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

    F0GBR06SJW -- India Fund  

    INR 10.14  0.01  0.1%

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

    Current PriceHorizonTarget PriceOdds to move above current price in 30 days
     10.14 30 days 10.14  ABOUT 37.67%
    Based on normal probability distribution, the odds of IDFC Money to move above current price in 30 days from now is about 37.67% (This IDFC Money Mgr Trs Reg Wk Div probability density function shows the probability of IDFC Money Fund to fall within a particular range of prices over 30 days) .
    Assuming 30 trading days horizon, IDFC Money has beta of 0.0041 suggesting as returns on market go up, IDFC Money average returns are expected to increase less than the benchmark. However during bear market, the loss on holding IDFC Money Mgr Trs Reg Wk Div will be expected to be much smaller as well. Additionally IDFC Money Mgr Trs Reg Wk Div has a negative alpha implying that the risk taken by holding this equity is not justified. The company is significantly underperforming DOW
     IDFC Money Price Density 
          Price 
    α
    Alpha over DOW
    =0.0083
    β
    Beta against DOW=0.0041
    σ
    Overall volatility
    =2.98
    Ir
    Information ratio =0.92

    IDFC Money Alerts

    IDFC Money Alerts and Suggestions

    IDFC Money Mgr is not yet fully synchronised with the market data
    The fund retains about 57.47% of its assets under management (AUM) in cash
    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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