Correlation Between MicroSectors FANG and AB Ultra
Can any of the company-specific risk be diversified away by investing in both MicroSectors FANG and AB Ultra 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 AB Ultra into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between MicroSectors FANG Index and AB Ultra Short, you can compare the effects of market volatilities on MicroSectors FANG and AB Ultra 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 AB Ultra. Check out your portfolio center. Please also check ongoing floating volatility patterns of MicroSectors FANG and AB Ultra.
Diversification Opportunities for MicroSectors FANG and AB Ultra
-0.82 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between MicroSectors and YEAR is -0.82. Overlapping area represents the amount of risk that can be diversified away by holding MicroSectors FANG Index and AB Ultra Short in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on AB Ultra Short 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 Index are associated (or correlated) with AB Ultra. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of AB Ultra Short has no effect on the direction of MicroSectors FANG i.e., MicroSectors FANG and AB Ultra go up and down completely randomly.
Pair Corralation between MicroSectors FANG and AB Ultra
Given the investment horizon of 90 days MicroSectors FANG Index is expected to under-perform the AB Ultra. In addition to that, MicroSectors FANG is 66.46 times more volatile than AB Ultra Short. It trades about -0.08 of its total potential returns per unit of risk. AB Ultra Short is currently generating about 0.31 per unit of volatility. If you would invest 4,924 in AB Ultra Short on August 29, 2024 and sell it today you would earn a total of 140.00 from holding AB Ultra Short or generate 2.84% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
MicroSectors FANG Index vs. AB Ultra Short
Performance |
| Timeline |
| MicroSectors FANG Index |
| AB Ultra Short |
MicroSectors FANG and AB Ultra Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with MicroSectors FANG and AB Ultra
The main advantage of trading using opposite MicroSectors FANG and AB Ultra positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if MicroSectors FANG position performs unexpectedly, AB Ultra 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 AB Ultra will offset losses from the drop in AB Ultra's long position.| MicroSectors FANG vs. MicroSectors FANG Index | MicroSectors FANG vs. Direxion Daily Semiconductor | MicroSectors FANG vs. Direxion Daily Technology | MicroSectors FANG vs. Direxion Daily SP |
| AB Ultra vs. Ab Tax Aware Short | AB Ultra vs. Simplify Exchange Traded | AB Ultra vs. Forestar Group | AB Ultra vs. Bondbloxx ETF Trust |
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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