Correlation Between First Trust and Mesirow Financial
Can any of the company-specific risk be diversified away by investing in both First Trust and Mesirow Financial 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 First Trust and Mesirow Financial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between First Trust Managed and Mesirow Financial High, you can compare the effects of market volatilities on First Trust and Mesirow Financial 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 First Trust with a short position of Mesirow Financial. Check out your portfolio center. Please also check ongoing floating volatility patterns of First Trust and Mesirow Financial.
Diversification Opportunities for First Trust and Mesirow Financial
0.17 | Correlation Coefficient |
Average diversification
The 3 months correlation between First and Mesirow is 0.17. Overlapping area represents the amount of risk that can be diversified away by holding First Trust Managed and Mesirow Financial High in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Mesirow Financial High and First Trust 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 First Trust Managed are associated (or correlated) with Mesirow Financial. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Mesirow Financial High has no effect on the direction of First Trust i.e., First Trust and Mesirow Financial go up and down completely randomly.
Pair Corralation between First Trust and Mesirow Financial
Assuming the 90 days horizon First Trust is expected to generate 9.46 times less return on investment than Mesirow Financial. But when comparing it to its historical volatility, First Trust Managed is 1.23 times less risky than Mesirow Financial. It trades about 0.06 of its potential returns per unit of risk. Mesirow Financial High is currently generating about 0.43 of returns per unit of risk over similar time horizon. If you would invest 804.00 in Mesirow Financial High on May 15, 2025 and sell it today you would earn a total of 35.00 from holding Mesirow Financial High or generate 4.35% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 98.39% |
Values | Daily Returns |
First Trust Managed vs. Mesirow Financial High
Performance |
Timeline |
First Trust Managed |
Mesirow Financial High |
First Trust and Mesirow Financial Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with First Trust and Mesirow Financial
The main advantage of trading using opposite First Trust and Mesirow Financial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if First Trust position performs unexpectedly, Mesirow Financial 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 Mesirow Financial will offset losses from the drop in Mesirow Financial's long position.First Trust vs. First Eagle Gold | First Trust vs. Precious Metals Ultrasector | First Trust vs. Goldman Sachs International | First Trust vs. Global Gold Fund |
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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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