Correlation Between Glg Intl and Blackrock High
Can any of the company-specific risk be diversified away by investing in both Glg Intl and Blackrock High 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 Glg Intl and Blackrock High into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Glg Intl Small and Blackrock High Yield, you can compare the effects of market volatilities on Glg Intl and Blackrock High 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 Glg Intl with a short position of Blackrock High. Check out your portfolio center. Please also check ongoing floating volatility patterns of Glg Intl and Blackrock High.
Diversification Opportunities for Glg Intl and Blackrock High
0.66 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Glg and Blackrock is 0.66. Overlapping area represents the amount of risk that can be diversified away by holding Glg Intl Small and Blackrock High Yield in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Blackrock High Yield and Glg Intl 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 Glg Intl Small are associated (or correlated) with Blackrock High. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Blackrock High Yield has no effect on the direction of Glg Intl i.e., Glg Intl and Blackrock High go up and down completely randomly.
Pair Corralation between Glg Intl and Blackrock High
Assuming the 90 days horizon Glg Intl Small is expected to generate 5.31 times more return on investment than Blackrock High. However, Glg Intl is 5.31 times more volatile than Blackrock High Yield. It trades about 0.05 of its potential returns per unit of risk. Blackrock High Yield is currently generating about -0.04 per unit of risk. If you would invest 8,134 in Glg Intl Small on September 20, 2024 and sell it today you would earn a total of 210.00 from holding Glg Intl Small or generate 2.58% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Glg Intl Small vs. Blackrock High Yield
Performance |
Timeline |
Glg Intl Small |
Blackrock High Yield |
Glg Intl and Blackrock High Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Glg Intl and Blackrock High
The main advantage of trading using opposite Glg Intl and Blackrock High positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Glg Intl position performs unexpectedly, Blackrock High 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 Blackrock High will offset losses from the drop in Blackrock High's long position.Glg Intl vs. Balanced Fund Investor | Glg Intl vs. Qs Large Cap | Glg Intl vs. Red Oak Technology | Glg Intl vs. Materials Portfolio Fidelity |
Blackrock High vs. Glg Intl Small | Blackrock High vs. Kinetics Small Cap | Blackrock High vs. Small Pany Growth | Blackrock High vs. Lebenthal Lisanti Small |
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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