Correlation Between NYSE Composite and MBGGR

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Can any of the company-specific risk be diversified away by investing in both NYSE Composite and MBGGR at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining NYSE Composite and MBGGR into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between NYSE Composite and MBGGR 35 03 AUG 25, you can compare the effects of market volatilities on NYSE Composite and MBGGR and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in NYSE Composite with a short position of MBGGR. Check out your portfolio center. Please also check ongoing floating volatility patterns of NYSE Composite and MBGGR.

Diversification Opportunities for NYSE Composite and MBGGR

0.33
  Correlation Coefficient

Weak diversification

The 3 months correlation between NYSE and MBGGR is 0.33. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and MBGGR 35 03 AUG 25 in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on MBGGR 35 03 and NYSE Composite is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on NYSE Composite are associated (or correlated) with MBGGR. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of MBGGR 35 03 has no effect on the direction of NYSE Composite i.e., NYSE Composite and MBGGR go up and down completely randomly.
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Pair Corralation between NYSE Composite and MBGGR

Assuming the 90 days trading horizon NYSE Composite is expected to generate 2.11 times more return on investment than MBGGR. However, NYSE Composite is 2.11 times more volatile than MBGGR 35 03 AUG 25. It trades about 0.06 of its potential returns per unit of risk. MBGGR 35 03 AUG 25 is currently generating about 0.0 per unit of risk. If you would invest  1,582,860  in NYSE Composite on October 1, 2024 and sell it today you would earn a total of  340,988  from holding NYSE Composite or generate 21.54% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthVery Weak
Accuracy26.74%
ValuesDaily Returns

NYSE Composite  vs.  MBGGR 35 03 AUG 25

 Performance 
       Timeline  

NYSE Composite and MBGGR Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with NYSE Composite and MBGGR

The main advantage of trading using opposite NYSE Composite and MBGGR positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NYSE Composite position performs unexpectedly, MBGGR can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in MBGGR will offset losses from the drop in MBGGR's long position.
The idea behind NYSE Composite and MBGGR 35 03 AUG 25 pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.
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Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.

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