Equity LifeStyle Payment of Dividends and Other Cash Distributions Trend

ELS -- USA Stock  

USD 92.72  0.31  0.33%

This module enables investors to look at Equity LifeStyle 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 177.7 M, Cost of Revenue of 374.1 M or Earning Before Interest and Taxes EBIT of 283.6 M, but also many exotic indicators such as Interest Coverage of 2.7334, Long Term Debt to Equity of 2.3121 or Receivables Turnover of 24.9699. This module is a perfect complement to use when analyzing Equity LifeStyle Valuation or Volatility. It can also complement various Equity LifeStyle Technical models. Additionally see analysis of Equity LifeStyle Correlation with competitors.
Showing smoothed Payment of Dividends and Other Cash Distributions of Equity LifeStyle Properties with missing and latest data points interpolated. A component of Net Cash Flow from Financing representing dividends and dividend equivalents paid on common stock and restricted stock units.
Payment of Dividends and Other Cash Distributions 10 Years Trend
Decreasing
Slightly volatile
 Payment of Dividends and Other Cash Distributions  
      Timeline 

Regression Statistics

Arithmetic Mean (92,117,886)
Geometric Mean  81,537,967
Coefficient Of Variation (49.41)
Mean Deviation  40,186,053
Median (68,194,000)
Standard Deviation  45,511,480
Range  113,024,000
R Value (0.93)
R Squared  0.86
Significance  0.00001586
Slope (11,683,108)

Equity LifeStyle Payment of Dividends and Other Cash Distributions Over Time

2016-12-31 (151,945,000) 
2017-12-31 (151,945,000) 
2018-12-31 (149,732,632) 

Other Fundumenentals

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Equity LifeStyle Upcoming Company Events

Upcoming Quarterly ReportApril 17, 2017
Next Earnings ReportJuly 17, 2017
Additionally see analysis of Equity LifeStyle Correlation with competitors. Please also try Watchlist Optimization module to optimize watchlists to build efficient portfolio or rebalance existing positions based on mean-variance optimization algorithm.