Automatic Operating Income from 2010 to 2025
| ADP Stock | USD 298.24 0.65 0.22% |
Operating Income | First Reported 1985-09-30 | Previous Quarter 1.3 B | Current Value -70.2 M | Quarterly Volatility 322.9 M |
Check Automatic Data financial statements over time to gain insight into future company performance. You can evaluate financial statements to find patterns among Automatic Data's main balance sheet or income statement drivers, such as Depreciation And Amortization of 323.3 M, Interest Expense of 436.4 M or Selling General Administrative of 4.5 B, as well as many indicators such as Price To Sales Ratio of 2.43, Dividend Yield of 0.0154 or PTB Ratio of 20.11. Automatic financial statements analysis is a perfect complement when working with Automatic Data Valuation or Volatility modules.
Automatic | Operating Income |
Latest Automatic Data's Operating Income Growth Pattern
Below is the plot of the Operating Income of Automatic Data Processing over the last few years. Operating Income is the amount of profit realized from Automatic Data Processing operations after accounting for operating expenses such as cost of goods sold (COGS), wages and depreciation. Operating income takes the gross income and subtracts other operating expenses and then removes depreciation. Operating Income of Automatic Data Processing is typically a synonym for earnings before interest and taxes (EBIT) and is also commonly referred to as operating profit or recurring profit. It is earnings before interest and taxes (EBIT), representing the amount of profit a company generates from its operations. Automatic Data's Operating 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.
| Operating Income | 10 Years Trend |
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Operating Income |
| Timeline |
Automatic Operating Income Regression Statistics
| Arithmetic Mean | 2,958,284,312 | |
| Geometric Mean | 2,375,920,262 | |
| Coefficient Of Variation | 56.73 | |
| Mean Deviation | 1,388,659,312 | |
| Median | 3,128,700,000 | |
| Standard Deviation | 1,678,225,657 | |
| Sample Variance | 2816441.4T | |
| Range | 5.8B | |
| R-Value | 0.96 | |
| Mean Square Error | 207634.8T | |
| R-Squared | 0.93 | |
| Slope | 340,154,522 | |
| Total Sum of Squares | 42246620.3T |
Automatic Operating Income History
Other Fundumenentals of Automatic Data Processing
Automatic Data Operating Income component correlations
Click cells to compare fundamentals
About Automatic Data Financial Statements
Automatic Data shareholders use historical fundamental indicators, such as Operating Income, to determine how well the company is positioned to perform in the future. Although Automatic Data investors may analyze each financial statement separately, they are all interrelated. The changes in Automatic Data's assets and liabilities, for example, are also reflected in the revenues and expenses on on Automatic Data's income statement. Understanding these patterns can help investors time the market effectively. Please read more on our fundamental analysis page.
| Last Reported | Projected for Next Year | ||
| Operating Income | 5.7 B | 6 B | |
| Non Operating Income Net Other | 165.2 M | 173.4 M |
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
| 0.67 | KBR | KBR Inc | PairCorr |
| 0.66 | TTEK | Tetra Tech | PairCorr |
| 0.63 | NL | NL Industries | PairCorr |
| 0.49 | DLX | Deluxe | PairCorr |
| 0.48 | BW | Babcock Wilcox Enter | 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.