Automatic Data Processing MACD



ADP MACDADP DMA
Automatic Data Processing is a provider of cloud-based human capital management (HCM) solutions. Co. classifies its operations into the following two reportable segments: Employer Services, which serves clients ranging from single-employee small businesses to large enterprises, providing a range of technology-based HCM solutions, including payroll services, human resources management, workforce management, compliance services, insurance services and retirement services; and Professional Employer Organization, which provides clients with employment administration outsourcing solutions through a relationship in which employees who work for a client are co-employed by Co. and the client.

When researching a stock like Automatic Data Processing, many investors are the most familiar with Fundamental Analysis — looking at a company's balance sheet, earnings, revenues, and what's happening in that company's underlying business. Investors who use Fundamental Analysis to identify good stocks to buy or sell can also benefit from ADP Technical Analysis to help find a good entry or exit point. Technical Analysis is blind to the fundamentals and looks only at the trading data for ADP stock — the real life supply and demand for the stock over time — and examines that data in different ways. One of those ways is to calculate a Simpe Moving Average ("SMA") by looking back a certain number of days. One of the most popular "longer look-backs" is the ADP 200 day moving average ("ADP 200 DMA"), while one of the most popular "shorter look-backs" is the ADP 50 day moving average ("ADP 50 DMA"). A chart showing both of these popular moving averages is shown on this page for Automatic Data Processing. Comparing two moving averages against each other can be a useful visualization tool: by calculating the difference between the ADP 200 DMA and the ADP 50 DMA, we get a moving average convergence divergence indicator ("ADP MACD"). The ADP MACD chart, in conjunction with the chart of the moving averages, basically helps in visualizing how the moving averages are showing convergence (moving closer together), or divergence (moving farther apart).
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