Correlation Between NYSE Composite and Lamar Advertising
Can any of the company-specific risk be diversified away by investing in both NYSE Composite and Lamar Advertising 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 Lamar Advertising into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between NYSE Composite and Lamar Advertising, you can compare the effects of market volatilities on NYSE Composite and Lamar Advertising 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 Lamar Advertising. Check out your portfolio center. Please also check ongoing floating volatility patterns of NYSE Composite and Lamar Advertising.
Diversification Opportunities for NYSE Composite and Lamar Advertising
0.58 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between NYSE and Lamar is 0.58. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Lamar Advertising in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Lamar Advertising 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 Lamar Advertising. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Lamar Advertising has no effect on the direction of NYSE Composite i.e., NYSE Composite and Lamar Advertising go up and down completely randomly.
Pair Corralation between NYSE Composite and Lamar Advertising
Assuming the 90 days trading horizon NYSE Composite is expected to generate 0.43 times more return on investment than Lamar Advertising. However, NYSE Composite is 2.33 times less risky than Lamar Advertising. It trades about 0.37 of its potential returns per unit of risk. Lamar Advertising is currently generating about 0.01 per unit of risk. If you would invest 1,924,339 in NYSE Composite on September 4, 2024 and sell it today you would earn a total of 94,242 from holding NYSE Composite or generate 4.9% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
NYSE Composite vs. Lamar Advertising
Performance |
Timeline |
NYSE Composite and Lamar Advertising Volatility Contrast
Predicted Return Density |
Returns |
NYSE Composite
Pair trading matchups for NYSE Composite
Lamar Advertising
Pair trading matchups for Lamar Advertising
Pair Trading with NYSE Composite and Lamar Advertising
The main advantage of trading using opposite NYSE Composite and Lamar Advertising positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NYSE Composite position performs unexpectedly, Lamar Advertising 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 Lamar Advertising will offset losses from the drop in Lamar Advertising's long position.NYSE Composite vs. Kite Realty Group | NYSE Composite vs. Tradeweb Markets | NYSE Composite vs. Meiwu Technology Co | NYSE Composite vs. Uber Technologies |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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