Correlation Between FT Cboe and AIM ETF
Can any of the company-specific risk be diversified away by investing in both FT Cboe 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 FT Cboe and AIM ETF into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between FT Cboe Vest and AIM ETF Products, you can compare the effects of market volatilities on FT Cboe 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 FT Cboe with a short position of AIM ETF. Check out your portfolio center. Please also check ongoing floating volatility patterns of FT Cboe and AIM ETF.
Diversification Opportunities for FT Cboe and AIM ETF
Almost no diversification
The 3 months correlation between DNOV and AIM is 0.96. Overlapping area represents the amount of risk that can be diversified away by holding FT Cboe Vest 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 FT Cboe 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 FT Cboe Vest 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 FT Cboe i.e., FT Cboe and AIM ETF go up and down completely randomly.
Pair Corralation between FT Cboe and AIM ETF
Given the investment horizon of 90 days FT Cboe Vest is expected to generate 0.49 times more return on investment than AIM ETF. However, FT Cboe Vest is 2.04 times less risky than AIM ETF. It trades about 0.69 of its potential returns per unit of risk. AIM ETF Products is currently generating about 0.26 per unit of risk. If you would invest 4,255 in FT Cboe Vest on August 29, 2024 and sell it today you would earn a total of 71.00 from holding FT Cboe Vest or generate 1.67% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
FT Cboe Vest vs. AIM ETF Products
Performance |
| Timeline |
| FT Cboe Vest |
| AIM ETF Products |
FT Cboe and AIM ETF Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with FT Cboe and AIM ETF
The main advantage of trading using opposite FT Cboe and AIM ETF positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if FT Cboe 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.The idea behind FT Cboe Vest and AIM ETF Products pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.| AIM ETF vs. AllianzIM Large Cap | AIM ETF vs. AIM ETF Products | AIM ETF vs. AIM ETF Products | AIM ETF vs. AIM ETF Products |
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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