Correlation Between SPORT LISBOA and CIG PANNONIA
Can any of the company-specific risk be diversified away by investing in both SPORT LISBOA and CIG PANNONIA 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 SPORT LISBOA and CIG PANNONIA into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SPORT LISBOA E and CIG PANNONIA LIFE, you can compare the effects of market volatilities on SPORT LISBOA and CIG PANNONIA 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 SPORT LISBOA with a short position of CIG PANNONIA. Check out your portfolio center. Please also check ongoing floating volatility patterns of SPORT LISBOA and CIG PANNONIA.
Diversification Opportunities for SPORT LISBOA and CIG PANNONIA
0.77 | Correlation Coefficient |
Poor diversification
The 3 months correlation between SPORT and CIG is 0.77. Overlapping area represents the amount of risk that can be diversified away by holding SPORT LISBOA E and CIG PANNONIA LIFE in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on CIG PANNONIA LIFE and SPORT LISBOA 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 SPORT LISBOA E are associated (or correlated) with CIG PANNONIA. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of CIG PANNONIA LIFE has no effect on the direction of SPORT LISBOA i.e., SPORT LISBOA and CIG PANNONIA go up and down completely randomly.
Pair Corralation between SPORT LISBOA and CIG PANNONIA
Assuming the 90 days horizon SPORT LISBOA E is expected to generate 1.23 times more return on investment than CIG PANNONIA. However, SPORT LISBOA is 1.23 times more volatile than CIG PANNONIA LIFE. It trades about 0.17 of its potential returns per unit of risk. CIG PANNONIA LIFE is currently generating about 0.07 per unit of risk. If you would invest 368.00 in SPORT LISBOA E on April 24, 2025 and sell it today you would earn a total of 176.00 from holding SPORT LISBOA E or generate 47.83% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
SPORT LISBOA E vs. CIG PANNONIA LIFE
Performance |
Timeline |
SPORT LISBOA E |
CIG PANNONIA LIFE |
SPORT LISBOA and CIG PANNONIA Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with SPORT LISBOA and CIG PANNONIA
The main advantage of trading using opposite SPORT LISBOA and CIG PANNONIA positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SPORT LISBOA position performs unexpectedly, CIG PANNONIA 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 CIG PANNONIA will offset losses from the drop in CIG PANNONIA's long position.SPORT LISBOA vs. Sixt Leasing SE | SPORT LISBOA vs. Lion One Metals | SPORT LISBOA vs. ANDRADA MINING LTD | SPORT LISBOA vs. Lendlease Group |
CIG PANNONIA vs. RESMINING UNSPADR10 | CIG PANNONIA vs. Globex Mining Enterprises | CIG PANNONIA vs. FOKUS MINING P | CIG PANNONIA vs. Monument Mining Limited |
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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