Correlation Between NYSE Composite and Oakmark Equity

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

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

Diversification Opportunities for NYSE Composite and Oakmark Equity

0.92

Correlation Coefficient

NYSE Composite  Oakmark Equity

Almost no diversification

The 3 months correlation between NYSE and Oakmark is 0.92. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Oakmark Equity And in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Oakmark Equity And 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 Oakmark Equity. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Oakmark Equity And has no effect on the direction of NYSE Composite i.e., NYSE Composite and Oakmark Equity go up and down completely randomly.

Pair Corralation between NYSE Composite and Oakmark Equity

Assuming the 90 days trading horizon NYSE Composite is expected to generate 1.15 times more return on investment than Oakmark Equity. However, NYSE Composite is 1.15 times more volatile than Oakmark Equity And. It trades about 0.17 of its potential returns per unit of risk. Oakmark Equity And is currently generating about 0.17 per unit of risk. If you would invest  1,901,742  in NYSE Composite on August 31, 2024 and sell it today you would earn a total of  119,240  from holding NYSE Composite or generate 6.27% return on investment over 90 days.

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

NYSE Composite  vs.  Oakmark Equity And

Pair Trading with NYSE Composite and Oakmark Equity

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

The idea behind NYSE Composite and Oakmark Equity And 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.