Correlation Between Goldman Sachs and Boston Partners
Can any of the company-specific risk be diversified away by investing in both Goldman Sachs and Boston Partners 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 Boston Partners 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 Boston Partners Emerging, you can compare the effects of market volatilities on Goldman Sachs and Boston Partners 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 Boston Partners. Check out your portfolio center. Please also check ongoing floating volatility patterns of Goldman Sachs and Boston Partners.
Diversification Opportunities for Goldman Sachs and Boston Partners
-0.67 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Goldman and Boston is -0.67. Overlapping area represents the amount of risk that can be diversified away by holding Goldman Sachs Large and Boston Partners Emerging in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Boston Partners Emerging 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 Boston Partners. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Boston Partners Emerging has no effect on the direction of Goldman Sachs i.e., Goldman Sachs and Boston Partners go up and down completely randomly.
Pair Corralation between Goldman Sachs and Boston Partners
Assuming the 90 days horizon Goldman Sachs Large is expected to generate 0.69 times more return on investment than Boston Partners. However, Goldman Sachs Large is 1.45 times less risky than Boston Partners. It trades about 0.16 of its potential returns per unit of risk. Boston Partners Emerging is currently generating about -0.09 per unit of risk. If you would invest 1,642 in Goldman Sachs Large on September 1, 2024 and sell it today you would earn a total of 238.00 from holding Goldman Sachs Large or generate 14.49% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 47.24% |
Values | Daily Returns |
Goldman Sachs Large vs. Boston Partners Emerging
Performance |
Timeline |
Goldman Sachs Large |
Boston Partners Emerging |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
Goldman Sachs and Boston Partners Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Goldman Sachs and Boston Partners
The main advantage of trading using opposite Goldman Sachs and Boston Partners positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Goldman Sachs position performs unexpectedly, Boston Partners 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 Boston Partners will offset losses from the drop in Boston Partners' long position.Goldman Sachs vs. Goldman Sachs Clean | Goldman Sachs vs. Goldman Sachs Clean | Goldman Sachs vs. Goldman Sachs Clean | Goldman Sachs vs. Goldman Sachs Clean |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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