Correlation Between Chung Hsin and Shinkong Synthetic
Can any of the company-specific risk be diversified away by investing in both Chung Hsin and Shinkong Synthetic at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining Chung Hsin and Shinkong Synthetic into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Chung Hsin Electric Machinery and Shinkong Synthetic Fiber, you can compare the effects of market volatilities on Chung Hsin and Shinkong Synthetic 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 Chung Hsin with a short position of Shinkong Synthetic. Check out your portfolio center. Please also check ongoing floating volatility patterns of Chung Hsin and Shinkong Synthetic.
Diversification Opportunities for Chung Hsin and Shinkong Synthetic
0.6 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Chung and Shinkong is 0.6. Overlapping area represents the amount of risk that can be diversified away by holding Chung Hsin Electric Machinery and Shinkong Synthetic Fiber in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shinkong Synthetic Fiber and Chung Hsin 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 Chung Hsin Electric Machinery are associated (or correlated) with Shinkong Synthetic. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shinkong Synthetic Fiber has no effect on the direction of Chung Hsin i.e., Chung Hsin and Shinkong Synthetic go up and down completely randomly.
Pair Corralation between Chung Hsin and Shinkong Synthetic
Assuming the 90 days trading horizon Chung Hsin Electric Machinery is expected to generate 1.93 times more return on investment than Shinkong Synthetic. However, Chung Hsin is 1.93 times more volatile than Shinkong Synthetic Fiber. It trades about 0.05 of its potential returns per unit of risk. Shinkong Synthetic Fiber is currently generating about 0.02 per unit of risk. If you would invest 12,650 in Chung Hsin Electric Machinery on September 2, 2024 and sell it today you would earn a total of 3,150 from holding Chung Hsin Electric Machinery or generate 24.9% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Chung Hsin Electric Machinery vs. Shinkong Synthetic Fiber
Performance |
Timeline |
Chung Hsin Electric |
Shinkong Synthetic Fiber |
Chung Hsin and Shinkong Synthetic Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Chung Hsin and Shinkong Synthetic
The main advantage of trading using opposite Chung Hsin and Shinkong Synthetic positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Chung Hsin position performs unexpectedly, Shinkong Synthetic 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 Shinkong Synthetic will offset losses from the drop in Shinkong Synthetic's long position.Chung Hsin vs. BES Engineering Co | Chung Hsin vs. Continental Holdings Corp | Chung Hsin vs. Kee Tai Properties | Chung Hsin vs. Hung Sheng Construction |
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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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