Correlation Between GigaDevice SemiconductorBei and Beijing Zhong
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By analyzing existing cross correlation between GigaDevice SemiconductorBeiji and Beijing Zhong Ke, you can compare the effects of market volatilities on GigaDevice SemiconductorBei and Beijing Zhong 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 GigaDevice SemiconductorBei with a short position of Beijing Zhong. Check out your portfolio center. Please also check ongoing floating volatility patterns of GigaDevice SemiconductorBei and Beijing Zhong.
Diversification Opportunities for GigaDevice SemiconductorBei and Beijing Zhong
0.59 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between GigaDevice and Beijing is 0.59. Overlapping area represents the amount of risk that can be diversified away by holding GigaDevice SemiconductorBeiji and Beijing Zhong Ke in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Beijing Zhong Ke and GigaDevice SemiconductorBei 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 GigaDevice SemiconductorBeiji are associated (or correlated) with Beijing Zhong. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Beijing Zhong Ke has no effect on the direction of GigaDevice SemiconductorBei i.e., GigaDevice SemiconductorBei and Beijing Zhong go up and down completely randomly.
Pair Corralation between GigaDevice SemiconductorBei and Beijing Zhong
Assuming the 90 days trading horizon GigaDevice SemiconductorBei is expected to generate 1.36 times less return on investment than Beijing Zhong. In addition to that, GigaDevice SemiconductorBei is 1.03 times more volatile than Beijing Zhong Ke. It trades about 0.13 of its total potential returns per unit of risk. Beijing Zhong Ke is currently generating about 0.19 per unit of volatility. If you would invest 803.00 in Beijing Zhong Ke on September 12, 2024 and sell it today you would earn a total of 300.00 from holding Beijing Zhong Ke or generate 37.36% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 96.55% |
Values | Daily Returns |
GigaDevice SemiconductorBeiji vs. Beijing Zhong Ke
Performance |
Timeline |
GigaDevice SemiconductorBei |
Beijing Zhong Ke |
GigaDevice SemiconductorBei and Beijing Zhong Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with GigaDevice SemiconductorBei and Beijing Zhong
The main advantage of trading using opposite GigaDevice SemiconductorBei and Beijing Zhong positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GigaDevice SemiconductorBei position performs unexpectedly, Beijing Zhong 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 Beijing Zhong will offset losses from the drop in Beijing Zhong's long position.The idea behind GigaDevice SemiconductorBeiji and Beijing Zhong Ke 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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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