Automatic Net Debt from 2010 to 2024

ADP Stock  USD 307.97  3.30  1.08%   
Automatic Data Net Debt yearly trend continues to be relatively stable with very little volatility. Net Debt is likely to grow to about 831.5 M 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
1986-06-30
Previous Quarter
885.2 M
Current Value
6.6 B
Quarterly Volatility
2.5 B
 
Black Monday
 
Oil Shock
 
Dot-com Bubble
 
Housing Crash
 
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 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.
  
Check out the analysis of Automatic Data Correlation against competitors.

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 Mean(177,213,667)
Geometric Mean708,206,462
Coefficient Of Variation(678.34)
Mean Deviation991,709,067
Median(167,600,000)
Standard Deviation1,202,105,350
Sample Variance1445057.3T
Range3.9B
R-Value0.82
Mean Square Error511235.5T
R-Squared0.67
Significance0.0002
Slope220,265,589
Total Sum of Squares20230801.8T

Automatic Net Debt History

2024831.5 M
2023791.9 M
20221.3 B
20212.1 B
2020753 M
2019440.5 M
201853 M

Other Fundumenentals of Automatic Data Processing

Automatic Data Net Debt component correlations

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0.23-0.410.310.20.840.130.190.060.20.70.30.03-0.23-0.130.14-0.320.38-0.15-0.070.210.0-0.20.250.270.05
0.61-0.410.60.71-0.160.660.590.190.78-0.64-0.320.320.81-0.730.53-0.44-0.770.830.61-0.59-0.650.780.55-0.85-0.32
0.950.310.60.980.460.930.920.30.90.1-0.10.580.77-0.950.48-0.87-0.690.850.4-0.6-0.660.730.6-0.72-0.52
0.940.20.710.980.370.940.910.280.92-0.04-0.070.620.81-0.950.53-0.8-0.750.910.47-0.61-0.690.780.64-0.79-0.57
0.410.84-0.160.460.370.340.370.250.420.560.270.07-0.05-0.330.26-0.4-0.020.030.070.2-0.02-0.010.08-0.090.05
0.990.130.660.930.940.340.990.180.840.07-0.180.480.84-0.940.6-0.87-0.760.810.51-0.64-0.650.820.46-0.74-0.42
1.00.190.590.920.910.370.990.140.810.16-0.170.450.81-0.920.61-0.89-0.710.760.49-0.63-0.610.790.43-0.68-0.4
0.180.060.190.30.280.250.180.140.33-0.160.38-0.030.14-0.33-0.18-0.16-0.230.29-0.24-0.09-0.170.15-0.01-0.460.04
0.850.20.780.90.920.420.840.810.33-0.19-0.170.420.75-0.940.55-0.77-0.690.830.56-0.57-0.70.760.66-0.8-0.3
0.140.7-0.640.1-0.040.560.070.16-0.16-0.190.17-0.12-0.280.040.08-0.290.33-0.35-0.140.140.11-0.3-0.230.440.07
-0.150.3-0.32-0.1-0.070.27-0.18-0.170.38-0.170.170.09-0.30.22-0.040.280.24-0.15-0.290.250.28-0.29-0.120.16-0.06
0.470.030.320.580.620.070.480.45-0.030.42-0.120.090.46-0.420.06-0.29-0.550.71-0.06-0.28-0.290.430.49-0.44-0.84
0.8-0.230.810.770.81-0.050.840.810.140.75-0.28-0.30.46-0.850.56-0.69-0.790.850.61-0.75-0.580.970.53-0.71-0.44
-0.93-0.13-0.73-0.95-0.95-0.33-0.94-0.92-0.33-0.940.040.22-0.42-0.85-0.540.910.74-0.85-0.540.720.72-0.85-0.560.790.34
0.60.140.530.480.530.260.60.61-0.180.550.08-0.040.060.56-0.54-0.46-0.330.380.74-0.33-0.450.560.44-0.26-0.05
-0.89-0.32-0.44-0.87-0.8-0.4-0.87-0.89-0.16-0.77-0.290.28-0.29-0.690.91-0.460.53-0.61-0.480.70.64-0.7-0.410.530.18
-0.70.38-0.77-0.69-0.75-0.02-0.76-0.71-0.23-0.690.330.24-0.55-0.790.74-0.330.53-0.84-0.40.540.45-0.76-0.190.90.52
0.78-0.150.830.850.910.030.810.760.290.83-0.35-0.150.710.85-0.850.38-0.61-0.840.36-0.64-0.680.80.65-0.86-0.69
0.49-0.070.610.40.470.070.510.49-0.240.56-0.14-0.29-0.060.61-0.540.74-0.48-0.40.36-0.51-0.460.60.39-0.360.04
-0.610.21-0.59-0.6-0.610.2-0.64-0.63-0.09-0.570.140.25-0.28-0.750.72-0.330.70.54-0.64-0.510.62-0.74-0.440.520.24
-0.630.0-0.65-0.66-0.69-0.02-0.65-0.61-0.17-0.70.110.28-0.29-0.580.72-0.450.640.45-0.68-0.460.62-0.59-0.640.60.22
0.78-0.20.780.730.78-0.010.820.790.150.76-0.3-0.290.430.97-0.850.56-0.7-0.760.80.6-0.74-0.590.51-0.68-0.28
0.480.250.550.60.640.080.460.43-0.010.66-0.23-0.120.490.53-0.560.44-0.41-0.190.650.39-0.44-0.640.51-0.32-0.44
-0.70.27-0.85-0.72-0.79-0.09-0.74-0.68-0.46-0.80.440.16-0.44-0.710.79-0.260.530.9-0.86-0.360.520.6-0.68-0.320.41
-0.420.05-0.32-0.52-0.570.05-0.42-0.40.04-0.30.07-0.06-0.84-0.440.34-0.050.180.52-0.690.040.240.22-0.28-0.440.41
Click cells to compare fundamentals

About Automatic Data Financial Statements

Automatic Data shareholders use historical fundamental indicators, such as 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. 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 ReportedProjected for Next Year
Net Debt791.9 M831.5 M
Net Debt To EBITDA 0.14  0.14 

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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Moving against 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

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.