Automatic Data Processing shows Risk Adjusted Performance of 0.033157 and Mean Deviation of 0.5916. Automatic Data Proce technical analysis gives you the methodology to make use of historical prices and volume patterns to determine a pattern that approximates the direction of the firm future prices. In plain English you can use this information to find out if the firm will indeed mirror its model of historical prices and volume momentum or the prices will eventually revert. We found nineteen technical drivers for Automatic Data Processing which can be compared to its rivals. Please confirm Automatic Data ProceVariance, Maximum Drawdown as well as the relationship between Maximum Drawdown and Semi Variance to decide if Automatic Data Proce is priced correctly providing market reflects its regular price of 137.06 per share. Given that Automatic Data has Jensen Alpha of 0.039814, we suggest you validate Automatic Data Processing prevailing market performance to make sure the company can sustain itself at future point.
The output start index for this execution was six with a total number of output elements of eleven. The Average True Range was developed by J. Welles Wilder in 1970s. It is one of components of the Welles Wilder Directional Movement indicators. The ATR is a measure of Automatic Data Proce volatility. High ATR values indicate high volatility, and low values indicate low volatility. View also all equity analysis or get more info about average true range volatility indicators indicator.
Automatic Data Proce Trend Analysis
Use this graph to draw trend lines for Automatic Data Processing. You can use it to identify possible trend reversals for Automatic Data as well as other signals and approximate when it will take place. Remember, you need at least two touches of the trend line with actual Automatic Data price movement. To start drawing, click on the pencil icon on top-right. To remove the trend, use eraser icon.
Automatic Data Best Fit Change Line
The following chart estimates an ordinary least squares regression model for Automatic Data Processing applied against its price change over selected period. The best fit line has a slop of 0.29 % which may imply that Automatic Data Processing will maintain its good market sentiment and make money for investors. It has 34 observation points and a regression sum of squares at 66.41, which is the sum of squared deviations for the predicted Automatic Data price change compared to its average price change.
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