Correlation Between Goldman Sachs and Baillie Gifford
Can any of the company-specific risk be diversified away by investing in both Goldman Sachs and Baillie Gifford 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 Baillie Gifford into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Goldman Sachs Large and Baillie Gifford China, you can compare the effects of market volatilities on Goldman Sachs and Baillie Gifford 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 Baillie Gifford. Check out your portfolio center. Please also check ongoing floating volatility patterns of Goldman Sachs and Baillie Gifford.
Diversification Opportunities for Goldman Sachs and Baillie Gifford
0.34 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Goldman and Baillie is 0.34. Overlapping area represents the amount of risk that can be diversified away by holding Goldman Sachs Large and Baillie Gifford China in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Baillie Gifford China 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 Large are associated (or correlated) with Baillie Gifford. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Baillie Gifford China has no effect on the direction of Goldman Sachs i.e., Goldman Sachs and Baillie Gifford go up and down completely randomly.
Pair Corralation between Goldman Sachs and Baillie Gifford
Assuming the 90 days horizon Goldman Sachs Large is expected to generate 0.65 times more return on investment than Baillie Gifford. However, Goldman Sachs Large is 1.53 times less risky than Baillie Gifford. It trades about 0.09 of its potential returns per unit of risk. Baillie Gifford China is currently generating about 0.01 per unit of risk. If you would invest 2,773 in Goldman Sachs Large on September 12, 2024 and sell it today you would earn a total of 1,103 from holding Goldman Sachs Large or generate 39.78% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Goldman Sachs Large vs. Baillie Gifford China
Performance |
| Timeline |
| Goldman Sachs Large |
| Baillie Gifford China |
Goldman Sachs and Baillie Gifford Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Goldman Sachs and Baillie Gifford
The main advantage of trading using opposite Goldman Sachs and Baillie Gifford positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Goldman Sachs position performs unexpectedly, Baillie Gifford 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 Baillie Gifford will offset losses from the drop in Baillie Gifford's long position.| Goldman Sachs vs. Pace Large Value | Goldman Sachs vs. Dodge Cox Stock | Goldman Sachs vs. Virtus Nfj Large Cap | Goldman Sachs vs. Large Cap Growth Profund |
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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 Competition Analyzer module to analyze and compare many basic indicators for a group of related or unrelated entities.
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