Correlation Between IBL HealthCare and Hi Tech
Can any of the company-specific risk be diversified away by investing in both IBL HealthCare and Hi Tech 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 IBL HealthCare and Hi Tech into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between IBL HealthCare and Hi Tech Lubricants, you can compare the effects of market volatilities on IBL HealthCare and Hi Tech 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 IBL HealthCare with a short position of Hi Tech. Check out your portfolio center. Please also check ongoing floating volatility patterns of IBL HealthCare and Hi Tech.
Diversification Opportunities for IBL HealthCare and Hi Tech
0.62 | Correlation Coefficient |
Poor diversification
The 3 months correlation between IBL and HTL is 0.62. Overlapping area represents the amount of risk that can be diversified away by holding IBL HealthCare and Hi Tech Lubricants in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Hi Tech Lubricants and IBL HealthCare 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 IBL HealthCare are associated (or correlated) with Hi Tech. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Hi Tech Lubricants has no effect on the direction of IBL HealthCare i.e., IBL HealthCare and Hi Tech go up and down completely randomly.
Pair Corralation between IBL HealthCare and Hi Tech
Assuming the 90 days trading horizon IBL HealthCare is expected to generate 1.48 times less return on investment than Hi Tech. But when comparing it to its historical volatility, IBL HealthCare is 1.14 times less risky than Hi Tech. It trades about 0.04 of its potential returns per unit of risk. Hi Tech Lubricants is currently generating about 0.06 of returns per unit of risk over similar time horizon. If you would invest 2,412 in Hi Tech Lubricants on August 27, 2024 and sell it today you would earn a total of 1,817 from holding Hi Tech Lubricants or generate 75.33% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 99.32% |
| Values | Daily Returns |
IBL HealthCare vs. Hi Tech Lubricants
Performance |
| Timeline |
| IBL HealthCare |
| Hi Tech Lubricants |
IBL HealthCare and Hi Tech Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with IBL HealthCare and Hi Tech
The main advantage of trading using opposite IBL HealthCare and Hi Tech positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if IBL HealthCare position performs unexpectedly, Hi Tech 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 Hi Tech will offset losses from the drop in Hi Tech's long position.| IBL HealthCare vs. Masood Textile Mills | IBL HealthCare vs. Fauji Foods | IBL HealthCare vs. KSB Pumps | IBL HealthCare vs. Mari Petroleum |
| Hi Tech vs. Al Ghazi Tractors | Hi Tech vs. Shell Pakistan | Hi Tech vs. Nestle Pakistan | Hi Tech vs. Hinopak Motors |
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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