Automatic Net Profit Margin vs Pretax Profit Margin Analysis

ADP Stock  USD 302.23  4.67  1.57%   
Automatic Data financial indicator trend analysis is way more than just evaluating Automatic Data Processing prevailing accounting drivers to predict future trends. We encourage investors to analyze account correlations over time for multiple indicators to determine whether Automatic Data Processing is a good investment. Please check the relationship between Automatic Data Net Profit Margin and its Pretax Profit Margin accounts. Check out Trending Equities to better understand how to build diversified portfolios, which includes a position in Automatic Data Processing. Also, note that the market value of any company could be closely tied with the direction of predictive economic indicators such as signals in unemployment.

Net Profit Margin vs Pretax Profit Margin

Net Profit Margin vs Pretax Profit Margin Correlation Analysis

The overlapping area represents the amount of trend that can be explained by analyzing historical patterns of Automatic Data Processing Net Profit Margin account and Pretax Profit Margin. At this time, the significance of the direction appears to have almost identical trend.
The correlation between Automatic Data's Net Profit Margin and Pretax Profit Margin is 0.96. Overlapping area represents the amount of variation of Net Profit Margin that can explain the historical movement of Pretax Profit Margin in the same time period over historical financial statements of Automatic Data Processing, assuming nothing else is changed. The correlation between historical values of Automatic Data's Net Profit Margin and Pretax Profit Margin is a relative statistical measure of the degree to which these accounts tend to move together. The correlation coefficient measures the extent to which Net Profit Margin of Automatic Data Processing are associated (or correlated) with its Pretax Profit Margin. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when Pretax Profit Margin has no effect on the direction of Net Profit Margin i.e., Automatic Data's Net Profit Margin and Pretax Profit Margin go up and down completely randomly.

Correlation Coefficient

0.96
Relationship DirectionPositive 
Relationship StrengthVery Strong

Net Profit Margin

The percentage of revenue left after all expenses have been deducted from sales. The measure is calculated by dividing net profit by revenue.

Pretax Profit Margin

Most indicators from Automatic Data's fundamental ratios are interrelated and interconnected. However, analyzing fundamental ratios indicators one by one will only give a small insight into Automatic Data Processing current financial condition. On the other hand, looking into the entire matrix of fundamental ratios indicators, and analyzing their relationships over time can provide a more complete picture of the company financial strength now and in the future. Check out Trending Equities to better understand how to build diversified portfolios, which includes a position in Automatic Data Processing. Also, note that the market value of any company could be closely tied with the direction of predictive economic indicators such as signals in unemployment.
At this time, Automatic Data's Sales General And Administrative To Revenue is relatively stable compared to the past year. As of 12/10/2024, Enterprise Value is likely to grow to about 61 B, while Selling General Administrative is likely to drop slightly above 2 B.
 2021 2022 2023 2024 (projected)
Gross Profit7.0B8.1B8.7B9.2B
Total Revenue16.5B18.0B19.2B20.2B

Automatic Data fundamental ratios Correlations

0.70.990.990.230.720.610.950.270.850.780.30.940.41-0.930.880.370.69-0.950.990.621.00.180.850.140.47
0.70.670.71-0.140.650.740.750.240.70.80.260.780.09-0.710.830.190.42-0.80.730.490.680.670.76-0.330.34
0.990.671.00.140.680.640.910.210.790.750.230.920.33-0.890.870.40.73-0.921.00.621.00.10.810.110.48
0.990.711.00.150.70.640.930.230.820.760.250.930.36-0.90.880.370.71-0.941.00.621.00.180.830.10.47
0.23-0.140.140.150.31-0.410.310.510.450.040.510.20.84-0.32-0.01-0.06-0.08-0.150.130.010.190.060.20.70.03
0.720.650.680.70.310.620.750.280.840.640.280.740.45-0.810.720.520.45-0.790.710.270.690.350.88-0.090.2
0.610.740.640.64-0.410.620.6-0.270.580.71-0.260.71-0.16-0.540.790.520.67-0.70.660.470.590.190.78-0.640.32
0.950.750.910.930.310.750.60.270.920.850.290.980.46-0.940.870.250.61-0.980.930.710.920.30.90.10.58
0.270.240.210.230.510.28-0.270.270.250.141.00.20.43-0.380.21-0.06-0.05-0.230.230.080.280.290.110.430.14
0.850.70.790.820.450.840.580.920.250.770.260.910.63-0.870.770.30.54-0.880.820.550.80.390.940.040.44
0.780.80.750.760.040.640.710.850.140.770.160.870.13-0.790.870.340.58-0.870.790.670.750.310.8-0.180.53
0.30.260.230.250.510.28-0.260.291.00.260.160.220.43-0.390.23-0.07-0.02-0.250.250.110.30.290.120.430.16
0.940.780.920.930.20.740.710.980.20.910.870.220.37-0.90.90.290.71-0.970.940.750.910.280.92-0.040.62
0.410.090.330.360.840.45-0.160.460.430.630.130.430.37-0.460.130.040.06-0.330.340.090.370.250.420.560.07
-0.93-0.71-0.89-0.9-0.32-0.81-0.54-0.94-0.38-0.87-0.79-0.39-0.9-0.46-0.88-0.42-0.470.96-0.91-0.5-0.91-0.25-0.88-0.16-0.4
0.880.830.870.88-0.010.720.790.870.210.770.870.230.90.13-0.880.470.65-0.930.90.60.880.190.86-0.180.45
0.370.190.40.37-0.060.520.520.25-0.060.30.34-0.070.290.04-0.420.470.33-0.350.38-0.170.38-0.240.45-0.14-0.27
0.690.420.730.71-0.080.450.670.61-0.050.540.58-0.020.710.06-0.470.650.33-0.630.710.730.69-0.180.59-0.140.6
-0.95-0.8-0.92-0.94-0.15-0.79-0.7-0.98-0.23-0.88-0.87-0.25-0.97-0.330.96-0.93-0.35-0.63-0.95-0.68-0.93-0.29-0.920.02-0.55
0.990.731.01.00.130.710.660.930.230.820.790.250.940.34-0.910.90.380.71-0.950.620.990.180.840.070.48
0.620.490.620.620.010.270.470.710.080.550.670.110.750.09-0.50.6-0.170.73-0.680.620.590.040.54-0.10.95
1.00.681.01.00.190.690.590.920.280.80.750.30.910.37-0.910.880.380.69-0.930.990.590.140.810.160.45
0.180.670.10.180.060.350.190.30.290.390.310.290.280.25-0.250.19-0.24-0.18-0.290.180.040.140.33-0.16-0.03
0.850.760.810.830.20.880.780.90.110.940.80.120.920.42-0.880.860.450.59-0.920.840.540.810.33-0.190.42
0.14-0.330.110.10.7-0.09-0.640.10.430.04-0.180.43-0.040.56-0.16-0.18-0.14-0.140.020.07-0.10.16-0.16-0.19-0.12
0.470.340.480.470.030.20.320.580.140.440.530.160.620.07-0.40.45-0.270.6-0.550.480.950.45-0.030.42-0.12
Click cells to compare fundamentals

