Correlation Between GM and Matricelf
Can any of the company-specific risk be diversified away by investing in both GM and Matricelf 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 Matricelf into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between General Motors and Matricelf, you can compare the effects of market volatilities on GM and Matricelf 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 Matricelf. Check out your portfolio center. Please also check ongoing floating volatility patterns of GM and Matricelf.
Diversification Opportunities for GM and Matricelf
Very weak diversification
The 3 months correlation between GM and Matricelf is 0.52. Overlapping area represents the amount of risk that can be diversified away by holding General Motors and Matricelf in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Matricelf 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 Matricelf. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Matricelf has no effect on the direction of GM i.e., GM and Matricelf go up and down completely randomly.
Pair Corralation between GM and Matricelf
Allowing for the 90-day total investment horizon General Motors is expected to generate 0.68 times more return on investment than Matricelf. However, General Motors is 1.47 times less risky than Matricelf. It trades about 0.05 of its potential returns per unit of risk. Matricelf is currently generating about -0.02 per unit of risk. If you would invest 3,778 in General Motors on September 1, 2024 and sell it today you would earn a total of 1,781 from holding General Motors or generate 47.14% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 78.18% |
| Values | Daily Returns |
General Motors vs. Matricelf
Performance |
| Timeline |
| General Motors |
| Matricelf |
GM and Matricelf Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with GM and Matricelf
The main advantage of trading using opposite GM and Matricelf positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GM position performs unexpectedly, Matricelf 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 Matricelf will offset losses from the drop in Matricelf's long position.The idea behind General Motors and Matricelf 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.| Matricelf vs. Nice | Matricelf vs. The Gold Bond | Matricelf vs. Bank Leumi Le Israel | Matricelf vs. ICL Israel Chemicals |
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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