Correlation Between Agha Steel and Pakistan Tobacco
Can any of the company-specific risk be diversified away by investing in both Agha Steel and Pakistan Tobacco 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 Agha Steel and Pakistan Tobacco into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Agha Steel Industries and Pakistan Tobacco, you can compare the effects of market volatilities on Agha Steel and Pakistan Tobacco 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 Agha Steel with a short position of Pakistan Tobacco. Check out your portfolio center. Please also check ongoing floating volatility patterns of Agha Steel and Pakistan Tobacco.
Diversification Opportunities for Agha Steel and Pakistan Tobacco
-0.19 | Correlation Coefficient |
Good diversification
The 3 months correlation between Agha and Pakistan is -0.19. Overlapping area represents the amount of risk that can be diversified away by holding Agha Steel Industries and Pakistan Tobacco in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Pakistan Tobacco and Agha Steel 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 Agha Steel Industries are associated (or correlated) with Pakistan Tobacco. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Pakistan Tobacco has no effect on the direction of Agha Steel i.e., Agha Steel and Pakistan Tobacco go up and down completely randomly.
Pair Corralation between Agha Steel and Pakistan Tobacco
Assuming the 90 days trading horizon Agha Steel Industries is expected to under-perform the Pakistan Tobacco. In addition to that, Agha Steel is 1.65 times more volatile than Pakistan Tobacco. It trades about -0.1 of its total potential returns per unit of risk. Pakistan Tobacco is currently generating about 0.0 per unit of volatility. If you would invest 124,682 in Pakistan Tobacco on October 26, 2024 and sell it today you would lose (679.00) from holding Pakistan Tobacco or give up 0.54% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Agha Steel Industries vs. Pakistan Tobacco
Performance |
| Timeline |
| Agha Steel Industries |
| Pakistan Tobacco |
Agha Steel and Pakistan Tobacco Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Agha Steel and Pakistan Tobacco
The main advantage of trading using opposite Agha Steel and Pakistan Tobacco positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Agha Steel position performs unexpectedly, Pakistan Tobacco 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 Pakistan Tobacco will offset losses from the drop in Pakistan Tobacco's long position.| Agha Steel vs. Engro Polymer Chemicals | Agha Steel vs. United Insurance | Agha Steel vs. National Foods | Agha Steel vs. Unity Foods |
| Pakistan Tobacco vs. Matco Foods | Pakistan Tobacco vs. Wah Nobel Chemicals | Pakistan Tobacco vs. Engro Polymer Chemicals | Pakistan Tobacco vs. Ghani Chemical Industries |
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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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