Correlation Between Jeronimo Martins and Mota Engil
Can any of the company-specific risk be diversified away by investing in both Jeronimo Martins and Mota Engil 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 Jeronimo Martins and Mota Engil into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Jeronimo Martins SGPS and Mota Engil SGPS SA, you can compare the effects of market volatilities on Jeronimo Martins and Mota Engil 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 Jeronimo Martins with a short position of Mota Engil. Check out your portfolio center. Please also check ongoing floating volatility patterns of Jeronimo Martins and Mota Engil.
Diversification Opportunities for Jeronimo Martins and Mota Engil
0.56 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Jeronimo and Mota is 0.56. Overlapping area represents the amount of risk that can be diversified away by holding Jeronimo Martins SGPS and Mota Engil SGPS SA in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Mota Engil SGPS and Jeronimo Martins 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 Jeronimo Martins SGPS are associated (or correlated) with Mota Engil. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Mota Engil SGPS has no effect on the direction of Jeronimo Martins i.e., Jeronimo Martins and Mota Engil go up and down completely randomly.
Pair Corralation between Jeronimo Martins and Mota Engil
Assuming the 90 days trading horizon Jeronimo Martins SGPS is expected to generate 0.54 times more return on investment than Mota Engil. However, Jeronimo Martins SGPS is 1.84 times less risky than Mota Engil. It trades about 0.35 of its potential returns per unit of risk. Mota Engil SGPS SA is currently generating about 0.14 per unit of risk. If you would invest 1,905 in Jeronimo Martins SGPS on December 5, 2024 and sell it today you would earn a total of 187.00 from holding Jeronimo Martins SGPS or generate 9.82% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 95.65% |
Values | Daily Returns |
Jeronimo Martins SGPS vs. Mota Engil SGPS SA
Performance |
Timeline |
Jeronimo Martins SGPS |
Mota Engil SGPS |
Jeronimo Martins and Mota Engil Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Jeronimo Martins and Mota Engil
The main advantage of trading using opposite Jeronimo Martins and Mota Engil positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Jeronimo Martins position performs unexpectedly, Mota Engil 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 Mota Engil will offset losses from the drop in Mota Engil's long position.Jeronimo Martins vs. Sonae SGPS SA | Jeronimo Martins vs. Galp Energia SGPS | Jeronimo Martins vs. EDP Energias | Jeronimo Martins vs. Altri SGPS SA |
Mota Engil vs. Sonae SGPS SA | Mota Engil vs. Altri SGPS SA | Mota Engil vs. Banco Comercial Portugues | Mota Engil vs. Semapa |
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 Portfolio Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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