Correlation Between VIVA WINE and AGF Management
Can any of the company-specific risk be diversified away by investing in both VIVA WINE and AGF Management 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 VIVA WINE and AGF Management into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between VIVA WINE GROUP and AGF Management Limited, you can compare the effects of market volatilities on VIVA WINE and AGF Management 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 VIVA WINE with a short position of AGF Management. Check out your portfolio center. Please also check ongoing floating volatility patterns of VIVA WINE and AGF Management.
Diversification Opportunities for VIVA WINE and AGF Management
-0.21 | Correlation Coefficient |
Very good diversification
The 3 months correlation between VIVA and AGF is -0.21. Overlapping area represents the amount of risk that can be diversified away by holding VIVA WINE GROUP and AGF Management Limited in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on AGF Management and VIVA WINE 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 VIVA WINE GROUP are associated (or correlated) with AGF Management. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of AGF Management has no effect on the direction of VIVA WINE i.e., VIVA WINE and AGF Management go up and down completely randomly.
Pair Corralation between VIVA WINE and AGF Management
Assuming the 90 days horizon VIVA WINE GROUP is expected to generate 1.13 times more return on investment than AGF Management. However, VIVA WINE is 1.13 times more volatile than AGF Management Limited. It trades about -0.16 of its potential returns per unit of risk. AGF Management Limited is currently generating about -0.2 per unit of risk. If you would invest 333.00 in VIVA WINE GROUP on October 19, 2024 and sell it today you would lose (13.00) from holding VIVA WINE GROUP or give up 3.9% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 94.44% |
| Values | Daily Returns |
VIVA WINE GROUP vs. AGF Management Limited
Performance |
| Timeline |
| VIVA WINE GROUP |
| AGF Management |
VIVA WINE and AGF Management Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with VIVA WINE and AGF Management
The main advantage of trading using opposite VIVA WINE and AGF Management positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if VIVA WINE position performs unexpectedly, AGF Management 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 AGF Management will offset losses from the drop in AGF Management's long position.| VIVA WINE vs. PENN Entertainment | VIVA WINE vs. WILLIS LEASE FIN | VIVA WINE vs. Townsquare Media | VIVA WINE vs. Nexstar Media Group |
| AGF Management vs. ITALIAN WINE BRANDS | AGF Management vs. ETFS Coffee ETC | AGF Management vs. VIVA WINE GROUP | AGF Management vs. SIEM OFFSHORE NEW |
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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