LIC MF (India) Probability of Target Price Finishing Over Current Price

    F00000VYUT -- India Fund  

    INR 19.42  0.04  0.21%

    LIC MF probability of target price tool provides mechanism to make assumptions about upside and downside potential of LIC MF ULIS 10Y RP UC Dir Mn Div performance during a given time horizon utilizing its historical volatility. Please specify LIC MF time horizon, a valid symbol (red box) and a target price (blue box) you would like LIC MF odds to be computed. Additionally see Investing Opportunities.
    Horizon     30 Days    Login   to change
    Refresh Odds

    LIC MF Target Price Odds to finish over

    Current PriceHorizonTarget PriceOdds to move above current price in 30 days
     19.42 30 days 19.42  ABOUT 18.45%
    Based on normal probability distribution, the odds of LIC MF to move above current price in 30 days from now is about 18.45% (This LIC MF ULIS 10Y RP UC Dir Mn Div probability density function shows the probability of LIC MF Fund to fall within a particular range of prices over 30 days) .
    Assuming 30 trading days horizon, LIC MF ULIS 10Y RP UC Dir Mn Div has beta of -0.0476 suggesting as returns on benchmark increase, returns on holding LIC MF are expected to decrease at a much smaller rate. During bear market, however, LIC MF ULIS 10Y RP UC Dir Mn Div is likely to outperform the market. Additionally LIC MF ULIS 10Y RP UC Dir Mn Div has an alpha of 0.0442 implying that it can potentially generate 0.0442% excess return over DOW after adjusting for the inherited market risk (beta).
     LIC MF Price Density 
    Current Price   Target Price   
    Alpha over DOW
    Beta against DOW=0.05
    Overall volatility
    Information ratio =0.19

    LIC MF Alerts

    LIC MF Alerts and Suggestions

    LIC MF ULIS generates negative expected return over the last 30 days
    The fund retains about 12.4% 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.