Correlation Between GM and Nano One
Can any of the company-specific risk be diversified away by investing in both GM and Nano One 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 Nano One into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between General Motors and Nano One Materials, you can compare the effects of market volatilities on GM and Nano One 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 Nano One. Check out your portfolio center. Please also check ongoing floating volatility patterns of GM and Nano One.
Diversification Opportunities for GM and Nano One
Good diversification
The 3 months correlation between GM and Nano is -0.07. Overlapping area represents the amount of risk that can be diversified away by holding General Motors and Nano One Materials in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Nano One Materials 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 Nano One. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Nano One Materials has no effect on the direction of GM i.e., GM and Nano One go up and down completely randomly.
Pair Corralation between GM and Nano One
Allowing for the 90-day total investment horizon General Motors is expected to generate 0.94 times more return on investment than Nano One. However, General Motors is 1.06 times less risky than Nano One. It trades about 0.16 of its potential returns per unit of risk. Nano One Materials is currently generating about -0.36 per unit of risk. If you would invest 5,096 in General Motors on September 2, 2024 and sell it today you would earn a total of 463.00 from holding General Motors or generate 9.09% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 95.45% |
| Values | Daily Returns |
General Motors vs. Nano One Materials
Performance |
| Timeline |
| General Motors |
| Nano One Materials |
GM and Nano One Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with GM and Nano One
The main advantage of trading using opposite GM and Nano One positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GM position performs unexpectedly, Nano One 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 Nano One will offset losses from the drop in Nano One's long position.The idea behind General Motors and Nano One Materials 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.| Nano One vs. Bird Construction | Nano One vs. CVS HEALTH CDR | Nano One vs. Mako Mining Corp | Nano One vs. Nicola Mining |
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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