Correlation Between DFS Furniture and COMBA TELECOM
Can any of the company-specific risk be diversified away by investing in both DFS Furniture and COMBA TELECOM 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 COMBA TELECOM 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 COMBA TELECOM SYST, you can compare the effects of market volatilities on DFS Furniture and COMBA TELECOM 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 COMBA TELECOM. Check out your portfolio center. Please also check ongoing floating volatility patterns of DFS Furniture and COMBA TELECOM.
Diversification Opportunities for DFS Furniture and COMBA TELECOM
0.17 | Correlation Coefficient |
Average diversification
The 3 months correlation between DFS and COMBA is 0.17. Overlapping area represents the amount of risk that can be diversified away by holding DFS Furniture PLC and COMBA TELECOM SYST in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on COMBA TELECOM SYST 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 COMBA TELECOM. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of COMBA TELECOM SYST has no effect on the direction of DFS Furniture i.e., DFS Furniture and COMBA TELECOM go up and down completely randomly.
Pair Corralation between DFS Furniture and COMBA TELECOM
Assuming the 90 days trading horizon DFS Furniture PLC is expected to generate 1.2 times more return on investment than COMBA TELECOM. However, DFS Furniture is 1.2 times more volatile than COMBA TELECOM SYST. It trades about 0.01 of its potential returns per unit of risk. COMBA TELECOM SYST is currently generating about 0.0 per unit of risk. If you would invest 167.00 in DFS Furniture PLC on October 13, 2024 and sell it today you would lose (14.00) from holding DFS Furniture PLC or give up 8.38% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
DFS Furniture PLC vs. COMBA TELECOM SYST
Performance |
| Timeline |
| DFS Furniture PLC |
| COMBA TELECOM SYST |
DFS Furniture and COMBA TELECOM Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with DFS Furniture and COMBA TELECOM
The main advantage of trading using opposite DFS Furniture and COMBA TELECOM positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if DFS Furniture position performs unexpectedly, COMBA TELECOM 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 COMBA TELECOM will offset losses from the drop in COMBA TELECOM's long position.| DFS Furniture vs. GWILLI FOOD | DFS Furniture vs. TIANDE CHEMICAL | DFS Furniture vs. Sanyo Chemical Industries | DFS Furniture vs. Sinopec Shanghai Petrochemical |
| COMBA TELECOM vs. CEOTRONICS | COMBA TELECOM vs. Aedas Homes SA | COMBA TELECOM vs. CITY OFFICE REIT | COMBA TELECOM vs. DFS Furniture PLC |
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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