Correlation Between Galva Technologies and Maming Enam
Can any of the company-specific risk be diversified away by investing in both Galva Technologies and Maming Enam 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 Galva Technologies and Maming Enam into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Galva Technologies Tbk and Maming Enam Sembilan, you can compare the effects of market volatilities on Galva Technologies and Maming Enam 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 Galva Technologies with a short position of Maming Enam. Check out your portfolio center. Please also check ongoing floating volatility patterns of Galva Technologies and Maming Enam.
Diversification Opportunities for Galva Technologies and Maming Enam
0.43 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Galva and Maming is 0.43. Overlapping area represents the amount of risk that can be diversified away by holding Galva Technologies Tbk and Maming Enam Sembilan in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Maming Enam Sembilan and Galva Technologies 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 Galva Technologies Tbk are associated (or correlated) with Maming Enam. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Maming Enam Sembilan has no effect on the direction of Galva Technologies i.e., Galva Technologies and Maming Enam go up and down completely randomly.
Pair Corralation between Galva Technologies and Maming Enam
Assuming the 90 days trading horizon Galva Technologies Tbk is expected to generate 0.64 times more return on investment than Maming Enam. However, Galva Technologies Tbk is 1.57 times less risky than Maming Enam. It trades about -0.01 of its potential returns per unit of risk. Maming Enam Sembilan is currently generating about -0.09 per unit of risk. If you would invest 33,200 in Galva Technologies Tbk on December 30, 2024 and sell it today you would lose (1,400) from holding Galva Technologies Tbk or give up 4.22% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Galva Technologies Tbk vs. Maming Enam Sembilan
Performance |
Timeline |
Galva Technologies Tbk |
Maming Enam Sembilan |
Galva Technologies and Maming Enam Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Galva Technologies and Maming Enam
The main advantage of trading using opposite Galva Technologies and Maming Enam positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Galva Technologies position performs unexpectedly, Maming Enam 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 Maming Enam will offset losses from the drop in Maming Enam's long position.Galva Technologies vs. Multipolar Technology Tbk | Galva Technologies vs. Nusantara Voucher Distribution | Galva Technologies vs. Hensel Davest Indonesia | Galva Technologies vs. Anabatic Technologies Tbk |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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