Automatic Change Receivables from 2010 to 2024

ADP Stock  USD 245.12  2.38  0.98%   
Automatic Data Change Receivables yearly trend continues to be relatively stable with very little volatility. Change Receivables is likely to grow to about 122.1 M this year. Change Receivables is the difference in the amount of accounts receivable from one accounting period to the next. A positive number indicates an increase in receivables, while a negative number indicates a decrease. View All Fundamentals
 
Change Receivables  
First Reported
2009-09-30
Previous Quarter
81.2 M
Current Value
49.1 M
Quarterly Volatility
151.6 M
 
Credit Downgrade
 
Yuan Drop
 
Covid
Check Automatic Data financial statements over time to gain insight into future company performance. You can evaluate financial statements to find patterns among Automatic main balance sheet or income statement drivers, such as Depreciation And Amortization of 663.3 M, Interest Expense of 305.9 M or Selling General Administrative of 4.3 B, as well as many exotic indicators such as Price To Sales Ratio of 2.41, Dividend Yield of 0.0153 or PTB Ratio of 24.49. Automatic financial statements analysis is a perfect complement when working with Automatic Data Valuation or Volatility modules.
  
This module can also supplement Automatic Data's financial leverage analysis and stock options assessment as well as various Automatic Data Technical models . Check out the analysis of Automatic Data Correlation against competitors.

Latest Automatic Data's Change Receivables Growth Pattern

Below is the plot of the Change Receivables of Automatic Data Processing over the last few years. It is the difference in the amount of accounts receivable from one accounting period to the next. A positive number indicates an increase in receivables, while a negative number indicates a decrease. Automatic Data's Change Receivables 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.
Change Receivables10 Years Trend
Pretty Stable
   Change Receivables   
       Timeline  

Automatic Change Receivables Regression Statistics

Arithmetic Mean(137,181,733)
Geometric Mean144,495,633
Coefficient Of Variation(150.09)
Mean Deviation170,310,382
Median(113,800,000)
Standard Deviation205,889,900
Sample Variance42390.7T
Range615.7M
R-Value0.13
Mean Square Error44852.2T
R-Squared0.02
Significance0.64
Slope6,091,564
Total Sum of Squares593469.1T

Automatic Change Receivables History

2024122.1 M
2023116.3 M
2022129.2 M
2021-486.5 M
2020-339.8 M
2019-113.8 M
2018-473.9 M

About Automatic Data Financial Statements

There are typically three primary documents that fall into the category of financial statements. These documents include Automatic Data income statement, its balance sheet, and the statement of cash flows. Automatic Data investors use historical funamental indicators, such as Automatic Data's Change Receivables, to determine how well the company is positioned to perform in the future. Although Automatic Data investors may use each financial statement separately, they are all related. The changes in Automatic Data's assets and liabilities, for example, are also reflected in the revenues and expenses that we see on Automatic Data's income statement, which results in the company's gains or losses. Cash flows can provide more information regarding cash listed on a balance sheet, but not equivalent to net income shown on the income statement. We offer a historical overview of the basic patterns found on Automatic Data Financial Statements. Understanding these patterns can help to make the right decision on long term investment in Automatic Data. Please read more on our technical analysis and fundamental analysis pages.
Last ReportedProjected for Next Year
Change Receivables116.3 M122.1 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

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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 the analysis of Automatic Data Correlation against competitors.
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.

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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
0.147
Dividend Share
5.3
Earnings Share
8.96
Revenue Per Share
45.972
Quarterly Revenue Growth
0.066
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.