Correlation Between Habib Metropolitan and Dow Jones
Can any of the company-specific risk be diversified away by investing in both Habib Metropolitan and Dow Jones 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 Habib Metropolitan and Dow Jones into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Habib Metropolitan Bank and Dow Jones Industrial, you can compare the effects of market volatilities on Habib Metropolitan and Dow Jones 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 Habib Metropolitan with a short position of Dow Jones. Check out your portfolio center. Please also check ongoing floating volatility patterns of Habib Metropolitan and Dow Jones.
Diversification Opportunities for Habib Metropolitan and Dow Jones
0.69 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Habib and Dow is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding Habib Metropolitan Bank and Dow Jones Industrial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dow Jones Industrial and Habib Metropolitan 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 Habib Metropolitan Bank are associated (or correlated) with Dow Jones. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dow Jones Industrial has no effect on the direction of Habib Metropolitan i.e., Habib Metropolitan and Dow Jones go up and down completely randomly.
Pair Corralation between Habib Metropolitan and Dow Jones
Assuming the 90 days trading horizon Habib Metropolitan Bank is expected to generate 2.35 times more return on investment than Dow Jones. However, Habib Metropolitan is 2.35 times more volatile than Dow Jones Industrial. It trades about 0.11 of its potential returns per unit of risk. Dow Jones Industrial is currently generating about 0.09 per unit of risk. If you would invest 6,515 in Habib Metropolitan Bank on September 29, 2024 and sell it today you would earn a total of 1,734 from holding Habib Metropolitan Bank or generate 26.62% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 98.43% |
Values | Daily Returns |
Habib Metropolitan Bank vs. Dow Jones Industrial
Performance |
Timeline |
Habib Metropolitan and Dow Jones Volatility Contrast
Predicted Return Density |
Returns |
Habib Metropolitan Bank
Pair trading matchups for Habib Metropolitan
Dow Jones Industrial
Pair trading matchups for Dow Jones
Pair Trading with Habib Metropolitan and Dow Jones
The main advantage of trading using opposite Habib Metropolitan and Dow Jones positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Habib Metropolitan position performs unexpectedly, Dow Jones 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 Dow Jones will offset losses from the drop in Dow Jones' long position.Habib Metropolitan vs. Habib Bank | Habib Metropolitan vs. National Bank of | Habib Metropolitan vs. United Bank | Habib Metropolitan vs. MCB Bank |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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