Correlation Between Pakistan Petroleum and Mari Petroleum
Can any of the company-specific risk be diversified away by investing in both Pakistan Petroleum and Mari Petroleum 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 Pakistan Petroleum and Mari Petroleum into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Pakistan Petroleum and Mari Petroleum, you can compare the effects of market volatilities on Pakistan Petroleum and Mari Petroleum 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 Pakistan Petroleum with a short position of Mari Petroleum. Check out your portfolio center. Please also check ongoing floating volatility patterns of Pakistan Petroleum and Mari Petroleum.
Diversification Opportunities for Pakistan Petroleum and Mari Petroleum
0.55 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Pakistan and Mari is 0.55. Overlapping area represents the amount of risk that can be diversified away by holding Pakistan Petroleum and Mari Petroleum in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Mari Petroleum and Pakistan Petroleum 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 Pakistan Petroleum are associated (or correlated) with Mari Petroleum. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Mari Petroleum has no effect on the direction of Pakistan Petroleum i.e., Pakistan Petroleum and Mari Petroleum go up and down completely randomly.
Pair Corralation between Pakistan Petroleum and Mari Petroleum
Assuming the 90 days trading horizon Pakistan Petroleum is expected to generate 1.08 times more return on investment than Mari Petroleum. However, Pakistan Petroleum is 1.08 times more volatile than Mari Petroleum. It trades about 0.28 of its potential returns per unit of risk. Mari Petroleum is currently generating about 0.17 per unit of risk. If you would invest 13,630 in Pakistan Petroleum on August 28, 2024 and sell it today you would earn a total of 1,777 from holding Pakistan Petroleum or generate 13.04% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Pakistan Petroleum vs. Mari Petroleum
Performance |
Timeline |
Pakistan Petroleum |
Mari Petroleum |
Pakistan Petroleum and Mari Petroleum Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Pakistan Petroleum and Mari Petroleum
The main advantage of trading using opposite Pakistan Petroleum and Mari Petroleum positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Pakistan Petroleum position performs unexpectedly, Mari Petroleum 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 Mari Petroleum will offset losses from the drop in Mari Petroleum's long position.Pakistan Petroleum vs. Al Ghazi Tractors | Pakistan Petroleum vs. Shell Pakistan | Pakistan Petroleum vs. Nestle Pakistan | Pakistan Petroleum vs. Hinopak Motors |
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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 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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