UTI Bond (India) Probability of Target Price Finishing Over Current Price

    F00000PE4P -- India Fund  

    INR 17.49  0.06  0.34%

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

    Current PriceHorizonTarget PriceOdds to move above current price in 30 days
     17.49 30 days 17.49  ABOUT 40.44%
    Based on normal probability distribution, the odds of UTI Bond to move above current price in 30 days from now is about 40.44% (This UTI Bond Dir Div probability density function shows the probability of UTI Bond Fund to fall within a particular range of prices over 30 days) .
    Assuming 30 trading days horizon, UTI Bond has beta of 0.011 suggesting as returns on market go up, UTI Bond average returns are expected to increase less than the benchmark. However during bear market, the loss on holding UTI Bond Dir Div will be expected to be much smaller as well. Additionally UTI Bond Dir Div has an alpha of 0.0164 implying that it can potentially generate 0.0164% excess return over DOW after adjusting for the inherited market risk (beta).
     UTI Bond Price Density 
          Price 
    α
    Alpha over DOW
    =0.016428
    β
    Beta against DOW=0.011
    σ
    Overall volatility
    =3.67
    Ir
    Information ratio =0.67

    UTI Bond Alerts

    UTI Bond Alerts and Suggestions

    UTI Bond Dir is not yet fully synchronised with the market data
    The fund retains about 9.85% of its assets under management (AUM) in cash

    Price Density Drivers

    UTI Bond 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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