Correlation Between Hertz Global and CF Industries
Can any of the company-specific risk be diversified away by investing in both Hertz Global and CF Industries 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 Hertz Global and CF Industries into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Hertz Global Holdings and CF Industries Holdings, you can compare the effects of market volatilities on Hertz Global and CF Industries 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 Hertz Global with a short position of CF Industries. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hertz Global and CF Industries.
Diversification Opportunities for Hertz Global and CF Industries
0.73 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Hertz and CF Industries is 0.73. Overlapping area represents the amount of risk that can be diversified away by holding Hertz Global Holdings and CF Industries Holdings in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on CF Industries Holdings and Hertz Global 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 Hertz Global Holdings are associated (or correlated) with CF Industries. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of CF Industries Holdings has no effect on the direction of Hertz Global i.e., Hertz Global and CF Industries go up and down completely randomly.
Pair Corralation between Hertz Global and CF Industries
Considering the 90-day investment horizon Hertz Global Holdings is expected to generate 3.14 times more return on investment than CF Industries. However, Hertz Global is 3.14 times more volatile than CF Industries Holdings. It trades about 0.08 of its potential returns per unit of risk. CF Industries Holdings is currently generating about -0.02 per unit of risk. If you would invest 324.00 in Hertz Global Holdings on October 2, 2024 and sell it today you would earn a total of 62.00 from holding Hertz Global Holdings or generate 19.14% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Hertz Global Holdings vs. CF Industries Holdings
Performance |
Timeline |
Hertz Global Holdings |
CF Industries Holdings |
Hertz Global and CF Industries Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Hertz Global and CF Industries
The main advantage of trading using opposite Hertz Global and CF Industries positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hertz Global position performs unexpectedly, CF Industries 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 CF Industries will offset losses from the drop in CF Industries' long position.Hertz Global vs. United Rentals | Hertz Global vs. Ryder System | Hertz Global vs. Herc Holdings | Hertz Global vs. Air Lease |
CF Industries vs. Nutrien | CF Industries vs. Intrepid Potash | CF Industries vs. Corteva | CF Industries vs. ICL Israel Chemicals |
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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