Correlation Between Changchun Faway and Shenzhen Bingchuan
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By analyzing existing cross correlation between Changchun Faway Automobile and Shenzhen Bingchuan Network, you can compare the effects of market volatilities on Changchun Faway and Shenzhen Bingchuan 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 Changchun Faway with a short position of Shenzhen Bingchuan. Check out your portfolio center. Please also check ongoing floating volatility patterns of Changchun Faway and Shenzhen Bingchuan.
Diversification Opportunities for Changchun Faway and Shenzhen Bingchuan
0.88 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Changchun and Shenzhen is 0.88. Overlapping area represents the amount of risk that can be diversified away by holding Changchun Faway Automobile and Shenzhen Bingchuan Network in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shenzhen Bingchuan and Changchun Faway 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 Changchun Faway Automobile are associated (or correlated) with Shenzhen Bingchuan. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shenzhen Bingchuan has no effect on the direction of Changchun Faway i.e., Changchun Faway and Shenzhen Bingchuan go up and down completely randomly.
Pair Corralation between Changchun Faway and Shenzhen Bingchuan
Assuming the 90 days trading horizon Changchun Faway is expected to generate 2.9 times less return on investment than Shenzhen Bingchuan. But when comparing it to its historical volatility, Changchun Faway Automobile is 2.09 times less risky than Shenzhen Bingchuan. It trades about 0.2 of its potential returns per unit of risk. Shenzhen Bingchuan Network is currently generating about 0.28 of returns per unit of risk over similar time horizon. If you would invest 1,303 in Shenzhen Bingchuan Network on September 17, 2024 and sell it today you would earn a total of 1,405 from holding Shenzhen Bingchuan Network or generate 107.83% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Changchun Faway Automobile vs. Shenzhen Bingchuan Network
Performance |
Timeline |
Changchun Faway Auto |
Shenzhen Bingchuan |
Changchun Faway and Shenzhen Bingchuan Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Changchun Faway and Shenzhen Bingchuan
The main advantage of trading using opposite Changchun Faway and Shenzhen Bingchuan positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Changchun Faway position performs unexpectedly, Shenzhen Bingchuan 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 Bingchuan will offset losses from the drop in Shenzhen Bingchuan's long position.Changchun Faway vs. BeiGene | Changchun Faway vs. Kweichow Moutai Co | Changchun Faway vs. Beijing Roborock Technology | Changchun Faway vs. G bits Network Technology |
Shenzhen Bingchuan vs. Changchun Faway Automobile | Shenzhen Bingchuan vs. GRG Banking Equipment | Shenzhen Bingchuan vs. Shenwu Energy Saving | Shenzhen Bingchuan vs. CIMC Vehicles Co |
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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