Correlation Between H2O Retailing and Comba Telecom
Can any of the company-specific risk be diversified away by investing in both H2O Retailing 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 H2O Retailing and Comba Telecom into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between H2O Retailing and Comba Telecom Systems, you can compare the effects of market volatilities on H2O Retailing 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 H2O Retailing with a short position of Comba Telecom. Check out your portfolio center. Please also check ongoing floating volatility patterns of H2O Retailing and Comba Telecom.
Diversification Opportunities for H2O Retailing and Comba Telecom
0.3 | Correlation Coefficient |
Weak diversification
The 3 months correlation between H2O and Comba is 0.3. Overlapping area represents the amount of risk that can be diversified away by holding H2O Retailing and Comba Telecom Systems in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Comba Telecom Systems and H2O Retailing 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 H2O Retailing 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 Systems has no effect on the direction of H2O Retailing i.e., H2O Retailing and Comba Telecom go up and down completely randomly.
Pair Corralation between H2O Retailing and Comba Telecom
Assuming the 90 days horizon H2O Retailing is expected to generate 0.64 times more return on investment than Comba Telecom. However, H2O Retailing is 1.57 times less risky than Comba Telecom. It trades about -0.01 of its potential returns per unit of risk. Comba Telecom Systems is currently generating about -0.13 per unit of risk. If you would invest 1,350 in H2O Retailing on October 13, 2024 and sell it today you would lose (10.00) from holding H2O Retailing or give up 0.74% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 94.44% |
| Values | Daily Returns |
H2O Retailing vs. Comba Telecom Systems
Performance |
| Timeline |
| H2O Retailing |
| Comba Telecom Systems |
H2O Retailing and Comba Telecom Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with H2O Retailing and Comba Telecom
The main advantage of trading using opposite H2O Retailing and Comba Telecom positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if H2O Retailing 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.| H2O Retailing vs. Scandinavian Tobacco Group | H2O Retailing vs. Tradeweb Markets | H2O Retailing vs. Eidesvik Offshore ASA | H2O Retailing vs. CSSC Offshore Marine |
| Comba Telecom vs. H2O Retailing | Comba Telecom vs. FAST RETAIL ADR | Comba Telecom vs. Burlington Stores | Comba Telecom vs. Retail Estates NV |
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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