Correlation Between Goldman Sachs and STRATS SM
Can any of the company-specific risk be diversified away by investing in both Goldman Sachs and STRATS SM 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 Goldman Sachs and STRATS SM into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Goldman Sachs Capital and STRATS SM Trust, you can compare the effects of market volatilities on Goldman Sachs and STRATS SM 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 Goldman Sachs with a short position of STRATS SM. Check out your portfolio center. Please also check ongoing floating volatility patterns of Goldman Sachs and STRATS SM.
Diversification Opportunities for Goldman Sachs and STRATS SM
0.04 | Correlation Coefficient |
Significant diversification
The 3 months correlation between Goldman and STRATS is 0.04. Overlapping area represents the amount of risk that can be diversified away by holding Goldman Sachs Capital and STRATS SM Trust in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on STRATS SM Trust and Goldman Sachs 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 Goldman Sachs Capital are associated (or correlated) with STRATS SM. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of STRATS SM Trust has no effect on the direction of Goldman Sachs i.e., Goldman Sachs and STRATS SM go up and down completely randomly.
Pair Corralation between Goldman Sachs and STRATS SM
Considering the 90-day investment horizon Goldman Sachs is expected to generate 4.76 times less return on investment than STRATS SM. In addition to that, Goldman Sachs is 3.39 times more volatile than STRATS SM Trust. It trades about 0.01 of its total potential returns per unit of risk. STRATS SM Trust is currently generating about 0.1 per unit of volatility. If you would invest 2,475 in STRATS SM Trust on August 31, 2024 and sell it today you would earn a total of 24.00 from holding STRATS SM Trust or generate 0.97% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Insignificant |
| Accuracy | 95.45% |
| Values | Daily Returns |
Goldman Sachs Capital vs. STRATS SM Trust
Performance |
| Timeline |
| Goldman Sachs Capital |
| STRATS SM Trust |
Goldman Sachs and STRATS SM Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Goldman Sachs and STRATS SM
The main advantage of trading using opposite Goldman Sachs and STRATS SM positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Goldman Sachs position performs unexpectedly, STRATS SM 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 STRATS SM will offset losses from the drop in STRATS SM's long position.| Goldman Sachs vs. Lindblad Expeditions Holdings | Goldman Sachs vs. Verra Mobility Corp | Goldman Sachs vs. Skillful Craftsman Education | Goldman Sachs vs. Afya |
| STRATS SM vs. STRATS SM Trust | STRATS SM vs. Strats SM Trust | STRATS SM vs. STRATSSM Certificates series | STRATS SM vs. Strats SM 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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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