Correlation Between Wizz Air and Regions Financial
Can any of the company-specific risk be diversified away by investing in both Wizz Air and Regions Financial 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 Wizz Air and Regions Financial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Wizz Air Holdings and Regions Financial Corp, you can compare the effects of market volatilities on Wizz Air and Regions Financial 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 Wizz Air with a short position of Regions Financial. Check out your portfolio center. Please also check ongoing floating volatility patterns of Wizz Air and Regions Financial.
Diversification Opportunities for Wizz Air and Regions Financial
0.18 | Correlation Coefficient |
Average diversification
The 3 months correlation between Wizz and Regions is 0.18. Overlapping area represents the amount of risk that can be diversified away by holding Wizz Air Holdings and Regions Financial Corp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Regions Financial Corp and Wizz Air 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 Wizz Air Holdings are associated (or correlated) with Regions Financial. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Regions Financial Corp has no effect on the direction of Wizz Air i.e., Wizz Air and Regions Financial go up and down completely randomly.
Pair Corralation between Wizz Air and Regions Financial
Assuming the 90 days trading horizon Wizz Air Holdings is expected to generate 2.16 times more return on investment than Regions Financial. However, Wizz Air is 2.16 times more volatile than Regions Financial Corp. It trades about 0.16 of its potential returns per unit of risk. Regions Financial Corp is currently generating about -0.36 per unit of risk. If you would invest 129,700 in Wizz Air Holdings on September 23, 2024 and sell it today you would earn a total of 14,600 from holding Wizz Air Holdings or generate 11.26% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Wizz Air Holdings vs. Regions Financial Corp
Performance |
Timeline |
Wizz Air Holdings |
Regions Financial Corp |
Wizz Air and Regions Financial Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Wizz Air and Regions Financial
The main advantage of trading using opposite Wizz Air and Regions Financial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Wizz Air position performs unexpectedly, Regions Financial 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 Regions Financial will offset losses from the drop in Regions Financial's long position.Wizz Air vs. Tyson Foods Cl | Wizz Air vs. Atalaya Mining | Wizz Air vs. Griffin Mining | Wizz Air vs. National Beverage Corp |
Regions Financial vs. Wizz Air Holdings | Regions Financial vs. Systemair AB | Regions Financial vs. Amedeo Air Four | Regions Financial vs. Alaska Air Group |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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