Correlation Between Meta Financial and InterContinental
Can any of the company-specific risk be diversified away by investing in both Meta Financial and InterContinental 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 Meta Financial and InterContinental into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Meta Financial Group and InterContinental Hotels Group, you can compare the effects of market volatilities on Meta Financial and InterContinental 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 Meta Financial with a short position of InterContinental. Check out your portfolio center. Please also check ongoing floating volatility patterns of Meta Financial and InterContinental.
Diversification Opportunities for Meta Financial and InterContinental
0.89 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Meta and InterContinental is 0.89. Overlapping area represents the amount of risk that can be diversified away by holding Meta Financial Group and InterContinental Hotels Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on InterContinental Hotels and Meta Financial 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 Meta Financial Group are associated (or correlated) with InterContinental. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of InterContinental Hotels has no effect on the direction of Meta Financial i.e., Meta Financial and InterContinental go up and down completely randomly.
Pair Corralation between Meta Financial and InterContinental
Assuming the 90 days horizon Meta Financial Group is expected to generate 0.99 times more return on investment than InterContinental. However, Meta Financial Group is 1.01 times less risky than InterContinental. It trades about -0.05 of its potential returns per unit of risk. InterContinental Hotels Group is currently generating about -0.16 per unit of risk. If you would invest 7,145 in Meta Financial Group on December 25, 2024 and sell it today you would lose (395.00) from holding Meta Financial Group or give up 5.53% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Meta Financial Group vs. InterContinental Hotels Group
Performance |
Timeline |
Meta Financial Group |
InterContinental Hotels |
Meta Financial and InterContinental Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Meta Financial and InterContinental
The main advantage of trading using opposite Meta Financial and InterContinental positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Meta Financial position performs unexpectedly, InterContinental 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 InterContinental will offset losses from the drop in InterContinental's long position.Meta Financial vs. POSBO UNSPADRS20YC1 | Meta Financial vs. Postal Savings Bank | Meta Financial vs. Truist Financial | Meta Financial vs. OVERSEA CHINUNSPADR2 |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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