Correlation Between Guangzhou Seagull and Sublime China
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By analyzing existing cross correlation between Guangzhou Seagull Kitchen and Sublime China Information, you can compare the effects of market volatilities on Guangzhou Seagull and Sublime China 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 Guangzhou Seagull with a short position of Sublime China. Check out your portfolio center. Please also check ongoing floating volatility patterns of Guangzhou Seagull and Sublime China.
Diversification Opportunities for Guangzhou Seagull and Sublime China
0.54 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Guangzhou and Sublime is 0.54. Overlapping area represents the amount of risk that can be diversified away by holding Guangzhou Seagull Kitchen and Sublime China Information in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sublime China Information and Guangzhou Seagull 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 Guangzhou Seagull Kitchen are associated (or correlated) with Sublime China. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sublime China Information has no effect on the direction of Guangzhou Seagull i.e., Guangzhou Seagull and Sublime China go up and down completely randomly.
Pair Corralation between Guangzhou Seagull and Sublime China
Assuming the 90 days trading horizon Guangzhou Seagull Kitchen is expected to under-perform the Sublime China. But the stock apears to be less risky and, when comparing its historical volatility, Guangzhou Seagull Kitchen is 1.17 times less risky than Sublime China. The stock trades about -0.27 of its potential returns per unit of risk. The Sublime China Information is currently generating about -0.02 of returns per unit of risk over similar time horizon. If you would invest 6,175 in Sublime China Information on October 14, 2024 and sell it today you would lose (280.00) from holding Sublime China Information or give up 4.53% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Guangzhou Seagull Kitchen vs. Sublime China Information
Performance |
| Timeline |
| Guangzhou Seagull Kitchen |
| Sublime China Information |
Guangzhou Seagull and Sublime China Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Guangzhou Seagull and Sublime China
The main advantage of trading using opposite Guangzhou Seagull and Sublime China positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Guangzhou Seagull position performs unexpectedly, Sublime China 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 Sublime China will offset losses from the drop in Sublime China's long position.| Guangzhou Seagull vs. Jilin Chemical Fibre | Guangzhou Seagull vs. Sichuan Hebang Biotechnology | Guangzhou Seagull vs. Changchun BCHT Biotechnology | Guangzhou Seagull vs. Dymatic Chemicals |
| Sublime China vs. Shenzhen Noposion Agrochemicals | Sublime China vs. Liaoning Dingjide Petrochemical | Sublime China vs. Guangzhou Dongfang Hotel | Sublime China vs. Guangzhou Seagull Kitchen |
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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