Correlation Between Yi Jinn and Everest Textile
Can any of the company-specific risk be diversified away by investing in both Yi Jinn and Everest Textile 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 Yi Jinn and Everest Textile into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Yi Jinn Industrial and Everest Textile Co, you can compare the effects of market volatilities on Yi Jinn and Everest Textile 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 Yi Jinn with a short position of Everest Textile. Check out your portfolio center. Please also check ongoing floating volatility patterns of Yi Jinn and Everest Textile.
Diversification Opportunities for Yi Jinn and Everest Textile
0.43 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between 1457 and Everest is 0.43. Overlapping area represents the amount of risk that can be diversified away by holding Yi Jinn Industrial and Everest Textile Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Everest Textile and Yi Jinn 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 Yi Jinn Industrial are associated (or correlated) with Everest Textile. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Everest Textile has no effect on the direction of Yi Jinn i.e., Yi Jinn and Everest Textile go up and down completely randomly.
Pair Corralation between Yi Jinn and Everest Textile
Assuming the 90 days trading horizon Yi Jinn Industrial is expected to generate 0.82 times more return on investment than Everest Textile. However, Yi Jinn Industrial is 1.22 times less risky than Everest Textile. It trades about -0.04 of its potential returns per unit of risk. Everest Textile Co is currently generating about -0.04 per unit of risk. If you would invest 2,020 in Yi Jinn Industrial on September 17, 2024 and sell it today you would lose (35.00) from holding Yi Jinn Industrial or give up 1.73% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Yi Jinn Industrial vs. Everest Textile Co
Performance |
Timeline |
Yi Jinn Industrial |
Everest Textile |
Yi Jinn and Everest Textile Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Yi Jinn and Everest Textile
The main advantage of trading using opposite Yi Jinn and Everest Textile positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Yi Jinn position performs unexpectedly, Everest Textile 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 Everest Textile will offset losses from the drop in Everest Textile's long position.Yi Jinn vs. Zig Sheng Industrial | Yi Jinn vs. Hong Yi Fiber | Yi Jinn vs. Lealea Enterprise Co | Yi Jinn vs. Shinkong Synthetic Fiber |
Everest Textile vs. Feng Tay Enterprises | Everest Textile vs. Ruentex Development Co | Everest Textile vs. WiseChip Semiconductor | Everest Textile vs. Novatek Microelectronics Corp |
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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