MNGLN 42 (Israel) Risk Analysis And Volatility

Our philosophy in estimating volatility of an etf is to use all available market data together with etf specific technical indicators that cannot be diversified away. We have found twenty-one technical indicators for MNGLN 42 which you can use to evaluate future volatility of the organization. Please verify MNGLN-42 to check out if risk estimate we provide are consistent with the epected return of 0.0%.
Horizon     30 Days    Login   to change

MNGLN-42 Technical Analysis

Transformation
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MNGLN 42 Projected Return Density Against Market

Assuming 30 trading days horizon, MNGLN 42 has beta of 0.0 indicating the returns on DOW and MNGLN 42 do not appear to be sensitive. Furthermore, It does not look like the company alpha can have any bearing on the equity current valuation.
Assuming 30 trading days horizon, the coefficient of variation of MNGLN 42 is 0.0. The daily returns are destributed with a variance of 0.0 and standard deviation of 0.0. The mean deviation of MNGLN-42 is currently at 0.0. For similar time horizon, the selected benchmark (DOW) has volatility of 0.69
α
Alpha over DOW
=0.00
β
Beta against DOW=0.00
σ
Overall volatility
=0.00
Ir
Information ratio =0.00

MNGLN 42 Return Volatility

the entity accepts 0.0% volatility on return distribution over the 30 days horizon. the entity inherits 0.6937% risk (volatility on return distribution) over the 30 days horizon.
 Performance (%) 
      Timeline 

MNGLN 42 Investment Opportunity

DOW has a standard deviation of returns of 0.69 and is 9.223372036854776E16 times more volatile than MNGLN-42. 0% of all equities and portfolios are less risky than MNGLN 42. Compared to the overall equity markets, volatility of historical daily returns of MNGLN-42 is lower than 0 (%) of all global equities and portfolios over the last 30 days.

MNGLN 42 Current Risk Indicators

MNGLN 42 Suggested Diversification Pairs

See also Stocks Correlation. Please also try Watchlist Optimization module to optimize watchlists to build efficient portfolio or rebalance existing positions based on mean-variance optimization algorithm.
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