Old Mutual (Ireland) Probability of Target Price Finishing Over Current Price

    F00000QQD8 -- Ireland Fund  

    GBp 1,569  56.00  3.70%

    Old Mutual probability of target price tool provides mechanism to make assumptions about upside and downside potential of Old Mutual US Dividend U1 GBP Acc performance during a given time horizon utilizing its historical volatility. Please specify Old Mutual time horizon, a valid symbol (red box) and a target price (blue box) you would like Old Mutual odds to be computed. Additionally see Investing Opportunities.
    Horizon     30 Days    Login   to change
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    Old Mutual Target Price Odds to finish over

    Current PriceHorizonTarget PriceOdds to move above current price in 30 days
     1,569 30 days 1,569  ROUGHLY 2.82%
    Based on normal probability distribution, the odds of Old Mutual to move above current price in 30 days from now is roughly 2.82% (This Old Mutual US Dividend U1 GBP Acc probability density function shows the probability of Old Mutual Fund to fall within a particular range of prices over 30 days) .
    Assuming 30 trading days horizon, Old Mutual US Dividend U1 GBP Acc has beta of -0.2004 suggesting as returns on benchmark increase, returns on holding Old Mutual are expected to decrease at a much smaller rate. During bear market, however, Old Mutual US Dividend U1 GBP Acc is likely to outperform the market. Additionally Old Mutual US Dividend U1 GBP Acc has an alpha of 0.0724 implying that it can potentially generate 0.0724% excess return over DOW after adjusting for the inherited market risk (beta).
     Old Mutual Price Density 
     
          
    Current Price   Target Price   
    α
    Alpha over DOW
    =0.07
    β
    Beta against DOW=0.2
    σ
    Overall volatility
    =643.52
    Ir
    Information ratio =0.19

    Old Mutual Alerts

    Old Mutual Alerts and Suggestions

    The fund retains 97.27% of its assets under management (AUM) in equities
    Additionally see Investing Opportunities. Please also try Portfolio Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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