Correlation Between CPE Technology and Bank Islam
Can any of the company-specific risk be diversified away by investing in both CPE Technology and Bank Islam 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 CPE Technology and Bank Islam into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between CPE Technology Berhad and Bank Islam Malaysia, you can compare the effects of market volatilities on CPE Technology and Bank Islam 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 CPE Technology with a short position of Bank Islam. Check out your portfolio center. Please also check ongoing floating volatility patterns of CPE Technology and Bank Islam.
Diversification Opportunities for CPE Technology and Bank Islam
-0.49 | Correlation Coefficient |
Very good diversification
The 3 months correlation between CPE and Bank is -0.49. Overlapping area represents the amount of risk that can be diversified away by holding CPE Technology Berhad and Bank Islam Malaysia in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bank Islam Malaysia and CPE Technology 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 CPE Technology Berhad are associated (or correlated) with Bank Islam. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bank Islam Malaysia has no effect on the direction of CPE Technology i.e., CPE Technology and Bank Islam go up and down completely randomly.
Pair Corralation between CPE Technology and Bank Islam
Assuming the 90 days trading horizon CPE Technology Berhad is expected to under-perform the Bank Islam. In addition to that, CPE Technology is 3.13 times more volatile than Bank Islam Malaysia. It trades about -0.16 of its total potential returns per unit of risk. Bank Islam Malaysia is currently generating about 0.05 per unit of volatility. If you would invest 244.00 in Bank Islam Malaysia on December 21, 2024 and sell it today you would earn a total of 6.00 from holding Bank Islam Malaysia or generate 2.46% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 98.31% |
Values | Daily Returns |
CPE Technology Berhad vs. Bank Islam Malaysia
Performance |
Timeline |
CPE Technology Berhad |
Bank Islam Malaysia |
CPE Technology and Bank Islam Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with CPE Technology and Bank Islam
The main advantage of trading using opposite CPE Technology and Bank Islam positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CPE Technology position performs unexpectedly, Bank Islam 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 Bank Islam will offset losses from the drop in Bank Islam's long position.CPE Technology vs. Shangri La Hotels | CPE Technology vs. Sports Toto Berhad | CPE Technology vs. Kobay Tech Bhd | CPE Technology vs. ES Ceramics Technology |
Bank Islam vs. Cloudpoint Technology Berhad | Bank Islam vs. Senheng New Retail | Bank Islam vs. Shangri La Hotels | Bank Islam vs. Coraza Integrated Technology |
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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