Correlation Between Fast Food and Mahaka Media
Can any of the company-specific risk be diversified away by investing in both Fast Food and Mahaka Media 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 Mahaka Media 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 Mahaka Media Tbk, you can compare the effects of market volatilities on Fast Food and Mahaka Media 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 Mahaka Media. Check out your portfolio center. Please also check ongoing floating volatility patterns of Fast Food and Mahaka Media.
Diversification Opportunities for Fast Food and Mahaka Media
0.84 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Fast and Mahaka is 0.84. Overlapping area represents the amount of risk that can be diversified away by holding Fast Food Indonesia and Mahaka Media Tbk in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Mahaka Media 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 Mahaka Media. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Mahaka Media Tbk has no effect on the direction of Fast Food i.e., Fast Food and Mahaka Media go up and down completely randomly.
Pair Corralation between Fast Food and Mahaka Media
Assuming the 90 days trading horizon Fast Food Indonesia is expected to under-perform the Mahaka Media. But the stock apears to be less risky and, when comparing its historical volatility, Fast Food Indonesia is 1.74 times less risky than Mahaka Media. The stock trades about -0.24 of its potential returns per unit of risk. The Mahaka Media Tbk is currently generating about -0.09 of returns per unit of risk over similar time horizon. If you would invest 3,800 in Mahaka Media Tbk on September 13, 2024 and sell it today you would lose (900.00) from holding Mahaka Media Tbk or give up 23.68% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Fast Food Indonesia vs. Mahaka Media Tbk
Performance |
Timeline |
Fast Food Indonesia |
Mahaka Media Tbk |
Fast Food and Mahaka Media Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Fast Food and Mahaka Media
The main advantage of trading using opposite Fast Food and Mahaka Media positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Fast Food position performs unexpectedly, Mahaka Media 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 Mahaka Media will offset losses from the drop in Mahaka Media's long position.Fast Food vs. Pembangunan Graha Lestari | Fast Food vs. Pembangunan Jaya Ancol | Fast Food vs. Hotel Sahid Jaya | Fast Food vs. Mitrabara Adiperdana PT |
Mahaka Media vs. Mnc Land Tbk | Mahaka Media vs. MNC Vision Networks | Mahaka Media vs. MD Pictures Tbk | Mahaka Media vs. Link Net 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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