Correlation Between Abrdn Bloomberg and UBS AG
Can any of the company-specific risk be diversified away by investing in both Abrdn Bloomberg and UBS AG 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 Abrdn Bloomberg and UBS AG into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between abrdn Bloomberg All and UBS AG London, you can compare the effects of market volatilities on Abrdn Bloomberg and UBS AG 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 Abrdn Bloomberg with a short position of UBS AG. Check out your portfolio center. Please also check ongoing floating volatility patterns of Abrdn Bloomberg and UBS AG.
Diversification Opportunities for Abrdn Bloomberg and UBS AG
0.95 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Abrdn and UBS is 0.95. Overlapping area represents the amount of risk that can be diversified away by holding abrdn Bloomberg All and UBS AG London in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on UBS AG London and Abrdn 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 abrdn Bloomberg All are associated (or correlated) with UBS AG. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of UBS AG London has no effect on the direction of Abrdn Bloomberg i.e., Abrdn Bloomberg and UBS AG go up and down completely randomly.
Pair Corralation between Abrdn Bloomberg and UBS AG
Considering the 90-day investment horizon Abrdn Bloomberg is expected to generate 1.64 times less return on investment than UBS AG. But when comparing it to its historical volatility, abrdn Bloomberg All is 4.31 times less risky than UBS AG. It trades about 0.27 of its potential returns per unit of risk. UBS AG London is currently generating about 0.1 of returns per unit of risk over similar time horizon. If you would invest 2,534 in UBS AG London on January 20, 2024 and sell it today you would earn a total of 120.00 from holding UBS AG London or generate 4.74% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
abrdn Bloomberg All vs. UBS AG London
Performance |
| Timeline |
| abrdn Bloomberg All |
| UBS AG London |
Abrdn Bloomberg and UBS AG Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Abrdn Bloomberg and UBS AG
The main advantage of trading using opposite Abrdn Bloomberg and UBS AG positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Abrdn Bloomberg position performs unexpectedly, UBS AG 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 UBS AG will offset losses from the drop in UBS AG's long position.| Abrdn Bloomberg vs. abrdn Bloomberg All | Abrdn Bloomberg vs. iShares Bloomberg Roll | Abrdn Bloomberg vs. GraniteShares Bloomberg Commodity | Abrdn Bloomberg vs. iShares GSCI Commodity |
| UBS AG vs. ETRACS Bloomberg Commodity | UBS AG vs. USCF SummerHaven Dynamic | UBS AG vs. First Trust Alternative | UBS AG vs. iShares Bloomberg Roll |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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