Correlation Between MicroSectors FANG and AIM ETF
Can any of the company-specific risk be diversified away by investing in both MicroSectors FANG and AIM ETF 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 AIM ETF 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 AIM ETF Products, you can compare the effects of market volatilities on MicroSectors FANG and AIM ETF 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 AIM ETF. Check out your portfolio center. Please also check ongoing floating volatility patterns of MicroSectors FANG and AIM ETF.
Diversification Opportunities for MicroSectors FANG and AIM ETF
0.97 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between MicroSectors and AIM is 0.97. Overlapping area represents the amount of risk that can be diversified away by holding MicroSectors FANG ETN and AIM ETF Products in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on AIM ETF Products 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 AIM ETF. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of AIM ETF Products has no effect on the direction of MicroSectors FANG i.e., MicroSectors FANG and AIM ETF go up and down completely randomly.
Pair Corralation between MicroSectors FANG and AIM ETF
Given the investment horizon of 90 days MicroSectors FANG ETN is expected to generate 3.21 times more return on investment than AIM ETF. However, MicroSectors FANG is 3.21 times more volatile than AIM ETF Products. It trades about 0.25 of its potential returns per unit of risk. AIM ETF Products is currently generating about 0.34 per unit of risk. If you would invest 5,024 in MicroSectors FANG ETN on September 1, 2024 and sell it today you would earn a total of 354.00 from holding MicroSectors FANG ETN or generate 7.05% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Strong |
| Accuracy | 95.45% |
| Values | Daily Returns |
MicroSectors FANG ETN vs. AIM ETF Products
Performance |
| Timeline |
| MicroSectors FANG ETN |
| AIM ETF Products |
MicroSectors FANG and AIM ETF Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with MicroSectors FANG and AIM ETF
The main advantage of trading using opposite MicroSectors FANG and AIM ETF positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if MicroSectors FANG position performs unexpectedly, AIM ETF 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 AIM ETF will offset losses from the drop in AIM ETF's long position.| MicroSectors FANG vs. Freedom Day Dividend | MicroSectors FANG vs. iShares MSCI China | MicroSectors FANG vs. iShares Dividend and | MicroSectors FANG vs. SmartETFs Dividend Builder |
| AIM ETF vs. Innovator ETFs Trust | AIM ETF vs. First Trust Cboe | AIM ETF vs. Innovator SP 500 | AIM ETF vs. Innovator SP 500 |
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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