Sundaram Money (India) Probability of Target Price Finishing Over Current Price

    F00000PX98 -- India Fund  

    INR 13.48  0.00  0.00%

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

    Current PriceHorizonTarget PriceOdds to move above current price in 30 days
     13.48 30 days 13.48  ABOUT 30.66%
    Based on normal probability distribution, the odds of Sundaram Money to move above current price in 30 days from now is about 30.66% (This Sundaram Money Reg Bns probability density function shows the probability of Sundaram Money Fund to fall within a particular range of prices over 30 days) .
    Assuming 30 trading days horizon, Sundaram Money Reg Bns has beta of -0.093 suggesting as returns on benchmark increase, returns on holding Sundaram Money are expected to decrease at a much smaller rate. During bear market, however, Sundaram Money Reg Bns is likely to outperform the market. Additionally Sundaram Money Reg Bns has an alpha of 0.0862 implying that it can potentially generate 0.0862% excess return over DOW after adjusting for the inherited market risk (beta).
     Sundaram Money Price Density 
     
          
    Current Price   Target Price   
    α
    Alpha over DOW
    =0.09
    β
    Beta against DOW=0.09
    σ
    Overall volatility
    =5.23
    Ir
    Information ratio =0.36

    Sundaram Money Alerts

    Sundaram Money Alerts and Suggestions

    The fund retains about 100.0% of its assets under management (AUM) in cash
    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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