Automatic Data Processing Stock Return On Asset

ADP Stock  USD 304.67  0.48  0.16%   
Automatic Data Processing fundamentals help investors to digest information that contributes to Automatic Data's financial success or failures. It also enables traders to predict the movement of Automatic Stock. The fundamental analysis module provides a way to measure Automatic Data's intrinsic value by examining its available economic and financial indicators, including the cash flow records, the balance sheet account changes, the income statement patterns, and various microeconomic indicators and financial ratios related to Automatic Data stock.
As of 11/25/2024, Return On Assets is likely to grow to 0.08.
  
This module does not cover all equities due to inconsistencies in global equity categorizations. Continue to Equity Screeners to view more equity screening tools.

Automatic Data Processing Company Return On Asset Analysis

Automatic Data's Return on Asset or ROA shows how effective is the management of the company in generating income from utilizing all of the assets at their disposal. It is a useful ratio to evaluate the performance of different departments of a company as well as to understand management performance over time.

Return On Asset

 = 

Net Income

Total Assets

More About Return On Asset | All Equity Analysis

Current Automatic Data Return On Asset

    
  0.0654  
Most of Automatic Data's fundamental indicators, such as Return On Asset, 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 Return On Asset 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 Return On Asset. 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.
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Return on Asset measures overall efficiency of a company in generating profits from its total assets. It is expressed as the percentage of profits earned per dollar of Asset. A low ROA typically means that a company is asset-intensive and therefore will needs more money to continue generating revenue in the future.
Competition

Automatic Total Assets

Total Assets

57.08 Billion

At this time, Automatic Data's Total Assets are relatively stable compared to the past year.
Based on the latest financial disclosure, Automatic Data Processing has a Return On Asset of 0.0654. This is 112.82% lower than that of the Professional Services sector and significantly higher than that of the Industrials industry. The return on asset for all United States stocks is 146.71% lower than that of the firm.

Automatic Return On Asset 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 Return On Asset 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 return on asset 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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  0.77DRVN Driven Brands HoldingsPairCorr

Moving against Automatic Stock

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  0.48MMS MaximusPairCorr
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.