Correlation Between Sanlam and Manulife Financial
Can any of the company-specific risk be diversified away by investing in both Sanlam 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 Sanlam and Manulife Financial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Sanlam Ltd PK and Manulife Financial, you can compare the effects of market volatilities on Sanlam 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 Sanlam with a short position of Manulife Financial. Check out your portfolio center. Please also check ongoing floating volatility patterns of Sanlam and Manulife Financial.
Diversification Opportunities for Sanlam and Manulife Financial
-0.2 | Correlation Coefficient |
Good diversification
The 3 months correlation between Sanlam and Manulife is -0.2. Overlapping area represents the amount of risk that can be diversified away by holding Sanlam Ltd PK and Manulife Financial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Manulife Financial and Sanlam 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 Sanlam Ltd PK 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 Sanlam i.e., Sanlam and Manulife Financial go up and down completely randomly.
Pair Corralation between Sanlam and Manulife Financial
Assuming the 90 days horizon Sanlam Ltd PK is expected to under-perform the Manulife Financial. In addition to that, Sanlam is 3.68 times more volatile than Manulife Financial. It trades about -0.09 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 | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Sanlam Ltd PK vs. Manulife Financial
Performance |
| Timeline |
| Sanlam Ltd PK |
| Manulife Financial |
Sanlam and Manulife Financial Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Sanlam and Manulife Financial
The main advantage of trading using opposite Sanlam and Manulife Financial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Sanlam 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.| Sanlam vs. Ping An Insurance | Sanlam vs. CNO Financial Group | Sanlam vs. Genworth Financial | Sanlam vs. MetLife Preferred Stock |
| 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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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