Correlation Between Identiv and Allianz SE
Can any of the company-specific risk be diversified away by investing in both Identiv and Allianz SE 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 Identiv and Allianz SE into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Identiv and Allianz SE VNA, you can compare the effects of market volatilities on Identiv and Allianz SE 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 Identiv with a short position of Allianz SE. Check out your portfolio center. Please also check ongoing floating volatility patterns of Identiv and Allianz SE.
Diversification Opportunities for Identiv and Allianz SE
0.33 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Identiv and Allianz is 0.33. Overlapping area represents the amount of risk that can be diversified away by holding Identiv and Allianz SE VNA in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Allianz SE VNA and Identiv 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 Identiv are associated (or correlated) with Allianz SE. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Allianz SE VNA has no effect on the direction of Identiv i.e., Identiv and Allianz SE go up and down completely randomly.
Pair Corralation between Identiv and Allianz SE
Assuming the 90 days trading horizon Identiv is expected to under-perform the Allianz SE. In addition to that, Identiv is 3.77 times more volatile than Allianz SE VNA. It trades about -0.17 of its total potential returns per unit of risk. Allianz SE VNA is currently generating about -0.08 per unit of volatility. If you would invest 30,010 in Allianz SE VNA on October 5, 2024 and sell it today you would lose (330.00) from holding Allianz SE VNA or give up 1.1% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Identiv vs. Allianz SE VNA
Performance |
Timeline |
Identiv |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Modest
Allianz SE VNA |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Insignificant
Identiv and Allianz SE Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Identiv and Allianz SE
The main advantage of trading using opposite Identiv and Allianz SE positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Identiv position performs unexpectedly, Allianz SE 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 Allianz SE will offset losses from the drop in Allianz SE's long position.The idea behind Identiv and Allianz SE VNA pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.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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