Automatic Accounts Payable from 2010 to 2024
| ADP Stock | USD 304.67 0.48 0.16% |
Accounts Payable | First Reported 1989-03-31 | Previous Quarter 100.6 M | Current Value 145.2 M | Quarterly Volatility 42.5 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 379.5 M or Selling General Administrative of 2 B, as well as many indicators such as Price To Sales Ratio of 5.29, Dividend Yield of 0.0154 or PTB Ratio of 22.34. Automatic financial statements analysis is a perfect complement when working with Automatic Data Valuation or Volatility modules.
Automatic | Accounts Payable |
Latest Automatic Data's Accounts Payable Growth Pattern
Below is the plot of the Accounts Payable of Automatic Data Processing over the last few years. An accounting item on the balance sheet that represents Automatic Data obligation to pay off a short-term debt to its creditors. The accounts payable entry is usually reported under current liabilities. If accounts payable of Automatic Data Processing are not paid within the agreed terms, the payables are considered to be in default, which may trigger a penalty or interest payment, or the revocation of additional credit from the supplier. Accounts payable may also be considered a source of cash, since they represent funds being borrowed from suppliers. Given these cash flow considerations, suppliers have a natural inclination to push for shorter payment terms, while creditors want to lengthen the payment terms. It is the amount a company owes to suppliers or vendors for products or services received but not yet paid for. It represents the company's short-term liabilities. Automatic Data's Accounts Payable 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.
| Accounts Payable | 10 Years Trend |
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Accounts Payable |
| Timeline |
Automatic Accounts Payable Regression Statistics
| Arithmetic Mean | 132,112,294 | |
| Geometric Mean | 125,368,280 | |
| Coefficient Of Variation | 28.55 | |
| Mean Deviation | 28,809,835 | |
| Median | 137,384,409 | |
| Standard Deviation | 37,724,010 | |
| Sample Variance | 1423.1T | |
| Range | 153M | |
| R-Value | (0.24) | |
| Mean Square Error | 1447.4T | |
| R-Squared | 0.06 | |
| Significance | 0.40 | |
| Slope | (1,988,961) | |
| Total Sum of Squares | 19923.4T |
Automatic Accounts Payable History
About Automatic Data Financial Statements
Automatic Data shareholders use historical fundamental indicators, such as Accounts Payable, 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 | ||
| Accounts Payable | 100.6 M | 137.4 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
| 0.81 | WHLM | Wilhelmina | PairCorr |
| 0.66 | MLKN | MillerKnoll | PairCorr |
| 0.65 | CVEO | Civeo Corp | PairCorr |
| 0.49 | AP | Ampco Pittsburgh | PairCorr |
| 0.48 | MMS | Maximus | 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.