Automatic Net Debt from 2010 to 2026

ADP Stock  EUR 197.60  1.66  0.85%   
Automatic Data's Net Debt is increasing over the last several years with slightly volatile swings. Net Debt is estimated to finish at about 6.5 B this year. Net Debt is the total debt of Automatic Data Processing minus its cash and cash equivalents. It represents the actual debt burden on the company after accounting for the liquid assets it holds. View All Fundamentals
 
Net Debt  
First Reported
2020-06-30
Previous Quarter
6.7 B
Current Value
1.6 B
Quarterly Volatility
1.9 B
 
Covid
 
Interest Hikes
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 Interest Expense of 550.5 M, Selling General Administrative of 3.4 B or Total Revenue of 16.1 B, as well as many indicators such as . Automatic financial statements analysis is a perfect complement when working with Automatic Data Valuation or Volatility modules.
  
This module can also supplement various Automatic Data Technical models . Check out the analysis of Automatic Data Correlation against competitors.
The Net Debt trend for Automatic Data Processing offers valuable insights into the company's financial trajectory and strategic direction. By examining multi-year patterns, investors can identify whether Automatic Data is strengthening or weakening its position, and how this metric correlates with broader market conditions and industry benchmarks.

Latest Automatic Data's Net Debt Growth Pattern

Below is the plot of the Net Debt of Automatic Data Processing over the last few years. It is the total debt of a company minus its cash and cash equivalents. It represents the actual debt burden on the company after accounting for the liquid assets it holds. Automatic Data's Net Debt 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.
Net Debt10 Years Trend
Slightly volatile
   Net Debt   
       Timeline  

Automatic Net Debt Regression Statistics

Arithmetic Mean1,303,371,353
Geometric Mean234,823,068
Coefficient Of Variation177.15
Mean Deviation1,716,720,893
Median53,000,000
Standard Deviation2,308,930,318
Sample Variance5331159.2T
Range6.5B
R-Value0.74
Mean Square Error2576769.1T
R-Squared0.55
Significance0.0007
Slope338,128,539
Total Sum of Squares85298547.4T

Automatic Net Debt History

20266.5 B
20256.2 B
20245.4 B
2023463.3 M
2022905.5 M
20211.7 B
2020409.8 M

About Automatic Data Financial Statements

Automatic Data stakeholders use historical fundamental indicators, such as Automatic Data's Net Debt, 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. For example, changes in Automatic Data's assets and liabilities are reflected in the revenues and expenses on Automatic Data's income statement, which ultimately affect the company's gains or losses. Understanding these patterns can help in making the right long-term investment decisions in Automatic Data Processing. Please read more on our technical analysis and fundamental analysis pages.
Last ReportedProjected for Next Year
Net Debt6.2 B6.5 B

Currently Active Assets on Macroaxis

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.
You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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.