BOI AXA (India) Alpha and Beta Analysis Overview

This module allows you to check different measures of market premium for BOI AXA Equity Reg Bns as well as systematic risk associated with investing in BOI AXA over a specified time horizon. Check also Trending Equities.
Horizon     30 Days    Login   to change
Run Premiums

BOI AXA Market Premiums

30 days against DJI
Risk Adjusted Performance  


Jensen Alpha  


Total Risk Alpha  


Sortino Ratio  


Treynor Ratio  


BOI AXA Fundamentals

 Better Than Average     
 Worse Than Average Compare BOI AXA to competition

BOI AXA Fundamental Vs Peers

FundamentalsBOI AXAPeer Average
Price to Earning18.33 times7.60 times
Price to Book2.82 times1.04 times
Price to Sales1.85 times1.03 times
One Year Return(7.37) % 2.30 %
Three Year Return18.53 % 3.97 %
Five Year Return14.25 % 1.27 %
Minimum Initial Investment5 K8.09 M

BOI AXA Opportunities

BOI AXA Return and Market Media

The median price of BOI AXA for the period between Mon, Aug 20, 2018 and Wed, Sep 19, 2018 is 35.99 with a coefficient of variation of 91.19. The daily time series for the period is distributed with a sample standard deviation of 19.0, arithmetic mean of 20.83, and mean deviation of 18.23. The Fund did not receive any noticable media coverage during the period.
 Price Growth (%)  

Current Sentiment - 110606

BOI AXA Equity Investor Sentiment

Macroaxis portfolio users are unresponsive in their sentiment towards investing in BOI AXA Equity Reg Bns. What is your opinion about investing in BOI AXA Equity Reg Bns? Are you bullish or bearish?
50% Bullish
50% Bearish

Build Diversified Portfolios

Align your risk with return expectations

Fix your portfolio
By capturing your risk tolerance and investment horizon Macroaxis technology of instant portfolio optimization will compute exactly how much risk is acceptable for your desired return expectations
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.