Correlation Between DFS Furniture and Capital Metals
Can any of the company-specific risk be diversified away by investing in both DFS Furniture and Capital Metals 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 DFS Furniture and Capital Metals into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between DFS Furniture PLC and Capital Metals PLC, you can compare the effects of market volatilities on DFS Furniture and Capital Metals 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 DFS Furniture with a short position of Capital Metals. Check out your portfolio center. Please also check ongoing floating volatility patterns of DFS Furniture and Capital Metals.
Diversification Opportunities for DFS Furniture and Capital Metals
0.34 | Correlation Coefficient |
Weak diversification
The 3 months correlation between DFS and Capital is 0.34. Overlapping area represents the amount of risk that can be diversified away by holding DFS Furniture PLC and Capital Metals PLC in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Capital Metals PLC and DFS 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 DFS Furniture PLC are associated (or correlated) with Capital Metals. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Capital Metals PLC has no effect on the direction of DFS Furniture i.e., DFS Furniture and Capital Metals go up and down completely randomly.
Pair Corralation between DFS Furniture and Capital Metals
Assuming the 90 days trading horizon DFS Furniture is expected to generate 5.39 times less return on investment than Capital Metals. But when comparing it to its historical volatility, DFS Furniture PLC is 4.93 times less risky than Capital Metals. It trades about 0.05 of its potential returns per unit of risk. Capital Metals PLC is currently generating about 0.05 of returns per unit of risk over similar time horizon. If you would invest 105.00 in Capital Metals PLC on September 3, 2024 and sell it today you would earn a total of 80.00 from holding Capital Metals PLC or generate 76.19% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
DFS Furniture PLC vs. Capital Metals PLC
Performance |
| Timeline |
| DFS Furniture PLC |
| Capital Metals PLC |
DFS Furniture and Capital Metals Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with DFS Furniture and Capital Metals
The main advantage of trading using opposite DFS Furniture and Capital Metals positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if DFS Furniture position performs unexpectedly, Capital Metals 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 Capital Metals will offset losses from the drop in Capital Metals' long position.| DFS Furniture vs. Rockfire Resources plc | DFS Furniture vs. Tlou Energy | DFS Furniture vs. Falcon Oil Gas | DFS Furniture vs. Helium One Global |
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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