Correlation Between China Shenhua and PICC Property
Can any of the company-specific risk be diversified away by investing in both China Shenhua and PICC Property 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 PICC Property 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 PICC Property and, you can compare the effects of market volatilities on China Shenhua and PICC Property 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 PICC Property. Check out your portfolio center. Please also check ongoing floating volatility patterns of China Shenhua and PICC Property.
Diversification Opportunities for China Shenhua and PICC Property
0.6 | Correlation Coefficient |
Poor diversification
The 3 months correlation between China and PICC is 0.6. Overlapping area represents the amount of risk that can be diversified away by holding China Shenhua Energy and PICC Property and in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on PICC Property 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 PICC Property. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of PICC Property has no effect on the direction of China Shenhua i.e., China Shenhua and PICC Property go up and down completely randomly.
Pair Corralation between China Shenhua and PICC Property
Assuming the 90 days horizon China Shenhua Energy is expected to under-perform the PICC Property. But the pink sheet apears to be less risky and, when comparing its historical volatility, China Shenhua Energy is 1.82 times less risky than PICC Property. The pink sheet trades about -0.03 of its potential returns per unit of risk. The PICC Property and is currently generating about 0.03 of returns per unit of risk over similar time horizon. If you would invest 3,812 in PICC Property and on August 31, 2024 and sell it today you would earn a total of 50.00 from holding PICC Property and or generate 1.31% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 95.65% |
| Values | Daily Returns |
China Shenhua Energy vs. PICC Property and
Performance |
| Timeline |
| China Shenhua Energy |
| PICC Property |
China Shenhua and PICC Property Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with China Shenhua and PICC Property
The main advantage of trading using opposite China Shenhua and PICC Property positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if China Shenhua position performs unexpectedly, PICC Property 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 PICC Property will offset losses from the drop in PICC Property's long position.| 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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