Correlation Between CF Industries and LOBO EV
Can any of the company-specific risk be diversified away by investing in both CF Industries and LOBO EV 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 CF Industries and LOBO EV into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between CF Industries Holdings and LOBO EV TECHNOLOGIES, you can compare the effects of market volatilities on CF Industries and LOBO EV 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 CF Industries with a short position of LOBO EV. Check out your portfolio center. Please also check ongoing floating volatility patterns of CF Industries and LOBO EV.
Diversification Opportunities for CF Industries and LOBO EV
0.14 | Correlation Coefficient |
Average diversification
The 3 months correlation between CF Industries and LOBO is 0.14. Overlapping area represents the amount of risk that can be diversified away by holding CF Industries Holdings and LOBO EV TECHNOLOGIES in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on LOBO EV TECHNOLOGIES and CF Industries 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 CF Industries Holdings are associated (or correlated) with LOBO EV. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of LOBO EV TECHNOLOGIES has no effect on the direction of CF Industries i.e., CF Industries and LOBO EV go up and down completely randomly.
Pair Corralation between CF Industries and LOBO EV
Allowing for the 90-day total investment horizon CF Industries is expected to generate 2.66 times less return on investment than LOBO EV. But when comparing it to its historical volatility, CF Industries Holdings is 6.65 times less risky than LOBO EV. It trades about 0.16 of its potential returns per unit of risk. LOBO EV TECHNOLOGIES is currently generating about 0.06 of returns per unit of risk over similar time horizon. If you would invest 193.00 in LOBO EV TECHNOLOGIES on September 12, 2024 and sell it today you would earn a total of 26.90 from holding LOBO EV TECHNOLOGIES or generate 13.94% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
CF Industries Holdings vs. LOBO EV TECHNOLOGIES
Performance |
| Timeline |
| CF Industries Holdings |
| LOBO EV TECHNOLOGIES |
CF Industries and LOBO EV Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with CF Industries and LOBO EV
The main advantage of trading using opposite CF Industries and LOBO EV positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CF Industries position performs unexpectedly, LOBO EV 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 LOBO EV will offset losses from the drop in LOBO EV's long position.| CF Industries vs. Nutrien | CF Industries vs. Intrepid Potash | CF Industries vs. Corteva | CF Industries vs. ICL Israel Chemicals |
| LOBO EV vs. Molson Coors Brewing | LOBO EV vs. The Coca Cola | LOBO EV vs. Hawkins | LOBO EV vs. Axalta Coating Systems |
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 Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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