Correlation Between NYSE Composite and Legg Mason
Can any of the company-specific risk be diversified away by investing in both NYSE Composite and Legg Mason 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 Legg Mason into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between NYSE Composite and Legg Mason Bw, you can compare the effects of market volatilities on NYSE Composite and Legg Mason 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 Legg Mason. Check out your portfolio center. Please also check ongoing floating volatility patterns of NYSE Composite and Legg Mason.
Diversification Opportunities for NYSE Composite and Legg Mason
0.6 | Correlation Coefficient |
Poor diversification
The 3 months correlation between NYSE and Legg is 0.6. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Legg Mason Bw in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Legg Mason Bw 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 Legg Mason. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Legg Mason Bw has no effect on the direction of NYSE Composite i.e., NYSE Composite and Legg Mason go up and down completely randomly.
Pair Corralation between NYSE Composite and Legg Mason
Assuming the 90 days trading horizon NYSE Composite is expected to generate 1.53 times more return on investment than Legg Mason. However, NYSE Composite is 1.53 times more volatile than Legg Mason Bw. It trades about 0.21 of its potential returns per unit of risk. Legg Mason Bw is currently generating about -0.14 per unit of risk. If you would invest 1,911,944 in NYSE Composite on October 21, 2024 and sell it today you would earn a total of 48,793 from holding NYSE Composite or generate 2.55% 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. Legg Mason Bw
Performance |
| Timeline |
NYSE Composite and Legg Mason Volatility Contrast
Predicted Return Density |
| Returns |
NYSE Composite
Pair trading matchups for NYSE Composite
Legg Mason Bw
Pair trading matchups for Legg Mason
Pair Trading with NYSE Composite and Legg Mason
The main advantage of trading using opposite NYSE Composite and Legg Mason positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NYSE Composite position performs unexpectedly, Legg Mason 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 Legg Mason will offset losses from the drop in Legg Mason's long position.| NYSE Composite vs. National Health Investors | NYSE Composite vs. Arbor Realty Trust | NYSE Composite vs. Canaf Investments | NYSE Composite vs. Freedom Holding Corp |
| Legg Mason vs. Clearbridge Aggressive Growth | Legg Mason vs. Clearbridge Small Cap | Legg Mason vs. Qs International Equity | Legg Mason vs. Clearbridge Appreciation Fund |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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