Automatic Data Processing Stock Z Score

ADP Stock

USD 241.99 0.91 0.37%

Altman Z Score is one of the simplest fundamental models to determine how likely your company is to fail. The module uses available fundamental data of a given equity to approximate the Altman Z score. Altman Z Score is determined by evaluating five fundamental price points available from the company's current public disclosure documents. Check out Automatic Data Piotroski F Score and Automatic Data Valuation analysis.

At this time, Automatic Data's Return On Capital Employed is relatively stable compared to the past year. At this time, Automatic Data's EBIT is relatively stable compared to the past year. As of 04/19/2024, EBITDA is likely to grow to about 6.3 B, while Research Development is likely to drop slightly above 591.6 M.

Automatic Data Processing Company Z Score Analysis

Automatic Data's Z-Score is a simple linear, multi-factor model that measures the financial health and economic stability of a company. The score is used to predict the probability of a firm going into bankruptcy within next 24 months or two fiscal years from the day stated on the accounting statements used to calculate it. The model uses five fundamental business ratios that are weighted according to algorithm of Professor Edward Altman who developed it in the late 1960s at New York University..

Z Score

Sum Of

5 Factors

More About Z Score | All Equity Analysis

First Factor

1.2 * (

Working Capital

/

Total Assets )

Second Factor

1.4 * (

Retained Earnings

/

Total Assets )

Thrid Factor

3.3 * (

EBITAD

/

Total Assets )

Fouth Factor

0.6 * (

Market Value of Equity

/

Total Liabilities )

Fifth Factor

0.99 * (

Revenue

/

Total Assets )

Automatic Z Score Driver Correlations

Understanding the fundamental principles of building solid financial models for Automatic Data is extremely important. It helps to project a fair market value of Automatic Stock properly, considering its historical fundamentals such as Z Score. Since Automatic Data's main accounts across its financial reports are all linked and dependent on each other, it is essential to analyze all possible correlations between related accounts. However, instead of reviewing all of Automatic Data's historical financial statements, investors can examine the correlated drivers to determine its overall health. This can be effectively done using a conventional correlation matrix of Automatic Data's interrelated accounts and indicators.

