The fund owns Beta (Systematic Risk) of 0.8236 which conveys that as returns on market increase, NE GLB returns are expected to increase less than the market. However during bear market, the loss on holding NE GLB will be expected to be smaller as well.. Although it is extremely important to respect NE GLB EQ
existing price patterns
, it is better to be realistic regarding the information on equity price patterns
. The way in which we are estimating future performance of any fund is to evaluate the business as a whole together with its past performance including all available fundamental and technical indicators
. By analyzing NE GLB EQ technical indicators
you can at this moment evaluate if the expected return of 0.0% will be sustainable into the future.
NE GLB EQ Relative Risk vs. Return Landscape
If you would invest 12,513
in NE GLB EQ USD AD AC on October 18, 2018
and sell it today you would earn a total of 0.00
from holding NE GLB EQ USD AD AC or generate 0.0%
return on investment over 30
days. NE GLB EQ USD AD AC is generating negative expected returns and assumes 0.0% volatility on return distribution over the 30 days horizon. Simply put, 0% of equities are less volatile than NE GLB EQ USD AD AC and 99% of equity instruments are likely to generate higher returns than the company over the next 30 trading days.
Daily Expected Return (%)
NE GLB Market Risk Analysis
Sharpe Ratio = 0.0
Based on monthly moving average NE GLB is performing at about 0% of its full potential. If added to a well diversified portfolio the total return can be enhanced and market risk can be reduced. You can increase risk-adjusted return of NE GLB
by adding it to a well-diversified
Risk-Adjusted Fund Performance
Over the last 30 days NE GLB EQ USD AD AC has generated negative risk-adjusted returns adding no value to fund investors.
|NE GLB EQ is not yet fully synchronised with the market data|
|NE GLB EQ has some characteristics of a very speculative penny stock|
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. Please also try Watchlist Optimization
module to optimize watchlists to build efficient portfolio or rebalance existing positions based on mean-variance optimization algorithm.