Correlation Between Global X and BMO Emerging
Can any of the company-specific risk be diversified away by investing in both Global X and BMO 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 Global X and BMO Emerging into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Global X Big and BMO Emerging Markets, you can compare the effects of market volatilities on Global X and BMO 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 Global X with a short position of BMO Emerging. Check out your portfolio center. Please also check ongoing floating volatility patterns of Global X and BMO Emerging.
Diversification Opportunities for Global X and BMO Emerging
-0.69 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Global and BMO is -0.69. Overlapping area represents the amount of risk that can be diversified away by holding Global X Big and BMO Emerging Markets in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BMO Emerging Markets and Global X 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 Global X Big are associated (or correlated) with BMO Emerging. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BMO Emerging Markets has no effect on the direction of Global X i.e., Global X and BMO Emerging go up and down completely randomly.
Pair Corralation between Global X and BMO Emerging
Assuming the 90 days trading horizon Global X Big is expected to generate 7.15 times more return on investment than BMO Emerging. However, Global X is 7.15 times more volatile than BMO Emerging Markets. It trades about 0.2 of its potential returns per unit of risk. BMO Emerging Markets is currently generating about -0.06 per unit of risk. If you would invest 2,935 in Global X Big on August 25, 2024 and sell it today you would earn a total of 358.00 from holding Global X Big or generate 12.2% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Global X Big vs. BMO Emerging Markets
Performance |
| Timeline |
| Global X Big |
| BMO Emerging Markets |
Global X and BMO Emerging Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Global X and BMO Emerging
The main advantage of trading using opposite Global X and BMO Emerging positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Global X position performs unexpectedly, BMO 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 BMO Emerging will offset losses from the drop in BMO Emerging's long position.| Global X vs. Blockchain Technologies ETF | Global X vs. Global X Robotics | Global X vs. Evolve Automobile Innovation | Global X vs. Evolve Innovation Index |
| BMO Emerging vs. iShares IG Corporate | BMO Emerging vs. iShares 1 10Yr Laddered | BMO Emerging vs. iShares Floating Rate | BMO Emerging vs. iShares Convertible Bond |
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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