Correlation Between JPMorgan Chase and Calamos ETF
Can any of the company-specific risk be diversified away by investing in both JPMorgan Chase and Calamos ETF 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 JPMorgan Chase and Calamos ETF into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between JPMorgan Chase Co and Calamos ETF Trust, you can compare the effects of market volatilities on JPMorgan Chase and Calamos ETF 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 JPMorgan Chase with a short position of Calamos ETF. Check out your portfolio center. Please also check ongoing floating volatility patterns of JPMorgan Chase and Calamos ETF.
Diversification Opportunities for JPMorgan Chase and Calamos ETF
0.89 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between JPMorgan and Calamos is 0.89. Overlapping area represents the amount of risk that can be diversified away by holding JPMorgan Chase Co and Calamos ETF Trust in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Calamos ETF Trust and JPMorgan Chase 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 JPMorgan Chase Co are associated (or correlated) with Calamos ETF. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Calamos ETF Trust has no effect on the direction of JPMorgan Chase i.e., JPMorgan Chase and Calamos ETF go up and down completely randomly.
Pair Corralation between JPMorgan Chase and Calamos ETF
Considering the 90-day investment horizon JPMorgan Chase Co is expected to generate 10.33 times more return on investment than Calamos ETF. However, JPMorgan Chase is 10.33 times more volatile than Calamos ETF Trust. It trades about 0.11 of its potential returns per unit of risk. Calamos ETF Trust is currently generating about 0.24 per unit of risk. If you would invest 16,452 in JPMorgan Chase Co on September 14, 2024 and sell it today you would earn a total of 7,533 from holding JPMorgan Chase Co or generate 45.79% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 28.92% |
| Values | Daily Returns |
JPMorgan Chase Co vs. Calamos ETF Trust
Performance |
| Timeline |
| JPMorgan Chase |
| Calamos ETF Trust |
JPMorgan Chase and Calamos ETF Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with JPMorgan Chase and Calamos ETF
The main advantage of trading using opposite JPMorgan Chase and Calamos ETF positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if JPMorgan Chase position performs unexpectedly, Calamos ETF 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 Calamos ETF will offset losses from the drop in Calamos ETF's long position.| JPMorgan Chase vs. Citigroup | JPMorgan Chase vs. Wells Fargo | JPMorgan Chase vs. Toronto Dominion Bank | JPMorgan Chase vs. Nu Holdings |
| Calamos ETF vs. FT Vest Equity | Calamos ETF vs. Northern Lights | Calamos ETF vs. Dimensional International High | Calamos ETF vs. JPMorgan Fundamental Data |
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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