Correlation Between Global X and Pacer Benchmark
Can any of the company-specific risk be diversified away by investing in both Global X and Pacer Benchmark 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 Pacer Benchmark into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Global X Cloud and Pacer Benchmark Data, you can compare the effects of market volatilities on Global X and Pacer Benchmark 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 Pacer Benchmark. Check out your portfolio center. Please also check ongoing floating volatility patterns of Global X and Pacer Benchmark.
Diversification Opportunities for Global X and Pacer Benchmark
0.23 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Global and Pacer is 0.23. Overlapping area represents the amount of risk that can be diversified away by holding Global X Cloud and Pacer Benchmark Data in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Pacer Benchmark Data 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 Cloud are associated (or correlated) with Pacer Benchmark. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Pacer Benchmark Data has no effect on the direction of Global X i.e., Global X and Pacer Benchmark go up and down completely randomly.
Pair Corralation between Global X and Pacer Benchmark
Given the investment horizon of 90 days Global X Cloud is expected to under-perform the Pacer Benchmark. In addition to that, Global X is 1.26 times more volatile than Pacer Benchmark Data. It trades about -0.05 of its total potential returns per unit of risk. Pacer Benchmark Data is currently generating about -0.03 per unit of volatility. If you would invest 3,177 in Pacer Benchmark Data on December 2, 2024 and sell it today you would lose (70.00) from holding Pacer Benchmark Data or give up 2.2% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Global X Cloud vs. Pacer Benchmark Data
Performance |
Timeline |
Global X Cloud |
Pacer Benchmark Data |
Global X and Pacer Benchmark Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Global X and Pacer Benchmark
The main advantage of trading using opposite Global X and Pacer Benchmark positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Global X position performs unexpectedly, Pacer Benchmark 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 Pacer Benchmark will offset losses from the drop in Pacer Benchmark's long position.Global X vs. WisdomTree Cloud Computing | Global X vs. First Trust Cloud | Global X vs. Global X FinTech | Global X vs. Global X Cybersecurity |
Pacer Benchmark vs. Pacer Benchmark Industrial | Pacer Benchmark vs. First Trust NASDAQ | Pacer Benchmark vs. Global X Internet | Pacer Benchmark vs. ProShares Online Retail |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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