Correlation Between Meta Financial and POSBO UNSPADRS/20YC1
Can any of the company-specific risk be diversified away by investing in both Meta Financial and POSBO UNSPADRS/20YC1 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 POSBO UNSPADRS/20YC1 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 POSBO UNSPADRS20YC1, you can compare the effects of market volatilities on Meta Financial and POSBO UNSPADRS/20YC1 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 POSBO UNSPADRS/20YC1. Check out your portfolio center. Please also check ongoing floating volatility patterns of Meta Financial and POSBO UNSPADRS/20YC1.
Diversification Opportunities for Meta Financial and POSBO UNSPADRS/20YC1
-0.54 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Meta and POSBO is -0.54. Overlapping area represents the amount of risk that can be diversified away by holding Meta Financial Group and POSBO UNSPADRS20YC1 in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on POSBO UNSPADRS/20YC1 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 POSBO UNSPADRS/20YC1. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of POSBO UNSPADRS/20YC1 has no effect on the direction of Meta Financial i.e., Meta Financial and POSBO UNSPADRS/20YC1 go up and down completely randomly.
Pair Corralation between Meta Financial and POSBO UNSPADRS/20YC1
Assuming the 90 days horizon Meta Financial Group is expected to generate 1.06 times more return on investment than POSBO UNSPADRS/20YC1. However, Meta Financial is 1.06 times more volatile than POSBO UNSPADRS20YC1. It trades about 0.27 of its potential returns per unit of risk. POSBO UNSPADRS20YC1 is currently generating about 0.06 per unit of risk. If you would invest 7,150 in Meta Financial Group on November 7, 2024 and sell it today you would earn a total of 600.00 from holding Meta Financial Group or generate 8.39% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 91.3% |
| Values | Daily Returns |
Meta Financial Group vs. POSBO UNSPADRS20YC1
Performance |
| Timeline |
| Meta Financial Group |
| POSBO UNSPADRS/20YC1 |
Meta Financial and POSBO UNSPADRS/20YC1 Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Meta Financial and POSBO UNSPADRS/20YC1
The main advantage of trading using opposite Meta Financial and POSBO UNSPADRS/20YC1 positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Meta Financial position performs unexpectedly, POSBO UNSPADRS/20YC1 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 POSBO UNSPADRS/20YC1 will offset losses from the drop in POSBO UNSPADRS/20YC1's long position.| Meta Financial vs. Plastic Omnium | Meta Financial vs. GOODYEAR T RUBBER | Meta Financial vs. KOBE STEEL LTD | Meta Financial vs. Vulcan Materials |
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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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