Correlation Between Fast Food and Global Mediacom
Can any of the company-specific risk be diversified away by investing in both Fast Food and Global Mediacom 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 Fast Food and Global Mediacom into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Fast Food Indonesia and Global Mediacom Tbk, you can compare the effects of market volatilities on Fast Food and Global Mediacom 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 Fast Food with a short position of Global Mediacom. Check out your portfolio center. Please also check ongoing floating volatility patterns of Fast Food and Global Mediacom.
Diversification Opportunities for Fast Food and Global Mediacom
0.94 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Fast and Global is 0.94. Overlapping area represents the amount of risk that can be diversified away by holding Fast Food Indonesia and Global Mediacom Tbk in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Global Mediacom Tbk and Fast Food 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 Fast Food Indonesia are associated (or correlated) with Global Mediacom. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Global Mediacom Tbk has no effect on the direction of Fast Food i.e., Fast Food and Global Mediacom go up and down completely randomly.
Pair Corralation between Fast Food and Global Mediacom
Assuming the 90 days trading horizon Fast Food Indonesia is expected to under-perform the Global Mediacom. In addition to that, Fast Food is 1.16 times more volatile than Global Mediacom Tbk. It trades about -0.07 of its total potential returns per unit of risk. Global Mediacom Tbk is currently generating about -0.03 per unit of volatility. If you would invest 27,600 in Global Mediacom Tbk on September 2, 2024 and sell it today you would lose (7,900) from holding Global Mediacom Tbk or give up 28.62% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Fast Food Indonesia vs. Global Mediacom Tbk
Performance |
| Timeline |
| Fast Food Indonesia |
| Global Mediacom Tbk |
Fast Food and Global Mediacom Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Fast Food and Global Mediacom
The main advantage of trading using opposite Fast Food and Global Mediacom positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Fast Food position performs unexpectedly, Global Mediacom 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 Global Mediacom will offset losses from the drop in Global Mediacom's long position.| Fast Food vs. Hero Supermarket Tbk | Fast Food vs. Indoritel Makmur Internasional | Fast Food vs. Enseval Putra Megatrading | Fast Food vs. Fks Multi Agro |
| Global Mediacom vs. Indosat Tbk | Global Mediacom vs. XL Axiata Tbk | Global Mediacom vs. Energi Mega Persada | Global Mediacom vs. Bakrie Brothers 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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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