Correlation Between Bank Al and Gatron Industries
Can any of the company-specific risk be diversified away by investing in both Bank Al and Gatron Industries 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 Al and Gatron Industries into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bank Al Habib and Gatron Industries, you can compare the effects of market volatilities on Bank Al and Gatron Industries 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 Al with a short position of Gatron Industries. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank Al and Gatron Industries.
Diversification Opportunities for Bank Al and Gatron Industries
-0.44 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Bank and Gatron is -0.44. Overlapping area represents the amount of risk that can be diversified away by holding Bank Al Habib and Gatron Industries in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Gatron Industries and Bank Al 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 Al Habib are associated (or correlated) with Gatron Industries. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Gatron Industries has no effect on the direction of Bank Al i.e., Bank Al and Gatron Industries go up and down completely randomly.
Pair Corralation between Bank Al and Gatron Industries
Assuming the 90 days trading horizon Bank Al Habib is expected to generate 2.02 times more return on investment than Gatron Industries. However, Bank Al is 2.02 times more volatile than Gatron Industries. It trades about 0.13 of its potential returns per unit of risk. Gatron Industries is currently generating about -0.43 per unit of risk. If you would invest 12,894 in Bank Al Habib on October 24, 2024 and sell it today you would earn a total of 661.00 from holding Bank Al Habib or generate 5.13% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Bank Al Habib vs. Gatron Industries
Performance |
| Timeline |
| Bank Al Habib |
| Gatron Industries |
Bank Al and Gatron Industries Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Bank Al and Gatron Industries
The main advantage of trading using opposite Bank Al and Gatron Industries positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank Al position performs unexpectedly, Gatron Industries 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 Gatron Industries will offset losses from the drop in Gatron Industries' long position.| Bank Al vs. United Insurance | Bank Al vs. Crescent Star Insurance | Bank Al vs. Premier Insurance | Bank Al vs. TPL Insurance |
| Gatron Industries vs. Pakistan Telecommunication | Gatron Industries vs. Century Insurance | Gatron Industries vs. Fateh Sports Wear | Gatron Industries vs. Sindh Modaraba Management |
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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