Correlation Between EFU General and Roshan Packages
Can any of the company-specific risk be diversified away by investing in both EFU General and Roshan Packages 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 EFU General and Roshan Packages into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between EFU General Insurance and Roshan Packages, you can compare the effects of market volatilities on EFU General and Roshan Packages 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 EFU General with a short position of Roshan Packages. Check out your portfolio center. Please also check ongoing floating volatility patterns of EFU General and Roshan Packages.
Diversification Opportunities for EFU General and Roshan Packages
0.54 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between EFU and Roshan is 0.54. Overlapping area represents the amount of risk that can be diversified away by holding EFU General Insurance and Roshan Packages in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Roshan Packages and EFU General 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 EFU General Insurance are associated (or correlated) with Roshan Packages. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Roshan Packages has no effect on the direction of EFU General i.e., EFU General and Roshan Packages go up and down completely randomly.
Pair Corralation between EFU General and Roshan Packages
Assuming the 90 days trading horizon EFU General Insurance is expected to generate 1.11 times more return on investment than Roshan Packages. However, EFU General is 1.11 times more volatile than Roshan Packages. It trades about 0.51 of its potential returns per unit of risk. Roshan Packages is currently generating about 0.18 per unit of risk. If you would invest 9,150 in EFU General Insurance on September 3, 2024 and sell it today you would earn a total of 4,575 from holding EFU General Insurance or generate 50.0% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
EFU General Insurance vs. Roshan Packages
Performance |
| Timeline |
| EFU General Insurance |
| Roshan Packages |
EFU General and Roshan Packages Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with EFU General and Roshan Packages
The main advantage of trading using opposite EFU General and Roshan Packages positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if EFU General position performs unexpectedly, Roshan Packages 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 Roshan Packages will offset losses from the drop in Roshan Packages' long position.| EFU General vs. Oil and Gas | EFU General vs. Pakistan State Oil | EFU General vs. Pakistan Petroleum | EFU General vs. Fauji Fertilizer |
| Roshan Packages vs. Masood Textile Mills | Roshan Packages vs. Fauji Foods | Roshan Packages vs. KSB Pumps | Roshan Packages vs. Mari Petroleum |
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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