Correlation Between SPDR Bloomberg and BNY Mellon
Can any of the company-specific risk be diversified away by investing in both SPDR Bloomberg 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 SPDR Bloomberg and BNY Mellon into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SPDR Bloomberg Short and BNY Mellon High, you can compare the effects of market volatilities on SPDR Bloomberg 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 SPDR Bloomberg with a short position of BNY Mellon. Check out your portfolio center. Please also check ongoing floating volatility patterns of SPDR Bloomberg and BNY Mellon.
Diversification Opportunities for SPDR Bloomberg and BNY Mellon
0.99 | Correlation Coefficient |
No risk reduction
The 3 months correlation between SPDR and BNY is 0.99. Overlapping area represents the amount of risk that can be diversified away by holding SPDR Bloomberg Short and BNY Mellon High in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BNY Mellon High and SPDR Bloomberg 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 SPDR Bloomberg Short 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 High has no effect on the direction of SPDR Bloomberg i.e., SPDR Bloomberg and BNY Mellon go up and down completely randomly.
Pair Corralation between SPDR Bloomberg and BNY Mellon
Given the investment horizon of 90 days SPDR Bloomberg is expected to generate 1.03 times less return on investment than BNY Mellon. But when comparing it to its historical volatility, SPDR Bloomberg Short is 1.06 times less risky than BNY Mellon. It trades about 0.07 of its potential returns per unit of risk. BNY Mellon High is currently generating about 0.07 of returns per unit of risk over similar time horizon. If you would invest 4,715 in BNY Mellon High on December 28, 2024 and sell it today you would earn a total of 51.00 from holding BNY Mellon High or generate 1.08% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 100.0% |
Values | Daily Returns |
SPDR Bloomberg Short vs. BNY Mellon High
Performance |
Timeline |
SPDR Bloomberg Short |
BNY Mellon High |
SPDR Bloomberg and BNY Mellon Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with SPDR Bloomberg and BNY Mellon
The main advantage of trading using opposite SPDR Bloomberg and BNY Mellon positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SPDR Bloomberg 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.SPDR Bloomberg vs. PIMCO 0 5 Year | SPDR Bloomberg vs. iShares 0 5 Year | SPDR Bloomberg vs. Invesco Senior Loan | SPDR Bloomberg vs. SPDR Blackstone Senior |
BNY Mellon vs. BNY Mellon International | BNY Mellon vs. BNY Mellon ETF | BNY Mellon vs. BNY Mellon Mid | BNY Mellon vs. BNY Mellon Large |
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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