Automatic Data Account Relationship Matchups

Automatic Data fundamental ratios Accounts

201920202021202220232024 (projected)
Total Assets39.2B48.8B63.1B51.0B54.4B57.1B
Short Long Term Debt Total2.3B3.3B3.5B6.8B3.7B3.9B
Other Current Liab28.8B37.7B54.6B38.6B44.3B46.5B
Total Current Liabilities30.1B38.1B55.2B42.8B45.1B47.3B
Total Stockholder Equity5.8B5.7B3.2B3.5B4.5B4.3B
Property Plant And Equipment Net1.2B1.1B1.1B1.1B1.1B723.2M
Net Debt440.5M753M2.1B1.3B791.9M831.5M
Retained Earnings18.4B19.5B20.7B22.1B23.6B24.8B
Cash1.9B2.6B1.4B2.1B2.9B1.5B
Non Current Assets Total7.6B8.0B8.3B8.8B8.8B7.8B
Non Currrent Assets Other2.9B3.3B3.3B4.0B4.1B4.6B
Cash And Short Term Investments1.9B2.6B1.5B2.1B2.9B1.6B
Net Receivables2.4B2.7B3.2B3.0B3.4B3.6B
Good Will2.3B2.3B2.3B2.3B2.4B1.9B
Common Stock Shares Outstanding432.7M428.1M421.1M415.7M412.2M431.7M
Liabilities And Stockholders Equity39.2B48.8B63.1B51.0B54.4B57.1B
Non Current Liabilities Total3.3B5.0B4.7B4.7B4.7B4.8B
Inventory26.7B34.9B49.6B1.01.151.09
Other Current Assets506.2M35.4B50.2B743.9M39.2B41.2B
Other Stockholder Equity(12.7B)(13.9B)(15.5B)(16.4B)(17.3B)(16.5B)
Total Liab33.4B43.1B59.8B47.5B49.8B52.3B
Property Plant And Equipment Gross703M1.1B1.1B1.1B2.9B3.0B
Total Current Assets31.6B40.7B54.8B42.2B45.5B47.8B
Accumulated Other Comprehensive Income(14.8M)10.6M(2.0B)(2.3B)(1.8B)(1.7B)
Short Term Debt1.0B23.5M232.7M3.8B478.7M506.5M
Intangible Assets1.2B1.2B1.3B1.3B1.3B1.1B
Accounts Payable102M141.1M110.2M96.8M100.6M137.4M
Short Term Investments0.010.4M32.7M14.7M384M231.6M
Other Liab1.9B1.7B1.3B1.4B1.2B1.2B
Other Assets2.9B3.3B3.5B4.0B1.00.95
Long Term Debt1.0B3.0B3.0B3.0B3.0B3.1B
Treasury Stock(14.1B)(15.4B)(17.3B)(18.5B)(16.6B)(15.8B)
Property Plant Equipment703.9M1.1B1.1B681.4M783.6M809.7M
Current Deferred Revenue212.5M203.9M188.2M188.6M199.8M223.3M
Net Tangible Assets2.2B2.1B(408.3M)(173.9M)(156.5M)(148.7M)
Retained Earnings Total Equity18.4B19.5B20.7B22.1B25.4B17.1B
Capital Surpluse1.3B1.5B1.8B2.1B2.4B2.5B

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

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.