Correlation Between Andalan Sakti and Trimitra Prawara
Can any of the company-specific risk be diversified away by investing in both Andalan Sakti and Trimitra Prawara 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 Andalan Sakti and Trimitra Prawara into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Andalan Sakti Primaindo and Trimitra Prawara Goldland, you can compare the effects of market volatilities on Andalan Sakti and Trimitra Prawara 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 Andalan Sakti with a short position of Trimitra Prawara. Check out your portfolio center. Please also check ongoing floating volatility patterns of Andalan Sakti and Trimitra Prawara.
Diversification Opportunities for Andalan Sakti and Trimitra Prawara
0.84 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Andalan and Trimitra is 0.84. Overlapping area represents the amount of risk that can be diversified away by holding Andalan Sakti Primaindo and Trimitra Prawara Goldland in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Trimitra Prawara Goldland and Andalan Sakti 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 Andalan Sakti Primaindo are associated (or correlated) with Trimitra Prawara. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Trimitra Prawara Goldland has no effect on the direction of Andalan Sakti i.e., Andalan Sakti and Trimitra Prawara go up and down completely randomly.
Pair Corralation between Andalan Sakti and Trimitra Prawara
Assuming the 90 days trading horizon Andalan Sakti Primaindo is expected to under-perform the Trimitra Prawara. In addition to that, Andalan Sakti is 1.46 times more volatile than Trimitra Prawara Goldland. It trades about -0.28 of its total potential returns per unit of risk. Trimitra Prawara Goldland is currently generating about -0.15 per unit of volatility. If you would invest 2,800 in Trimitra Prawara Goldland on August 28, 2024 and sell it today you would lose (300.00) from holding Trimitra Prawara Goldland or give up 10.71% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Andalan Sakti Primaindo vs. Trimitra Prawara Goldland
Performance |
Timeline |
Andalan Sakti Primaindo |
Trimitra Prawara Goldland |
Andalan Sakti and Trimitra Prawara Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Andalan Sakti and Trimitra Prawara
The main advantage of trading using opposite Andalan Sakti and Trimitra Prawara positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Andalan Sakti position performs unexpectedly, Trimitra Prawara 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 Trimitra Prawara will offset losses from the drop in Trimitra Prawara's long position.Andalan Sakti vs. Bumi Benowo Sukses | Andalan Sakti vs. Makmur Berkah Amanda | Andalan Sakti vs. Royalindo Investa Wijaya | Andalan Sakti vs. Cahayaputra Asa Keramik |
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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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