Correlation Between Vistra Energy and GMéxico Transportes
Can any of the company-specific risk be diversified away by investing in both Vistra Energy 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 Vistra Energy and GMéxico Transportes into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Vistra Energy Corp and GMxico Transportes SAB, you can compare the effects of market volatilities on Vistra Energy 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 Vistra Energy with a short position of GMéxico Transportes. Check out your portfolio center. Please also check ongoing floating volatility patterns of Vistra Energy and GMéxico Transportes.
Diversification Opportunities for Vistra Energy and GMéxico Transportes
0.66 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Vistra and GMéxico is 0.66. Overlapping area represents the amount of risk that can be diversified away by holding Vistra Energy Corp 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 Vistra Energy 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 Vistra Energy Corp 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 Vistra Energy i.e., Vistra Energy and GMéxico Transportes go up and down completely randomly.
Pair Corralation between Vistra Energy and GMéxico Transportes
Considering the 90-day investment horizon Vistra Energy Corp is expected to generate 0.63 times more return on investment than GMéxico Transportes. However, Vistra Energy Corp is 1.59 times less risky than GMéxico Transportes. It trades about 0.2 of its potential returns per unit of risk. GMxico Transportes SAB is currently generating about 0.12 per unit of risk. If you would invest 12,383 in Vistra Energy Corp on October 25, 2024 and sell it today you would earn a total of 6,303 from holding Vistra Energy Corp or generate 50.9% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 95.24% |
Values | Daily Returns |
Vistra Energy Corp vs. GMxico Transportes SAB
Performance |
Timeline |
Vistra Energy Corp |
GMxico Transportes SAB |
Vistra Energy and GMéxico Transportes Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Vistra Energy and GMéxico Transportes
The main advantage of trading using opposite Vistra Energy and GMéxico Transportes positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Vistra Energy 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.Vistra Energy vs. Pampa Energia SA | Vistra Energy vs. TransAlta Corp | Vistra Energy vs. Kenon Holdings | Vistra Energy vs. NRG Energy |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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