Automatic Balance Sheet Analysis From 2010 to 2024
ADP Stock | USD 246.84 0.53 0.22% |
Automatic |
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 Automatic Data Valuation and Automatic Data Correlation analysis. At this time, Automatic Data's Selling General Administrative is relatively stable compared to the past year. As of 04/24/2024, Sales General And Administrative To Revenue is likely to grow to 0.29, while Enterprise Value Over EBITDA is likely to drop 9.76.
2021 | 2022 | 2023 | 2024 (projected) | Gross Profit | 6.6B | 7.2B | 8.3B | 8.7B | Total Revenue | 16.0B | 17.2B | 19.8B | 20.8B |
Automatic Data fundamental ratios Correlations
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Automatic Data Account Relationship Matchups
High Positive Relationship
High Negative Relationship
Automatic Data fundamental ratios Accounts
2019 | 2020 | 2021 | 2022 | 2023 | 2024 (projected) | ||
Total Assets | 39.2B | 48.8B | 63.1B | 51.0B | 58.6B | 61.5B | |
Other Current Liab | 28.8B | 37.7B | 54.7B | 38.6B | 44.4B | 46.7B | |
Net Debt | 440.5M | 753M | 2.1B | 1.3B | 1.1B | 1.2B | |
Retained Earnings | 18.4B | 19.5B | 20.7B | 22.1B | 25.4B | 26.7B | |
Cash | 1.9B | 2.6B | 1.4B | 2.1B | 2.4B | 1.4B | |
Net Receivables | 2.4B | 2.7B | 3.2B | 3.0B | 3.5B | 3.6B | |
Good Will | 2.3B | 2.3B | 2.3B | 2.3B | 2.7B | 1.9B | |
Inventory | 26.7B | 34.9B | 49.6B | 36.3B | 41.8B | 43.9B | |
Other Current Assets | 506.2M | 533.4M | 50.2B | 743.9M | 855.5M | 812.7M | |
Total Liab | 33.4B | 43.1B | 59.8B | 47.5B | 54.6B | 57.3B | |
Total Current Assets | 31.6B | 40.7B | 54.8B | 42.2B | 48.5B | 50.9B | |
Short Term Debt | 1.0B | 23.5M | 136.4M | 3.8B | 4.4B | 4.6B | |
Intangible Assets | 1.2B | 1.2B | 1.3B | 1.3B | 1.5B | 1.1B | |
Accounts Payable | 102M | 141.1M | 110.2M | 96.8M | 111.3M | 138.1M | |
Other Liab | 1.9B | 1.7B | 1.3B | 1.4B | 1.2B | 1.2B | |
Other Assets | 2.9B | 3.3B | 3.5B | 4.0B | 3.6B | 5.1B | |
Long Term Debt | 1.0B | 3.0B | 3.0B | 3.0B | 3.4B | 3.6B | |
Treasury Stock | (14.1B) | (15.4B) | (17.3B) | (18.5B) | (16.6B) | (15.8B) | |
Net Tangible Assets | 2.2B | 2.1B | (408.3M) | (173.9M) | (156.5M) | (148.7M) | |
Capital Surpluse | 1.3B | 1.5B | 1.8B | 2.1B | 2.4B | 2.5B |
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 against Automatic Stock
0.67 | EXPO | Exponent Earnings Call Tomorrow | PairCorr |
0.54 | VIRC | Virco Manufacturing Report 26th of April 2024 | PairCorr |
0.53 | FC | Franklin Covey Financial Report 26th of June 2024 | PairCorr |
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.Check out Automatic Data Valuation and Automatic Data Correlation 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 Sign In To Macroaxis module to sign in to explore Macroaxis' wealth optimization platform and fintech modules.
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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.