China Ming Asset Turnover Trend

China Ming Yang Wind Power Group Limited -- USA Stock  

USD 2.44  2.44  9,223,372,036,855%

This module enables investors to look at China Ming 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 277.5 M, Cost of Revenue of 6.7 B or Earning Before Interest and Taxes EBIT of 455.5 M, but also many exotic indicators such as Asset Turnover of 0.5494, Book Value per Share of 40.8694 or Current Ratio of 1.06. This module is a perfect complement to use when analyzing China Ming Valuation or Volatility. It can also complement various China Ming Technical models. Please see also analysis of China Ming Correlation with competitors.
Showing smoothed Asset Turnover of China Ming Yang Wind Power Group Limited with missing and latest data points interpolated. Asset turnover is a measure of a firms operating efficiency, calculated by dividing Revenues by Average Assets. Often a component of DuPont return on equity analysis.
Asset Turnover  10 Years Trend  
Decreasing
Slightly volatile
 Asset Turnover 
      Timeline 

Regression Statistics

Arithmetic Mean  0.59
Geometric Mean  0.55
Coefficient Of Variation  36.52
Mean Deviation  0.18
Median  0.55
Standard Deviation  0.21
Sample Variance  0.045901
Range  0.58
R Value (0.68)
Mean Square Error  0.026625
R Squared  0.47
Significance  0.009882
Slope (0.037646)
Total Sum of Squares  0.55

China Ming Asset Turnover Over Time

2016-12-31  0.47 
2017-12-31  0.47 
2018-12-31  0.55 

Other Fundumenentals

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MY - USA Stock
China Ming Yang Wind Power Group Limited
Fama & French Machinery
Machinery
Fama & French Classification
LocationCHINA
ExchangeNew York Stock Exchange
$2.44

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Please see also analysis of China Ming 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.