Correlation Between Alam Sutera and Surya Semesta
Can any of the company-specific risk be diversified away by investing in both Alam Sutera and Surya Semesta 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 Alam Sutera and Surya Semesta into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Alam Sutera Realty and Surya Semesta Internusa, you can compare the effects of market volatilities on Alam Sutera and Surya Semesta 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 Alam Sutera with a short position of Surya Semesta. Check out your portfolio center. Please also check ongoing floating volatility patterns of Alam Sutera and Surya Semesta.
Diversification Opportunities for Alam Sutera and Surya Semesta
0.66 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Alam and Surya is 0.66. Overlapping area represents the amount of risk that can be diversified away by holding Alam Sutera Realty and Surya Semesta Internusa in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Surya Semesta Internusa and Alam Sutera 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 Alam Sutera Realty are associated (or correlated) with Surya Semesta. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Surya Semesta Internusa has no effect on the direction of Alam Sutera i.e., Alam Sutera and Surya Semesta go up and down completely randomly.
Pair Corralation between Alam Sutera and Surya Semesta
Assuming the 90 days trading horizon Alam Sutera Realty is expected to generate 0.76 times more return on investment than Surya Semesta. However, Alam Sutera Realty is 1.32 times less risky than Surya Semesta. It trades about -0.11 of its potential returns per unit of risk. Surya Semesta Internusa is currently generating about -0.2 per unit of risk. If you would invest 13,400 in Alam Sutera Realty on December 30, 2024 and sell it today you would lose (2,400) from holding Alam Sutera Realty or give up 17.91% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Alam Sutera Realty vs. Surya Semesta Internusa
Performance |
Timeline |
Alam Sutera Realty |
Surya Semesta Internusa |
Alam Sutera and Surya Semesta Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Alam Sutera and Surya Semesta
The main advantage of trading using opposite Alam Sutera and Surya Semesta positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Alam Sutera position performs unexpectedly, Surya Semesta 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 Surya Semesta will offset losses from the drop in Surya Semesta's long position.Alam Sutera vs. Bumi Serpong Damai | Alam Sutera vs. Summarecon Agung Tbk | Alam Sutera vs. Lippo Karawaci Tbk | Alam Sutera vs. Ciputra Development Tbk |
Surya Semesta vs. Summarecon Agung Tbk | Surya Semesta vs. Lippo Cikarang Tbk | Surya Semesta vs. Alam Sutera Realty | Surya Semesta vs. Lippo Karawaci Tbk |
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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