Correlation Between Hong Yi and Shinkong Synthetic
Can any of the company-specific risk be diversified away by investing in both Hong Yi 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 Hong Yi and Shinkong Synthetic into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Hong Yi Fiber and Shinkong Synthetic Fiber, you can compare the effects of market volatilities on Hong Yi 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 Hong Yi with a short position of Shinkong Synthetic. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hong Yi and Shinkong Synthetic.
Diversification Opportunities for Hong Yi and Shinkong Synthetic
0.24 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Hong and Shinkong is 0.24. Overlapping area represents the amount of risk that can be diversified away by holding Hong Yi Fiber 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 Hong Yi 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 Hong Yi Fiber 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 Hong Yi i.e., Hong Yi and Shinkong Synthetic go up and down completely randomly.
Pair Corralation between Hong Yi and Shinkong Synthetic
Assuming the 90 days trading horizon Hong Yi Fiber is expected to under-perform the Shinkong Synthetic. But the stock apears to be less risky and, when comparing its historical volatility, Hong Yi Fiber is 1.34 times less risky than Shinkong Synthetic. The stock trades about -0.07 of its potential returns per unit of risk. The Shinkong Synthetic Fiber is currently generating about -0.02 of returns per unit of risk over similar time horizon. If you would invest 1,470 in Shinkong Synthetic Fiber on December 28, 2024 and sell it today you would lose (15.00) from holding Shinkong Synthetic Fiber or give up 1.02% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Hong Yi Fiber vs. Shinkong Synthetic Fiber
Performance |
Timeline |
Hong Yi Fiber |
Shinkong Synthetic Fiber |
Hong Yi and Shinkong Synthetic Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Hong Yi and Shinkong Synthetic
The main advantage of trading using opposite Hong Yi and Shinkong Synthetic positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hong Yi 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.Hong Yi vs. Yi Jinn Industrial | Hong Yi vs. Zig Sheng Industrial | Hong Yi vs. Lan Fa Textile | Hong Yi vs. Tainan Spinning Co |
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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 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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