Correlation Between Qs Small and Ashmore Emerging
Can any of the company-specific risk be diversified away by investing in both Qs Small 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 Small 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 Small Capitalization and Ashmore Emerging Markets, you can compare the effects of market volatilities on Qs Small 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 Small with a short position of Ashmore Emerging. Check out your portfolio center. Please also check ongoing floating volatility patterns of Qs Small and Ashmore Emerging.
Diversification Opportunities for Qs Small and Ashmore Emerging
0.08 | Correlation Coefficient |
Significant diversification
The 3 months correlation between LMBMX and Ashmore is 0.08. Overlapping area represents the amount of risk that can be diversified away by holding Qs Small Capitalization 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 Small 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 Small Capitalization 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 Small i.e., Qs Small and Ashmore Emerging go up and down completely randomly.
Pair Corralation between Qs Small and Ashmore Emerging
Assuming the 90 days horizon Qs Small Capitalization is expected to under-perform the Ashmore Emerging. In addition to that, Qs Small is 1.5 times more volatile than Ashmore Emerging Markets. It trades about -0.31 of its total potential returns per unit of risk. Ashmore Emerging Markets is currently generating about -0.22 per unit of volatility. If you would invest 782.00 in Ashmore Emerging Markets on October 7, 2024 and sell it today you would lose (36.00) from holding Ashmore Emerging Markets or give up 4.6% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Qs Small Capitalization vs. Ashmore Emerging Markets
Performance |
Timeline |
Qs Small Capitalization |
Ashmore Emerging Markets |
Qs Small and Ashmore Emerging Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Qs Small and Ashmore Emerging
The main advantage of trading using opposite Qs Small and Ashmore Emerging positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Qs Small 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 Small vs. Virtus Seix Government | Qs Small vs. Intermediate Government Bond | Qs Small vs. Short Term Government Fund | Qs Small vs. Nationwide Government Bond |
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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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