	Total Assets	Total Stockholder Equity	Net Debt	Retained Earnings	Net Receivables	Good Will	Total Liab	Total Current Assets	Short Term Debt	Intangible Assets	Accounts Payable	Short Term Investments	Common Stock	Long Term Debt	Treasury Stock	Property Plant Equipment	Current Deferred Revenue	Net Tangible Assets	Capital Surpluse	Deferred Long Term Liab	Long Term Investments	Short Long Term Debt	Long Term Debt Total	Net Invested Capital	Net Working Capital	Capital Stock
Total Assets		0.19	0.64	0.95	0.95	0.42	1.0	1.0	0.51	0.84	0.15	-0.23	0.55	0.82	-0.93	0.58	-0.87	-0.73	0.82	0.46	-0.6	-0.68	0.78	0.47	-0.74	-0.49
Total Stockholder Equity	0.19		-0.42	0.25	0.16	0.81	0.09	0.15	-0.03	0.15	0.72	0.31	-0.04	-0.25	-0.11	0.14	-0.29	0.41	-0.19	-0.07	0.22	0.03	-0.24	0.24	0.29	0.09
Net Debt	0.64	-0.42		0.63	0.74	-0.12	0.69	0.62	0.46	0.78	-0.61	-0.38	0.4	0.82	-0.74	0.51	-0.45	-0.8	0.87	0.58	-0.6	-0.68	0.79	0.56	-0.85	-0.4
Retained Earnings	0.95	0.25	0.63		0.98	0.46	0.94	0.92	0.67	0.89	0.12	-0.2	0.65	0.79	-0.93	0.45	-0.86	-0.71	0.88	0.37	-0.59	-0.71	0.73	0.57	-0.78	-0.59
Net Receivables	0.95	0.16	0.74	0.98		0.4	0.95	0.93	0.66	0.92	-0.01	-0.18	0.62	0.84	-0.94	0.53	-0.82	-0.75	0.91	0.46	-0.6	-0.75	0.77	0.61	-0.83	-0.58
Good Will	0.42	0.81	-0.12	0.46	0.4		0.35	0.38	0.21	0.46	0.54	0.26	0.16	-0.01	-0.35	0.26	-0.42	-0.06	0.08	0.06	0.18	-0.04	0.04	0.11	-0.12	0.01
Total Liab	1.0	0.09	0.69	0.94	0.95	0.35		0.99	0.52	0.84	0.08	-0.26	0.56	0.86	-0.93	0.57	-0.86	-0.78	0.85	0.47	-0.64	-0.69	0.82	0.45	-0.78	-0.51
Total Current Assets	1.0	0.15	0.62	0.92	0.93	0.38	0.99		0.46	0.8	0.17	-0.25	0.51	0.83	-0.92	0.59	-0.88	-0.73	0.79	0.47	-0.63	-0.66	0.79	0.42	-0.72	-0.46
Short Term Debt	0.51	-0.03	0.46	0.67	0.66	0.21	0.52	0.46		0.54	-0.18	0.15	0.77	0.5	-0.49	-0.09	-0.29	-0.58	0.76	-0.19	-0.24	-0.43	0.37	0.33	-0.75	-0.75
Intangible Assets	0.84	0.15	0.78	0.89	0.92	0.46	0.84	0.8	0.54		-0.18	-0.25	0.51	0.75	-0.92	0.52	-0.77	-0.7	0.85	0.52	-0.57	-0.71	0.78	0.64	-0.8	-0.33
Accounts Payable	0.15	0.72	-0.61	0.12	-0.01	0.54	0.08	0.17	-0.18	-0.18		0.21	-0.05	-0.26	0.02	0.08	-0.29	0.3	-0.31	-0.15	0.13	0.09	-0.27	-0.2	0.4	0.04
Short Term Investments	-0.23	0.31	-0.38	-0.2	-0.18	0.26	-0.26	-0.25	0.15	-0.25	0.21		-0.12	-0.37	0.3	-0.04	0.32	0.35	-0.29	-0.26	0.3	0.33	-0.38	-0.22	0.24	0.11
Common Stock	0.55	-0.04	0.4	0.65	0.62	0.16	0.56	0.51	0.77	0.51	-0.05	-0.12		0.55	-0.42	0.06	-0.35	-0.55	0.71	-0.06	-0.28	-0.41	0.43	0.39	-0.62	-0.84
Long Term Debt	0.82	-0.25	0.82	0.79	0.84	-0.01	0.86	0.83	0.5	0.75	-0.26	-0.37	0.55		-0.84	0.53	-0.68	-0.81	0.89	0.56	-0.74	-0.63	0.96	0.53	-0.76	-0.52
Treasury Stock	-0.93	-0.11	-0.74	-0.93	-0.94	-0.35	-0.93	-0.92	-0.49	-0.92	0.02	0.3	-0.42	-0.84		-0.54	0.91	0.74	-0.85	-0.54	0.72	0.74	-0.85	-0.54	0.78	0.34
Property Plant Equipment	0.58	0.14	0.51	0.45	0.53	0.26	0.57	0.59	-0.09	0.52	0.08	-0.04	0.06	0.53	-0.54		-0.45	-0.33	0.38	0.74	-0.33	-0.43	0.56	0.46	-0.24	-0.05
Current Deferred Revenue	-0.87	-0.29	-0.45	-0.86	-0.82	-0.42	-0.86	-0.88	-0.29	-0.77	-0.29	0.32	-0.35	-0.68	0.91	-0.45		0.55	-0.63	-0.47	0.7	0.65	-0.73	-0.42	0.54	0.2
Net Tangible Assets	-0.73	0.41	-0.8	-0.71	-0.75	-0.06	-0.78	-0.73	-0.58	-0.7	0.3	0.35	-0.55	-0.81	0.74	-0.33	0.55		-0.84	-0.4	0.54	0.51	-0.76	-0.14	0.91	0.52
Capital Surpluse	0.82	-0.19	0.87	0.88	0.91	0.08	0.85	0.79	0.76	0.85	-0.31	-0.29	0.71	0.89	-0.85	0.38	-0.63	-0.84		0.36	-0.64	-0.76	0.8	0.61	-0.92	-0.69
Deferred Long Term Liab	0.46	-0.07	0.58	0.37	0.46	0.06	0.47	0.47	-0.19	0.52	-0.15	-0.26	-0.06	0.56	-0.54	0.74	-0.47	-0.4	0.36		-0.51	-0.44	0.6	0.42	-0.3	0.04
Long Term Investments	-0.6	0.22	-0.6	-0.59	-0.6	0.18	-0.64	-0.63	-0.24	-0.57	0.13	0.3	-0.28	-0.74	0.72	-0.33	0.7	0.54	-0.64	-0.51		0.63	-0.74	-0.43	0.51	0.24
Short Long Term Debt	-0.68	0.03	-0.68	-0.71	-0.75	-0.04	-0.69	-0.66	-0.43	-0.71	0.09	0.33	-0.41	-0.63	0.74	-0.43	0.65	0.51	-0.76	-0.44	0.63		-0.6	-0.67	0.65	0.38
Long Term Debt Total	0.78	-0.24	0.79	0.73	0.77	0.04	0.82	0.79	0.37	0.78	-0.27	-0.38	0.43	0.96	-0.85	0.56	-0.73	-0.76	0.8	0.6	-0.74	-0.6		0.48	-0.69	-0.28
Net Invested Capital	0.47	0.24	0.56	0.57	0.61	0.11	0.45	0.42	0.33	0.64	-0.2	-0.22	0.39	0.53	-0.54	0.46	-0.42	-0.14	0.61	0.42	-0.43	-0.67	0.48		-0.35	-0.38
Net Working Capital	-0.74	0.29	-0.85	-0.78	-0.83	-0.12	-0.78	-0.72	-0.75	-0.8	0.4	0.24	-0.62	-0.76	0.78	-0.24	0.54	0.91	-0.92	-0.3	0.51	0.65	-0.69	-0.35		0.58
Capital Stock	-0.49	0.09	-0.4	-0.59	-0.58	0.01	-0.51	-0.46	-0.75	-0.33	0.04	0.11	-0.84	-0.52	0.34	-0.05	0.2	0.52	-0.69	0.04	0.24	0.38	-0.28	-0.38	0.58

