Correlation Between Bank Mandiri and GMéxico Transportes
Can any of the company-specific risk be diversified away by investing in both Bank Mandiri and GMéxico Transportes 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 Bank Mandiri and GMéxico Transportes into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bank Mandiri Persero and GMxico Transportes SAB, you can compare the effects of market volatilities on Bank Mandiri and GMéxico Transportes 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 Bank Mandiri with a short position of GMéxico Transportes. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank Mandiri and GMéxico Transportes.
Diversification Opportunities for Bank Mandiri and GMéxico Transportes
-0.75 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Bank and GMéxico is -0.75. Overlapping area represents the amount of risk that can be diversified away by holding Bank Mandiri Persero and GMxico Transportes SAB in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on GMxico Transportes SAB and Bank Mandiri 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 Bank Mandiri Persero are associated (or correlated) with GMéxico Transportes. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of GMxico Transportes SAB has no effect on the direction of Bank Mandiri i.e., Bank Mandiri and GMéxico Transportes go up and down completely randomly.
Pair Corralation between Bank Mandiri and GMéxico Transportes
Assuming the 90 days horizon Bank Mandiri Persero is expected to generate 4.67 times more return on investment than GMéxico Transportes. However, Bank Mandiri is 4.67 times more volatile than GMxico Transportes SAB. It trades about 0.1 of its potential returns per unit of risk. GMxico Transportes SAB is currently generating about -0.22 per unit of risk. If you would invest 36.00 in Bank Mandiri Persero on October 25, 2024 and sell it today you would earn a total of 2.00 from holding Bank Mandiri Persero or generate 5.56% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 85.71% |
Values | Daily Returns |
Bank Mandiri Persero vs. GMxico Transportes SAB
Performance |
Timeline |
Bank Mandiri Persero |
GMxico Transportes SAB |
Bank Mandiri and GMéxico Transportes Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Bank Mandiri and GMéxico Transportes
The main advantage of trading using opposite Bank Mandiri and GMéxico Transportes positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank Mandiri position performs unexpectedly, GMéxico Transportes 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 GMéxico Transportes will offset losses from the drop in GMéxico Transportes' long position.Bank Mandiri vs. PT Bank Rakyat | Bank Mandiri vs. Piraeus Bank SA | Bank Mandiri vs. Eurobank Ergasias Services | Bank Mandiri vs. Zions Bancorporation |
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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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