Correlation Between Ristia Bintang and PT Data
Can any of the company-specific risk be diversified away by investing in both Ristia Bintang and PT Data 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 Ristia Bintang and PT Data into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ristia Bintang Mahkotasejati and PT Data Sinergitama, you can compare the effects of market volatilities on Ristia Bintang and PT Data 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 Ristia Bintang with a short position of PT Data. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ristia Bintang and PT Data.
Diversification Opportunities for Ristia Bintang and PT Data
-0.48 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Ristia and ELIT is -0.48. Overlapping area represents the amount of risk that can be diversified away by holding Ristia Bintang Mahkotasejati and PT Data Sinergitama in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on PT Data Sinergitama and Ristia Bintang 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 Ristia Bintang Mahkotasejati are associated (or correlated) with PT Data. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of PT Data Sinergitama has no effect on the direction of Ristia Bintang i.e., Ristia Bintang and PT Data go up and down completely randomly.
Pair Corralation between Ristia Bintang and PT Data
Assuming the 90 days trading horizon Ristia Bintang Mahkotasejati is expected to generate 0.18 times more return on investment than PT Data. However, Ristia Bintang Mahkotasejati is 5.48 times less risky than PT Data. It trades about -0.3 of its potential returns per unit of risk. PT Data Sinergitama is currently generating about -0.13 per unit of risk. If you would invest 3,000 in Ristia Bintang Mahkotasejati on December 29, 2024 and sell it today you would lose (400.00) from holding Ristia Bintang Mahkotasejati or give up 13.33% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Ristia Bintang Mahkotasejati vs. PT Data Sinergitama
Performance |
Timeline |
Ristia Bintang Mahko |
PT Data Sinergitama |
Ristia Bintang and PT Data Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Ristia Bintang and PT Data
The main advantage of trading using opposite Ristia Bintang and PT Data positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ristia Bintang position performs unexpectedly, PT Data 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 PT Data will offset losses from the drop in PT Data's long position.Ristia Bintang vs. Bekasi Fajar Industrial | Ristia Bintang vs. Trinitan Metals and | Ristia Bintang vs. Ace Hardware Indonesia | Ristia Bintang vs. Cisarua Mountain Dairy |
PT Data vs. Ace Hardware Indonesia | PT Data vs. Optima Prima Metal | PT Data vs. Lippo General Insurance | PT Data vs. Kedawung Setia Industrial |
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 Optimization module to compute new portfolio that will generate highest expected return given your specified tolerance for risk.
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