Correlation Between Sawit Sumbermas and Jakarta Int
Can any of the company-specific risk be diversified away by investing in both Sawit Sumbermas and Jakarta Int 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 Sawit Sumbermas and Jakarta Int into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Sawit Sumbermas Sarana and Jakarta Int Hotels, you can compare the effects of market volatilities on Sawit Sumbermas and Jakarta Int 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 Sawit Sumbermas with a short position of Jakarta Int. Check out your portfolio center. Please also check ongoing floating volatility patterns of Sawit Sumbermas and Jakarta Int.
Diversification Opportunities for Sawit Sumbermas and Jakarta Int
-0.48 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Sawit and Jakarta is -0.48. Overlapping area represents the amount of risk that can be diversified away by holding Sawit Sumbermas Sarana and Jakarta Int Hotels in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Jakarta Int Hotels and Sawit Sumbermas 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 Sawit Sumbermas Sarana are associated (or correlated) with Jakarta Int. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Jakarta Int Hotels has no effect on the direction of Sawit Sumbermas i.e., Sawit Sumbermas and Jakarta Int go up and down completely randomly.
Pair Corralation between Sawit Sumbermas and Jakarta Int
Assuming the 90 days trading horizon Sawit Sumbermas Sarana is expected to generate 0.94 times more return on investment than Jakarta Int. However, Sawit Sumbermas Sarana is 1.07 times less risky than Jakarta Int. It trades about 0.09 of its potential returns per unit of risk. Jakarta Int Hotels is currently generating about -0.13 per unit of risk. If you would invest 130,000 in Sawit Sumbermas Sarana on December 30, 2024 and sell it today you would earn a total of 30,500 from holding Sawit Sumbermas Sarana or generate 23.46% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Sawit Sumbermas Sarana vs. Jakarta Int Hotels
Performance |
Timeline |
Sawit Sumbermas Sarana |
Jakarta Int Hotels |
Sawit Sumbermas and Jakarta Int Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Sawit Sumbermas and Jakarta Int
The main advantage of trading using opposite Sawit Sumbermas and Jakarta Int positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Sawit Sumbermas position performs unexpectedly, Jakarta Int 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 Jakarta Int will offset losses from the drop in Jakarta Int's long position.Sawit Sumbermas vs. Surya Citra Media | Sawit Sumbermas vs. Matahari Department Store | Sawit Sumbermas vs. Salim Ivomas Pratama | Sawit Sumbermas vs. Akr Corporindo Tbk |
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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 My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.
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