Automatic Data Processing Stock Book Value Per Share

ADP Stock  USD 260.22  3.04  1.18%   
Automatic Data's fundamental analysis aims to assess its intrinsic value by examining key economic and financial indicators - such as cash flow records, changes in balance sheet accounts, income statement trends, financial ratios, and relevant microeconomic factors affecting Automatic Stock price.
Last ReportedProjected for Next Year
Book Value Per Share 15.20  15.96 
Tangible Book Value Per Share 3.22  2.73 
As of 12/04/2025, Book Value Per Share is likely to grow to 15.96, while Tangible Book Value Per Share is likely to drop 2.73.
  
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Automatic Data Processing Company Book Value Per Share Analysis

Automatic Data's Book Value per Share (B/S) can be calculated by subtracting liabilities from assets, and then dividing it by the total number of currently outstanding shares. It indicates the level of safety associated with each common share after removing the effects of liabilities. In other words, a shareholder can use this ratio to see how much he or she can sell the stake in the company in the event of a liquidation.

Book Value per Share

 = 

Common Equity

Average Shares

More About Book Value Per Share | All Equity Analysis

Current Automatic Data Book Value Per Share

    
  15.74 X  
Most of Automatic Data's fundamental indicators, such as Book Value Per Share, are part of a valuation analysis module that helps investors searching for stocks that are currently trading at higher or lower prices than their real value. If the real value is higher than the market price, Automatic Data Processing is considered to be undervalued, and we provide a buy recommendation. Otherwise, we render a sell signal.

Automatic Book Value Per Share 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 Book Value Per Share. 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.
The naive approach to look at Book Value per Share is to compare it to current stock price. If Book Value per Share is higher than the currently traded stock price, the company can be considered undervalued. However, investors must be aware that conventional calculation of Book Value does not include intangible assets such as goodwill, intellectual property, trademarks or brands and may not be an appropriate measure for many firms.
Competition

Automatic Common Stock Shares Outstanding

Common Stock Shares Outstanding

429.05 Million

At this time, Automatic Data's Common Stock Shares Outstanding is relatively stable compared to the past year.
In accordance with the recently published financial statements, the book value per share of Automatic Data Processing is about 15.742 times. This is 99.97% lower than that of the Professional Services sector and significantly higher than that of the Industrials industry. The book value per share for all United States stocks is 99.19% higher than that of the company.

Automatic Book Value Per Share Peer Comparison

Stock peer comparison is one of the most widely used and accepted methods of equity analyses. It analyses Automatic Data's direct or indirect competition against its Book Value Per Share to detect undervalued stocks with similar characteristics or determine the stocks which would be a good addition to a portfolio. Peer analysis of Automatic Data could also be used in its relative valuation, which is a method of valuing Automatic Data by comparing valuation metrics of similar companies.
Automatic Data is currently under evaluation in book value per share category among its peers.

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.
Environmental
Governance
Social

Automatic Fundamentals

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 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.