Correlation Between Cshg Logistica and Cshg Atrium
Can any of the company-specific risk be diversified away by investing in both Cshg Logistica and Cshg Atrium 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 Cshg Logistica and Cshg Atrium into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Cshg Logistica and Cshg Atrium Shopping, you can compare the effects of market volatilities on Cshg Logistica and Cshg Atrium 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 Cshg Logistica with a short position of Cshg Atrium. Check out your portfolio center. Please also check ongoing floating volatility patterns of Cshg Logistica and Cshg Atrium.
Diversification Opportunities for Cshg Logistica and Cshg Atrium
0.86 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Cshg and Cshg is 0.86. Overlapping area represents the amount of risk that can be diversified away by holding Cshg Logistica and Cshg Atrium Shopping in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Cshg Atrium Shopping and Cshg Logistica 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 Cshg Logistica are associated (or correlated) with Cshg Atrium. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Cshg Atrium Shopping has no effect on the direction of Cshg Logistica i.e., Cshg Logistica and Cshg Atrium go up and down completely randomly.
Pair Corralation between Cshg Logistica and Cshg Atrium
Assuming the 90 days trading horizon Cshg Logistica is expected to generate 0.64 times more return on investment than Cshg Atrium. However, Cshg Logistica is 1.56 times less risky than Cshg Atrium. It trades about -0.03 of its potential returns per unit of risk. Cshg Atrium Shopping is currently generating about -0.24 per unit of risk. If you would invest 15,382 in Cshg Logistica on August 28, 2024 and sell it today you would lose (44.00) from holding Cshg Logistica or give up 0.29% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Cshg Logistica vs. Cshg Atrium Shopping
Performance |
| Timeline |
| Cshg Logistica |
| Cshg Atrium Shopping |
Cshg Logistica and Cshg Atrium Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Cshg Logistica and Cshg Atrium
The main advantage of trading using opposite Cshg Logistica and Cshg Atrium positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Cshg Logistica position performs unexpectedly, Cshg Atrium 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 Cshg Atrium will offset losses from the drop in Cshg Atrium's long position.| Cshg Logistica vs. Cshg Jhsf Prime | Cshg Logistica vs. Cshg Atrium Shopping | Cshg Logistica vs. Real Estate Investment | Cshg Logistica vs. NAVI CRDITO IMOBILIRIO |
| Cshg Atrium vs. Cshg Jhsf Prime | Cshg Atrium vs. Cshg Logistica | Cshg Atrium vs. Real Estate Investment | Cshg Atrium vs. NAVI CRDITO IMOBILIRIO |
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 Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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