Correlation Between E For and City Sports
Can any of the company-specific risk be diversified away by investing in both E For and City Sports 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 E For and City Sports into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between E for L and City Sports and, you can compare the effects of market volatilities on E For and City Sports 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 E For with a short position of City Sports. Check out your portfolio center. Please also check ongoing floating volatility patterns of E For and City Sports.
Diversification Opportunities for E For and City Sports
0.7 | Correlation Coefficient |
Poor diversification
The 3 months correlation between EFORL and City is 0.7. Overlapping area represents the amount of risk that can be diversified away by holding E for L and City Sports and in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on City Sports and E For 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 E for L are associated (or correlated) with City Sports. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of City Sports has no effect on the direction of E For i.e., E For and City Sports go up and down completely randomly.
Pair Corralation between E For and City Sports
Assuming the 90 days trading horizon E for L is expected to generate 11.66 times more return on investment than City Sports. However, E For is 11.66 times more volatile than City Sports and. It trades about 0.23 of its potential returns per unit of risk. City Sports and is currently generating about 0.41 per unit of risk. If you would invest 13.00 in E for L on September 15, 2024 and sell it today you would earn a total of 16.00 from holding E for L or generate 123.08% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
E for L vs. City Sports and
Performance |
Timeline |
E for L |
City Sports |
E For and City Sports Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with E For and City Sports
The main advantage of trading using opposite E For and City Sports positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if E For position performs unexpectedly, City Sports 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 City Sports will offset losses from the drop in City Sports' long position.The idea behind E for L and City Sports and pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.City Sports vs. Chiangmai Frozen Foods | City Sports vs. Dynasty Ceramic Public | City Sports vs. Castle Peak Holdings | City Sports vs. CPL Group Public |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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