Correlation Between Bank Islami and Agha Steel
Can any of the company-specific risk be diversified away by investing in both Bank Islami and Agha Steel 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 Islami and Agha Steel into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bank Islami Pakistan and Agha Steel Industries, you can compare the effects of market volatilities on Bank Islami and Agha Steel 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 Islami with a short position of Agha Steel. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank Islami and Agha Steel.
Diversification Opportunities for Bank Islami and Agha Steel
-0.08 | Correlation Coefficient |
Good diversification
The 3 months correlation between Bank and Agha is -0.08. Overlapping area represents the amount of risk that can be diversified away by holding Bank Islami Pakistan and Agha Steel Industries in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Agha Steel Industries and Bank Islami 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 Islami Pakistan are associated (or correlated) with Agha Steel. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Agha Steel Industries has no effect on the direction of Bank Islami i.e., Bank Islami and Agha Steel go up and down completely randomly.
Pair Corralation between Bank Islami and Agha Steel
Assuming the 90 days trading horizon Bank Islami Pakistan is expected to generate 0.52 times more return on investment than Agha Steel. However, Bank Islami Pakistan is 1.92 times less risky than Agha Steel. It trades about -0.14 of its potential returns per unit of risk. Agha Steel Industries is currently generating about -0.2 per unit of risk. If you would invest 2,348 in Bank Islami Pakistan on August 24, 2024 and sell it today you would lose (131.00) from holding Bank Islami Pakistan or give up 5.58% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Bank Islami Pakistan vs. Agha Steel Industries
Performance |
| Timeline |
| Bank Islami Pakistan |
| Agha Steel Industries |
Bank Islami and Agha Steel Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Bank Islami and Agha Steel
The main advantage of trading using opposite Bank Islami and Agha Steel positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank Islami position performs unexpectedly, Agha Steel 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 Agha Steel will offset losses from the drop in Agha Steel's long position.| Bank Islami vs. Beco Steel | Bank Islami vs. JS Investments | Bank Islami vs. ITTEFAQ Iron Industries | Bank Islami vs. 786 Investment Limited |
| Agha Steel vs. Dost Steels | Agha Steel vs. Air Link Communication | Agha Steel vs. JS Investments | Agha Steel vs. Pak Gulf Leasing |
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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