Correlation Between GM and Data#3
Can any of the company-specific risk be diversified away by investing in both GM and Data#3 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 Data#3 into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between General Motors and Data3 Limited, you can compare the effects of market volatilities on GM and Data#3 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 Data#3. Check out your portfolio center. Please also check ongoing floating volatility patterns of GM and Data#3.
Diversification Opportunities for GM and Data#3
Weak diversification
The 3 months correlation between GM and Data#3 is 0.33. Overlapping area represents the amount of risk that can be diversified away by holding General Motors and Data3 Limited in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Data3 Limited 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 Data#3. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Data3 Limited has no effect on the direction of GM i.e., GM and Data#3 go up and down completely randomly.
Pair Corralation between GM and Data#3
Allowing for the 90-day total investment horizon General Motors is expected to generate 7.31 times more return on investment than Data#3. However, GM is 7.31 times more volatile than Data3 Limited. It trades about 0.05 of its potential returns per unit of risk. Data3 Limited is currently generating about 0.1 per unit of risk. If you would invest 3,805 in General Motors on September 3, 2024 and sell it today you would earn a total of 1,699 from holding General Motors or generate 44.65% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
General Motors vs. Data3 Limited
Performance |
| Timeline |
| General Motors |
| Data3 Limited |
GM and Data#3 Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with GM and Data#3
The main advantage of trading using opposite GM and Data#3 positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GM position performs unexpectedly, Data#3 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 Data#3 will offset losses from the drop in Data#3's long position.The idea behind General Motors and Data3 Limited 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.| Data#3 vs. Celsius Holdings | Data#3 vs. Apogee Enterprises | Data#3 vs. The Coca Cola | Data#3 vs. Brandywine Realty Trust |
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 Idea Breakdown module to analyze constituents of all Macroaxis ideas. Macroaxis investment ideas are predefined, sector-focused investing themes.
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