Correlation Between Sawit Sumbermas and Mitra Pinasthika
Can any of the company-specific risk be diversified away by investing in both Sawit Sumbermas and Mitra Pinasthika 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 Mitra Pinasthika 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 Mitra Pinasthika Mustika, you can compare the effects of market volatilities on Sawit Sumbermas and Mitra Pinasthika 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 Mitra Pinasthika. Check out your portfolio center. Please also check ongoing floating volatility patterns of Sawit Sumbermas and Mitra Pinasthika.
Diversification Opportunities for Sawit Sumbermas and Mitra Pinasthika
0.23 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Sawit and Mitra is 0.23. Overlapping area represents the amount of risk that can be diversified away by holding Sawit Sumbermas Sarana and Mitra Pinasthika Mustika in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Mitra Pinasthika Mustika 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 Mitra Pinasthika. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Mitra Pinasthika Mustika has no effect on the direction of Sawit Sumbermas i.e., Sawit Sumbermas and Mitra Pinasthika go up and down completely randomly.
Pair Corralation between Sawit Sumbermas and Mitra Pinasthika
Assuming the 90 days trading horizon Sawit Sumbermas Sarana is expected to generate 8.38 times more return on investment than Mitra Pinasthika. However, Sawit Sumbermas is 8.38 times more volatile than Mitra Pinasthika Mustika. It trades about 0.02 of its potential returns per unit of risk. Mitra Pinasthika Mustika is currently generating about -0.1 per unit of risk. If you would invest 105,000 in Sawit Sumbermas Sarana on September 4, 2024 and sell it today you would lose (1,500) from holding Sawit Sumbermas Sarana or give up 1.43% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Sawit Sumbermas Sarana vs. Mitra Pinasthika Mustika
Performance |
Timeline |
Sawit Sumbermas Sarana |
Mitra Pinasthika Mustika |
Sawit Sumbermas and Mitra Pinasthika Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Sawit Sumbermas and Mitra Pinasthika
The main advantage of trading using opposite Sawit Sumbermas and Mitra Pinasthika positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Sawit Sumbermas position performs unexpectedly, Mitra Pinasthika 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 Mitra Pinasthika will offset losses from the drop in Mitra Pinasthika'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 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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