Sarasin IE (Ireland) Probability of Target Price Finishing Over Current Price

    F00000OWJO -- Ireland Fund  

    USD 25.23  0.58  2.25%

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

    Current PriceHorizonTarget PriceOdds to move above current price in 30 days
     25.23 30 days 25.23  ABOUT 42.78%
    Based on normal probability distribution, the odds of Sarasin IE to move above current price in 30 days from now is about 42.78% (This Sarasin IE EquiSar Glbl Thmtc P probability density function shows the probability of Sarasin IE Fund to fall within a particular range of prices over 30 days) .
    Assuming 30 trading days horizon, Sarasin IE EquiSar Glbl Thmtc P has beta of -0.4418 suggesting as returns on benchmark increase, returns on holding Sarasin IE are expected to decrease at a much smaller rate. During bear market, however, Sarasin IE EquiSar Glbl Thmtc P is likely to outperform the market. Additionally The company has an alpha of 0.0999 implying that it can potentially generate 0.0999% excess return over DOW after adjusting for the inherited market risk (beta).
     Sarasin IE Price Density 
    Alpha over DOW
    Beta against DOW=0.44
    Overall volatility
    Information ratio =0.13

    Sarasin IE Alerts

    Sarasin IE Alerts and Suggestions

    Sarasin IE EquiSar is not yet fully synchronised with the market data
    The fund retains 98.11% of its assets under management (AUM) in equities
    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.