ATT Return on Average Assets Trend

T -- USA Stock  

USD 33.78  0.34  1.02%

This module enables investors to look at ATT various fundamental indicators over time in order to gain insight into the company future performance. Macroaxis historical fundamental analysis tools allow evaluation of not only typical financial statement drivers such as Consolidated Income of 14.2 B or Cost of Revenue of 55.7 B, but also many exotic indicators such as Interest Coverage of 5.776 or Long Term Debt to Equity of 0.9944. This module is a perfect complement to use when analyzing ATT Valuation or Volatility. It can also complement various ATT Technical models. Also please take a look at analysis of ATT Correlation with competitors.
Showing smoothed Return on Average Assets of ATT with missing and latest data points interpolated. Return on assets measures how profitable a company is Net Income Common Stock relative to its total assets Average Assets.
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3.11 %

          10 Years Trend
Decreasing
Stable
 Return on Average Assets 
      Timeline 

ATT Regression Statistics

Arithmetic Mean 0.037092
Coefficient Of Variation 66.46
Mean Deviation 0.018439
Median 0.032
Standard Deviation 0.02465
Sample Variance 0.00060761
Range 0.08
R Value(0.16)
Mean Square Error 0.00065222
R Squared 0.024157
Significance 0.63
Slope(0.001063)
Total Sum of Squares 0.006684

ATT Return on Average Assets Over Time

2016-12-31  0.032 
2017-12-31  0.032 
2018-12-31  0.0311 

Other Fundumenentals of ATT

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ATT Fundamental Drivers

ATT Fundamental Market Drivers

Short Percent Of Float1.18%
Forward Price Earnings9.17
Shares Short Prior Month84.47M
Average Daily Volume Last 10 Day30M
Average Daily Volume In Three Month39.69M
Date Short InterestAugust 15, 2018
Fifty Day Average32.353
Two Hundred Day Average33.384

ATT Upcoming Events

ATT Upcoming Company Events

Upcoming Quarterly ReportOctober 27, 2017
Next Earnings ReportJanuary 24, 2018
Also please take a look at analysis of ATT Correlation with competitors. Please also try Pattern Recognition module to use different pattern recognition models to time the market across multiple global exchanges.
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