Correlation Between GM and Dividend
Can any of the company-specific risk be diversified away by investing in both GM and Dividend 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 Dividend into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between General Motors and Dividend 15 Split, you can compare the effects of market volatilities on GM and Dividend 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 Dividend. Check out your portfolio center. Please also check ongoing floating volatility patterns of GM and Dividend.
Diversification Opportunities for GM and Dividend
Very weak diversification
The 3 months correlation between GM and Dividend is 0.56. Overlapping area represents the amount of risk that can be diversified away by holding General Motors and Dividend 15 Split in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dividend 15 Split 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 Dividend. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dividend 15 Split has no effect on the direction of GM i.e., GM and Dividend go up and down completely randomly.
Pair Corralation between GM and Dividend
Allowing for the 90-day total investment horizon General Motors is expected to under-perform the Dividend. In addition to that, GM is 4.43 times more volatile than Dividend 15 Split. It trades about -0.08 of its total potential returns per unit of risk. Dividend 15 Split is currently generating about 0.21 per unit of volatility. If you would invest 348.00 in Dividend 15 Split on September 21, 2024 and sell it today you would earn a total of 10.00 from holding Dividend 15 Split or generate 2.87% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 95.65% |
Values | Daily Returns |
General Motors vs. Dividend 15 Split
Performance |
Timeline |
General Motors |
Dividend 15 Split |
GM and Dividend Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with GM and Dividend
The main advantage of trading using opposite GM and Dividend positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GM position performs unexpectedly, Dividend 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 Dividend will offset losses from the drop in Dividend's long position.The idea behind General Motors and Dividend 15 Split 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.Dividend vs. Copa Holdings SA | Dividend vs. United Airlines Holdings | Dividend vs. Delta Air Lines | Dividend vs. SkyWest |
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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