Correlation Between State Street and Gmo Emerging
Can any of the company-specific risk be diversified away by investing in both State Street and Gmo Emerging 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 State Street and Gmo Emerging into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between State Street Target and Gmo Emerging Markets, you can compare the effects of market volatilities on State Street and Gmo Emerging 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 State Street with a short position of Gmo Emerging. Check out your portfolio center. Please also check ongoing floating volatility patterns of State Street and Gmo Emerging.
Diversification Opportunities for State Street and Gmo Emerging
0.96 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between State and Gmo is 0.96. Overlapping area represents the amount of risk that can be diversified away by holding State Street Target and Gmo Emerging Markets in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Gmo Emerging Markets and State Street 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 State Street Target are associated (or correlated) with Gmo Emerging. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Gmo Emerging Markets has no effect on the direction of State Street i.e., State Street and Gmo Emerging go up and down completely randomly.
Pair Corralation between State Street and Gmo Emerging
Assuming the 90 days horizon State Street is expected to generate 1.33 times less return on investment than Gmo Emerging. But when comparing it to its historical volatility, State Street Target is 1.31 times less risky than Gmo Emerging. It trades about 0.21 of its potential returns per unit of risk. Gmo Emerging Markets is currently generating about 0.22 of returns per unit of risk over similar time horizon. If you would invest 1,205 in Gmo Emerging Markets on May 4, 2025 and sell it today you would earn a total of 125.00 from holding Gmo Emerging Markets or generate 10.37% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 100.0% |
Values | Daily Returns |
State Street Target vs. Gmo Emerging Markets
Performance |
Timeline |
State Street Target |
Gmo Emerging Markets |
State Street and Gmo Emerging Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with State Street and Gmo Emerging
The main advantage of trading using opposite State Street and Gmo Emerging positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if State Street position performs unexpectedly, Gmo Emerging 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 Gmo Emerging will offset losses from the drop in Gmo Emerging's long position.State Street vs. Queens Road Small | State Street vs. Valic Company I | State Street vs. Great West Loomis Sayles | State Street vs. Pace Smallmedium Value |
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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 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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