Correlation Between NYSE Composite and Oppenheimer Main
Can any of the company-specific risk be diversified away by investing in both NYSE Composite and Oppenheimer Main 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 Oppenheimer Main into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between NYSE Composite and Oppenheimer Main Street, you can compare the effects of market volatilities on NYSE Composite and Oppenheimer Main 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 Oppenheimer Main. Check out your portfolio center. Please also check ongoing floating volatility patterns of NYSE Composite and Oppenheimer Main.
Diversification Opportunities for NYSE Composite and Oppenheimer Main
0.88 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between NYSE and Oppenheimer is 0.88. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Oppenheimer Main Street in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Oppenheimer Main Street 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 Oppenheimer Main. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Oppenheimer Main Street has no effect on the direction of NYSE Composite i.e., NYSE Composite and Oppenheimer Main go up and down completely randomly.
Pair Corralation between NYSE Composite and Oppenheimer Main
Assuming the 90 days trading horizon NYSE Composite is expected to generate 0.58 times more return on investment than Oppenheimer Main. However, NYSE Composite is 1.73 times less risky than Oppenheimer Main. It trades about 0.07 of its potential returns per unit of risk. Oppenheimer Main Street is currently generating about 0.02 per unit of risk. If you would invest 1,919,556 in NYSE Composite on September 18, 2024 and sell it today you would earn a total of 43,212 from holding NYSE Composite or generate 2.25% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 98.44% |
Values | Daily Returns |
NYSE Composite vs. Oppenheimer Main Street
Performance |
Timeline |
NYSE Composite and Oppenheimer Main Volatility Contrast
Predicted Return Density |
Returns |
NYSE Composite
Pair trading matchups for NYSE Composite
Oppenheimer Main Street
Pair trading matchups for Oppenheimer Main
Pair Trading with NYSE Composite and Oppenheimer Main
The main advantage of trading using opposite NYSE Composite and Oppenheimer Main positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NYSE Composite position performs unexpectedly, Oppenheimer Main 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 Oppenheimer Main will offset losses from the drop in Oppenheimer Main's long position.NYSE Composite vs. Siriuspoint | NYSE Composite vs. Fomento Economico Mexicano | NYSE Composite vs. Boston Beer | NYSE Composite vs. Ambev SA ADR |
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Check out your portfolio center.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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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