Correlation Between Goldman Sachs and Ashmore Emerging
Can any of the company-specific risk be diversified away by investing in both Goldman Sachs and Ashmore Emerging 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 Ashmore Emerging into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Goldman Sachs Inflation and Ashmore Emerging Markets, you can compare the effects of market volatilities on Goldman Sachs and Ashmore Emerging 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 Ashmore Emerging. Check out your portfolio center. Please also check ongoing floating volatility patterns of Goldman Sachs and Ashmore Emerging.
Diversification Opportunities for Goldman Sachs and Ashmore Emerging
0.84 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Goldman and Ashmore is 0.84. Overlapping area represents the amount of risk that can be diversified away by holding Goldman Sachs Inflation and Ashmore Emerging Markets in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Ashmore Emerging Markets 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 Inflation are associated (or correlated) with Ashmore Emerging. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Ashmore Emerging Markets has no effect on the direction of Goldman Sachs i.e., Goldman Sachs and Ashmore Emerging go up and down completely randomly.
Pair Corralation between Goldman Sachs and Ashmore Emerging
Assuming the 90 days horizon Goldman Sachs Inflation is expected to generate 0.71 times more return on investment than Ashmore Emerging. However, Goldman Sachs Inflation is 1.4 times less risky than Ashmore Emerging. It trades about 0.16 of its potential returns per unit of risk. Ashmore Emerging Markets is currently generating about 0.07 per unit of risk. If you would invest 938.00 in Goldman Sachs Inflation on October 27, 2024 and sell it today you would earn a total of 7.00 from holding Goldman Sachs Inflation or generate 0.75% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Goldman Sachs Inflation vs. Ashmore Emerging Markets
Performance |
Timeline |
Goldman Sachs Inflation |
Ashmore Emerging Markets |
Goldman Sachs and Ashmore Emerging Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Goldman Sachs and Ashmore Emerging
The main advantage of trading using opposite Goldman Sachs and Ashmore Emerging positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Goldman Sachs position performs unexpectedly, Ashmore Emerging 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 Ashmore Emerging will offset losses from the drop in Ashmore Emerging's long position.Goldman Sachs vs. Technology Ultrasector Profund | Goldman Sachs vs. Blackrock Science Technology | Goldman Sachs vs. Vanguard Information Technology | Goldman Sachs vs. Dreyfus Technology Growth |
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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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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