Correlation Between NYSE Composite and 91324PES7

Can any of the company-specific risk be diversified away by investing in both NYSE Composite and 91324PES7 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 91324PES7 into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between NYSE Composite and UNH 5875 15 FEB 53, you can compare the effects of market volatilities on NYSE Composite and 91324PES7 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 91324PES7. Check out your portfolio center. Please also check ongoing floating volatility patterns of NYSE Composite and 91324PES7.

Diversification Opportunities for NYSE Composite and 91324PES7

0.19

Correlation Coefficient

NYSE Composite  91324PES7

Average diversification

The 3 months correlation between NYSE and 91324PES7 is 0.19. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and UNH 5875 15 FEB 53 in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on UNH 5875 15 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 91324PES7. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of UNH 5875 15 has no effect on the direction of NYSE Composite i.e., NYSE Composite and 91324PES7 go up and down completely randomly.

Pair Corralation between NYSE Composite and 91324PES7

Assuming the 90 days trading horizon NYSE Composite is expected to generate 5.97 times less return on investment than 91324PES7. But when comparing it to its historical volatility, NYSE Composite is 1.81 times less risky than 91324PES7. It trades about 0.04 of its potential returns per unit of risk. UNH 5875 15 FEB 53 is currently generating about 0.12 of returns per unit of risk over similar time horizon. If you would invest  10,047  in UNH 5875 15 FEB 53 on December 25, 2024 and sell it today you would earn a total of  1,071  from holding UNH 5875 15 FEB 53 or generate 10.66% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Insignificant
Accuracy	100.0%
Values	Daily Returns

NYSE Composite  vs.  UNH 5875 15 FEB 53

Pair Trading with NYSE Composite and 91324PES7

The main advantage of trading using opposite NYSE Composite and 91324PES7 positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NYSE Composite position performs unexpectedly, 91324PES7 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 91324PES7 will offset losses from the drop in 91324PES7's long position.

The idea behind NYSE Composite and UNH 5875 15 FEB 53 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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.