Correlation Between Agricultural Bank and Shannon Semiconductor
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By analyzing existing cross correlation between Agricultural Bank of and Shannon Semiconductor Technology, you can compare the effects of market volatilities on Agricultural Bank and Shannon Semiconductor 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 Agricultural Bank with a short position of Shannon Semiconductor. Check out your portfolio center. Please also check ongoing floating volatility patterns of Agricultural Bank and Shannon Semiconductor.
Diversification Opportunities for Agricultural Bank and Shannon Semiconductor
-0.6 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Agricultural and Shannon is -0.6. Overlapping area represents the amount of risk that can be diversified away by holding Agricultural Bank of and Shannon Semiconductor Technolo in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shannon Semiconductor and Agricultural Bank 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 Agricultural Bank of are associated (or correlated) with Shannon Semiconductor. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shannon Semiconductor has no effect on the direction of Agricultural Bank i.e., Agricultural Bank and Shannon Semiconductor go up and down completely randomly.
Pair Corralation between Agricultural Bank and Shannon Semiconductor
Assuming the 90 days trading horizon Agricultural Bank of is expected to generate 0.59 times more return on investment than Shannon Semiconductor. However, Agricultural Bank of is 1.69 times less risky than Shannon Semiconductor. It trades about 0.01 of its potential returns per unit of risk. Shannon Semiconductor Technology is currently generating about -0.11 per unit of risk. If you would invest 504.00 in Agricultural Bank of on October 20, 2024 and sell it today you would earn a total of 1.00 from holding Agricultural Bank of or generate 0.2% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Agricultural Bank of vs. Shannon Semiconductor Technolo
Performance |
Timeline |
Agricultural Bank |
Shannon Semiconductor |
Agricultural Bank and Shannon Semiconductor Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Agricultural Bank and Shannon Semiconductor
The main advantage of trading using opposite Agricultural Bank and Shannon Semiconductor positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Agricultural Bank position performs unexpectedly, Shannon Semiconductor 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 Shannon Semiconductor will offset losses from the drop in Shannon Semiconductor's long position.Agricultural Bank vs. Jinlong Machinery Electronic | Agricultural Bank vs. JCHX Mining Management | Agricultural Bank vs. Xingguang Agricultural Mach | Agricultural Bank vs. Shantui Construction Machinery |
Shannon Semiconductor vs. Aba Chemicals Corp | Shannon Semiconductor vs. Hua Xia Bank | Shannon Semiconductor vs. GRG Banking Equipment | Shannon Semiconductor vs. Jinhui Liquor 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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