Correlation Between Jiangsu Financial and Shaanxi Construction
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By analyzing existing cross correlation between Jiangsu Financial Leasing and Shaanxi Construction Machinery, you can compare the effects of market volatilities on Jiangsu Financial and Shaanxi Construction 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 Jiangsu Financial with a short position of Shaanxi Construction. Check out your portfolio center. Please also check ongoing floating volatility patterns of Jiangsu Financial and Shaanxi Construction.
Diversification Opportunities for Jiangsu Financial and Shaanxi Construction
0.55 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Jiangsu and Shaanxi is 0.55. Overlapping area represents the amount of risk that can be diversified away by holding Jiangsu Financial Leasing and Shaanxi Construction Machinery in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shaanxi Construction and Jiangsu Financial 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 Jiangsu Financial Leasing are associated (or correlated) with Shaanxi Construction. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shaanxi Construction has no effect on the direction of Jiangsu Financial i.e., Jiangsu Financial and Shaanxi Construction go up and down completely randomly.
Pair Corralation between Jiangsu Financial and Shaanxi Construction
Assuming the 90 days trading horizon Jiangsu Financial Leasing is expected to generate 0.21 times more return on investment than Shaanxi Construction. However, Jiangsu Financial Leasing is 4.87 times less risky than Shaanxi Construction. It trades about 0.26 of its potential returns per unit of risk. Shaanxi Construction Machinery is currently generating about 0.05 per unit of risk. If you would invest 496.00 in Jiangsu Financial Leasing on September 26, 2024 and sell it today you would earn a total of 24.00 from holding Jiangsu Financial Leasing or generate 4.84% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Jiangsu Financial Leasing vs. Shaanxi Construction Machinery
Performance |
Timeline |
Jiangsu Financial Leasing |
Shaanxi Construction |
Jiangsu Financial and Shaanxi Construction Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Jiangsu Financial and Shaanxi Construction
The main advantage of trading using opposite Jiangsu Financial and Shaanxi Construction positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Jiangsu Financial position performs unexpectedly, Shaanxi Construction 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 Shaanxi Construction will offset losses from the drop in Shaanxi Construction's long position.Jiangsu Financial vs. Zijin Mining Group | Jiangsu Financial vs. Lier Chemical Co | Jiangsu Financial vs. Tianjin Silvery Dragon | Jiangsu Financial vs. Jiangxi Naipu Mining |
Shaanxi Construction vs. Cambricon Technologies Corp | Shaanxi Construction vs. Loongson Technology Corp | Shaanxi Construction vs. Shenzhen Fortune Trend | Shaanxi Construction vs. Chongqing Road Bridge |
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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