0.26 % | |||
| Current Valuation | 102.88 B | |||
| Shares Outstanding | 403 M | |||
| Shares Owned By Insiders | 0.15 % | |||
| Shares Owned By Institutions | 86.80 % | |||
| Number Of Shares Shorted | 7.38 M | |||
| Price To Earning | 35.60 X | |||
| Price To Book | 15.80 X | |||
| Price To Sales | 4.69 X | |||
| Revenue | 20.56 B | |||
| Gross Profit | 10.26 B | |||
| EBITDA | 6.24 B | |||
| Net Income | 4.08 B | |||
| Cash And Equivalents | 1.23 B | |||
| Cash Per Share | 2.97 X | |||
| Total Debt | 9.07 B | |||
| Debt To Equity | 1.40 % | |||
| Current Ratio | 0.97 X | |||
| Book Value Per Share | 15.86 X | |||
| Cash Flow From Operations | 4.94 B | |||
| Short Ratio | 3.31 X | |||
| Earnings Per Share | 10.40 X | |||
| Price To Earnings To Growth | 2.99 X | |||
| Target Price | 280.71 | |||
| Number Of Employees | 67 K | |||
| Beta | 0.86 | |||
| Market Capitalization | 99.48 B | |||
| Total Asset | 53.37 B | |||
| Retained Earnings | 25.24 B | |||
| Working Capital | 1.97 B | |||
| Current Asset | 3.68 B | |||
| Current Liabilities | 2 B | |||
| Annual Yield | 0.03 % | |||
| Five Year Return | 1.95 % | |||
| Net Asset | 53.37 B | |||
| Last Dividend Paid | 3.08 |
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
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.