Correlation Between Shannon Semiconductor and Shengtak New
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By analyzing existing cross correlation between Shannon Semiconductor Technology and Shengtak New Material, you can compare the effects of market volatilities on Shannon Semiconductor and Shengtak New 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 Shannon Semiconductor with a short position of Shengtak New. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shannon Semiconductor and Shengtak New.
Diversification Opportunities for Shannon Semiconductor and Shengtak New
0.83 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Shannon and Shengtak is 0.83. Overlapping area represents the amount of risk that can be diversified away by holding Shannon Semiconductor Technolo and Shengtak New Material in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shengtak New Material and Shannon Semiconductor 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 Shannon Semiconductor Technology are associated (or correlated) with Shengtak New. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shengtak New Material has no effect on the direction of Shannon Semiconductor i.e., Shannon Semiconductor and Shengtak New go up and down completely randomly.
Pair Corralation between Shannon Semiconductor and Shengtak New
Assuming the 90 days trading horizon Shannon Semiconductor Technology is expected to under-perform the Shengtak New. But the stock apears to be less risky and, when comparing its historical volatility, Shannon Semiconductor Technology is 1.04 times less risky than Shengtak New. The stock trades about -0.13 of its potential returns per unit of risk. The Shengtak New Material is currently generating about -0.08 of returns per unit of risk over similar time horizon. If you would invest 3,117 in Shengtak New Material on October 30, 2024 and sell it today you would lose (151.00) from holding Shengtak New Material or give up 4.84% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Shannon Semiconductor Technolo vs. Shengtak New Material
Performance |
| Timeline |
| Shannon Semiconductor |
| Shengtak New Material |
Shannon Semiconductor and Shengtak New Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Shannon Semiconductor and Shengtak New
The main advantage of trading using opposite Shannon Semiconductor and Shengtak New positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shannon Semiconductor position performs unexpectedly, Shengtak New 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 Shengtak New will offset losses from the drop in Shengtak New's long position.| Shannon Semiconductor vs. Anhui Transport Consulting | Shannon Semiconductor vs. CITIC Metal Co | Shannon Semiconductor vs. Heilongjiang Transport Development | Shannon Semiconductor vs. Shanghai Yanpu Metal |
| Shengtak New vs. Zijin Mining Group | Shengtak New vs. Wanhua Chemical Group | Shengtak New vs. Baoshan Iron Steel | Shengtak New vs. Shandong Gold Mining |
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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