Correlation Between Hooker Furniture and MSP Recovery
Can any of the company-specific risk be diversified away by investing in both Hooker Furniture and MSP Recovery 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 Hooker Furniture and MSP Recovery into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Hooker Furniture and MSP Recovery, you can compare the effects of market volatilities on Hooker Furniture and MSP Recovery 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 Hooker Furniture with a short position of MSP Recovery. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hooker Furniture and MSP Recovery.
Diversification Opportunities for Hooker Furniture and MSP Recovery
-0.53 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Hooker and MSP is -0.53. Overlapping area represents the amount of risk that can be diversified away by holding Hooker Furniture and MSP Recovery in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on MSP Recovery and Hooker Furniture 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 Hooker Furniture are associated (or correlated) with MSP Recovery. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of MSP Recovery has no effect on the direction of Hooker Furniture i.e., Hooker Furniture and MSP Recovery go up and down completely randomly.
Pair Corralation between Hooker Furniture and MSP Recovery
Given the investment horizon of 90 days Hooker Furniture is expected to generate 0.2 times more return on investment than MSP Recovery. However, Hooker Furniture is 4.97 times less risky than MSP Recovery. It trades about 0.14 of its potential returns per unit of risk. MSP Recovery is currently generating about -0.06 per unit of risk. If you would invest 1,659 in Hooker Furniture on September 5, 2024 and sell it today you would earn a total of 160.00 from holding Hooker Furniture or generate 9.64% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Hooker Furniture vs. MSP Recovery
Performance |
Timeline |
Hooker Furniture |
MSP Recovery |
Hooker Furniture and MSP Recovery Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Hooker Furniture and MSP Recovery
The main advantage of trading using opposite Hooker Furniture and MSP Recovery positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hooker Furniture position performs unexpectedly, MSP Recovery 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 MSP Recovery will offset losses from the drop in MSP Recovery's long position.Hooker Furniture vs. Bassett Furniture Industries | Hooker Furniture vs. Natuzzi SpA | Hooker Furniture vs. Flexsteel Industries | Hooker Furniture vs. Hamilton Beach Brands |
MSP Recovery vs. Hooker Furniture | MSP Recovery vs. Archrock | MSP Recovery vs. Live Ventures | MSP Recovery vs. Vistra Energy Corp |
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 Rebalancing module to analyze risk-adjusted returns against different time horizons to find asset-allocation targets.
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