Correlation Between Chandra Asri and Habco Trans
Can any of the company-specific risk be diversified away by investing in both Chandra Asri and Habco Trans 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 Chandra Asri and Habco Trans into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Chandra Asri Petrochemical and Habco Trans Maritima, you can compare the effects of market volatilities on Chandra Asri and Habco Trans 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 Chandra Asri with a short position of Habco Trans. Check out your portfolio center. Please also check ongoing floating volatility patterns of Chandra Asri and Habco Trans.
Diversification Opportunities for Chandra Asri and Habco Trans
-0.13 | Correlation Coefficient |
Good diversification
The 3 months correlation between Chandra and Habco is -0.13. Overlapping area represents the amount of risk that can be diversified away by holding Chandra Asri Petrochemical and Habco Trans Maritima in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Habco Trans Maritima and Chandra Asri 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 Chandra Asri Petrochemical are associated (or correlated) with Habco Trans. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Habco Trans Maritima has no effect on the direction of Chandra Asri i.e., Chandra Asri and Habco Trans go up and down completely randomly.
Pair Corralation between Chandra Asri and Habco Trans
Assuming the 90 days trading horizon Chandra Asri Petrochemical is expected to generate 2.69 times more return on investment than Habco Trans. However, Chandra Asri is 2.69 times more volatile than Habco Trans Maritima. It trades about 0.04 of its potential returns per unit of risk. Habco Trans Maritima is currently generating about -0.02 per unit of risk. If you would invest 750,000 in Chandra Asri Petrochemical on December 4, 2024 and sell it today you would earn a total of 0.00 from holding Chandra Asri Petrochemical or generate 0.0% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Chandra Asri Petrochemical vs. Habco Trans Maritima
Performance |
Timeline |
Chandra Asri Petroch |
Habco Trans Maritima |
Chandra Asri and Habco Trans Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Chandra Asri and Habco Trans
The main advantage of trading using opposite Chandra Asri and Habco Trans positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Chandra Asri position performs unexpectedly, Habco Trans 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 Habco Trans will offset losses from the drop in Habco Trans' long position.Chandra Asri vs. Barito Pacific Tbk | Chandra Asri vs. Pabrik Kertas Tjiwi | Chandra Asri vs. Charoen Pokphand Indonesia | Chandra Asri vs. Indah Kiat Pulp |
Habco Trans vs. PT Dewi Shri | Habco Trans vs. PT Arkora Hydro | Habco Trans vs. PT Hasnur Internasional | Habco Trans vs. PT Sari Kreasi |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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