Correlation Between CA Modas and Starbucks
Can any of the company-specific risk be diversified away by investing in both CA Modas and Starbucks 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 CA Modas and Starbucks into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between CA Modas SA and Starbucks, you can compare the effects of market volatilities on CA Modas and Starbucks 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 CA Modas with a short position of Starbucks. Check out your portfolio center. Please also check ongoing floating volatility patterns of CA Modas and Starbucks.
Diversification Opportunities for CA Modas and Starbucks
Very weak diversification
The 3 months correlation between CEAB3 and Starbucks is 0.59. Overlapping area represents the amount of risk that can be diversified away by holding CA Modas SA and Starbucks in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Starbucks and CA Modas 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 CA Modas SA are associated (or correlated) with Starbucks. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Starbucks has no effect on the direction of CA Modas i.e., CA Modas and Starbucks go up and down completely randomly.
Pair Corralation between CA Modas and Starbucks
Assuming the 90 days trading horizon CA Modas SA is expected to generate 1.79 times more return on investment than Starbucks. However, CA Modas is 1.79 times more volatile than Starbucks. It trades about 0.07 of its potential returns per unit of risk. Starbucks is currently generating about 0.04 per unit of risk. If you would invest 490.00 in CA Modas SA on September 2, 2024 and sell it today you would earn a total of 567.00 from holding CA Modas SA or generate 115.71% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 98.14% |
| Values | Daily Returns |
CA Modas SA vs. Starbucks
Performance |
| Timeline |
| CA Modas SA |
| Starbucks |
CA Modas and Starbucks Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with CA Modas and Starbucks
The main advantage of trading using opposite CA Modas and Starbucks positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CA Modas position performs unexpectedly, Starbucks 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 Starbucks will offset losses from the drop in Starbucks' long position.The idea behind CA Modas SA and Starbucks 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.| Starbucks vs. BTG Pactual Logstica | Starbucks vs. Plano Plano Desenvolvimento | Starbucks vs. Cable One | Starbucks vs. ATMA Participaes SA |
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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