Correlation Between Fidelity Covington and ProShares
Can any of the company-specific risk be diversified away by investing in both Fidelity Covington and ProShares 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 Fidelity Covington and ProShares into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Fidelity Covington Trust and ProShares On Demand ETF, you can compare the effects of market volatilities on Fidelity Covington and ProShares 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 Fidelity Covington with a short position of ProShares. Check out your portfolio center. Please also check ongoing floating volatility patterns of Fidelity Covington and ProShares.
Diversification Opportunities for Fidelity Covington and ProShares
-0.62 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Fidelity and ProShares is -0.62. Overlapping area represents the amount of risk that can be diversified away by holding Fidelity Covington Trust and ProShares On Demand ETF in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on ProShares On Demand and Fidelity Covington 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 Fidelity Covington Trust are associated (or correlated) with ProShares. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of ProShares On Demand has no effect on the direction of Fidelity Covington i.e., Fidelity Covington and ProShares go up and down completely randomly.
Pair Corralation between Fidelity Covington and ProShares
Given the investment horizon of 90 days Fidelity Covington Trust is expected to under-perform the ProShares. In addition to that, Fidelity Covington is 1.4 times more volatile than ProShares On Demand ETF. It trades about -0.09 of its total potential returns per unit of risk. ProShares On Demand ETF is currently generating about 0.2 per unit of volatility. If you would invest 2,986 in ProShares On Demand ETF on September 14, 2024 and sell it today you would earn a total of 480.00 from holding ProShares On Demand ETF or generate 16.08% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Fidelity Covington Trust vs. ProShares On Demand ETF
Performance |
Timeline |
Fidelity Covington Trust |
ProShares On Demand |
Fidelity Covington and ProShares Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Fidelity Covington and ProShares
The main advantage of trading using opposite Fidelity Covington and ProShares positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Fidelity Covington position performs unexpectedly, ProShares 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 ProShares will offset losses from the drop in ProShares' long position.Fidelity Covington vs. Fidelity Covington Trust | Fidelity Covington vs. Fidelity Covington Trust | Fidelity Covington vs. Fidelity Covington Trust | Fidelity Covington vs. Fidelity Crypto Industry |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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