Correlation Between Oxford Industries and Ree Automotive
Can any of the company-specific risk be diversified away by investing in both Oxford Industries and Ree Automotive 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 Oxford Industries and Ree Automotive into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Oxford Industries and Ree Automotive Holding, you can compare the effects of market volatilities on Oxford Industries and Ree Automotive 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 Oxford Industries with a short position of Ree Automotive. Check out your portfolio center. Please also check ongoing floating volatility patterns of Oxford Industries and Ree Automotive.
Diversification Opportunities for Oxford Industries and Ree Automotive
0.83 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Oxford and Ree is 0.83. Overlapping area represents the amount of risk that can be diversified away by holding Oxford Industries and Ree Automotive Holding in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Ree Automotive Holding and Oxford 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 Oxford Industries are associated (or correlated) with Ree Automotive. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Ree Automotive Holding has no effect on the direction of Oxford Industries i.e., Oxford Industries and Ree Automotive go up and down completely randomly.
Pair Corralation between Oxford Industries and Ree Automotive
Considering the 90-day investment horizon Oxford Industries is expected to generate 0.43 times more return on investment than Ree Automotive. However, Oxford Industries is 2.32 times less risky than Ree Automotive. It trades about -0.13 of its potential returns per unit of risk. Ree Automotive Holding is currently generating about -0.2 per unit of risk. If you would invest 7,828 in Oxford Industries on December 26, 2024 and sell it today you would lose (1,651) from holding Oxford Industries or give up 21.09% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Oxford Industries vs. Ree Automotive Holding
Performance |
Timeline |
Oxford Industries |
Ree Automotive Holding |
Oxford Industries and Ree Automotive Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Oxford Industries and Ree Automotive
The main advantage of trading using opposite Oxford Industries and Ree Automotive positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Oxford Industries position performs unexpectedly, Ree Automotive 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 Ree Automotive will offset losses from the drop in Ree Automotive's long position.Oxford Industries vs. VF Corporation | Oxford Industries vs. Levi Strauss Co | Oxford Industries vs. Under Armour A | Oxford Industries vs. Columbia Sportswear |
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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 Equity Valuation module to check real value of public entities based on technical and fundamental data.
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