Correlation Between Shaheen Insurance and Sitara Chemical
Can any of the company-specific risk be diversified away by investing in both Shaheen Insurance and Sitara Chemical 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 Shaheen Insurance and Sitara Chemical into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Shaheen Insurance and Sitara Chemical Industries, you can compare the effects of market volatilities on Shaheen Insurance and Sitara Chemical 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 Shaheen Insurance with a short position of Sitara Chemical. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shaheen Insurance and Sitara Chemical.
Diversification Opportunities for Shaheen Insurance and Sitara Chemical
0.28 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Shaheen and Sitara is 0.28. Overlapping area represents the amount of risk that can be diversified away by holding Shaheen Insurance and Sitara Chemical Industries in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sitara Chemical Indu and Shaheen Insurance 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 Shaheen Insurance are associated (or correlated) with Sitara Chemical. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sitara Chemical Indu has no effect on the direction of Shaheen Insurance i.e., Shaheen Insurance and Sitara Chemical go up and down completely randomly.
Pair Corralation between Shaheen Insurance and Sitara Chemical
Assuming the 90 days trading horizon Shaheen Insurance is expected to generate 1.27 times more return on investment than Sitara Chemical. However, Shaheen Insurance is 1.27 times more volatile than Sitara Chemical Industries. It trades about 0.14 of its potential returns per unit of risk. Sitara Chemical Industries is currently generating about 0.05 per unit of risk. If you would invest 468.00 in Shaheen Insurance on September 5, 2024 and sell it today you would earn a total of 116.00 from holding Shaheen Insurance or generate 24.79% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 98.41% |
Values | Daily Returns |
Shaheen Insurance vs. Sitara Chemical Industries
Performance |
Timeline |
Shaheen Insurance |
Sitara Chemical Indu |
Shaheen Insurance and Sitara Chemical Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Shaheen Insurance and Sitara Chemical
The main advantage of trading using opposite Shaheen Insurance and Sitara Chemical positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shaheen Insurance position performs unexpectedly, Sitara Chemical 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 Sitara Chemical will offset losses from the drop in Sitara Chemical's long position.Shaheen Insurance vs. Oil and Gas | Shaheen Insurance vs. Pakistan State Oil | Shaheen Insurance vs. Pakistan Petroleum | Shaheen Insurance vs. Fauji Fertilizer |
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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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