Correlation Between MicroSectors FANG and BNY Mellon
Can any of the company-specific risk be diversified away by investing in both MicroSectors FANG and BNY Mellon 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 BNY Mellon 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 BNY Mellon ETF, you can compare the effects of market volatilities on MicroSectors FANG and BNY Mellon 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 BNY Mellon. Check out your portfolio center. Please also check ongoing floating volatility patterns of MicroSectors FANG and BNY Mellon.
Diversification Opportunities for MicroSectors FANG and BNY Mellon
-0.88 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between MicroSectors and BNY is -0.88. Overlapping area represents the amount of risk that can be diversified away by holding MicroSectors FANG Index and BNY Mellon ETF in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BNY Mellon ETF 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 BNY Mellon. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BNY Mellon ETF has no effect on the direction of MicroSectors FANG i.e., MicroSectors FANG and BNY Mellon go up and down completely randomly.
Pair Corralation between MicroSectors FANG and BNY Mellon
Given the investment horizon of 90 days MicroSectors FANG Index is expected to under-perform the BNY Mellon. In addition to that, MicroSectors FANG is 125.36 times more volatile than BNY Mellon ETF. It trades about -0.11 of its total potential returns per unit of risk. BNY Mellon ETF is currently generating about 0.55 per unit of volatility. If you would invest 4,463 in BNY Mellon ETF on August 27, 2024 and sell it today you would earn a total of 506.00 from holding BNY Mellon ETF or generate 11.34% 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. BNY Mellon ETF
Performance |
| Timeline |
| MicroSectors FANG Index |
| BNY Mellon ETF |
MicroSectors FANG and BNY Mellon Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with MicroSectors FANG and BNY Mellon
The main advantage of trading using opposite MicroSectors FANG and BNY Mellon positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if MicroSectors FANG position performs unexpectedly, BNY Mellon 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 BNY Mellon will offset losses from the drop in BNY Mellon'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 |
| BNY Mellon vs. SPDR SSgA Ultra | BNY Mellon vs. SPDR Bloomberg Barclays | BNY Mellon vs. American Century ETF |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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