Correlation Between Shandong Ruifeng and StarPower Semiconductor
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By analyzing existing cross correlation between Shandong Ruifeng Chemical and StarPower Semiconductor, you can compare the effects of market volatilities on Shandong Ruifeng and StarPower 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 Shandong Ruifeng with a short position of StarPower Semiconductor. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shandong Ruifeng and StarPower Semiconductor.
Diversification Opportunities for Shandong Ruifeng and StarPower Semiconductor
0.88 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Shandong and StarPower is 0.88. Overlapping area represents the amount of risk that can be diversified away by holding Shandong Ruifeng Chemical and StarPower Semiconductor in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on StarPower Semiconductor and Shandong Ruifeng 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 Shandong Ruifeng Chemical are associated (or correlated) with StarPower Semiconductor. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of StarPower Semiconductor has no effect on the direction of Shandong Ruifeng i.e., Shandong Ruifeng and StarPower Semiconductor go up and down completely randomly.
Pair Corralation between Shandong Ruifeng and StarPower Semiconductor
Assuming the 90 days trading horizon Shandong Ruifeng is expected to generate 1.25 times less return on investment than StarPower Semiconductor. In addition to that, Shandong Ruifeng is 1.11 times more volatile than StarPower Semiconductor. It trades about 0.03 of its total potential returns per unit of risk. StarPower Semiconductor is currently generating about 0.04 per unit of volatility. If you would invest 8,899 in StarPower Semiconductor on August 25, 2024 and sell it today you would earn a total of 961.00 from holding StarPower Semiconductor or generate 10.8% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Shandong Ruifeng Chemical vs. StarPower Semiconductor
Performance |
Timeline |
Shandong Ruifeng Chemical |
StarPower Semiconductor |
Shandong Ruifeng and StarPower Semiconductor Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Shandong Ruifeng and StarPower Semiconductor
The main advantage of trading using opposite Shandong Ruifeng and StarPower Semiconductor positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shandong Ruifeng position performs unexpectedly, StarPower 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 StarPower Semiconductor will offset losses from the drop in StarPower Semiconductor's long position.Shandong Ruifeng vs. Dalian Thermal Power | Shandong Ruifeng vs. Shandong Mining Machinery | Shandong Ruifeng vs. Changjiang Publishing Media | Shandong Ruifeng vs. Guangzhou Restaurants Group |
StarPower Semiconductor vs. Lutian Machinery Co | StarPower Semiconductor vs. China Longyuan Power | StarPower Semiconductor vs. Changshu Tongrun Auto | StarPower Semiconductor vs. PetroChina Co Ltd |
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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