Correlation Between Goldman Sachs and Litman Gregory
Can any of the company-specific risk be diversified away by investing in both Goldman Sachs and Litman Gregory 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 Litman Gregory into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Goldman Sachs Future and Litman Gregory Funds, you can compare the effects of market volatilities on Goldman Sachs and Litman Gregory 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 Litman Gregory. Check out your portfolio center. Please also check ongoing floating volatility patterns of Goldman Sachs and Litman Gregory.
Diversification Opportunities for Goldman Sachs and Litman Gregory
0.7 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Goldman and Litman is 0.7. Overlapping area represents the amount of risk that can be diversified away by holding Goldman Sachs Future and Litman Gregory Funds in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Litman Gregory Funds 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 Future are associated (or correlated) with Litman Gregory. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Litman Gregory Funds has no effect on the direction of Goldman Sachs i.e., Goldman Sachs and Litman Gregory go up and down completely randomly.
Pair Corralation between Goldman Sachs and Litman Gregory
Given the investment horizon of 90 days Goldman Sachs is expected to generate 1.32 times less return on investment than Litman Gregory. But when comparing it to its historical volatility, Goldman Sachs Future is 1.31 times less risky than Litman Gregory. It trades about 0.34 of its potential returns per unit of risk. Litman Gregory Funds is currently generating about 0.34 of returns per unit of risk over similar time horizon. If you would invest 1,141 in Litman Gregory Funds on September 4, 2024 and sell it today you would earn a total of 68.00 from holding Litman Gregory Funds or generate 5.96% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Goldman Sachs Future vs. Litman Gregory Funds
Performance |
| Timeline |
| Goldman Sachs Future |
| Litman Gregory Funds |
Goldman Sachs and Litman Gregory Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Goldman Sachs and Litman Gregory
The main advantage of trading using opposite Goldman Sachs and Litman Gregory positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Goldman Sachs position performs unexpectedly, Litman Gregory 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 Litman Gregory will offset losses from the drop in Litman Gregory's long position.| Goldman Sachs vs. Franklin Templeton ETF | Goldman Sachs vs. TrueShares Technology AI | Goldman Sachs vs. Franklin Exponential Data | Goldman Sachs vs. Franklin Genomic Advancements |
| Litman Gregory vs. Franklin Templeton ETF | Litman Gregory vs. TrueShares Technology AI | Litman Gregory vs. Franklin Exponential Data | Litman Gregory vs. Franklin Genomic Advancements |
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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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