As of 22 of February Old Mutual holds Risk Adjusted Performance of
(0.08) and Coefficient Of Variation of (2,479). Old Mutual Global technical analysis gives you tools to exploit past prices in attempt to determine a pattern that determines the direction of the fund future prices. In other words you can use this information to find out if the fund will indeed mirror its model of past market data or the prices will eventually revert. We found nineteen technical drivers for Old Mutual Global which can be compared to its competitors. Please check Old Mutual Global Mean Deviation and the relationship between Semi Deviation and Coefficient Of VariationMean Deviation, Downside Deviation, Standard Deviation, as well as the relationship between Semi Deviation and Coefficient Of Variation to decide if Old Mutual Global is priced some-what accurately providing market reflects its current price of 1.26 per share.
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Old Mutual Global Technical Analysis
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Old Mutual Global Trend AnalysisUse this graph to draw trend lines for Old Mutual Global Eq AR A Acc. You can use it to identify possible trend reversals for Old Mutual as well as other signals and approximate when it will take place. Remember, you need at least two touches of the trend line with actual Old Mutual price movement. To start drawing, click on the pencil icon on top-right. To remove the trend, use eraser icon.
Old Mutual Best Fit Change LineThe following chart estimates an ordinary least squares regression model for Old Mutual Global Eq AR A Acc applied against its price change over selected period. The best fit line has a slop of ? % . It has 0 observation points and a regression sum of squares at 0.0, which is the sum of squared deviations for the predicted Old Mutual price change compared to its average price change.
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|Risk Adjusted Performance||(0.08)|
|Market Risk Adjusted Performance||15.17|
|Coefficient Of Variation||(2,479)|
|Total Risk Alpha||(0.16)|
|Value At Risk||(1.54)|
Additionally see Investing Opportunities. Please also try Watchlist Optimization module to optimize watchlists to build efficient portfolio or rebalance existing positions based on mean-variance optimization algorithm.