Correlation Between GM and Braskem SA
Can any of the company-specific risk be diversified away by investing in both GM and Braskem SA 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 GM and Braskem SA into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between General Motors and Braskem SA, you can compare the effects of market volatilities on GM and Braskem SA 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 GM with a short position of Braskem SA. Check out your portfolio center. Please also check ongoing floating volatility patterns of GM and Braskem SA.
Diversification Opportunities for GM and Braskem SA
-0.82 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between GM and Braskem is -0.82. Overlapping area represents the amount of risk that can be diversified away by holding General Motors and Braskem SA in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Braskem SA and GM 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 General Motors are associated (or correlated) with Braskem SA. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Braskem SA has no effect on the direction of GM i.e., GM and Braskem SA go up and down completely randomly.
Pair Corralation between GM and Braskem SA
Allowing for the 90-day total investment horizon General Motors is expected to generate 0.53 times more return on investment than Braskem SA. However, General Motors is 1.88 times less risky than Braskem SA. It trades about 0.13 of its potential returns per unit of risk. Braskem SA is currently generating about -0.25 per unit of risk. If you would invest 5,154 in General Motors on August 30, 2024 and sell it today you would earn a total of 396.00 from holding General Motors or generate 7.68% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Significant |
| Accuracy | 91.3% |
| Values | Daily Returns |
General Motors vs. Braskem SA
Performance |
| Timeline |
| General Motors |
| Braskem SA |
GM and Braskem SA Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with GM and Braskem SA
The main advantage of trading using opposite GM and Braskem SA positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GM position performs unexpectedly, Braskem SA 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 Braskem SA will offset losses from the drop in Braskem SA's long position.The idea behind General Motors and Braskem SA 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.| Braskem SA vs. Braskem SA | Braskem SA vs. Braskem SA | Braskem SA vs. Bradespar SA | Braskem SA vs. Unipar Carbocloro 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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