Correlation Between Japfa Comfeed and Map Boga
Can any of the company-specific risk be diversified away by investing in both Japfa Comfeed and Map Boga 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 Japfa Comfeed and Map Boga into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Japfa Comfeed Indonesia and Map Boga Adiperkasa, you can compare the effects of market volatilities on Japfa Comfeed and Map Boga 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 Japfa Comfeed with a short position of Map Boga. Check out your portfolio center. Please also check ongoing floating volatility patterns of Japfa Comfeed and Map Boga.
Diversification Opportunities for Japfa Comfeed and Map Boga
-0.83 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Japfa and Map is -0.83. Overlapping area represents the amount of risk that can be diversified away by holding Japfa Comfeed Indonesia and Map Boga Adiperkasa in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Map Boga Adiperkasa and Japfa Comfeed 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 Japfa Comfeed Indonesia are associated (or correlated) with Map Boga. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Map Boga Adiperkasa has no effect on the direction of Japfa Comfeed i.e., Japfa Comfeed and Map Boga go up and down completely randomly.
Pair Corralation between Japfa Comfeed and Map Boga
Assuming the 90 days trading horizon Japfa Comfeed Indonesia is expected to generate 0.82 times more return on investment than Map Boga. However, Japfa Comfeed Indonesia is 1.22 times less risky than Map Boga. It trades about 0.05 of its potential returns per unit of risk. Map Boga Adiperkasa is currently generating about -0.01 per unit of risk. If you would invest 117,284 in Japfa Comfeed Indonesia on September 3, 2024 and sell it today you would earn a total of 54,216 from holding Japfa Comfeed Indonesia or generate 46.23% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Significant |
| Accuracy | 99.79% |
| Values | Daily Returns |
Japfa Comfeed Indonesia vs. Map Boga Adiperkasa
Performance |
| Timeline |
| Japfa Comfeed Indonesia |
| Map Boga Adiperkasa |
Japfa Comfeed and Map Boga Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Japfa Comfeed and Map Boga
The main advantage of trading using opposite Japfa Comfeed and Map Boga positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Japfa Comfeed position performs unexpectedly, Map Boga 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 Map Boga will offset losses from the drop in Map Boga's long position.| Japfa Comfeed vs. Charoen Pokphand Indonesia | Japfa Comfeed vs. Kalbe Farma Tbk | Japfa Comfeed vs. Indofood Cbp Sukses | Japfa Comfeed vs. PT Indofood Sukses |
| Map Boga vs. Mitra Pinasthika Mustika | Map Boga vs. Jakarta Int Hotels | Map Boga vs. Asuransi Harta Aman | Map Boga vs. Indosterling Technomedia Tbk |
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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