Correlation Between Thinkon Semiconductor and Qijing Machinery
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By analyzing existing cross correlation between Thinkon Semiconductor Jinzhou and Qijing Machinery, you can compare the effects of market volatilities on Thinkon Semiconductor and Qijing Machinery 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 Thinkon Semiconductor with a short position of Qijing Machinery. Check out your portfolio center. Please also check ongoing floating volatility patterns of Thinkon Semiconductor and Qijing Machinery.
Diversification Opportunities for Thinkon Semiconductor and Qijing Machinery
0.37 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Thinkon and Qijing is 0.37. Overlapping area represents the amount of risk that can be diversified away by holding Thinkon Semiconductor Jinzhou and Qijing Machinery in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Qijing Machinery and Thinkon 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 Thinkon Semiconductor Jinzhou are associated (or correlated) with Qijing Machinery. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Qijing Machinery has no effect on the direction of Thinkon Semiconductor i.e., Thinkon Semiconductor and Qijing Machinery go up and down completely randomly.
Pair Corralation between Thinkon Semiconductor and Qijing Machinery
Assuming the 90 days trading horizon Thinkon Semiconductor Jinzhou is expected to under-perform the Qijing Machinery. But the stock apears to be less risky and, when comparing its historical volatility, Thinkon Semiconductor Jinzhou is 1.47 times less risky than Qijing Machinery. The stock trades about -0.08 of its potential returns per unit of risk. The Qijing Machinery is currently generating about 0.16 of returns per unit of risk over similar time horizon. If you would invest 1,257 in Qijing Machinery on October 27, 2024 and sell it today you would earn a total of 134.00 from holding Qijing Machinery or generate 10.66% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Thinkon Semiconductor Jinzhou vs. Qijing Machinery
Performance |
| Timeline |
| Thinkon Semiconductor |
| Qijing Machinery |
Thinkon Semiconductor and Qijing Machinery Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Thinkon Semiconductor and Qijing Machinery
The main advantage of trading using opposite Thinkon Semiconductor and Qijing Machinery positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Thinkon Semiconductor position performs unexpectedly, Qijing Machinery 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 Qijing Machinery will offset losses from the drop in Qijing Machinery's long position.The idea behind Thinkon Semiconductor Jinzhou and Qijing Machinery 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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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