United States Price to Earnings Ratio Trend

UNG -- USA Etf  

USD 23.19  0.07  0.30%

This module enables investors to look at United States 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 Operating Expenses of 7.3 M, Selling General and Administrative Expense of 6.7 M or Weighted Average Shares of 49.2 M, but also many exotic indicators such as Book Value per Share of 8.7621, Debt to Equity Ratio of 0.13 or EBITDA Margin of 1.0389. This module is a perfect complement to use when analyzing United States Valuation or Volatility. It can also complement various United States Technical models. Also please take a look at analysis of United States Correlation with competitors.
Showing smoothed Price to Earnings Ratio of United States Natural Gas with missing and latest data points interpolated. An alternative to [PE] representing the ratio between Adjusted Share Price and Earnings per Basic Share USD.

3.38 times

          10 Years Trend
Slightly volatile
 Price to Earnings Ratio 

Regression Statistics

Arithmetic Mean  1.75
Geometric Mean  2.99
Coefficient Of Variation  417.45
Mean Deviation  5.83
Median (1.47)
Standard Deviation  7.29
Sample Variance  53.15
Range  18.95
R Value  0.48
Mean Square Error  44.39
R Squared  0.23
Significance  0.09
Slope  0.91
Total Sum of Squares  637.78

United States Price to Earnings Ratio Over Time

2016-12-31  15.57 
2017-12-31  15.57 
2018-12-31 (3.38) 

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  
Also please take a look at analysis of United States Correlation with competitors. Please also try Portfolio Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.