Correlation Between GreenTech Environmental and Suzhou Longway
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By analyzing existing cross correlation between GreenTech Environmental Co and Suzhou Longway Electronic, you can compare the effects of market volatilities on GreenTech Environmental and Suzhou Longway 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 GreenTech Environmental with a short position of Suzhou Longway. Check out your portfolio center. Please also check ongoing floating volatility patterns of GreenTech Environmental and Suzhou Longway.
Diversification Opportunities for GreenTech Environmental and Suzhou Longway
0.05 | Correlation Coefficient |
Significant diversification
The 3 months correlation between GreenTech and Suzhou is 0.05. Overlapping area represents the amount of risk that can be diversified away by holding GreenTech Environmental Co and Suzhou Longway Electronic in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Suzhou Longway Electronic and GreenTech Environmental 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 GreenTech Environmental Co are associated (or correlated) with Suzhou Longway. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Suzhou Longway Electronic has no effect on the direction of GreenTech Environmental i.e., GreenTech Environmental and Suzhou Longway go up and down completely randomly.
Pair Corralation between GreenTech Environmental and Suzhou Longway
Assuming the 90 days trading horizon GreenTech Environmental Co is expected to generate 0.74 times more return on investment than Suzhou Longway. However, GreenTech Environmental Co is 1.35 times less risky than Suzhou Longway. It trades about -0.01 of its potential returns per unit of risk. Suzhou Longway Electronic is currently generating about -0.01 per unit of risk. If you would invest 1,426 in GreenTech Environmental Co on October 8, 2024 and sell it today you would lose (101.00) from holding GreenTech Environmental Co or give up 7.08% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
GreenTech Environmental Co vs. Suzhou Longway Electronic
Performance |
Timeline |
GreenTech Environmental |
Suzhou Longway Electronic |
GreenTech Environmental and Suzhou Longway Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with GreenTech Environmental and Suzhou Longway
The main advantage of trading using opposite GreenTech Environmental and Suzhou Longway positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GreenTech Environmental position performs unexpectedly, Suzhou Longway 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 Suzhou Longway will offset losses from the drop in Suzhou Longway's long position.The idea behind GreenTech Environmental Co and Suzhou Longway Electronic pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.
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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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