Correlation Between Geely Automobile and Wells Fargo
Can any of the company-specific risk be diversified away by investing in both Geely Automobile and Wells Fargo 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 Geely Automobile and Wells Fargo into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Geely Automobile Holdings and West Fraser Timber, you can compare the effects of market volatilities on Geely Automobile and Wells Fargo 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 Geely Automobile with a short position of Wells Fargo. Check out your portfolio center. Please also check ongoing floating volatility patterns of Geely Automobile and Wells Fargo.
Diversification Opportunities for Geely Automobile and Wells Fargo
-0.5 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Geely and Wells is -0.5. Overlapping area represents the amount of risk that can be diversified away by holding Geely Automobile Holdings and West Fraser Timber in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on West Fraser Timber and Geely Automobile 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 Geely Automobile Holdings are associated (or correlated) with Wells Fargo. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of West Fraser Timber has no effect on the direction of Geely Automobile i.e., Geely Automobile and Wells Fargo go up and down completely randomly.
Pair Corralation between Geely Automobile and Wells Fargo
Assuming the 90 days horizon Geely Automobile Holdings is expected to under-perform the Wells Fargo. In addition to that, Geely Automobile is 1.09 times more volatile than West Fraser Timber. It trades about -0.22 of its total potential returns per unit of risk. West Fraser Timber is currently generating about -0.09 per unit of volatility. If you would invest 8,741 in West Fraser Timber on October 11, 2024 and sell it today you would lose (266.00) from holding West Fraser Timber or give up 3.04% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Geely Automobile Holdings vs. West Fraser Timber
Performance |
Timeline |
Geely Automobile Holdings |
West Fraser Timber |
Geely Automobile and Wells Fargo Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Geely Automobile and Wells Fargo
The main advantage of trading using opposite Geely Automobile and Wells Fargo positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Geely Automobile position performs unexpectedly, Wells Fargo 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 Wells Fargo will offset losses from the drop in Wells Fargo's long position.Geely Automobile vs. Information Services International Dentsu | Geely Automobile vs. Synchrony Financial | Geely Automobile vs. Virtu Financial | Geely Automobile vs. The Hanover Insurance |
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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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