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 Automatic Data Altman Z Score, Automatic Data Correlation, Automatic Data Valuation, as well as analyze Automatic Data Alpha and Beta and Automatic Data Hype Analysis.
Automatic
Piotroski F Score
Market Cap
Enterprise Value
Price To Sales Ratio
Dividend Yield
Ptb Ratio
Days Sales Outstanding
Book Value Per Share
Free Cash Flow Yield
Operating Cash Flow Per Share
Stock Based Compensation To Revenue
Capex To Depreciation
Pb Ratio
Ev To Sales
Free Cash Flow Per Share
Roic
Inventory Turnover
Net Income Per Share
Days Of Inventory On Hand
Payables Turnover
Sales General And Administrative To Revenue
Research And Ddevelopement To Revenue
Capex To Revenue
Cash Per Share
Pocfratio
Interest Coverage
Payout Ratio
Capex To Operating Cash Flow
Pfcf Ratio
Days Payables Outstanding
Income Quality
Roe
Ev To Operating Cash Flow
Pe Ratio
Return On Tangible Assets
Ev To Free Cash Flow
Earnings Yield
Intangibles To Total Assets
Net Debt To E B I T D A
Current Ratio
Tangible Book Value Per Share
Receivables Turnover
Graham Number
Shareholders Equity Per Share
Debt To Equity
Capex Per Share
Graham Net Net
Average Receivables
Revenue Per Share
Interest Debt Per Share
Debt To Assets
Enterprise Value Over E B I T D A
Short Term Coverage Ratios
Price Earnings Ratio
Operating Cycle
Price Book Value Ratio
Price Earnings To Growth Ratio
Days Of Payables Outstanding
Dividend Payout Ratio
Price To Operating Cash Flows Ratio
Price To Free Cash Flows Ratio
Pretax Profit Margin
Ebt Per Ebit
Operating Profit Margin
Effective Tax Rate
Company Equity Multiplier
Long Term Debt To Capitalization
Total Debt To Capitalization
Return On Capital Employed
Debt Equity Ratio
Ebit Per Revenue
Quick Ratio
Dividend Paid And Capex Coverage Ratio
Net Income Per E B T
Cash Ratio
Cash Conversion Cycle
Operating Cash Flow Sales Ratio
Days Of Inventory Outstanding
Days Of Sales Outstanding
Free Cash Flow Operating Cash Flow Ratio
Cash Flow Coverage Ratios
Price To Book Ratio
Fixed Asset Turnover
Capital Expenditure Coverage Ratio
Price Cash Flow Ratio
Enterprise Value Multiple
Debt Ratio
Cash Flow To Debt Ratio
Price Sales Ratio
Return On Assets
Asset Turnover
Net Profit Margin
Gross Profit Margin
Price Fair Value
Return On Equity
Sale Purchase Of Stock
Change In Cash
Free Cash Flow
Change In Working Capital
Begin Period Cash Flow
Other Cashflows From Financing Activities
Depreciation
Dividends Paid
Capital Expenditures
Total Cash From Operating Activities
Net Income
Total Cash From Financing Activities
End Period Cash Flow
Other Non Cash Items
Other Cashflows From Investing Activities
Change To Liabilities
Total Cashflows From Investing Activities
Stock Based Compensation
Change To Account Receivables
Change To Inventory
Investments
Change Receivables
Net Borrowings
Exchange Rate Changes
Cash And Cash Equivalents Changes
Cash Flows Other Operating
Change To Netincome
Change To Operating Activities
Total Assets
Short Long Term Debt Total
Other Current Liab
Total Current Liabilities
Total Stockholder Equity
Property Plant And Equipment Net
Net Debt
Retained Earnings
Cash
Non Current Assets Total
Non Currrent Assets Other
Cash And Short Term Investments
Net Receivables
Good Will
Common Stock Shares Outstanding
Liabilities And Stockholders Equity
Non Current Liabilities Total
Inventory
Other Current Assets
Other Stockholder Equity
Total Liab
Property Plant And Equipment Gross
Total Current Assets
Accumulated Other Comprehensive Income
Short Term Debt
Intangible Assets
Accounts Payable
Common Stock Total Equity
Short Term Investments
Common Stock
Other Liab
Other Assets
Long Term Debt
Treasury Stock
Property Plant Equipment
Current Deferred Revenue
Net Tangible Assets
Retained Earnings Total Equity
Capital Surpluse
Additional Paid In Capital
Deferred Long Term Liab
Long Term Investments
Short Long Term Debt
Long Term Debt Total
Non Current Liabilities Other
Net Invested Capital
Net Working Capital
Capital Lease Obligations
Depreciation And Amortization
Interest Expense
Selling General Administrative
Total Revenue
Gross Profit
Other Operating Expenses
Operating Income
Ebit
Ebitda
Cost Of Revenue
Total Operating Expenses
Income Before Tax
Total Other Income Expense Net
Income Tax Expense
Research Development
Net Income Applicable To Common Shares
Discontinued Operations
Net Income From Continuing Ops
Non Recurring
Non Operating Income Net Other
Tax Provision
Extraordinary Items
Net Interest Income
Interest Income
Reconciled Depreciation
Probability Of Bankruptcy
At this time, Automatic Data's Interest Debt Per Share is relatively stable compared to the past year. As of 11/30/2024, Debt To Assets is likely to grow to 0.07, while Long Term Debt Total is likely to drop slightly above 2.1 B. At this time, Automatic Data's ROIC is relatively stable compared to the past year. As of 11/30/2024, Net Income Per Share is likely to grow to 9.59, while Book Value Per Share is likely to drop 6.13.
At this time, it appears that Automatic Data's Piotroski F Score is Healthy. 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.
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
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.
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.
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.
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.