Correlation Between GigaCloud Technology and Check Point
Can any of the company-specific risk be diversified away by investing in both GigaCloud Technology and Check Point 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 GigaCloud Technology and Check Point into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between GigaCloud Technology Class and Check Point Software, you can compare the effects of market volatilities on GigaCloud Technology and Check Point 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 GigaCloud Technology with a short position of Check Point. Check out your portfolio center. Please also check ongoing floating volatility patterns of GigaCloud Technology and Check Point.
Diversification Opportunities for GigaCloud Technology and Check Point
0.13 | Correlation Coefficient |
Average diversification
The 3 months correlation between GigaCloud and Check is 0.13. Overlapping area represents the amount of risk that can be diversified away by holding GigaCloud Technology Class and Check Point Software in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Check Point Software and GigaCloud Technology 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 GigaCloud Technology Class are associated (or correlated) with Check Point. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Check Point Software has no effect on the direction of GigaCloud Technology i.e., GigaCloud Technology and Check Point go up and down completely randomly.
Pair Corralation between GigaCloud Technology and Check Point
Considering the 90-day investment horizon GigaCloud Technology Class is expected to generate 1.96 times more return on investment than Check Point. However, GigaCloud Technology is 1.96 times more volatile than Check Point Software. It trades about 0.01 of its potential returns per unit of risk. Check Point Software is currently generating about -0.14 per unit of risk. If you would invest 2,481 in GigaCloud Technology Class on August 24, 2024 and sell it today you would lose (69.00) from holding GigaCloud Technology Class or give up 2.78% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
GigaCloud Technology Class vs. Check Point Software
Performance |
Timeline |
GigaCloud Technology |
Check Point Software |
GigaCloud Technology and Check Point Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with GigaCloud Technology and Check Point
The main advantage of trading using opposite GigaCloud Technology and Check Point positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GigaCloud Technology position performs unexpectedly, Check Point 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 Check Point will offset losses from the drop in Check Point's long position.GigaCloud Technology vs. Arqit Quantum | GigaCloud Technology vs. Telos Corp | GigaCloud Technology vs. Cemtrex | GigaCloud Technology vs. Alarum Technologies |
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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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