Sprint Book Value per Share Trend

S -- USA Stock  

USD 6.12  0.11  1.83%

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 Book Value per Share of Sprint Corporation with missing and latest data points interpolated. Measures the ratio between Shareholders Equity and Weighted Average Shares.
Book Value per Share10 Years Trend
Slightly volatile
 Book Value per Share 

Regression Statistics

Arithmetic Mean  5.82
Geometric Mean  5.41
Coefficient Of Variation  43.82
Mean Deviation  1.55
Median  5.48
Standard Deviation  2.55
Sample Variance  6.51
Range  10.59
R Value (0.46)
Mean Square Error  5.62
R Squared  0.22
Significance  0.13
Slope (0.33)
Total Sum of Squares  71.64

Sprint Book Value per Share Over Time

2016-12-31  4.98 
2017-12-31  4.98 
2018-12-31  5.86 

Other Fundumenentals

Thematic Opportunities

Explore Investment Opportunities
Build portfolios using Macroaxis predefined set of investing ideas. Many of Macroaxis investing ideas can easily outperform a given market. Ideas can also be optimized per your risk profile before portfolio origination is invoked.
Explore Thematic Ideas
Explore Investing Ideas  

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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.