Correlation Between Shanghai Zhangjiang and Semiconductor Manufacturing
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By analyzing existing cross correlation between Shanghai Zhangjiang Hi Tech and Semiconductor Manufacturing Electronics, you can compare the effects of market volatilities on Shanghai Zhangjiang and Semiconductor Manufacturing 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 Shanghai Zhangjiang with a short position of Semiconductor Manufacturing. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shanghai Zhangjiang and Semiconductor Manufacturing.
Diversification Opportunities for Shanghai Zhangjiang and Semiconductor Manufacturing
0.94 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Shanghai and Semiconductor is 0.94. Overlapping area represents the amount of risk that can be diversified away by holding Shanghai Zhangjiang Hi Tech and Semiconductor Manufacturing El in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Semiconductor Manufacturing and Shanghai Zhangjiang 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 Shanghai Zhangjiang Hi Tech are associated (or correlated) with Semiconductor Manufacturing. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Semiconductor Manufacturing has no effect on the direction of Shanghai Zhangjiang i.e., Shanghai Zhangjiang and Semiconductor Manufacturing go up and down completely randomly.
Pair Corralation between Shanghai Zhangjiang and Semiconductor Manufacturing
Assuming the 90 days trading horizon Shanghai Zhangjiang is expected to generate 1.56 times less return on investment than Semiconductor Manufacturing. In addition to that, Shanghai Zhangjiang is 1.51 times more volatile than Semiconductor Manufacturing Electronics. It trades about 0.07 of its total potential returns per unit of risk. Semiconductor Manufacturing Electronics is currently generating about 0.17 per unit of volatility. If you would invest 479.00 in Semiconductor Manufacturing Electronics on August 29, 2024 and sell it today you would earn a total of 57.00 from holding Semiconductor Manufacturing Electronics or generate 11.9% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Shanghai Zhangjiang Hi Tech vs. Semiconductor Manufacturing El
Performance |
| Timeline |
| Shanghai Zhangjiang |
| Semiconductor Manufacturing |
Shanghai Zhangjiang and Semiconductor Manufacturing Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Shanghai Zhangjiang and Semiconductor Manufacturing
The main advantage of trading using opposite Shanghai Zhangjiang and Semiconductor Manufacturing positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shanghai Zhangjiang position performs unexpectedly, Semiconductor Manufacturing 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 Semiconductor Manufacturing will offset losses from the drop in Semiconductor Manufacturing's long position.| Shanghai Zhangjiang vs. Industrial and Commercial | Shanghai Zhangjiang vs. China Construction Bank | Shanghai Zhangjiang vs. Bank of China | Shanghai Zhangjiang vs. Agricultural Bank of |
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 Portfolio Optimization module to compute new portfolio that will generate highest expected return given your specified tolerance for risk.
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