Correlation Between Ping An and Manulife Financial
Can any of the company-specific risk be diversified away by investing in both Ping An and Manulife Financial 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 Ping An and Manulife Financial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ping An Insurance and Manulife Financial, you can compare the effects of market volatilities on Ping An and Manulife Financial 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 Ping An with a short position of Manulife Financial. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ping An and Manulife Financial.
Diversification Opportunities for Ping An and Manulife Financial
-0.44 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Ping and Manulife is -0.44. Overlapping area represents the amount of risk that can be diversified away by holding Ping An Insurance and Manulife Financial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Manulife Financial and Ping An 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 Ping An Insurance are associated (or correlated) with Manulife Financial. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Manulife Financial has no effect on the direction of Ping An i.e., Ping An and Manulife Financial go up and down completely randomly.
Pair Corralation between Ping An and Manulife Financial
Assuming the 90 days horizon Ping An Insurance is expected to under-perform the Manulife Financial. In addition to that, Ping An is 3.14 times more volatile than Manulife Financial. It trades about -0.38 of its total potential returns per unit of risk. Manulife Financial is currently generating about 0.22 per unit of volatility. If you would invest 1,457 in Manulife Financial on August 28, 2024 and sell it today you would earn a total of 28.00 from holding Manulife Financial or generate 1.92% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Ping An Insurance vs. Manulife Financial
Performance |
Timeline |
Ping An Insurance |
Manulife Financial |
Ping An and Manulife Financial Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Ping An and Manulife Financial
The main advantage of trading using opposite Ping An and Manulife Financial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ping An position performs unexpectedly, Manulife Financial 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 Manulife Financial will offset losses from the drop in Manulife Financial's long position.Ping An vs. CNO Financial Group | Ping An vs. Genworth Financial | Ping An vs. MetLife Preferred Stock | Ping An vs. Prudential Public Limited |
Manulife Financial vs. CNO Financial Group | Manulife Financial vs. Genworth Financial | Manulife Financial vs. MetLife Preferred Stock | Manulife Financial vs. Prudential Public Limited |
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 Competition Analyzer module to analyze and compare many basic indicators for a group of related or unrelated entities.
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