Correlation Between Qs Growth and Ashmore Emerging
Can any of the company-specific risk be diversified away by investing in both Qs Growth 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 Qs Growth and Ashmore Emerging into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Qs Growth Fund and Ashmore Emerging Markets, you can compare the effects of market volatilities on Qs Growth 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 Qs Growth with a short position of Ashmore Emerging. Check out your portfolio center. Please also check ongoing floating volatility patterns of Qs Growth and Ashmore Emerging.
Diversification Opportunities for Qs Growth and Ashmore Emerging
0.44 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between LANIX and Ashmore is 0.44. Overlapping area represents the amount of risk that can be diversified away by holding Qs Growth Fund 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 Qs Growth 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 Qs Growth Fund 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 Qs Growth i.e., Qs Growth and Ashmore Emerging go up and down completely randomly.
Pair Corralation between Qs Growth and Ashmore Emerging
Assuming the 90 days horizon Qs Growth Fund is expected to generate 0.7 times more return on investment than Ashmore Emerging. However, Qs Growth Fund is 1.43 times less risky than Ashmore Emerging. It trades about 0.1 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 1,719 in Qs Growth Fund on September 1, 2024 and sell it today you would earn a total of 157.00 from holding Qs Growth Fund or generate 9.13% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 99.21% |
| Values | Daily Returns |
Qs Growth Fund vs. Ashmore Emerging Markets
Performance |
| Timeline |
| Qs Growth Fund |
| Ashmore Emerging Markets |
Qs Growth and Ashmore Emerging Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Qs Growth and Ashmore Emerging
The main advantage of trading using opposite Qs Growth and Ashmore Emerging positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Qs Growth 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.| Qs Growth vs. Gmo High Yield | Qs Growth vs. Artisan High Income | Qs Growth vs. Western Asset High | Qs Growth vs. Pace High Yield |
| Ashmore Emerging vs. Ashmore Emerging Markets | Ashmore Emerging vs. Ashmore Emerging Markets | Ashmore Emerging vs. Ashmore Emerging Markets | Ashmore Emerging vs. Ashmore Emerging Markets |
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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