Newpark Resources Total Debt USD Trend

NR -- USA Stock  

USD 9.80  0.50  4.85%

This module enables investors to look at Newpark Resources 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 515.1 M, Gross Profit of 139.9 M or Interest Expense of 11.6 M, but also many exotic indicators such as Interest Coverage of 14.7294, Long Term Debt to Equity of 0.3872 or Calculated Tax Rate of 40.224. This module is a perfect complement to use when analyzing Newpark Resources Valuation or Volatility. It can also complement various Newpark Resources Technical models. Please see also analysis of Newpark Resources Correlation with competitors.
Showing smoothed Total Debt USD of Newpark Resources with missing and latest data points interpolated. Total Debt in USD
Total Debt USD10 Years Trend
Decreasing
Stable
 Total Debt USD 
      Timeline 

Regression Statistics

Arithmetic Mean  161,999,475
Geometric Mean  161,517,983
Coefficient Of Variation  8.46
Mean Deviation  9,552,459
Median  156,268,000
Standard Deviation  13,697,377
Range  41,201,000
R Value (0.14)
R Squared  0.019026
Significance  0.67
Slope (524,011)

Newpark Resources Total Debt USD Over Time

2016-12-31  156,268,000 
2017-12-31  156,268,000 
2018-12-31  183,844,706 

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  

Newpark Resources Upcoming Company Events

Upcoming Quarterly ReportApril 27, 2017
Next Earnings ReportJuly 27, 2017
Please see also analysis of Newpark Resources Correlation with competitors. Please also try Portfolio Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.