Correlation Between Travelers Companies and CNA Financial
Can any of the company-specific risk be diversified away by investing in both Travelers Companies and CNA 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 Travelers Companies and CNA Financial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between The Travelers Companies and CNA Financial, you can compare the effects of market volatilities on Travelers Companies and CNA 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 Travelers Companies with a short position of CNA Financial. Check out your portfolio center. Please also check ongoing floating volatility patterns of Travelers Companies and CNA Financial.
Diversification Opportunities for Travelers Companies and CNA Financial
-0.26 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Travelers and CNA is -0.26. Overlapping area represents the amount of risk that can be diversified away by holding The Travelers Companies and CNA Financial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on CNA Financial and Travelers Companies 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 The Travelers Companies are associated (or correlated) with CNA Financial. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of CNA Financial has no effect on the direction of Travelers Companies i.e., Travelers Companies and CNA Financial go up and down completely randomly.
Pair Corralation between Travelers Companies and CNA Financial
Considering the 90-day investment horizon The Travelers Companies is expected to generate 1.05 times more return on investment than CNA Financial. However, Travelers Companies is 1.05 times more volatile than CNA Financial. It trades about 0.13 of its potential returns per unit of risk. CNA Financial is currently generating about 0.09 per unit of risk. If you would invest 25,237 in The Travelers Companies on August 27, 2024 and sell it today you would earn a total of 885.00 from holding The Travelers Companies or generate 3.51% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
The Travelers Companies vs. CNA Financial
Performance |
Timeline |
The Travelers Companies |
CNA Financial |
Travelers Companies and CNA Financial Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Travelers Companies and CNA Financial
The main advantage of trading using opposite Travelers Companies and CNA Financial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Travelers Companies position performs unexpectedly, CNA 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 CNA Financial will offset losses from the drop in CNA Financial's long position.Travelers Companies vs. Fiverr International | Travelers Companies vs. Pinterest | Travelers Companies vs. Upstart Holdings | Travelers Companies vs. Fastly Inc |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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