Correlation Between Scharf Fund and Aggressive Growth
Can any of the company-specific risk be diversified away by investing in both Scharf Fund and Aggressive Growth 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 Scharf Fund and Aggressive Growth into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Scharf Fund Retail and Aggressive Growth Allocation, you can compare the effects of market volatilities on Scharf Fund and Aggressive Growth 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 Scharf Fund with a short position of Aggressive Growth. Check out your portfolio center. Please also check ongoing floating volatility patterns of Scharf Fund and Aggressive Growth.
Diversification Opportunities for Scharf Fund and Aggressive Growth
0.86 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Scharf and Aggressive is 0.86. Overlapping area represents the amount of risk that can be diversified away by holding Scharf Fund Retail and Aggressive Growth Allocation in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Aggressive Growth and Scharf Fund 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 Scharf Fund Retail are associated (or correlated) with Aggressive Growth. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Aggressive Growth has no effect on the direction of Scharf Fund i.e., Scharf Fund and Aggressive Growth go up and down completely randomly.
Pair Corralation between Scharf Fund and Aggressive Growth
Assuming the 90 days horizon Scharf Fund Retail is expected to under-perform the Aggressive Growth. In addition to that, Scharf Fund is 1.23 times more volatile than Aggressive Growth Allocation. It trades about -0.13 of its total potential returns per unit of risk. Aggressive Growth Allocation is currently generating about 0.12 per unit of volatility. If you would invest 1,168 in Aggressive Growth Allocation on September 12, 2024 and sell it today you would earn a total of 12.00 from holding Aggressive Growth Allocation or generate 1.03% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Scharf Fund Retail vs. Aggressive Growth Allocation
Performance |
Timeline |
Scharf Fund Retail |
Aggressive Growth |
Scharf Fund and Aggressive Growth Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Scharf Fund and Aggressive Growth
The main advantage of trading using opposite Scharf Fund and Aggressive Growth positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Scharf Fund position performs unexpectedly, Aggressive Growth 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 Aggressive Growth will offset losses from the drop in Aggressive Growth's long position.Scharf Fund vs. Gmo Resources | Scharf Fund vs. Icon Natural Resources | Scharf Fund vs. Fidelity Advisor Energy | Scharf Fund vs. Oil Gas Ultrasector |
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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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