Correlation Between Citic Offshore and Dazhong Transportation
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By analyzing existing cross correlation between Citic Offshore Helicopter and Dazhong Transportation Group, you can compare the effects of market volatilities on Citic Offshore and Dazhong Transportation 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 Citic Offshore with a short position of Dazhong Transportation. Check out your portfolio center. Please also check ongoing floating volatility patterns of Citic Offshore and Dazhong Transportation.
Diversification Opportunities for Citic Offshore and Dazhong Transportation
0.62 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Citic and Dazhong is 0.62. Overlapping area represents the amount of risk that can be diversified away by holding Citic Offshore Helicopter and Dazhong Transportation Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dazhong Transportation and Citic Offshore 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 Citic Offshore Helicopter are associated (or correlated) with Dazhong Transportation. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dazhong Transportation has no effect on the direction of Citic Offshore i.e., Citic Offshore and Dazhong Transportation go up and down completely randomly.
Pair Corralation between Citic Offshore and Dazhong Transportation
Assuming the 90 days trading horizon Citic Offshore Helicopter is expected to generate 1.62 times more return on investment than Dazhong Transportation. However, Citic Offshore is 1.62 times more volatile than Dazhong Transportation Group. It trades about -0.2 of its potential returns per unit of risk. Dazhong Transportation Group is currently generating about -0.35 per unit of risk. If you would invest 2,655 in Citic Offshore Helicopter on October 10, 2024 and sell it today you would lose (396.00) from holding Citic Offshore Helicopter or give up 14.92% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Citic Offshore Helicopter vs. Dazhong Transportation Group
Performance |
Timeline |
Citic Offshore Helicopter |
Dazhong Transportation |
Citic Offshore and Dazhong Transportation Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Citic Offshore and Dazhong Transportation
The main advantage of trading using opposite Citic Offshore and Dazhong Transportation positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Citic Offshore position performs unexpectedly, Dazhong Transportation 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 Dazhong Transportation will offset losses from the drop in Dazhong Transportation's long position.Citic Offshore vs. Xinxiang Chemical Fiber | Citic Offshore vs. Guotai Epoint Software | Citic Offshore vs. Dareway Software Co | Citic Offshore vs. Ningxia Younglight Chemicals |
Dazhong Transportation vs. Fujian Anjoy Foods | Dazhong Transportation vs. Northern United Publishing | Dazhong Transportation vs. Guangdong Wens Foodstuff | Dazhong Transportation vs. Jiahe Foods Industry |
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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