Correlation Between Federal Home and Loans4Less
Can any of the company-specific risk be diversified away by investing in both Federal Home and Loans4Less 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 Federal Home and Loans4Less into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Federal Home Loan and Loans4LessCom, you can compare the effects of market volatilities on Federal Home and Loans4Less 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 Federal Home with a short position of Loans4Less. Check out your portfolio center. Please also check ongoing floating volatility patterns of Federal Home and Loans4Less.
Diversification Opportunities for Federal Home and Loans4Less
-0.06 | Correlation Coefficient |
Good diversification
The 3 months correlation between Federal and Loans4Less is -0.06. Overlapping area represents the amount of risk that can be diversified away by holding Federal Home Loan and Loans4LessCom in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Loans4LessCom and Federal Home 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 Federal Home Loan are associated (or correlated) with Loans4Less. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Loans4LessCom has no effect on the direction of Federal Home i.e., Federal Home and Loans4Less go up and down completely randomly.
Pair Corralation between Federal Home and Loans4Less
Assuming the 90 days horizon Federal Home is expected to generate 6.12 times less return on investment than Loans4Less. But when comparing it to its historical volatility, Federal Home Loan is 5.03 times less risky than Loans4Less. It trades about 0.03 of its potential returns per unit of risk. Loans4LessCom is currently generating about 0.04 of returns per unit of risk over similar time horizon. If you would invest 9.00 in Loans4LessCom on January 17, 2025 and sell it today you would lose (3.42) from holding Loans4LessCom or give up 38.0% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Federal Home Loan vs. Loans4LessCom
Performance |
| Timeline |
| Federal Home Loan |
| Loans4LessCom |
Federal Home and Loans4Less Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Federal Home and Loans4Less
The main advantage of trading using opposite Federal Home and Loans4Less positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Federal Home position performs unexpectedly, Loans4Less 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 Loans4Less will offset losses from the drop in Loans4Less' long position.| Federal Home vs. Federal Home Loan | Federal Home vs. Federal Home Loan | Federal Home vs. Federal Home Loan | Federal Home vs. Federal Home Loan |
| Loans4Less vs. Guild Holdings Co | Loans4Less vs. Rocket Companies | Loans4Less vs. UWM Holdings Corp | Loans4Less vs. Mr Cooper Group |
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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