Click cells to compare fundamentals

To calculate a Z-Score, one would need to know a company's current working capital, its total assets and liabilities, and the amount of its latest earnings as well as earnings before interest and tax. Z-Scores can be used to compare the odds of bankruptcy of companies in a similar line of business or firms operating in the same industry. Companies with Z-Scores above 3.1 are generally considered to be stable and healthy with a low probability of bankruptcy. Scores that fall between 1.8 and 3.1 lie in a so-called 'grey area,' with scores of less than 1 indicating the highest probability of distress. Z Score is a used widely measure by financial auditors, accountants, money managers, loan processors, wealth advisers, and day traders. In the last 25 years, many financial models that utilize z-scores proved it to be successful as a predictor of corporate bankruptcy.

Competition

Predict Automatic Data

In accordance with the company's disclosures, Automatic Data Processing has a Z Score of 0.0. This is 100.0% lower than that of the Professional Services sector and about the same as Industrials (which currently averages 0.0) industry. The z score for all United States stocks is 100.0% higher than that of the company.

Automatic Data ESG Sustainability

Some studies have found that companies with high sustainability scores are getting higher valuations than competitors with lower social-engagement activities. While most ESG disclosures are voluntary and do not directly affect the long term financial condition, Automatic Data's sustainability indicators can be used to identify proper investment strategies using environmental, social, and governance scores that are crucial to Automatic Data's managers, analysts, and investors.

Environment Score

Governance Score

Social Score

Automatic Data Institutional Holders

Institutional Holdings refers to the ownership stake in Automatic Data that is held by large financial organizations, pension funds or endowments. Institutions may purchase large blocks of Automatic Data's outstanding shares and can exert considerable influence upon its management. Institutional holders may also work to push the share price higher once they own the stock. Extensive social media coverage, TV shows, articles in high-profile magazines, and presentations at investor conferences help move the stock higher, increasing Automatic Data's value.

