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

    111752 -- India Fund  

    INR 1,035  0.67  0.06%

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

    Current PriceHorizonTarget PriceOdds to move above current price in 30 days
     1,035 30 days 1,035  ABOUT 32.05%
    Based on normal probability distribution, the odds of Reliance Money to move above current price in 30 days from now is about 32.05% (This Reliance Money Mgr Retl Qtr Div probability density function shows the probability of Reliance Money Fund to fall within a particular range of prices over 30 days) .
    Assuming 30 trading days horizon, Reliance Money Mgr Retl Qtr Div has beta of -0.0419 . This suggests as returns on benchmark increase, returns on holding Reliance Money are expected to decrease at a much smaller rate. During bear market, however, Reliance Money Mgr Retl Qtr Div is likely to outperform the market. Additionally Reliance Money Mgr Retl Qtr Div has an alpha of 0.0398 implying that it can potentially generate 0.0398% excess return over DOW after adjusting for the inherited market risk (beta).
     Reliance Money Price Density 
    Alpha over DOW
    Beta against DOW=0.04
    Overall volatility
    Information ratio =0.29

    Reliance Money Alerts

    Reliance Money Alerts and Suggestions

    Reliance Money Mgr is not yet fully synchronised with the market data
    The fund holds about 60.75% 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.