Correlation Between MicroSectors FANG and FT Cboe
Can any of the company-specific risk be diversified away by investing in both MicroSectors FANG and FT Cboe 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 MicroSectors FANG and FT Cboe into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between MicroSectors FANG ETN and FT Cboe Vest, you can compare the effects of market volatilities on MicroSectors FANG and FT Cboe 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 MicroSectors FANG with a short position of FT Cboe. Check out your portfolio center. Please also check ongoing floating volatility patterns of MicroSectors FANG and FT Cboe.
Diversification Opportunities for MicroSectors FANG and FT Cboe
0.8 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between MicroSectors and DSEP is 0.8. Overlapping area represents the amount of risk that can be diversified away by holding MicroSectors FANG ETN and FT Cboe Vest in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on FT Cboe Vest and MicroSectors FANG 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 MicroSectors FANG ETN are associated (or correlated) with FT Cboe. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of FT Cboe Vest has no effect on the direction of MicroSectors FANG i.e., MicroSectors FANG and FT Cboe go up and down completely randomly.
Pair Corralation between MicroSectors FANG and FT Cboe
Given the investment horizon of 90 days MicroSectors FANG ETN is expected to generate 4.03 times more return on investment than FT Cboe. However, MicroSectors FANG is 4.03 times more volatile than FT Cboe Vest. It trades about 0.12 of its potential returns per unit of risk. FT Cboe Vest is currently generating about 0.13 per unit of risk. If you would invest 2,453 in MicroSectors FANG ETN on November 2, 2024 and sell it today you would earn a total of 3,427 from holding MicroSectors FANG ETN or generate 139.71% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
MicroSectors FANG ETN vs. FT Cboe Vest
Performance |
| Timeline |
| MicroSectors FANG ETN |
| FT Cboe Vest |
MicroSectors FANG and FT Cboe Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with MicroSectors FANG and FT Cboe
The main advantage of trading using opposite MicroSectors FANG and FT Cboe positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if MicroSectors FANG position performs unexpectedly, FT Cboe 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 FT Cboe will offset losses from the drop in FT Cboe's long position.| MicroSectors FANG vs. MicroSectors Gold 3X | MicroSectors FANG vs. MicroSectors Travel 3X | MicroSectors FANG vs. MicroSectors Travel 3X | MicroSectors FANG vs. MicroSectors FANG Index |
| FT Cboe vs. FT Cboe Vest | FT Cboe vs. FT Cboe Vest | FT Cboe vs. First Trust Exchange Traded | FT Cboe vs. FT Cboe Vest |
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 Headlines Timeline module to stay connected to all market stories and filter out noise. Drill down to analyze hype elasticity.
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