Correlation Between Bank Central and AGF Management
Can any of the company-specific risk be diversified away by investing in both Bank Central and AGF Management 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 Bank Central and AGF Management into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bank Central Asia and AGF Management Limited, you can compare the effects of market volatilities on Bank Central and AGF Management 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 Bank Central with a short position of AGF Management. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank Central and AGF Management.
Diversification Opportunities for Bank Central and AGF Management
-0.43 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Bank and AGF is -0.43. Overlapping area represents the amount of risk that can be diversified away by holding Bank Central Asia and AGF Management Limited in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on AGF Management and Bank Central 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 Bank Central Asia are associated (or correlated) with AGF Management. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of AGF Management has no effect on the direction of Bank Central i.e., Bank Central and AGF Management go up and down completely randomly.
Pair Corralation between Bank Central and AGF Management
Assuming the 90 days horizon Bank Central Asia is expected to under-perform the AGF Management. But the pink sheet apears to be less risky and, when comparing its historical volatility, Bank Central Asia is 1.31 times less risky than AGF Management. The pink sheet trades about -0.04 of its potential returns per unit of risk. The AGF Management Limited is currently generating about 0.25 of returns per unit of risk over similar time horizon. If you would invest 582.00 in AGF Management Limited on September 2, 2024 and sell it today you would earn a total of 216.00 from holding AGF Management Limited or generate 37.11% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Bank Central Asia vs. AGF Management Limited
Performance |
Timeline |
Bank Central Asia |
AGF Management |
Bank Central and AGF Management Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Bank Central and AGF Management
The main advantage of trading using opposite Bank Central and AGF Management positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank Central position performs unexpectedly, AGF Management 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 AGF Management will offset losses from the drop in AGF Management's long position.Bank Central vs. Nedbank Group | Bank Central vs. Standard Bank Group | Bank Central vs. Kasikornbank Public Co | Bank Central vs. KBC Groep NV |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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