Correlation Between WILLIS LEASE and CHINA HUARONG
Can any of the company-specific risk be diversified away by investing in both WILLIS LEASE and CHINA HUARONG 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 WILLIS LEASE and CHINA HUARONG into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between WILLIS LEASE FIN and CHINA HUARONG ENERHD 50, you can compare the effects of market volatilities on WILLIS LEASE and CHINA HUARONG 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 WILLIS LEASE with a short position of CHINA HUARONG. Check out your portfolio center. Please also check ongoing floating volatility patterns of WILLIS LEASE and CHINA HUARONG.
Diversification Opportunities for WILLIS LEASE and CHINA HUARONG
0.57 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between WILLIS and CHINA is 0.57. Overlapping area represents the amount of risk that can be diversified away by holding WILLIS LEASE FIN and CHINA HUARONG ENERHD 50 in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on CHINA HUARONG ENERHD and WILLIS LEASE 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 WILLIS LEASE FIN are associated (or correlated) with CHINA HUARONG. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of CHINA HUARONG ENERHD has no effect on the direction of WILLIS LEASE i.e., WILLIS LEASE and CHINA HUARONG go up and down completely randomly.
Pair Corralation between WILLIS LEASE and CHINA HUARONG
Assuming the 90 days horizon WILLIS LEASE FIN is expected to under-perform the CHINA HUARONG. But the stock apears to be less risky and, when comparing its historical volatility, WILLIS LEASE FIN is 6.19 times less risky than CHINA HUARONG. The stock trades about -0.05 of its potential returns per unit of risk. The CHINA HUARONG ENERHD 50 is currently generating about 0.02 of returns per unit of risk over similar time horizon. If you would invest 0.15 in CHINA HUARONG ENERHD 50 on December 28, 2024 and sell it today you would lose (0.10) from holding CHINA HUARONG ENERHD 50 or give up 66.67% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
WILLIS LEASE FIN vs. CHINA HUARONG ENERHD 50
Performance |
Timeline |
WILLIS LEASE FIN |
CHINA HUARONG ENERHD |
WILLIS LEASE and CHINA HUARONG Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with WILLIS LEASE and CHINA HUARONG
The main advantage of trading using opposite WILLIS LEASE and CHINA HUARONG positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if WILLIS LEASE position performs unexpectedly, CHINA HUARONG 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 CHINA HUARONG will offset losses from the drop in CHINA HUARONG's long position.WILLIS LEASE vs. Mitsui Chemicals | WILLIS LEASE vs. LIFEWAY FOODS | WILLIS LEASE vs. Collins Foods Limited | WILLIS LEASE vs. High Liner Foods |
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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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