		Shares
Northern Trust Corp	2023-12-31	5.7 M
Wellington Management Company Llp	2023-12-31	5.5 M
Goldman Sachs Group Inc	2023-12-31	4.8 M
State Farm Mutual Automobile Ins Co	2023-12-31	3.7 M
Ameriprise Financial Inc	2023-12-31	3.5 M
Capital Research Global Investors	2023-12-31	3.5 M
Legal & General Group Plc	2023-12-31	3.4 M
Amundi	2023-12-31	3.2 M
Amvescap Plc.	2023-12-31	3.2 M
Vanguard Group Inc	2023-12-31	39.6 M
Blackrock Inc	2023-12-31	33.3 M

Automatic Fundamentals

	Return On Equity	0.97
	Return On Asset	0.0532
	Profit Margin	0.19 %
	Operating Margin	0.26 %
	Current Valuation	101.18 B
	Shares Outstanding	410.79 M
	Shares Owned By Insiders	0.11 %
	Shares Owned By Institutions	83.67 %
	Number Of Shares Shorted	4.56 M
	Price To Earning	35.60 X
	Price To Book	23.20 X
	Price To Sales	5.39 X
	Revenue	17.2 B
	Gross Profit	8.51 B
	EBITDA	5.26 B
	Net Income	3.41 B
	Cash And Equivalents	1.23 B
	Cash Per Share	2.97 X
	Total Debt	3.34 B
	Debt To Equity	1.40 %
	Current Ratio	0.97 X
	Book Value Per Share	10.52 X
	Cash Flow From Operations	4.21 B
	Short Ratio	1.94 X
	Earnings Per Share	8.60 X
	Price To Earnings To Growth	2.69 X
	Target Price	259.16
	Number Of Employees	63 K
	Beta	0.79
	Market Capitalization	99.41 B
	Total Asset	50.97 B
	Retained Earnings	22.12 B
	Working Capital	(597 M)
	Current Asset	3.68 B
	Current Liabilities	2 B
	Annual Yield	0.02 %
	Five Year Return	1.95 %
	Net Asset	50.97 B
	Last Dividend Paid	5.15

About Automatic Data Fundamental Analysis

The Macroaxis Fundamental Analysis modules help investors analyze Automatic Data Processing's financials across various querterly and yearly statements, indicators and fundamental ratios. We help investors to determine the real value of Automatic Data using virtually all public information available. We use both quantitative as well as qualitative analysis to arrive at the intrinsic value of Automatic Data Processing based on its fundamental data. In general, a quantitative approach, as applied to this company, focuses on analyzing financial statements comparatively, whereas a qaualitative method uses data that is important to a company's growth but cannot be measured and presented in a numerical way.

Please read more on our fundamental analysis page.

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 against Automatic Stock

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0.47	ARC	ARC Document Solutions Financial Report 1st of May 2024	Pair Corr
0.41	BE	Bloom Energy Corp Financial Report 14th of May 2024	Pair Corr

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 Correlation Correlation 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:

How To Invest In Automatic

Details

Is Automatic Stock a good investment?

Details

What is Automatic Data's Net Worth?

Details

Largest Human Resource & Employment Services Companies

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Largest Commercial & Professional Services Companies

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Check out Automatic Data Piotroski F Score and Automatic Data Valuation analysis.

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 Global Markets Map module to get a quick overview of global market snapshot using zoomable world map. Drill down to check world indexes.

Complementary 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.

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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

0.092

Dividend Share

5.15

Earnings Share

8.6

Revenue Per Share

45.09

Quarterly Revenue Growth

0.063

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.

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Automatic Data Processing Stock Z Score

Automatic Data Processing Company Z Score Analysis

Z Score

Sum Of

5 Factors

First Factor

1.2 * (

Working Capital

/

Total Assets )

Second Factor

1.4 * (

Retained Earnings

/

Total Assets )

Thrid Factor

3.3 * (

EBITAD

/

Total Assets )

Fouth Factor

0.6 * (

Market Value of Equity

/

Total Liabilities )

Fifth Factor

0.99 * (

Revenue

/

Total Assets )

Automatic Z Score Driver Correlations

Automatic Data ESG Sustainability

Automatic Data Institutional Holders

Automatic Fundamentals

About Automatic Data Fundamental Analysis

Pair Trading with Automatic Data

Moving against Automatic Stock

Complementary Tools for Automatic Stock analysis

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