Sprint Accrued Expenses Turnover Trend

S -- USA Stock  

USD 6.40  0.12  1.91%

This module enables investors to look at Sprint 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 Cost of Revenue of 17.9 B or Earning Before Interest and Taxes EBIT of 385.9 M, but also many exotic indicators such as Interest Coverage of 0.1671 or Long Term Debt to Equity of 1.7405. This module is a perfect complement to use when analyzing Sprint Valuation or Volatility. It can also complement various Sprint Technical models. Also please take a look at analysis of Sprint Correlation with competitors.
Showing smoothed Accrued Expenses Turnover of Sprint Corporation with missing and latest data points interpolated.
Accrued Expenses Turnover10 Years Trend
Slightly volatile
 Accrued Expenses Turnover 

Sprint Regression Statistics

Arithmetic Mean 16.18
Geometric Mean 15.92
Coefficient Of Variation 19.63
Mean Deviation 2.72
Median 13.93
Standard Deviation 3.18
Sample Variance 10.09
Range 8.89
R Value 0.86
Mean Square Error 2.80
R Squared 0.75
Significance 0.00028322
Slope 0.76
Total Sum of Squares 110.97

Sprint Accrued Expenses Turnover Over Time

2016-12-31  19.40 
2017-12-31  19.40 
2018-12-31  22.82 

Other Fundumenentals of Sprint

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Sprint Fundamental Drivers

Sprint Corporation Fundamental Market Drivers

Short Percent Of Float18.20%
Forward Price Earnings309.50
Shares Short Prior Month112.94M
Average Daily Volume Last 10 Day10.79M
Average Daily Volume In Three Month9.75M
Date Short InterestSeptember 14, 2018
Fifty Day Average6.29
Two Hundred Day Average5.70

Sprint Upcoming Events

Sprint Upcoming Company Events

Upcoming Quarterly ReportMay 2, 2017
Next Earnings ReportJuly 24, 2017
Also please take a look at analysis of Sprint Correlation with competitors. Please also try Equity Valuation module to check real value of public entities based on technical and fundamental data.
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