Correlation Between Jinsanjiang (Zhaoqing) and Anhui Transport
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By analyzing existing cross correlation between Jinsanjiang Silicon Material and Anhui Transport Consulting, you can compare the effects of market volatilities on Jinsanjiang (Zhaoqing) and Anhui Transport 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 Jinsanjiang (Zhaoqing) with a short position of Anhui Transport. Check out your portfolio center. Please also check ongoing floating volatility patterns of Jinsanjiang (Zhaoqing) and Anhui Transport.
Diversification Opportunities for Jinsanjiang (Zhaoqing) and Anhui Transport
0.36 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Jinsanjiang and Anhui is 0.36. Overlapping area represents the amount of risk that can be diversified away by holding Jinsanjiang Silicon Material and Anhui Transport Consulting in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Anhui Transport Cons and Jinsanjiang (Zhaoqing) 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 Jinsanjiang Silicon Material are associated (or correlated) with Anhui Transport. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Anhui Transport Cons has no effect on the direction of Jinsanjiang (Zhaoqing) i.e., Jinsanjiang (Zhaoqing) and Anhui Transport go up and down completely randomly.
Pair Corralation between Jinsanjiang (Zhaoqing) and Anhui Transport
Assuming the 90 days trading horizon Jinsanjiang Silicon Material is expected to under-perform the Anhui Transport. In addition to that, Jinsanjiang (Zhaoqing) is 2.62 times more volatile than Anhui Transport Consulting. It trades about -0.01 of its total potential returns per unit of risk. Anhui Transport Consulting is currently generating about 0.03 per unit of volatility. If you would invest 911.00 in Anhui Transport Consulting on December 26, 2024 and sell it today you would earn a total of 14.00 from holding Anhui Transport Consulting or generate 1.54% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Jinsanjiang Silicon Material vs. Anhui Transport Consulting
Performance |
Timeline |
Jinsanjiang (Zhaoqing) |
Anhui Transport Cons |
Jinsanjiang (Zhaoqing) and Anhui Transport Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Jinsanjiang (Zhaoqing) and Anhui Transport
The main advantage of trading using opposite Jinsanjiang (Zhaoqing) and Anhui Transport positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Jinsanjiang (Zhaoqing) position performs unexpectedly, Anhui Transport 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 Anhui Transport will offset losses from the drop in Anhui Transport's long position.The idea behind Jinsanjiang Silicon Material and Anhui Transport Consulting 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 Volatility Analysis module to get historical volatility and risk analysis based on latest market data.
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