Correlation Between First Trust and ProShares Big
Can any of the company-specific risk be diversified away by investing in both First Trust and ProShares Big 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 First Trust and ProShares Big into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between First Trust NASDAQ and ProShares Big Data, you can compare the effects of market volatilities on First Trust and ProShares Big 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 First Trust with a short position of ProShares Big. Check out your portfolio center. Please also check ongoing floating volatility patterns of First Trust and ProShares Big.
Diversification Opportunities for First Trust and ProShares Big
0.83 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between First and ProShares is 0.83. Overlapping area represents the amount of risk that can be diversified away by holding First Trust NASDAQ and ProShares Big Data in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on ProShares Big Data and First Trust 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 First Trust NASDAQ are associated (or correlated) with ProShares Big. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of ProShares Big Data has no effect on the direction of First Trust i.e., First Trust and ProShares Big go up and down completely randomly.
Pair Corralation between First Trust and ProShares Big
Given the investment horizon of 90 days First Trust is expected to generate 5.18 times less return on investment than ProShares Big. But when comparing it to its historical volatility, First Trust NASDAQ is 1.21 times less risky than ProShares Big. It trades about 0.14 of its potential returns per unit of risk. ProShares Big Data is currently generating about 0.62 of returns per unit of risk over similar time horizon. If you would invest 3,691 in ProShares Big Data on August 26, 2024 and sell it today you would earn a total of 879.00 from holding ProShares Big Data or generate 23.81% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
First Trust NASDAQ vs. ProShares Big Data
Performance |
| Timeline |
| First Trust NASDAQ |
| ProShares Big Data |
First Trust and ProShares Big Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with First Trust and ProShares Big
The main advantage of trading using opposite First Trust and ProShares Big positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if First Trust position performs unexpectedly, ProShares Big 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 Big will offset losses from the drop in ProShares Big's long position.| First Trust vs. Amplify ETF Trust | First Trust vs. Global X Cybersecurity | First Trust vs. iShares Cybersecurity and | First Trust vs. First Trust Cloud |
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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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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