Correlation Between UNIQA INSURANCE and Meta Financial
Can any of the company-specific risk be diversified away by investing in both UNIQA INSURANCE and Meta 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 UNIQA INSURANCE and Meta Financial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between UNIQA INSURANCE GR and Meta Financial Group, you can compare the effects of market volatilities on UNIQA INSURANCE and Meta 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 UNIQA INSURANCE with a short position of Meta Financial. Check out your portfolio center. Please also check ongoing floating volatility patterns of UNIQA INSURANCE and Meta Financial.
Diversification Opportunities for UNIQA INSURANCE and Meta Financial
-0.06 | Correlation Coefficient |
Good diversification
The 3 months correlation between UNIQA and Meta is -0.06. Overlapping area represents the amount of risk that can be diversified away by holding UNIQA INSURANCE GR and Meta Financial Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Meta Financial Group and UNIQA INSURANCE 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 UNIQA INSURANCE GR are associated (or correlated) with Meta Financial. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Meta Financial Group has no effect on the direction of UNIQA INSURANCE i.e., UNIQA INSURANCE and Meta Financial go up and down completely randomly.
Pair Corralation between UNIQA INSURANCE and Meta Financial
Assuming the 90 days trading horizon UNIQA INSURANCE GR is expected to generate 1.32 times more return on investment than Meta Financial. However, UNIQA INSURANCE is 1.32 times more volatile than Meta Financial Group. It trades about 0.15 of its potential returns per unit of risk. Meta Financial Group is currently generating about 0.02 per unit of risk. If you would invest 960.00 in UNIQA INSURANCE GR on April 24, 2025 and sell it today you would earn a total of 190.00 from holding UNIQA INSURANCE GR or generate 19.79% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
UNIQA INSURANCE GR vs. Meta Financial Group
Performance |
Timeline |
UNIQA INSURANCE GR |
Meta Financial Group |
UNIQA INSURANCE and Meta Financial Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with UNIQA INSURANCE and Meta Financial
The main advantage of trading using opposite UNIQA INSURANCE and Meta Financial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if UNIQA INSURANCE position performs unexpectedly, Meta 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 Meta Financial will offset losses from the drop in Meta Financial's long position.UNIQA INSURANCE vs. BII Railway Transportation | UNIQA INSURANCE vs. Gaztransport Technigaz SA | UNIQA INSURANCE vs. NTG Nordic Transport | UNIQA INSURANCE vs. TAL Education Group |
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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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