Correlation Between Guangzhou Automobile and Jiangsu Xinri
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By analyzing existing cross correlation between Guangzhou Automobile Group and Jiangsu Xinri E Vehicle, you can compare the effects of market volatilities on Guangzhou Automobile and Jiangsu Xinri 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 Automobile with a short position of Jiangsu Xinri. Check out your portfolio center. Please also check ongoing floating volatility patterns of Guangzhou Automobile and Jiangsu Xinri.
Diversification Opportunities for Guangzhou Automobile and Jiangsu Xinri
0.61 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Guangzhou and Jiangsu is 0.61. Overlapping area represents the amount of risk that can be diversified away by holding Guangzhou Automobile Group and Jiangsu Xinri E Vehicle in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Jiangsu Xinri E and Guangzhou Automobile 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 Automobile Group are associated (or correlated) with Jiangsu Xinri. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Jiangsu Xinri E has no effect on the direction of Guangzhou Automobile i.e., Guangzhou Automobile and Jiangsu Xinri go up and down completely randomly.
Pair Corralation between Guangzhou Automobile and Jiangsu Xinri
Assuming the 90 days trading horizon Guangzhou Automobile Group is expected to under-perform the Jiangsu Xinri. But the stock apears to be less risky and, when comparing its historical volatility, Guangzhou Automobile Group is 1.23 times less risky than Jiangsu Xinri. The stock trades about -0.29 of its potential returns per unit of risk. The Jiangsu Xinri E Vehicle is currently generating about -0.2 of returns per unit of risk over similar time horizon. If you would invest 1,142 in Jiangsu Xinri E Vehicle on October 10, 2024 and sell it today you would lose (128.00) from holding Jiangsu Xinri E Vehicle or give up 11.21% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Guangzhou Automobile Group vs. Jiangsu Xinri E Vehicle
Performance |
Timeline |
Guangzhou Automobile |
Jiangsu Xinri E |
Guangzhou Automobile and Jiangsu Xinri Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Guangzhou Automobile and Jiangsu Xinri
The main advantage of trading using opposite Guangzhou Automobile and Jiangsu Xinri positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Guangzhou Automobile position performs unexpectedly, Jiangsu Xinri 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 Jiangsu Xinri will offset losses from the drop in Jiangsu Xinri's long position.The idea behind Guangzhou Automobile Group and Jiangsu Xinri E Vehicle pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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