Correlation Between Al Ghazi and Pakistan State
Can any of the company-specific risk be diversified away by investing in both Al Ghazi and Pakistan State 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 Al Ghazi and Pakistan State into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Al Ghazi Tractors and Pakistan State Oil, you can compare the effects of market volatilities on Al Ghazi and Pakistan State 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 Al Ghazi with a short position of Pakistan State. Check out your portfolio center. Please also check ongoing floating volatility patterns of Al Ghazi and Pakistan State.
Diversification Opportunities for Al Ghazi and Pakistan State
0.75 | Correlation Coefficient |
Poor diversification
The 3 months correlation between AGTL and Pakistan is 0.75. Overlapping area represents the amount of risk that can be diversified away by holding Al Ghazi Tractors and Pakistan State Oil in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Pakistan State Oil and Al Ghazi 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 Al Ghazi Tractors are associated (or correlated) with Pakistan State. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Pakistan State Oil has no effect on the direction of Al Ghazi i.e., Al Ghazi and Pakistan State go up and down completely randomly.
Pair Corralation between Al Ghazi and Pakistan State
Assuming the 90 days trading horizon Al Ghazi Tractors is expected to under-perform the Pakistan State. But the stock apears to be less risky and, when comparing its historical volatility, Al Ghazi Tractors is 2.18 times less risky than Pakistan State. The stock trades about -0.33 of its potential returns per unit of risk. The Pakistan State Oil is currently generating about 0.21 of returns per unit of risk over similar time horizon. If you would invest 23,397 in Pakistan State Oil on August 28, 2024 and sell it today you would earn a total of 2,774 from holding Pakistan State Oil or generate 11.86% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Al Ghazi Tractors vs. Pakistan State Oil
Performance |
| Timeline |
| Al Ghazi Tractors |
| Pakistan State Oil |
Al Ghazi and Pakistan State Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Al Ghazi and Pakistan State
The main advantage of trading using opposite Al Ghazi and Pakistan State positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Al Ghazi position performs unexpectedly, Pakistan State 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 State will offset losses from the drop in Pakistan State's long position.| Al Ghazi vs. Masood Textile Mills | Al Ghazi vs. Fauji Foods | Al Ghazi vs. Mari Petroleum | Al Ghazi vs. Loads |
| Pakistan State vs. Dost Steels | Pakistan State vs. Allied Bank | Pakistan State vs. Adamjee Insurance | Pakistan State vs. Metropolitan Steel Corp |
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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