Correlation Between Vienna Insurance and GEVORKYAN
Can any of the company-specific risk be diversified away by investing in both Vienna Insurance and GEVORKYAN 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 Vienna Insurance and GEVORKYAN into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Vienna Insurance Group and GEVORKYAN as, you can compare the effects of market volatilities on Vienna Insurance and GEVORKYAN 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 Vienna Insurance with a short position of GEVORKYAN. Check out your portfolio center. Please also check ongoing floating volatility patterns of Vienna Insurance and GEVORKYAN.
Diversification Opportunities for Vienna Insurance and GEVORKYAN
-0.54 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Vienna and GEVORKYAN is -0.54. Overlapping area represents the amount of risk that can be diversified away by holding Vienna Insurance Group and GEVORKYAN as in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on GEVORKYAN as and Vienna 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 Vienna Insurance Group are associated (or correlated) with GEVORKYAN. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of GEVORKYAN as has no effect on the direction of Vienna Insurance i.e., Vienna Insurance and GEVORKYAN go up and down completely randomly.
Pair Corralation between Vienna Insurance and GEVORKYAN
Assuming the 90 days trading horizon Vienna Insurance Group is expected to under-perform the GEVORKYAN. But the stock apears to be less risky and, when comparing its historical volatility, Vienna Insurance Group is 1.24 times less risky than GEVORKYAN. The stock trades about -0.01 of its potential returns per unit of risk. The GEVORKYAN as is currently generating about 0.13 of returns per unit of risk over similar time horizon. If you would invest 25,400 in GEVORKYAN as on September 19, 2024 and sell it today you would earn a total of 2,200 from holding GEVORKYAN as or generate 8.66% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Vienna Insurance Group vs. GEVORKYAN as
Performance |
Timeline |
Vienna Insurance |
GEVORKYAN as |
Vienna Insurance and GEVORKYAN Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Vienna Insurance and GEVORKYAN
The main advantage of trading using opposite Vienna Insurance and GEVORKYAN positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Vienna Insurance position performs unexpectedly, GEVORKYAN 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 GEVORKYAN will offset losses from the drop in GEVORKYAN's long position.Vienna Insurance vs. Cez AS | Vienna Insurance vs. MT 1997 AS | Vienna Insurance vs. Kofola CeskoSlovensko as | Vienna Insurance vs. HARDWARIO as |
GEVORKYAN vs. UNIQA Insurance Group | GEVORKYAN vs. JT ARCH INVESTMENTS | GEVORKYAN vs. Vienna Insurance 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 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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