Correlation Between Legg Mason and Ab Pennsylvania
Can any of the company-specific risk be diversified away by investing in both Legg Mason and Ab Pennsylvania 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 Legg Mason and Ab Pennsylvania into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Legg Mason Bw and Ab Pennsylvania Portfolio, you can compare the effects of market volatilities on Legg Mason and Ab Pennsylvania 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 Legg Mason with a short position of Ab Pennsylvania. Check out your portfolio center. Please also check ongoing floating volatility patterns of Legg Mason and Ab Pennsylvania.
Diversification Opportunities for Legg Mason and Ab Pennsylvania
0.61 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Legg and APACX is 0.61. Overlapping area represents the amount of risk that can be diversified away by holding Legg Mason Bw and Ab Pennsylvania Portfolio in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Ab Pennsylvania Portfolio and Legg Mason 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 Legg Mason Bw are associated (or correlated) with Ab Pennsylvania. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Ab Pennsylvania Portfolio has no effect on the direction of Legg Mason i.e., Legg Mason and Ab Pennsylvania go up and down completely randomly.
Pair Corralation between Legg Mason and Ab Pennsylvania
Assuming the 90 days horizon Legg Mason Bw is expected to generate 3.52 times more return on investment than Ab Pennsylvania. However, Legg Mason is 3.52 times more volatile than Ab Pennsylvania Portfolio. It trades about 0.09 of its potential returns per unit of risk. Ab Pennsylvania Portfolio is currently generating about 0.06 per unit of risk. If you would invest 1,511 in Legg Mason Bw on December 2, 2024 and sell it today you would earn a total of 606.00 from holding Legg Mason Bw or generate 40.11% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Legg Mason Bw vs. Ab Pennsylvania Portfolio
Performance |
Timeline |
Legg Mason Bw |
Ab Pennsylvania Portfolio |
Legg Mason and Ab Pennsylvania Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Legg Mason and Ab Pennsylvania
The main advantage of trading using opposite Legg Mason and Ab Pennsylvania positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Legg Mason position performs unexpectedly, Ab Pennsylvania 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 Ab Pennsylvania will offset losses from the drop in Ab Pennsylvania's long position.Legg Mason vs. T Rowe Price | Legg Mason vs. High Yield Fund Investor | Legg Mason vs. Payden High Income | Legg Mason vs. City National Rochdale |
Ab Pennsylvania vs. T Rowe Price | Ab Pennsylvania vs. Alternative Asset Allocation | Ab Pennsylvania vs. Rbb Fund | Ab Pennsylvania vs. Rbc Emerging Markets |
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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