This module uses fundamental data of Automatic Data to approximate its Piotroski F score. Automatic Data F Score is determined by combining nine binary scores representing 3 distinct fundamental categories of Automatic Data Processing. These three categories are profitability, efficiency, and funding. Some research analysts and sophisticated value traders use Piotroski F Score to find opportunities outside of the conventional market and financial statement analysis.They believe that some of the new information about Automatic Data financial position does not get reflected in the current market share price suggesting a possibility of arbitrage. 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 board of governors.
Automatic
Piotroski F Score
Sale Purchase Of Stock
Change In Cash
Stock Based Compensation
Free Cash Flow
Change In Working Capital
Begin Period Cash Flow
Other Cashflows From Financing Activities
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Other Non Cash Items
Dividends Paid
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Total Cash From Operating Activities
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Net Income
Total Cash From Financing Activities
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Other Cashflows From Investing Activities
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Investments
Net Borrowings
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Total Current Liabilities
Total Stockholder Equity
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Common Stock Shares Outstanding
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Treasury Stock
Property Plant Equipment
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Total Revenue
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Ebit
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Cost Of Revenue
Total Operating Expenses
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Income Tax Expense
Research Development
Tax Provision
Interest Income
Interest Expense
Selling General Administrative
Net Income Applicable To Common Shares
Extraordinary Items
Probability Of Bankruptcy
At this time, Automatic Data's Net Debt is comparatively stable compared to the past year. Short Term Debt is likely to gain to about 524.8 M in 2024, whereas Short and Long Term Debt Total is likely to drop slightly above 2.5 B in 2024.
At this time, it appears that Automatic Data's Piotroski F Score is Inapplicable. Although some professional money managers and academia have recently criticized Piotroski F-Score model, we still consider it an effective method of predicting the state of the financial strength of any organization that is not predisposed to accounting gimmicks and manipulations. Using this score on the criteria to originate an efficient long-term portfolio can help investors filter out the purely speculative stocks or equities playing fundamental games by manipulating their earnings..
The critical factor to consider when applying the Piotroski F Score to Automatic Data is to make sure Automatic is not a subject of accounting manipulations and runs a healthy internal audit department. So, if Automatic Data's auditors report directly to the board (not management), the managers will be reluctant to manipulate simply due to the fear of punishment. On the other hand, the auditors will be free to investigate the ledgers properly because they know that the board has their back. Below are the main accounts that are used in the Piotroski F Score model. By analyzing the historical trends of the mains drivers, investors can determine if Automatic Data's financial numbers are properly reported.
One of the toughest challenges investors face today is learning how to quickly synthesize historical financial statements and information provided by the company, SEC reporting, and various external parties in order to project the various growth rates. Understanding the correlation between Automatic Data's different financial indicators related to revenue, expenses, operating profit, and net earnings helps investors identify and prioritize their investing strategies towards Automatic Data in a much-optimized way.
F-Score is one of many stock grading techniques developed by Joseph Piotroski, a professor of accounting at the Stanford University Graduate School of Business. It was published in 2002 under the paper titled Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers. Piotroski F Score is based on binary analysis strategy in which stocks are given one point for passing 9 very simple fundamental tests, and zero point otherwise. According to Mr. Piotroski's analysis, his F-Score binary model can help to predict the performance of low price-to-book stocks.
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.
Analyzing currently trending equities could be an opportunity to develop a better portfolio based on different market momentums that they can trigger. Utilizing the top trending stocks is also useful when creating a market-neutral strategy or pair trading technique involving a short or a long position in a currently trending equity.
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.