Correlation Between China Shenhua and China Shenhua
Can any of the company-specific risk be diversified away by investing in both China Shenhua and China Shenhua 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 China Shenhua and China Shenhua into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between China Shenhua Energy and China Shenhua Energy, you can compare the effects of market volatilities on China Shenhua and China Shenhua 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 China Shenhua with a short position of China Shenhua. Check out your portfolio center. Please also check ongoing floating volatility patterns of China Shenhua and China Shenhua.
Diversification Opportunities for China Shenhua and China Shenhua
0.56 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between China and China is 0.56. Overlapping area represents the amount of risk that can be diversified away by holding China Shenhua Energy and China Shenhua Energy in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on China Shenhua Energy and China Shenhua 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 China Shenhua Energy are associated (or correlated) with China Shenhua. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of China Shenhua Energy has no effect on the direction of China Shenhua i.e., China Shenhua and China Shenhua go up and down completely randomly.
Pair Corralation between China Shenhua and China Shenhua
Assuming the 90 days horizon China Shenhua Energy is expected to under-perform the China Shenhua. In addition to that, China Shenhua is 1.34 times more volatile than China Shenhua Energy. It trades about -0.13 of its total potential returns per unit of risk. China Shenhua Energy is currently generating about -0.04 per unit of volatility. If you would invest 1,698 in China Shenhua Energy on December 19, 2024 and sell it today you would lose (78.00) from holding China Shenhua Energy or give up 4.59% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
China Shenhua Energy vs. China Shenhua Energy
Performance |
Timeline |
China Shenhua Energy |
China Shenhua Energy |
China Shenhua and China Shenhua Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with China Shenhua and China Shenhua
The main advantage of trading using opposite China Shenhua and China Shenhua positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if China Shenhua position performs unexpectedly, China Shenhua 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 Shenhua will offset losses from the drop in China Shenhua's long position.China Shenhua vs. Bukit Asam Tbk | China Shenhua vs. Indo Tambangraya Megah | China Shenhua vs. Thungela Resources Limited |
China Shenhua vs. Adaro Energy Tbk | China Shenhua vs. Bukit Asam Tbk | China Shenhua vs. Indo Tambangraya Megah | China Shenhua vs. Yanzhou Coal Mining |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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