Correlation Between United Insurance and Pakistan Synthetics
Can any of the company-specific risk be diversified away by investing in both United Insurance and Pakistan Synthetics 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 United Insurance and Pakistan Synthetics into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between United Insurance and Pakistan Synthetics, you can compare the effects of market volatilities on United Insurance and Pakistan Synthetics 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 United Insurance with a short position of Pakistan Synthetics. Check out your portfolio center. Please also check ongoing floating volatility patterns of United Insurance and Pakistan Synthetics.
Diversification Opportunities for United Insurance and Pakistan Synthetics
0.04 | Correlation Coefficient |
Significant diversification
The 3 months correlation between United and Pakistan is 0.04. Overlapping area represents the amount of risk that can be diversified away by holding United Insurance and Pakistan Synthetics in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Pakistan Synthetics and United 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 United Insurance are associated (or correlated) with Pakistan Synthetics. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Pakistan Synthetics has no effect on the direction of United Insurance i.e., United Insurance and Pakistan Synthetics go up and down completely randomly.
Pair Corralation between United Insurance and Pakistan Synthetics
Assuming the 90 days trading horizon United Insurance is expected to generate 0.62 times more return on investment than Pakistan Synthetics. However, United Insurance is 1.62 times less risky than Pakistan Synthetics. It trades about 0.12 of its potential returns per unit of risk. Pakistan Synthetics is currently generating about 0.05 per unit of risk. If you would invest 467.00 in United Insurance on October 10, 2024 and sell it today you would earn a total of 1,144 from holding United Insurance or generate 244.97% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 81.3% |
Values | Daily Returns |
United Insurance vs. Pakistan Synthetics
Performance |
Timeline |
United Insurance |
Pakistan Synthetics |
United Insurance and Pakistan Synthetics Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with United Insurance and Pakistan Synthetics
The main advantage of trading using opposite United Insurance and Pakistan Synthetics positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if United Insurance position performs unexpectedly, Pakistan Synthetics 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 Pakistan Synthetics will offset losses from the drop in Pakistan Synthetics' long position.United Insurance vs. Agritech | United Insurance vs. JS Investments | United Insurance vs. Ghandhara Automobile | United Insurance vs. NetSol Technologies |
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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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