Correlation Between Al Ghazi and Shell Pakistan
Can any of the company-specific risk be diversified away by investing in both Al Ghazi and Shell Pakistan 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 Shell Pakistan 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 Shell Pakistan, you can compare the effects of market volatilities on Al Ghazi and Shell Pakistan 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 Shell Pakistan. Check out your portfolio center. Please also check ongoing floating volatility patterns of Al Ghazi and Shell Pakistan.
Diversification Opportunities for Al Ghazi and Shell Pakistan
0.53 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between AGTL and Shell is 0.53. Overlapping area represents the amount of risk that can be diversified away by holding Al Ghazi Tractors and Shell Pakistan in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shell Pakistan 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 Shell Pakistan. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shell Pakistan has no effect on the direction of Al Ghazi i.e., Al Ghazi and Shell Pakistan go up and down completely randomly.
Pair Corralation between Al Ghazi and Shell Pakistan
Assuming the 90 days trading horizon Al Ghazi Tractors is expected to under-perform the Shell Pakistan. But the stock apears to be less risky and, when comparing its historical volatility, Al Ghazi Tractors is 1.09 times less risky than Shell Pakistan. The stock trades about -0.12 of its potential returns per unit of risk. The Shell Pakistan is currently generating about 0.18 of returns per unit of risk over similar time horizon. If you would invest 14,576 in Shell Pakistan on August 30, 2024 and sell it today you would earn a total of 935.00 from holding Shell Pakistan or generate 6.41% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Al Ghazi Tractors vs. Shell Pakistan
Performance |
| Timeline |
| Al Ghazi Tractors |
| Shell Pakistan |
Al Ghazi and Shell Pakistan Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Al Ghazi and Shell Pakistan
The main advantage of trading using opposite Al Ghazi and Shell Pakistan positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Al Ghazi position performs unexpectedly, Shell Pakistan 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 Shell Pakistan will offset losses from the drop in Shell Pakistan's long position.| Al Ghazi vs. Oil and Gas | Al Ghazi vs. Pakistan State Oil | Al Ghazi vs. Pakistan Petroleum | Al Ghazi vs. Engro |
| Shell Pakistan vs. Sardar Chemical Industries | Shell Pakistan vs. Air Link Communication | Shell Pakistan vs. Unilever Pakistan Foods | Shell Pakistan vs. Avanceon |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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