Vanguard US (Ireland) Probability of Target Price Finishing Over

    F00000SK63 -- Ireland Fund  

    GBp 10,355  20.00  0.19%

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

    Current PriceHorizonTarget PriceOdds to move above current price in 30 days
     10,355 30 days 10,355  ABOUT 19.36%
    Based on normal probability distribution, the odds of Vanguard US to move above current price in 30 days from now is about 19.36% (This Vanguard US Gov Bond Index GBP Hg Acc probability density function shows the probability of Vanguard US Fund to fall within a particular range of prices over 30 days) .
    Assuming 30 trading days horizon, Vanguard US has beta of 0.0 suggesting unless we do not have required data, the returns on DOW and Vanguard US are completely uncorrelated. Additionally Vanguard US Gov Bond Index GBP Hg AccIt does not look like Vanguard US alpha can have any bearing on the equity current valuation.
     Vanguard US Price Density 
     
          
    Current Price   Target Price   
    α
    Alpha over DOW
    =0.00
    β
    Beta against DOW=0.00
    σ
    Overall volatility
    =5,307
    Ir
    Information ratio =0.00

    Vanguard US Alerts

    Vanguard US Alerts and Suggestions
    Vanguard US Gov generates negative expected return over the last 30 days
    The fund retains about 96.9% of its assets under management (AUM) in fixed income securities
    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.