JM High (India) Probability of Target Price Finishing Over Current Price

    120407 -- India Fund  

    INR 11.01  0.00  0.00%

    JM High probability of target price tool provides mechanism to make assumptions about upside and downside potential of JM High Liquidity Dir Wk Div performance during a given time horizon utilizing its historical volatility. Please specify JM High time horizon, a valid symbol (red box) and a target price (blue box) you would like JM High odds to be computed. Check also Trending Equities.
    Horizon     30 Days    Login   to change
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    JM High Target Price Odds to finish over

    Current PriceHorizonTarget PriceOdds to move above current price in 30 days
     11.01 30 days 11.01  ABOUT 56.99%
    Based on normal probability distribution, the odds of JM High to move above current price in 30 days from now is about 56.99% (This JM High Liquidity Dir Wk Div probability density function shows the probability of JM High Fund to fall within a particular range of prices over 30 days) .
    Assuming 30 trading days horizon, JM High Liquidity Dir Wk Div has beta of -0.0071 . This suggests as returns on benchmark increase, returns on holding JM High are expected to decrease at a much smaller rate. During bear market, however, JM High Liquidity Dir Wk Div is likely to outperform the market. Additionally JM High Liquidity Dir Wk Div has a negative alpha implying that the risk taken by holding this equity is not justified. The company is significantly underperforming DOW
     JM High Price Density 
          Price 
    α
    Alpha over DOW
    =0.0088
    β
    Beta against DOW=0.0071
    σ
    Overall volatility
    =0.005676
    Ir
    Information ratio =2.86

    JM High Alerts

    JM High Alerts and Suggestions

    JM High Liquidity is not yet fully synchronised with the market data
    The fund holds about 100.0% of its total net assets in cash
    Check also Trending Equities. Please also try Watchlist Optimization module to optimize watchlists to build efficient portfolio or rebalance existing positions based on mean-variance optimization algorithm.
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