Correlation Between G-bits Network and Shenzhen Hifuture
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By analyzing existing cross correlation between G bits Network Technology and Shenzhen Hifuture Electric, you can compare the effects of market volatilities on G-bits Network and Shenzhen Hifuture 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 G-bits Network with a short position of Shenzhen Hifuture. Check out your portfolio center. Please also check ongoing floating volatility patterns of G-bits Network and Shenzhen Hifuture.
Diversification Opportunities for G-bits Network and Shenzhen Hifuture
0.47 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between G-bits and Shenzhen is 0.47. Overlapping area represents the amount of risk that can be diversified away by holding G bits Network Technology and Shenzhen Hifuture Electric in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shenzhen Hifuture and G-bits Network 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 G bits Network Technology are associated (or correlated) with Shenzhen Hifuture. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shenzhen Hifuture has no effect on the direction of G-bits Network i.e., G-bits Network and Shenzhen Hifuture go up and down completely randomly.
Pair Corralation between G-bits Network and Shenzhen Hifuture
Assuming the 90 days trading horizon G bits Network Technology is expected to under-perform the Shenzhen Hifuture. But the stock apears to be less risky and, when comparing its historical volatility, G bits Network Technology is 1.17 times less risky than Shenzhen Hifuture. The stock trades about -0.05 of its potential returns per unit of risk. The Shenzhen Hifuture Electric is currently generating about -0.03 of returns per unit of risk over similar time horizon. If you would invest 450.00 in Shenzhen Hifuture Electric on January 24, 2025 and sell it today you would lose (235.00) from holding Shenzhen Hifuture Electric or give up 52.22% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 99.79% |
| Values | Daily Returns |
G bits Network Technology vs. Shenzhen Hifuture Electric
Performance |
| Timeline |
| G bits Network |
| Shenzhen Hifuture |
G-bits Network and Shenzhen Hifuture Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with G-bits Network and Shenzhen Hifuture
The main advantage of trading using opposite G-bits Network and Shenzhen Hifuture positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if G-bits Network position performs unexpectedly, Shenzhen Hifuture 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 Shenzhen Hifuture will offset losses from the drop in Shenzhen Hifuture's long position.| G-bits Network vs. Kangping Technology Co | G-bits Network vs. Linkage Software Co | G-bits Network vs. Fujian Boss Software | G-bits Network vs. Jiangsu Hoperun Software |
| Shenzhen Hifuture vs. Sunwave Communications Co | Shenzhen Hifuture vs. Xinjiang Communications Construction | Shenzhen Hifuture vs. Wuxi Online Offline | Shenzhen Hifuture vs. Shaanxi Energy Investment |
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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