Correlation Between Goldman Sachs and Great-west Loomis
Can any of the company-specific risk be diversified away by investing in both Goldman Sachs and Great-west Loomis 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 Great-west Loomis into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Goldman Sachs Small and Great West Loomis Sayles, you can compare the effects of market volatilities on Goldman Sachs and Great-west Loomis 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 Great-west Loomis. Check out your portfolio center. Please also check ongoing floating volatility patterns of Goldman Sachs and Great-west Loomis.
Diversification Opportunities for Goldman Sachs and Great-west Loomis
0.99 | Correlation Coefficient |
No risk reduction
The 3 months correlation between GOLDMAN and Great-west is 0.99. Overlapping area represents the amount of risk that can be diversified away by holding Goldman Sachs Small and Great West Loomis Sayles in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Great West Loomis 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 Small are associated (or correlated) with Great-west Loomis. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Great West Loomis has no effect on the direction of Goldman Sachs i.e., Goldman Sachs and Great-west Loomis go up and down completely randomly.
Pair Corralation between Goldman Sachs and Great-west Loomis
Assuming the 90 days horizon Goldman Sachs Small is expected to under-perform the Great-west Loomis. In addition to that, Goldman Sachs is 1.11 times more volatile than Great West Loomis Sayles. It trades about -0.08 of its total potential returns per unit of risk. Great West Loomis Sayles is currently generating about -0.07 per unit of volatility. If you would invest 3,825 in Great West Loomis Sayles on December 28, 2024 and sell it today you would lose (171.00) from holding Great West Loomis Sayles or give up 4.47% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Goldman Sachs Small vs. Great West Loomis Sayles
Performance |
Timeline |
Goldman Sachs Small |
Great West Loomis |
Goldman Sachs and Great-west Loomis Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Goldman Sachs and Great-west Loomis
The main advantage of trading using opposite Goldman Sachs and Great-west Loomis positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Goldman Sachs position performs unexpectedly, Great-west Loomis 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 Great-west Loomis will offset losses from the drop in Great-west Loomis' long position.Goldman Sachs vs. Morningstar Municipal Bond | Goldman Sachs vs. Morgan Stanley Government | Goldman Sachs vs. Goldman Sachs Short | Goldman Sachs vs. Franklin Adjustable Government |
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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 Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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