Correlation Between Citra Putra and Perusahaan Perkebunan
Can any of the company-specific risk be diversified away by investing in both Citra Putra and Perusahaan Perkebunan 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 Citra Putra and Perusahaan Perkebunan into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Citra Putra Realty and Perusahaan Perkebunan London, you can compare the effects of market volatilities on Citra Putra and Perusahaan Perkebunan 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 Citra Putra with a short position of Perusahaan Perkebunan. Check out your portfolio center. Please also check ongoing floating volatility patterns of Citra Putra and Perusahaan Perkebunan.
Diversification Opportunities for Citra Putra and Perusahaan Perkebunan
-0.58 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Citra and Perusahaan is -0.58. Overlapping area represents the amount of risk that can be diversified away by holding Citra Putra Realty and Perusahaan Perkebunan London in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Perusahaan Perkebunan and Citra Putra 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 Citra Putra Realty are associated (or correlated) with Perusahaan Perkebunan. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Perusahaan Perkebunan has no effect on the direction of Citra Putra i.e., Citra Putra and Perusahaan Perkebunan go up and down completely randomly.
Pair Corralation between Citra Putra and Perusahaan Perkebunan
Assuming the 90 days trading horizon Citra Putra Realty is expected to generate 1.61 times more return on investment than Perusahaan Perkebunan. However, Citra Putra is 1.61 times more volatile than Perusahaan Perkebunan London. It trades about 0.85 of its potential returns per unit of risk. Perusahaan Perkebunan London is currently generating about -0.28 per unit of risk. If you would invest 11,100 in Citra Putra Realty on August 28, 2024 and sell it today you would earn a total of 13,100 from holding Citra Putra Realty or generate 118.02% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Citra Putra Realty vs. Perusahaan Perkebunan London
Performance |
| Timeline |
| Citra Putra Realty |
| Perusahaan Perkebunan |
Citra Putra and Perusahaan Perkebunan Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Citra Putra and Perusahaan Perkebunan
The main advantage of trading using opposite Citra Putra and Perusahaan Perkebunan positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Citra Putra position performs unexpectedly, Perusahaan Perkebunan 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 Perusahaan Perkebunan will offset losses from the drop in Perusahaan Perkebunan's long position.| Citra Putra vs. Jasa Armada Indonesia | Citra Putra vs. Cahayaputra Asa Keramik | Citra Putra vs. Campina Ice Cream |
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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