SBI Magnum (India) Probability of Target Price Finishing Over Current Price

    F00000PDCS -- India Fund  

    INR 13.70  0.02  0.15%

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

    Current PriceHorizonTarget PriceOdds to move above current price in 30 days
     13.70 30 days 13.70  ABOUT 31.66%
    Based on normal probability distribution, the odds of SBI Magnum to move above current price in 30 days from now is about 31.66% (This SBI Magnum Income Dir Div probability density function shows the probability of SBI Magnum Fund to fall within a particular range of prices over 30 days) .
    Assuming 30 trading days horizon, the fund has beta coefficient of 1.903 suggesting as the benchmark fluctuates upward, the company is expected to outperform it on average . However, if the benchmark returns are expected to be negative, SBI Magnum will likely underperform. Additionally The company has an alpha of 0.2405 implying that it can potentially generate 0.2405% excess return over DOW after adjusting for the inherited market risk (beta).
     SBI Magnum Price Density 
          Price 
    α
    Alpha over DOW
    =0.24
    β
    Beta against DOW=1.90
    σ
    Overall volatility
    =3.49
    Ir
    Information ratio =0.037674

    SBI Magnum Alerts

    SBI Magnum Alerts and Suggestions

    SBI Magnum Income is not yet fully synchronised with the market data
    The fund retains about 5.08% of its assets under management (AUM) in cash

    Price Density Drivers

    SBI Magnum Health Indicators

    Additionally see Investing Opportunities. Please also try Pattern Recognition module to use different pattern recognition models to time the market across multiple global exchanges.
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