Correlation Between Loandepot and China Aircraft
Can any of the company-specific risk be diversified away by investing in both Loandepot and China Aircraft 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 Loandepot and China Aircraft into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Loandepot and China Aircraft Leasing, you can compare the effects of market volatilities on Loandepot and China Aircraft 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 Loandepot with a short position of China Aircraft. Check out your portfolio center. Please also check ongoing floating volatility patterns of Loandepot and China Aircraft.
Diversification Opportunities for Loandepot and China Aircraft
0.5 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Loandepot and China is 0.5. Overlapping area represents the amount of risk that can be diversified away by holding Loandepot and China Aircraft Leasing in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on China Aircraft Leasing and Loandepot 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 Loandepot are associated (or correlated) with China Aircraft. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of China Aircraft Leasing has no effect on the direction of Loandepot i.e., Loandepot and China Aircraft go up and down completely randomly.
Pair Corralation between Loandepot and China Aircraft
Considering the 90-day investment horizon Loandepot is expected to under-perform the China Aircraft. In addition to that, Loandepot is 5.07 times more volatile than China Aircraft Leasing. It trades about -0.1 of its total potential returns per unit of risk. China Aircraft Leasing is currently generating about -0.12 per unit of volatility. If you would invest 43.00 in China Aircraft Leasing on September 16, 2024 and sell it today you would lose (3.00) from holding China Aircraft Leasing or give up 6.98% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Loandepot vs. China Aircraft Leasing
Performance |
Timeline |
Loandepot |
China Aircraft Leasing |
Loandepot and China Aircraft Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Loandepot and China Aircraft
The main advantage of trading using opposite Loandepot and China Aircraft positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Loandepot position performs unexpectedly, China Aircraft 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 China Aircraft will offset losses from the drop in China Aircraft's long position.Loandepot vs. CNFinance Holdings | Loandepot vs. Security National Financial | Loandepot vs. Encore Capital Group | Loandepot vs. UWM Holdings Corp |
China Aircraft vs. United Rentals | China Aircraft vs. Ashtead Gro | China Aircraft vs. AerCap Holdings NV | China Aircraft vs. Fortress Transp Infra |
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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