Correlation Between XPO Logistics and Copa Holdings
Can any of the company-specific risk be diversified away by investing in both XPO Logistics and Copa Holdings 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 XPO Logistics and Copa Holdings into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between XPO Logistics and Copa Holdings SA, you can compare the effects of market volatilities on XPO Logistics and Copa Holdings 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 XPO Logistics with a short position of Copa Holdings. Check out your portfolio center. Please also check ongoing floating volatility patterns of XPO Logistics and Copa Holdings.
Diversification Opportunities for XPO Logistics and Copa Holdings
-0.16 | Correlation Coefficient |
Good diversification
The 3 months correlation between XPO and Copa is -0.16. Overlapping area represents the amount of risk that can be diversified away by holding XPO Logistics and Copa Holdings SA in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Copa Holdings SA and XPO Logistics 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 XPO Logistics are associated (or correlated) with Copa Holdings. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Copa Holdings SA has no effect on the direction of XPO Logistics i.e., XPO Logistics and Copa Holdings go up and down completely randomly.
Pair Corralation between XPO Logistics and Copa Holdings
Considering the 90-day investment horizon XPO Logistics is expected to under-perform the Copa Holdings. In addition to that, XPO Logistics is 1.53 times more volatile than Copa Holdings SA. It trades about -0.18 of its total potential returns per unit of risk. Copa Holdings SA is currently generating about -0.08 per unit of volatility. If you would invest 9,116 in Copa Holdings SA on September 22, 2024 and sell it today you would lose (256.00) from holding Copa Holdings SA or give up 2.81% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
XPO Logistics vs. Copa Holdings SA
Performance |
Timeline |
XPO Logistics |
Copa Holdings SA |
XPO Logistics and Copa Holdings Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with XPO Logistics and Copa Holdings
The main advantage of trading using opposite XPO Logistics and Copa Holdings positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if XPO Logistics position performs unexpectedly, Copa Holdings 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 Copa Holdings will offset losses from the drop in Copa Holdings' long position.XPO Logistics vs. Copa Holdings SA | XPO Logistics vs. SkyWest | XPO Logistics vs. Sun Country Airlines | XPO Logistics vs. Frontier Group Holdings |
Copa Holdings vs. SkyWest | Copa Holdings vs. Sun Country Airlines | Copa Holdings vs. Air Transport Services | Copa Holdings vs. Frontier Group Holdings |
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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