Correlation Between Mulia Boga and Panca Mitra
Can any of the company-specific risk be diversified away by investing in both Mulia Boga and Panca Mitra 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 Mulia Boga and Panca Mitra into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Mulia Boga Raya and Panca Mitra Multiperdana, you can compare the effects of market volatilities on Mulia Boga and Panca Mitra 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 Mulia Boga with a short position of Panca Mitra. Check out your portfolio center. Please also check ongoing floating volatility patterns of Mulia Boga and Panca Mitra.
Diversification Opportunities for Mulia Boga and Panca Mitra
-0.51 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Mulia and Panca is -0.51. Overlapping area represents the amount of risk that can be diversified away by holding Mulia Boga Raya and Panca Mitra Multiperdana in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Panca Mitra Multiperdana and Mulia Boga 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 Mulia Boga Raya are associated (or correlated) with Panca Mitra. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Panca Mitra Multiperdana has no effect on the direction of Mulia Boga i.e., Mulia Boga and Panca Mitra go up and down completely randomly.
Pair Corralation between Mulia Boga and Panca Mitra
Assuming the 90 days trading horizon Mulia Boga Raya is expected to generate 1.11 times more return on investment than Panca Mitra. However, Mulia Boga is 1.11 times more volatile than Panca Mitra Multiperdana. It trades about 0.1 of its potential returns per unit of risk. Panca Mitra Multiperdana is currently generating about -0.17 per unit of risk. If you would invest 148,500 in Mulia Boga Raya on September 16, 2024 and sell it today you would earn a total of 49,000 from holding Mulia Boga Raya or generate 33.0% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Mulia Boga Raya vs. Panca Mitra Multiperdana
Performance |
Timeline |
Mulia Boga Raya |
Panca Mitra Multiperdana |
Mulia Boga and Panca Mitra Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Mulia Boga and Panca Mitra
The main advantage of trading using opposite Mulia Boga and Panca Mitra positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Mulia Boga position performs unexpectedly, Panca Mitra 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 Panca Mitra will offset losses from the drop in Panca Mitra's long position.Mulia Boga vs. Sentra Food Indonesia | Mulia Boga vs. Garudafood Putra Putri | Mulia Boga vs. Wijaya Karya Bangunan | Mulia Boga vs. Mitrabara Adiperdana PT |
Panca Mitra vs. Sentra Food Indonesia | Panca Mitra vs. Garudafood Putra Putri | Panca Mitra vs. Wijaya Karya Bangunan | Panca Mitra vs. Mitrabara Adiperdana PT |
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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