Correlation Between Barings Active and Fidelity Growth
Can any of the company-specific risk be diversified away by investing in both Barings Active and Fidelity 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 Barings Active and Fidelity Growth into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Barings Active Short and Fidelity Growth Pany, you can compare the effects of market volatilities on Barings Active and Fidelity 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 Barings Active with a short position of Fidelity Growth. Check out your portfolio center. Please also check ongoing floating volatility patterns of Barings Active and Fidelity Growth.
Diversification Opportunities for Barings Active and Fidelity Growth
0.54 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Barings and Fidelity is 0.54. Overlapping area represents the amount of risk that can be diversified away by holding Barings Active Short and Fidelity Growth Pany in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Fidelity Growth Pany and Barings Active 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 Barings Active Short are associated (or correlated) with Fidelity Growth. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Fidelity Growth Pany has no effect on the direction of Barings Active i.e., Barings Active and Fidelity Growth go up and down completely randomly.
Pair Corralation between Barings Active and Fidelity Growth
Assuming the 90 days horizon Barings Active is expected to generate 4.28 times less return on investment than Fidelity Growth. But when comparing it to its historical volatility, Barings Active Short is 8.75 times less risky than Fidelity Growth. It trades about 0.23 of its potential returns per unit of risk. Fidelity Growth Pany is currently generating about 0.11 of returns per unit of risk over similar time horizon. If you would invest 3,202 in Fidelity Growth Pany on September 4, 2024 and sell it today you would earn a total of 1,208 from holding Fidelity Growth Pany or generate 37.73% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 99.6% |
Values | Daily Returns |
Barings Active Short vs. Fidelity Growth Pany
Performance |
Timeline |
Barings Active Short |
Fidelity Growth Pany |
Barings Active and Fidelity Growth Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Barings Active and Fidelity Growth
The main advantage of trading using opposite Barings Active and Fidelity Growth positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Barings Active position performs unexpectedly, Fidelity 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 Fidelity Growth will offset losses from the drop in Fidelity Growth's long position.Barings Active vs. Tiaa Cref Smallmid Cap Equity | Barings Active vs. Small Cap Stock | Barings Active vs. The Gabelli Small | Barings Active vs. T Rowe Price |
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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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