Correlation Between NYSE Composite and Pnc Balanced

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

By analyzing existing cross correlation between NYSE Composite and Pnc Balanced Allocation, you can compare the effects of market volatilities on NYSE Composite and Pnc Balanced 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 Pnc Balanced. Check out your portfolio center. Please also check ongoing floating volatility patterns of NYSE Composite and Pnc Balanced.

Diversification Opportunities for NYSE Composite and Pnc Balanced

0.77

Correlation Coefficient

NYSE Composite  Pnc Balanced

Poor diversification

The 3 months correlation between NYSE and Pnc is 0.77. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Pnc Balanced Allocation in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Pnc Balanced Allocation 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 Pnc Balanced. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Pnc Balanced Allocation has no effect on the direction of NYSE Composite i.e., NYSE Composite and Pnc Balanced go up and down completely randomly.

Pair Corralation between NYSE Composite and Pnc Balanced

Assuming the 90 days trading horizon NYSE Composite is expected to generate 0.92 times more return on investment than Pnc Balanced. However, NYSE Composite is 1.09 times less risky than Pnc Balanced. It trades about 0.09 of its potential returns per unit of risk. Pnc Balanced Allocation is currently generating about 0.07 per unit of risk. If you would invest  1,663,938  in NYSE Composite on October 9, 2024 and sell it today you would earn a total of  257,450  from holding NYSE Composite or generate 15.47% return on investment over 90 days.

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

NYSE Composite  vs.  Pnc Balanced Allocation

Pair Trading with NYSE Composite and Pnc Balanced

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

The idea behind NYSE Composite and Pnc Balanced Allocation 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.