Correlation Between Monster Beverage and VIENNA INSURANCE
Can any of the company-specific risk be diversified away by investing in both Monster Beverage and VIENNA INSURANCE 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 Monster Beverage and VIENNA INSURANCE into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Monster Beverage Corp and VIENNA INSURANCE GR, you can compare the effects of market volatilities on Monster Beverage and VIENNA INSURANCE 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 Monster Beverage with a short position of VIENNA INSURANCE. Check out your portfolio center. Please also check ongoing floating volatility patterns of Monster Beverage and VIENNA INSURANCE.
Diversification Opportunities for Monster Beverage and VIENNA INSURANCE
-0.14 | Correlation Coefficient |
Good diversification
The 3 months correlation between Monster and VIENNA is -0.14. Overlapping area represents the amount of risk that can be diversified away by holding Monster Beverage Corp and VIENNA INSURANCE GR in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on VIENNA INSURANCE and Monster Beverage 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 Monster Beverage Corp are associated (or correlated) with VIENNA INSURANCE. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of VIENNA INSURANCE has no effect on the direction of Monster Beverage i.e., Monster Beverage and VIENNA INSURANCE go up and down completely randomly.
Pair Corralation between Monster Beverage and VIENNA INSURANCE
Assuming the 90 days trading horizon Monster Beverage Corp is expected to under-perform the VIENNA INSURANCE. In addition to that, Monster Beverage is 1.32 times more volatile than VIENNA INSURANCE GR. It trades about -0.02 of its total potential returns per unit of risk. VIENNA INSURANCE GR is currently generating about 0.32 per unit of volatility. If you would invest 2,930 in VIENNA INSURANCE GR on October 8, 2024 and sell it today you would earn a total of 115.00 from holding VIENNA INSURANCE GR or generate 3.92% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Monster Beverage Corp vs. VIENNA INSURANCE GR
Performance |
Timeline |
Monster Beverage Corp |
VIENNA INSURANCE |
Monster Beverage and VIENNA INSURANCE Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Monster Beverage and VIENNA INSURANCE
The main advantage of trading using opposite Monster Beverage and VIENNA INSURANCE positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Monster Beverage position performs unexpectedly, VIENNA INSURANCE 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 VIENNA INSURANCE will offset losses from the drop in VIENNA INSURANCE's long position.Monster Beverage vs. Apple Inc | Monster Beverage vs. Apple Inc | Monster Beverage vs. Apple Inc | Monster Beverage vs. Apple Inc |
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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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